SnowPro-Core SnowPro Core Certification Exam Questions and Answers

 In Snowflake, a schema is a logical grouping of database objects, which can include streams and external tables. A stream is an object that allows users to query data that has changed in specified tables or views, and an external table is a table that references data stored outside of Snowflake. Roles, warehouses, users, and shares are not contained within a schema. References: SHOW OBJECTS, Database, Schema, & Share DDL

 Data encryption and Time Travel are part of Snowflake’s Continuous Data Protection (CDP) feature set that do not require additional configuration. Data encryption is automatically applied to all files stored on internal stages, and Time Travel allows for querying and restoring data without any extra setup

 When cloning a database in Snowflake, the clone includes all privileges on the database as well as existing child objects within the database, such as schemas, tables, views, etc. However, it does not include future child objects or privileges on schemas within the database2.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

The default value for auto-suspending a Virtual Warehouse in the Snowflake Web Interface (UI) is 10 minutes. This setting helps manage compute costs by automatically suspending warehouses that are not in use, ensuring that compute resources are efficiently allocated and not wasted on idle warehouses.

References:

• Snowflake Documentation: Virtual Warehouses

To easily transfer data from a production account to a non-production account in Snowflake within the same cloud provider region, creating a data share is the most efficient approach. Data sharing allows for live, read-only access to selected data objects from the production account to the non-production account without the need to duplicate or move the actual data. This method facilitates seamless access to the data for development, testing, or analytics purposes in the non-production environment.

References:

• Snowflake Documentation: Data Sharing

A well-clustered table in Snowflake means that the data is organized in such a way that related data points are stored close to each other within the micro-partitions. This optimizes query performance by reducing the amount of scanned data. The diagram indicated by option C shows a well-clustered table, as it likely represents a more evenly distributed range of values across the micro-partitions1.

References = Snowflake Micro-partitions & Table Clustering

Snowflake virtual warehouses support a configuration file and command line options in SnowSQL to specify a default warehouse, which is characteristic C. Additionally, the size of a virtual warehouse can be changed at any time, which is characteristic E. These features provide flexibility and ease of use in managing compute resources2.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

The error “Max LOB size (16777216) exceeded” indicates that the size of the parsed column exceeds the maximum size allowed for a single column value in Snowflake, which is 16 MB. To resolve this issue, the file should be split into multiple smaller files that are within the recommended size range of 100 MB to 250 MB. This will ensure that each JSON document within the files is smaller than the maximum LOB size allowed. Compressing the file, using a larger-sized warehouse, or setting STRIP_OUTER_ARRAY=TRUE will not resolve the issue of the column size exceeding the maximum allowed. References: COPY INTO Error during Structured Data Load: “Max LOB size (16777216) exceeded…”

 Users are responsible for data storage costs in Snowflake until the data expires from the Fail-safe period. Fail-safe is the final stage in the data lifecycle, following Time Travel, and provides additional protection against accidental data loss. Once data exits the Fail-safe state, users are no longer billed for its storage

When publishing a Snowflake Data Marketplace listing into a remote region, it’s important to note that while the listing is replicated into all selected regions automatically, the data itself is not. Therefore, the data must be replicated separately. Additionally, the user must have the ORGADMIN role in at least one account to manage the replication of accounts1.

 In an auto-scaling multi-cluster virtual warehouse with the SCALING_POLICY set to ECONOMY, another cluster is started when the system has enough load for 2 minutes (A). This policy is designed to optimize the balance between performance and cost, starting additional clusters only when the sustained load justifies it2.

The SQL command used to delete files from a Snowflake internal stage is REMOVE. This command can be used to remove files from either an internal or external stage within Snowflake

Snowflake’s architecture is designed to operate entirely on cloud infrastructure. It uses the core compute and storage services of each cloud provider, which allows it to leverage the scalability and reliability of cloud resources. Snowflake’s services are deployed across multiple availability zones within a cloud provider’s region to ensure high availability and fault tolerance. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The maximum Time Travel retention period for a temporary Snowflake table is 1 day. This is the standard retention period for temporary tables, which allows for accessing historical data within a 24-hour window

Reader accounts in Snowflake are designed to allow users to read data shared with them but do not have the capability to extract data for use outside of Snowflake. They are intended for consuming shared data within the Snowflake environment only.

 One of the key features of Snowflake’s architecture is its unique approach to eliminating resource contention through the use of virtual warehouses. This is achieved by separating storage and compute resources, allowing multiple virtual warehouses to operate independently on the same data without affecting each other. This means that different workloads, such as loading data, running queries, or performing complex analytics, can be processed simultaneously without any performance degradation due to resource contention.

References:

• Snowflake Documentation on Virtual Warehouses
• SnowPro® Core Certification Study Guide

The list command in Snowflake is used to validate and display the list of files in a specified stage. When a user has unloaded data to a stage, running the list @file__stage command will show all the files that have been uploaded to that stage, allowing the user to verify the data that was unloaded.

References:

• Snowflake Documentation on Stages
• SnowPro® Core Certification Study Guide

 User-level network policies in Snowflake can be created by roles with the necessary privileges to manage security and account settings. The ACCOUNTADMIN role has the highest level of privileges across the account, including the ability to manage network policies. The SECURITYADMIN role is specifically responsible for managing security objects within Snowflake, which includes the creation and management of network policies.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Network Policies1
• Section 1.3 - SnowPro Core Certification Study Guide

 Materialized views in Snowflake are a feature that allows for the pre-computation and storage of query results for faster query performance. This feature is available starting from the Enterprise Edition of Snowflake. It is not available in the Standard Edition, and while it is also available in higher editions like Business Critical and Virtual Private Snowflake, the Enterprise Edition is the minimum requirement.

References:

• Snowflake Documentation on CREATE MATERIALIZED VIEW1.
• Snowflake Documentation on Working with Materialized Views

https://docs.snowflake.com/en/sql-reference/sql/create-materialized-view.html#:~:text=Materialized%20views%20require%20Enterprise%20Edition,upgrading%2C%20please%20contact%20Snowflake%20Support .

 The COPY INTO command is used in Snowflake to unload data from a table into a file in a stage. This command allows for the export of data from Snowflake tables into flat files, which can then be used for further analysis, processing, or storage in external systems.

References:

• Snowflake Documentation on Unloading Data
• Snowflake SnowPro Core: Copy Into Command to Unload Rows to Files in Named Stage

 Snowflake supports querying semi-structured data using specific data types that are capable of handling the flexibility and structure of such data. The data types supported for this purpose are:

• A. VARIANT: This is a universal data type that can store values of any other type, including structured and semi-structured types. It is particularly useful for handling JSON, Avro, ORC, Parquet, and XML data formats1.
• B. ARRAY: An array is a list of elements that can be of any data type, including VARIANT, and is used to handle semi-structured data that is naturally represented as a list1.

These data types are part of Snowflake’s built-in support for semi-structured data, allowing for the storage, querying, and analysis of data that does not fit into the traditional row-column format.

References:

• Snowflake Documentation on Semi-Structured Data
• [COF-C02] SnowPro Core Certification Exam Study Guide

 The command list @mystage; is used to view the list of files that have been uploaded to an internal stage in Snowflake. The list command displays the metadata for all files in the specified stage, which in this case is mystage. This command is particularly useful for verifying that files have been successfully unloaded from a Snowflake table to the stage and for managing the files within the stage.

References:

• Snowflake Documentation on Stages
• SnowPro® Core Certification Study Guide

To return results from the results cache in Snowflake, certain conditions must be met:

• Privileges: The user must have the appropriate privileges on the objects associated with the query. This ensures that only authorized users can access cached data.
• Time Frame: The query must have been run within 24 hours of the previously-run query. Snowflake’s results cache is designed to store the results of queries for a short period, typically 24 hours, to improve performance for repeated queries.

 To address a high volume of queries queuing in a warehouse, Snowflake recommends two best practices:

• A. Use multi-clustered warehousing to scale out warehouse capacity: This approach allows for the distribution of queries across multiple clusters within a warehouse, effectively managing the load and reducing the queue volume.
• B. Scale up the warehouse size to allow Queries to execute faster: Increasing the size of the warehouse provides more compute resources, which can reduce the time it takes for queries to execute and thus decrease the number of queries waiting in the queue.

These strategies help to optimize the performance of the warehouse by ensuring that resources are scaled appropriately to meet demand.

References:

• Snowflake Documentation on Multi-Cluster Warehousing
• SnowPro Core Certification best practices

 When a query in Snowflake is too large to fit into the available memory, it will start spilling to remote storage. This is an indication that the memory allocated for the query is insufficient for its execution, and as a result, Snowflake uses remote disk storage to handle the overflow. This spill to remote storage can lead to slower query performance due to the additional I/O operations required.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Query Profile1
• Snowpro Core Certification Exam Flashcards2

The auto-suspend and auto-resume settings in Snowflake apply to the entire virtual warehouse. These settings allow the warehouse to automatically suspend when it’s not in use, helping to save on compute costs. When queries or tasks are submitted to the warehouse, it can automatically resume operation. This functionality is designed to optimize resource usage and cost-efficiency.

References:

• SnowPro Core Certification Exam Study Guide (as of 2021)
• Snowflake documentation on virtual warehouses and their settings (as of 2021)

The Snowflake storage layer is responsible for storing data in an optimized, compressed, columnar format. This includes micro-partitions, which are the fundamental storage units that contain the actual data stored in Snowflake. Additionally, persisted query results, which are the results of queries that have been materialized and stored for future use, are also kept within this layer. This design allows for efficient data retrieval and management within the Snowflake architecture1.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Key Concepts & Architecture | Snowflake Documentation2

Secure sharing in Snowflake allows users to share specific objects with other Snowflake accounts without physically copying the data, thus not consuming additional storage. Tables and Secure User Defined Functions (UDFs) are among the objects that can be shared using this feature. Materialized views, sequences, and procedures are not shareable objects in Snowflake.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Secure Data Sharing1

Snowflake’s Time Travel feature is used for both querying historical data in tables and restoring and cloning historical data in databases, schemas, and tables3. It allows users to access historical data within a defined period (1 day by default, up to 90 days for Snowflake Enterprise Edition) and is a key feature for data recovery and management. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Clustering keys in Snowflake work by sorting the designated columns over time. This process is done in the background and does not block data manipulation language (DML) operations, allowing for normal database operations to continue without interruption. The purpose of clustering keys is to organize the data within micro-partitions to optimize query performance1.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Clustering1

When a cloned table is replicated to a secondary database in Snowflake, the following occurs:

• C. The physical data is replicated: The actual data of the cloned table is physically replicated to the secondary database. This ensures that the secondary database has its own copy of the data, which can be used for read-only purposes or failover scenarios1.
• E. Metadata pointers to cloned tables are replicated: Along with the physical data, the metadata pointers that refer to the cloned tables are also replicated. This metadata includes information about the structure of the table and any associated properties2.

It’s important to note that while the physical data and metadata are replicated, the secondary database is typically read-only and cannot be used for write operations. Additionally, while there may be additional storage costs associated with the secondary account, this is not a direct result of the replication process but rather a consequence of storing additional data.

References:

• SnowPro Core Exam Prep — Answers to Snowflake’s LEVEL UP: Backup and Recovery
• Snowflake SnowPro Core Certification Exam Questions Set 10

 In Snowflake, a reader account is a type of account that is intended for consuming shared data rather than performing any data management or DDL operations. The CREATE SHARE command is used to share data from your account with another account, which is not a capability provided to reader accounts. Reader accounts are typically restricted from creating shares, as their primary purpose is to read shared data rather than to share it themselves.

References:

• Snowflake Documentation on Reader Accounts
• SnowPro® Core Certification Study Guide

 Snowflake supports several methods for authenticating users, including federated authentication, key-pair authentication, and OAuth. Federated authentication allows users to authenticate using their organization’s identity provider. Key-pair authentication uses a public-private key pair for secure login, and OAuth is an open standard for access delegation commonly used for token-based authentication. References: Authentication policies | Snowflake Documentation, Authenticating to the server | Snowflake Documentation, External API authentication and secrets | Snowflake Documentation.

Parquet is a columnar storage file format that is optimized for performance in Snowflake. It is designed to be efficient for both storage and query performance, particularly for complex queries on large datasets. Parquet files support efficient compression and encoding schemes, which can lead to significant savings in storage and speed in query processing, making it the most performant file format for loading data into Snowflake.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Data Loading1

The correct command to view all roles granted to a specific user in Snowflake is SHOW GRANTS TO USER ;. This command lists all access control privileges that have been explicitly granted to the specified user. References: SHOW GRANTS | Snowflake Documentation

SnowSQL is the command-line client for Snowflake that allows users to execute SQL queries and perform all DDL and DML operations, including staging files for bulk data loading. It is specifically designed for scripting and automating tasks.

References:

• SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on SnowSQL

https://docs.snowflake.com/en/user-guide/snowsql-use.html

Snowflake’s Continuous Data Protection (CDP) encompasses a set of features that help protect data stored in Snowflake against human error, malicious acts, and software failure. Time Travel allows users to access historical data (i.e., data that has been changed or deleted) for a defined period, enabling querying and restoring of data. Fail-safe is an additional layer of data protection that provides a recovery option in the event of significant data loss or corruption, which can only be performed by Snowflake.

References:

• Continuous Data Protection | Snowflake Documentation1
• Data Storage Considerations | Snowflake Documentation2
• Snowflake SnowPro Core Certification Study Guide3
• Snowflake Data Cloud Glossary

https://docs.snowflake.com/en/user-guide/data-availability.html

Snowflake supports two geospatial data types: GEOGRAPHY and GEOMETRY. The GEOGRAPHY data type is used to store geospatial data that models the Earth as a perfect sphere, which is suitable for global geospatial data. This data type follows the WGS 84 standard and is used for storing points, lines, and polygons on the Earth’s surface. The GEOMETRY data type, on the other hand, represents features in a planar (Euclidean, Cartesian) coordinate system and is typically used for local spatial reference systems. Since the question specifically asks about geospatial data, which commonly refers to Earth-related spatial data, the correct answer is GEOGRAPHY3. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The result set cache in Snowflake is invalidated and no longer available when the underlying data of the query results has changed, ensuring that queries return the most current data. Additionally, the cache expires after 24 hours to maintain the efficiency and accuracy of data retrieval1.

The parameter STATEMENT_QUEUED_TIMEOUT_IN_SECONDS sets the limit for a query to wait in the queue in order to get its chance of running on the warehouse. The query will quit after reaching this limit. By default, the value of this parameter is 0 which mean the queries will wait indefinitely in the waiting queue

https://community.snowflake.com/s/article/Warehouse-Concurrency-and-Statement-Timeout-Parameters#:~:text=The%20parameter%20STATEMENT_QUEUED_TIMEOUT_IN_SECONDS%20sets%20the,indefinitely%20in%20the%20waiting%20queue .

The purpose of an External Function in Snowflake is to call code that executes outside of the Snowflake environment. This allows Snowflake to interact with external services and leverage functionalities that are not natively available within Snowflake, such as calling APIs or running custom code hosted on cloud services3.

https://docs.snowflake.com/en/sql-reference/external-functions.html

 The correct statement to accommodate the request for a marketing co-worker to change the size of their medium virtual warehouse called mktg__WH is to grant the MODIFY privilege on the warehouse to the ROLE MARKETING. This privilege allows the role to change the warehouse size among other properties.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Access Control Privileges1

When a virtual warehouse in Snowflake is resized, specifically when it is increased in size, the additional compute resources become immediately available to all running and queued queries. This means that the performance of these queries can improve due to the increased resources. Conversely, when the size of a warehouse is reduced, the compute resources are not removed until they are no longer being used by any current operations1.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Virtual Warehouses2

Network policies in Snowflake are defined as a set of rules that manage the network-level access to Snowflake accounts. These rules specify which IP addresses or IP ranges are permitted to connect to Snowflake, enhancing the security of Snowflake accounts by preventing unauthorized access. Network policies are an essential aspect of Snowflake's security model, allowing administrators to enforce access controls based on network locations.

References:

• Snowflake Documentation: Network Policies

To unload data from a table into one or more files in a named stage, the COPY INTO <location> command should be used. This command exports the result of a query, such as selecting all rows from a table, into files stored in the specified stage. The COPY INTO command is versatile, supporting various file formats and compression options for efficient data unloading.

References:

• Snowflake Documentation: COPY INTO Location

Characteristics of Snowsight worksheets in Snowflake include:

• A. Worksheets can be grouped under folders, and a folder of folders: This organizational feature allows users to efficiently manage and categorize their worksheets within Snowsight, Snowflake's web-based UI, enhancing the user experience by keeping related worksheets together.
• E. Users can import worksheets and share them with other users: Snowsight supports the sharing of worksheets among users, fostering collaboration by allowing users to share queries, analyses, and findings. This feature is crucial for collaborative data exploration and analysis workflows.

References:

• Snowflake Documentation: Snowsight (UI for Snowflake)

Snowflake's architecture is unique in that it combines elements of both traditional shared-disk and shared-nothing database architectures. This hybrid approach allows Snowflake to offer the scalability and performance benefits of a shared-nothing architecture (with compute and storage separated) while maintaining the simplicity and flexibility of a shared-disk architecture in managing data across all nodes in the system. This results in an architecture that provides on-demand scalability, both vertically and horizontally, without sacrificing performance or data cohesion.

References:

• Snowflake Documentation: Snowflake Architecture

In Snowflake, overloading refers to the creation of multiple User-Defined Functions (UDFs) with the same name but differing in the number or types of their arguments. This feature allows for more flexible function usage, as Snowflake can differentiate between functions based on the context of their invocation, such as the types or the number of arguments passed. Overloading helps to create more adaptable and readable code, as the same function name can be used for similar operations on different types of data.

References:

• Snowflake Documentation: User-Defined Functions

To insert JSON formatted string data into a VARIANT field in Snowflake, the correct function to use is PARSE_JSON. The PARSE_JSON function is specifically designed to interpret a JSON formatted string and convert it into a VARIANT type, which is Snowflake's flexible format for handling semi-structured data like JSON, XML, and Avro. This function is essential for loading and querying JSON data within Snowflake, allowing users to store and manage JSON data efficiently while preserving its structure for querying purposes. This function's usage and capabilities are detailed in the Snowflake documentation, providing users with guidance on how to handle semi-structured data effectively within their Snowflake environments.

References:

• Snowflake Documentation: PARSE_JSON

The Query Profile in Snowflake provides a graphical representation and statistics for each component of the query's execution plan. This includes details such as the execution time, the number of rows processed, and the amount of data scanned for each operation within the query. The Query Profile is a crucial tool for understanding and optimizing the performance of queries, as it helps identify potential bottlenecks and inefficiencies.

References:

• Snowflake Documentation: Understanding the Query Profile

The worksheet and database explorer feature in Snowsight allows users to tag frequently accessed worksheets for ease of access. This functionality helps users organize and quickly navigate to the worksheets they use most often, enhancing productivity and streamlining the data exploration and analysis process within Snowsight, Snowflake's web-based query and visualization interface.

References:

• Snowflake Documentation: Snowsight (UI for Snowflake)

When initiating a session in Snowflake, the system determines the current role based on the user's connection details and role assignments. If a user specifies a role during the connection, and that role is already granted to them, Snowflake sets it as the current role for the session. Alternatively, if no role is specified during the connection, but the user has a default role assigned, Snowflake will use this default role as the current session role. These mechanisms ensure that users operate within their permissions, enhancing security and governance within Snowflake environments.

References:

• Snowflake Documentation: Understanding Roles

In Snowflake, a Pipe is the object designed to enable the continuous, near-real-time loading of data from files as soon as they are available in a cloud storage location. Pipes use Snowflake’s COPY command to load data and can be associated with a Stage object to monitor for new files. When new data files appear in the stage, the pipe automatically loads the data into the target table.

References:

• Snowflake Documentation on Pipes
• SnowPro® Core Certification Study Guide

https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro.html

The ACCOUNTADMIN role is the most powerful role in Snowflake and should be limited to a select number of users within an organization. It is responsible for account-level configurations and should not be used for day-to-day object creation or management. Granting a custom role to ACCOUNTADMIN could inadvertently give broad access to users with this role, which is not a recommended security practice.

To temporarily bypass a network policy by configuring the user object property MINS_TO_BYPASS_NETWORK_POLICY, the USERADMIN role should be used. This role has the necessary privileges to modify user properties, including setting a temporary bypass for network policies, which can be crucial for enabling access under specific circumstances without permanently altering the network security configuration.

References:

• Snowflake Documentation: User Management

A Pre-signed URL provides access to files stored in Snowflake without requiring authorization at the time of access. This feature allows users to generate a URL with a limited validity period that grants temporary access to a file in a secure manner. It's particularly useful for sharing data with external parties or applications without the need for them to authenticate directly with Snowflake.

References:

• Snowflake Documentation: Using Pre-signed URLs

When creating a CSV file format in Snowflake and the requirement is to wrap columns by single quotes or double quotes, the FIELD_OPTIONALLY_ENCLOSED_BY parameter should be used in the file format specification. This parameter allows you to define a character (either a single quote or a double quote) that can optionally enclose each field in the CSV file, providing flexibility in handling fields that contain special characters or delimiters as part of their data.

References:

• Snowflake Documentation: CSV File Format

When unloading data, the Parquet file format is known for its efficiency in preserving the data values for floating-point number columns. Parquet is a columnar storage file format that offers high compression ratios and efficient data encoding schemes. It is especially effective for floating-point data, as it maintains high precision and supports efficient querying and analysis.

References:

• Snowflake Documentation: Using the Parquet File Format for Unloading Data

Snowflake's unique architecture is described as a multi-cluster, shared data architecture that leverages massively parallel processing (MPP). This architecture separates compute and storage resources, enabling Snowflake to scale them independently. It does not use a single cluster or rely solely on symmetric multiprocessing (SMP); rather, it uses a combination of shared-nothing architecture for compute clusters (virtual warehouses) and a centralized storage layer for data, optimizing for both performance and scalability.

References:

• Snowflake Documentation: Snowflake Architecture Overview

To get the most detailed information about individual table storage details in Snowflake, the TABLE STORAGE METRICS view should be used. This Information Schema view provides granular storage metrics for tables within Snowflake, including data related to the size of the table, the amount of data stored, and storage usage over time. It's an essential tool for administrators and users looking to monitor and optimize storage consumption and costs.

References:

• Snowflake Documentation: Information Schema - TABLE STORAGE METRICS View

To verify the privileges that have been granted to a specific role in Snowflake, the correct SQL command is SHOW GRANTS TO ROLE <Role Name>. This command lists all the privileges granted to the specified role, including access to schemas, tables, and other database objects. This is a useful command for administrators and users with sufficient privileges to audit and manage role permissions within the Snowflake environment.

References:

• Snowflake Documentation: SHOW GRANTS

The Search Optimization Service in Snowflake is designed to improve the performance of specific types of queries, particularly those involving extensive sets of WHERE conditions. By maintaining a search index on tables, this service can accelerate lookup and analytical queries, making it a valuable feature for optimizing query performance and reducing execution times for complex searches.

References:

• Snowflake Documentation: Search Optimization Service

Tri-secret secure encryption is a security model employed by Snowflake that involves combining a customer-managed key with a Snowflake-managed key to create a composite key for encrypting data. This model enhances data security by requiring both the customer-managed key and the Snowflake-managed key to decrypt data, thus ensuring that neither party can access the data independently. It represents a balanced approach to key management, leveraging both customer control and Snowflake's managed services for robust data encryption.

References:

• Snowflake Documentation: Encryption and Key Management

In the Snowflake Web Interface (UI), the Query History page displays the history of queries executed in Snowflake for up to 14 days. This allows users to review and analyze their query performance, troubleshoot issues, and understand their query patterns over a two-week period. The Query History page is a critical tool for monitoring and optimizing the use of Snowflake.

References:

• Snowflake Documentation: Using the Web Interface

A materialized view in Snowflake is a database object derived from a query specification, stored for later use, and can significantly speed up expensive aggregations on large data sets. Materialized views store the result of their underlying query, reducing the need to recompute the result each time the view is accessed. This makes them ideal for improving the performance of read-heavy, aggregate-intensive queries.

References:

• Snowflake Documentation: Using Materialized Views

When querying semi-structured data in Snowflake, the behavior towards case sensitivity is distinct between column names and the names of elements within the semi-structured data. Column names follow the general SQL norm of being case-insensitive, meaning you can reference them in any case without affecting the query. However, element names within JSON, XML, or other semi-structured data are case-sensitive. This distinction is crucial for accurate data retrieval and manipulation in Snowflake, especially when working with JSON objects where the case of keys can significantly alter the outcome of queries.

References:

• Snowflake Documentation: Querying Semi-structured Data

Characteristics of reader accounts in Snowflake include:

• A. Reader account users cannot add new data to the account: Reader accounts are intended for data consumption only. Users of these accounts can query and analyze the data shared with them but cannot upload or add new data to the account.
• E. Reader accounts enable data consumers to access and query data shared by the provider: One of the primary purposes of reader accounts is to allow data consumers to access and perform queries on the data shared by another Snowflake account, facilitating secure and controlled data sharing.

References:

• Snowflake Documentation: Reader Accounts

The SYSTEM$GET_PRIVATELINK function in Snowflake provides proxy information necessary for configuring PrivateLink connections, which can protect Snowsight as well as other Snowflake services. PrivateLink enhances security by allowing Snowflake to be accessed via a private connection within a cloud provider’s network, reducing exposure to the public internet.

References:

• Snowflake Documentation: PrivateLink Setup

The search optimization service in Snowflake supports use cases involving conjunctions (AND) of multiple equality predicates. This service enhances the performance of queries that include multiple equality conditions by utilizing search indexes to quickly filter data without scanning entire tables or partitions. It's particularly beneficial for improving the response times of complex queries that rely on specific data matching across multiple conditions.

References:

• Snowflake Documentation: Search Optimization Service

• Object Name Resolution: When unqualified object names (e.g., table name without schema) are used in a UDF, Snowflake follows a specific hierarchy to resolve them. Here's the order:
• Note: The SEARCH_PATH parameter influences object resolution for queries, not within UDFs.

References:

• Snowflake Documentation (Object Naming Resolution): https://docs.snowflake.com/en/sql-reference/name-resolution.html

When unloading floating-point number columns to CSV or JSON files, Snowflake truncates the values to approximately 15 significant digits with 9 digits following the decimal point, which can be represented as (15,9). This ensures a balance between accuracy and efficiency in representing floating-point numbers in text-based formats, which is essential for data interchange and processing applications that consume these files.

References:

• Snowflake Documentation: Data Unloading Considerations

In Snowflake, when setting up a network policy that specifies both allowed and blocked IP address lists, if an IP address appears in both lists, access from that IP address will be denied. The reason is that Snowflake prioritizes security, and the presence of an IP address in the blocked list indicates it should not be allowed regardless of its presence in the allowed list. This ensures that access controls remain stringent and that any potentially unsafe IP addresses are not inadvertently permitted access.

References:

• Snowflake Documentation: Network Policies

The VALIDATE table function in Snowflake would typically use a unique identifier, such as a UUID_STRING, as an input argument. This function is designed to validate the data within a table against a set of constraints or conditions, often requiring a specific identifier to reference the particular data or job being validated.

References:

• There is no direct reference to a VALIDATE table function with these specific parameters in Snowflake documentation. It seems like a theoretical example for understanding function arguments. Snowflake documentation on UDFs and system functions can provide guidance on how to create and use custom functions for similar purposes.

The command to list all the file formats for which a user has access privileges in Snowflake is SHOW FILE FORMATS. This command provides a list of all file formats defined in the user's current session or specified database/schema, along with details such as the name, type, and creation time of each file format. It is a valuable tool for users to understand and manage the file formats available for data loading and unloading operations.

References:

• Snowflake Documentation: SHOW FILE FORMATS

The search optimization service improves the performance of point lookup queries on large tables by using selective filters to quickly return a small number of rows. It creates an optimized data structure that helps in pruning the micro-partitions that do not contain the queried values3. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 In a regular schema within Snowflake, the object owner has the privilege to grant object privileges. The object owner is typically the role that created the object or to whom the ownership of the object has been transferred78.

References = [COF-C02] SnowPro Core Certification Exam Study Guide

The minimum Snowflake Edition that supports secure storage of Protected Health Information (PHI) data is the Business Critical Edition. This edition offers enhanced security features necessary for compliance with regulations such as HIPAA and HITRUST CSF4.

 In Snowflake’s data sharing model, any full Snowflake account can both provide and consume shared data. Additionally, the data consumer pays only for the compute resources used to query the shared data. No actual data is copied or transferred between accounts, and shared data does not take up any storage in a consumer account, so the consumer does not pay for data storage1.

References = Introduction to Secure Data Sharing | Snowflake Documentation

The STRIP_NULL_VALUE function in Snowflake is used to convert a JSON null value into a SQL NULL value1.

 To view and modify resource monitors, the least privileges needed are MONITOR and MODIFY. These privileges allow a user to monitor credit usage and make changes to resource monitors3.

The LOOP type of loop in Snowflake Scripting does not have a built-in termination condition and requires a BREAK statement to stop executing4.

To start configuring Snowflake for Single Sign-On (SSO), the CREATE SECURITY INTEGRATION command is used. This command sets up a security integration object in Snowflake, which is necessary for enabling SSO with external identity providers using SAML 2.01.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 A Pre-signed URL should be used to access files stored in a Snowflake stage without requiring authentication into Snowflake. Pre-signed URLs are simple HTTPS URLs that provide temporary access to a file via a web browser, using a pre-signed access token. The expiration time for the access token is configurable, and this type of URL allows users or applications to directly access or download the files without needing to authenticate into Snowflake5.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Statistic output in the Query Profile Overview of Snowflake provides detailed insights into the performance of different parts of the query. Specifically, it highlights the "Most expensive nodes," which are the operations or steps within the query execution that consume the most resources, such as CPU and memory. Identifying these nodes helps in pinpointing performance bottlenecks and optimizing query execution by focusing efforts on the most resource-intensive parts of the query.

References:

• Snowflake Documentation on Query Profile Overview: It details the components of the profile overview, emphasizing how to interpret the statistics section to improve query performance by understanding which nodes are most resource-intensive.

QUSTION NO: 582

How do secure views compare to non-secure views in Snowflake?

A. Secure views execute slowly compared to non-secure views.

B. Non-secure views are preferred over secure views when sharing data.

C. Secure views are similar to materialized views in that they are the most performant.

D. There are no performance differences between secure and non-secure views.

Answer: D

Secure views and non-secure views in Snowflake are differentiated primarily by their handling of data access and security rather than performance characteristics. A secure view enforces row-level security and ensures that the view definition is hidden from the users. However, in terms of performance, secure views do not inherently execute slower or faster than non-secure views. The performance of both types of views depends more on other factors such as underlying table design, query complexity, and system workload rather than the security features embedded in the views themselves.

References:

• Snowflake Documentation on Views: This section provides an overview of both secure and non-secure views, clarifying that the main difference lies in security features rather than performance, thus supporting the assertion that there are no inherent performance differences.

QUSTION NO: 583

When using SnowSQL, which configuration options are required when unloading data from a SQL query run on a local machine? {Select TWO).

A. echo

B. quiet

C. output_file

D. output_format

E. force_put_overwrite

Answer: C, D

When unloading data from SnowSQL (Snowflake's command-line client), to a file on a local machine, you need to specify certain configuration options to determine how and where the data should be outputted. The correct configuration options required are:

• C. output_file: This configuration option specifies the file path where the output from the query should be stored. It is essential for directing the results of your SQL query into a local file, rather than just displaying it on the screen.
• D. output_format: This option determines the format of the output file (e.g., CSV, JSON, etc.). It is crucial for ensuring that the data is unloaded in a structured format that meets the requirements of downstream processes or systems.

These options are specified in the SnowSQL configuration file or directly in the SnowSQL command line. The configuration file allows users to set defaults and customize their usage of SnowSQL, including output preferences for unloading data.

References:

• Snowflake Documentation: SnowSQL (CLI Client) at Snowflake Documentation
• Snowflake Documentation: Configuring SnowSQL at Snowflake Documentation

QUSTION NO: 584

How can a Snowflake user post-process the result of SHOW FILE FORMATS?

A. Use the RESULT_SCAN function.

B. Create a CURSOR for the command.

C. Put it in the FROM clause in brackets.

D. Assign the command to RESULTSET.

Answer: A

first run SHOW FILE FORMATS

then SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID(-1)))

https://docs.snowflake.com/en/sql-reference/functions/result_scan#usage-notes

QUSTION NO: 585

Which file function gives a user or application access to download unstructured data from a Snowflake stage?

A. BUILD_SCOPED_FILE_URL

B. BUILD_STAGE_FILE_URL

C. GET_PRESIGNED_URL

D. GET STAGE LOCATION

Answer: C

The function that provides access to download unstructured data from a Snowflake stage is:

• C. GET_PRESIGNED_URL: This function generates a presigned URL for a single file within a stage. The generated URL can be used to directly access or download the file without needing to go through Snowflake. This is particularly useful for unstructured data such as images, videos, or large text files, where direct access via a URL is needed outside of the Snowflake environment.

Example usage:

SELECT GET_PRESIGNED_URL('stage_name', 'file_path');

This function simplifies the process of securely sharing or accessing files stored in Snowflake stages with external systems or users.

References:

• Snowflake Documentation: GET_PRESIGNED_URL Function at Snowflake Documentation

QUSTION NO: 586

When should a multi-cluster virtual warehouse be used in Snowflake?

A. When queuing is delaying query execution on the warehouse

B. When there is significant disk spilling shown on the Query Profile

C. When dynamic vertical scaling is being used in the warehouse

D. When there are no concurrent queries running on the warehouse

Answer: A

A multi-cluster virtual warehouse in Snowflake is designed to handle high concurrency and workload demands by allowing multiple clusters of compute resources to operate simultaneously. The correct scenario to use a multi-cluster virtual warehouse is:

• A. When queuing is delaying query execution on the warehouse: Multi-cluster warehouses are ideal when the demand for compute resources exceeds the capacity of a single cluster, leading to query queuing. By enabling additional clusters, you can distribute the workload across multiple compute clusters, thereby reducing queuing and improving query performance.

This is especially useful in scenarios with fluctuating workloads or where it's critical to maintain low response times for a large number of concurrent queries.

References:

• Snowflake Documentation: Multi-Cluster Warehouses at Snowflake Documentation

QUSTION NO: 587

A JSON object is loaded into a column named data using a Snowflake variant datatype. The root node of the object is BIKE. The child attribute for this root node is BIKEID.

Which statement will allow the user to access BIKEID?

A. select data:BIKEID

B. select data.BIKE.BIKEID

C. select data:BIKE.BIKEID

D. select data:BIKE:BIKEID

Answer: C

In Snowflake, when accessing elements within a JSON object stored in a variant column, the correct syntax involves using a colon (:) to navigate the JSON structure. The BIKEID attribute, which is a child of the BIKE root node in the JSON object, is accessed using data:BIKE.BIKEID. This syntax correctly references the path through the JSON object, utilizing the colon for JSON field access and dot notation to traverse the hierarchy within the variant structure.References: Snowflake documentation on accessing semi-structured data, which outlines how to use the colon and dot notations for navigating JSON structures stored in variant columns.

QUSTION NO: 588

Which Snowflake tool is recommended for data batch processing?

A. SnowCD

B. SnowSQL

C. Snowsight

D. The Snowflake API

Answer: B

For data batch processing in Snowflake, the recommended tool is:

• B. SnowSQL: SnowSQL is the command-line client for Snowflake. It allows for executing SQL queries, scripts, and managing database objects. It's particularly suitable for batch processing tasks due to its ability to run SQL scripts that can execute multiple commands or queries in sequence, making it ideal for automated or scheduled tasks that require bulk data operations.

SnowSQL provides a flexible and powerful way to interact with Snowflake, supporting operations such as loading and unloading data, executing complex queries, and managing Snowflake objects from the command line or through scripts.

References:

• Snowflake Documentation: SnowSQL (CLI Client) at Snowflake Documentation

QUSTION NO: 589

How does the Snowflake search optimization service improve query performance?

A. It improves the performance of range searches.

B. It defines different clustering keys on the same source table.

C. It improves the performance of all queries running against a given table.

D. It improves the performance of equality searches.

Answer: D

The Snowflake Search Optimization Service is designed to enhance the performance of specific types of queries on large tables. The correct answer is:

• D. It improves the performance of equality searches: The service optimizes the performance of queries that use equality search conditions (e.g., WHERE column = value). It creates and maintains a search index on the table's columns, which significantly speeds up the retrieval of rows based on those equality search conditions.

This optimization is particularly beneficial for large tables where traditional scans might be inefficient for equality searches. By using the Search Optimization Service, Snowflake can leverage the search indexes to quickly locate the rows that match the search criteria without scanning the entire table.

References:

• Snowflake Documentation: Search Optimization Service at Snowflake Documentation

QUSTION NO: 590

What compute resource is used when loading data using Snowpipe?

A. Snowpipe uses virtual warehouses provided by the user.

B. Snowpipe uses an Apache Kafka server for its compute resources.

C. Snowpipe uses compute resources provided by Snowflake.

D. Snowpipe uses cloud platform compute resources provided by the user.

Answer: C

Snowpipe is Snowflake's continuous data ingestion service that allows for loading data as soon as it's available in a cloud storage stage. Snowpipe uses compute resources managed by Snowflake, separate from the virtual warehouses that users create for querying data. This means that Snowpipe operations do not consume the computational credits of user-created virtual warehouses, offering an efficient and cost-effective way to continuously load data into Snowflake.

References:

• Snowflake Documentation: Understanding Snowpipe

QUSTION NO: 591

What is one of the characteristics of data shares?

A. Data shares support full DML operations.

B. Data shares work by copying data to consumer accounts.

C. Data shares utilize secure views for sharing view objects.

D. Data shares are cloud agnostic and can cross regions by default.

Answer: C

Data sharing in Snowflake allows for live, read-only access to data across different Snowflake accounts without the need to copy or transfer the data. One of the characteristics of data shares is the ability to use secure views. Secure views are used within data shares to restrict the access of shared data, ensuring that consumers can only see the data that the provider intends to share, thereby preserving privacy and security.

References:

• Snowflake Documentation: Understanding Secure Views in Data Sharing

QUSTION NO: 592

Which DDL/DML operation is allowed on an inbound data share?

A. ALTER TA3LE

B. INSERT INTO

C. MERGE

D. SELECT

Answer: D

In Snowflake, an inbound data share refers to the data shared with an account by another account. The only DDL/DML operation allowed on an inbound data share is SELECT. This restriction ensures that the shared data remains read-only for the consuming account, maintaining the integrity and ownership of the data by the sharing account.

References:

• Snowflake Documentation: Using Data Shares

QUSTION NO: 593

In Snowflake, the use of federated authentication enables which Single Sign-On (SSO) workflow activities? (Select TWO).

A. Authorizing users

B. Initiating user sessions

C. Logging into Snowflake

D. Logging out of Snowflake

E. Performing role authentication

Answer: B C

Federated authentication in Snowflake allows users to use their organizational credentials to log in to Snowflake, leveraging Single Sign-On (SSO). The key activities enabled by this setup include:

• B. Initiating user sessions: Federated authentication streamlines the process of starting a user session in Snowflake by using the existing authentication mechanisms of an organization.
• C. Logging into Snowflake: It simplifies the login process, allowing users to authenticate with their organization's identity provider instead of managing separate credentials for Snowflake.

References:

• Snowflake Documentation: Configuring Federated Authentication

QUSTION NO: 594

A user wants to upload a file to an internal Snowflake stage using a put command.

Which tools and or connectors could be used to execute this command? (Select TWO).

A. SnowCD

B. SnowSQL

C. SQL API

D. Python connector

E. Snowsight worksheets

Answer: B, E

To upload a file to an internal Snowflake stage using a PUT command, you can use:

• B. SnowSQL: SnowSQL, the command-line client for Snowflake, supports the PUT command, allowing users to upload files directly to Snowflake stages from their local file systems.
• E. Snowsight worksheets: Snowsight, the web interface for Snowflake, provides a user-friendly environment for executing SQL commands, including the PUT command, through its interactive worksheets.

References:

• Snowflake Documentation: Loading Data into Snowflake using SnowSQL
• Snowflake Documentation: Using Snowsight

 In a managed access schema, the schema owner retains the OWNERSHIP privilege of any tables added to the schema. This means that while object owners have certain privileges over the objects they create, only the schema owner can manage privilege grants on these objects1.

Snowflake’s Time Travel feature is used for the maintenance of historical data, allowing users to access and restore data that has been changed or deleted within a defined period4.

The Snowflake data types that can be used to build nested hierarchical data are OBJECT and VARIANT. These data types support the storage and querying of semi-structured data, allowing for the creation of complex, nested data structures

 To validate data unloaded using the COPY INTO command, a Snowflake user can use the VALIDATION_MODE parameter within the SQL statement to test the files for errors without loading them3.

 Once an internal stage is dropped in Snowflake, it cannot be recovered or restored using Time Travel or UNDROP commands. The only option is to recreate the dropped stage

 One of the characteristics of materialized views in Snowflake is that they allow multiple tables to be joined in the underlying query. This enables the pre-computation of complex queries involving joins, which can significantly improve the performance of subsequent queries that access the materialized view4. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Snowflake command to unload the result of a query to a single file is COPY INTO with SINGLE = TRUE followed by a GET command to download the file. This command unloads the query result into a single file in the specified internal stage

A directory table in Snowflake is used to store file-level metadata about the data files in a stage. It is conceptually similar to an external table and provides information such as file size, last modified timestamp, and file URL. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 A Scoped URL permits temporary access to a staged file without the need to grant privileges to the stage or to issue access tokens. It provides a secure way to share access to files stored in Snowflake

When federated authentication is activated in Snowflake, users authenticate via an external identity provider (IdP) rather than using Snowflake-managed credentials. Therefore, a user with a password defined by Snowflake will be unable to enter a password and must use their IdP credentials to log in.

QUSTION NO: 579

What value provides information about disk usage for operations where intermediate results do not fit in memory in a Query Profile?

A. IO

B. Network

C. Pruning

D. Spilling

Answer: D

In Snowflake, when a query execution requires more memory than what is available, Snowflake handles these situations by spilling the intermediate results to disk. This process is known as "spilling." The Query Profile in Snowflake includes a metric that helps users identify when and how much data spilling occurs during the execution of a query. This information is crucial for optimizing queries as excessive spilling can significantly slow down query performance. The value that provides this information about disk usage due to intermediate results not fitting in memory is appropriately labeled as "Spilling" in the Query Profile.

References:

• Snowflake Documentation on Query Profile and Performance: This section explains the various components of the query profile, including the spilling metric, which indicates disk usage for operations where intermediate results exceed available memory.

The LATERAL modifier for the FLATTEN function allows joining information outside the object (such as other columns in the source table) with the flattened data, creating a lateral view that correlates with the preceding tables in the FROM clause2345. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake’s architecture allows for the independent scaling of compute resources, meaning you can increase or decrease the computational power as needed without affecting storage. This separation also means that storage can grow independently of compute usage, allowing for more flexible and cost-effective data management. 

SnowCD is a connectivity diagnostic tool that helps users troubleshoot network connections to Snowflake. It performs a series of checks to evaluate the network connection and provides suggestions for resolving any issues4.

The RETURN_ERRORS value in the VALIDATION_MODE option of the COPY command instructs Snowflake to validate the data files and return errors encountered across all specified files, including those that were partially loaded during an earlier load2. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 A Query Profile provides detailed execution information for a query, which can be used to analyze the performance and behavior of queries. This information can help users optimize and right-size their virtual warehouses for better efficiency. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Storing JSON data in a table with a VARIANT data type is optimal for querying because it allows Snowflake to leverage its semi-structured data capabilities. This approach enables efficient storage and querying without the need for flattening the data, which can be performance-intensive1.

 Snowflake supports the storage of semi-structured data using the ARRAY and VARIANT data types. The ARRAY data type can directly contain VARIANT, and thus indirectly contain any other data type, including itself. The VARIANT data type can store a value of any other type, including OBJECT and ARRAY, and is often used to represent semi-structured data formats like JSON, Avro, ORC, Parquet, or XML34.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The purpose of a Query Profile is to provide a detailed analysis of a particular query’s execution plan, including the mechanics, behavior, and performance. It helps in identifying potential performance bottlenecks and areas for optimization

A Stream object in Snowflake is used for change data capture (CDC), which records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes3.

 Virtual warehouses in Snowflake can be started and stopped at any time, providing flexibility in managing compute resources. They can also be resized at any time, even while running, to accommodate varying workloads910. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 The reader account compute will be billed to the provider account. Very Comprehensive Explanation: In Snowflake, when a provider creates a reader account for a consumer who does not have a Snowflake account, the compute resources used by the reader account are billed to the provider’s account. This allows the consumer to query the shared data without incurring any costs. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The SnowSQL file that can store connection information is named ‘config’. It is used to store user credentials and connection details for easy access to Snowflake instances. References: Based on general database knowledge as of 2021.

 Stored procedures in Snowflake can perform a variety of database operations, including DDL and DML, whereas UDFs are designed to return values and cannot execute database operations1.

 Snowflake maintains a result cache that stores the results of every query for 24 hours. If the same query is executed again within this time frame and the data has not changed, Snowflake will retrieve the data from the result cache instead of using a virtual warehouse to recompute the results2.

The minimum privilege required on an external stage for any role to access unstructured data files using a file URL in the GET REST API is READ. This allows the role to retrieve or download data files from the stage.

Disabling auto-suspend for a virtual warehouse is recommended when there is a steady workload, which ensures that compute resources are always available. Additionally, it is advisable to disable auto-suspend when immediate availability of compute resources is critical, eliminating any startup delay

 In the Information Schema and Account Usage, the column that identifies whether a view is secure or not is IS_SECURE2.

The Kafka connector stores formatted information in a single column of type VARIANT. This column type is used to store semi-structured data like JSON or Avro, which allows for flexibility in the data structure

The Snowflake Cloud Services layer coordinates activities within the Snowflake account. It is responsible for tasks such as authentication, infrastructure management, metadata management, query parsing and optimization, and access control. References: Based on general cloud database architecture knowledge.

 An outer join, specifically a left outer join, will list all rows from the left table and match them with rows from the right table. If there is no match, the result will still include the row from the left table, with NULLs for columns from the right table. References: Based on general SQL knowledge as of 2021.

 In a multi-cluster warehouse with an economy scaling policy, queries will wait in the queue for 2 minutes before another cluster is started. This is to minimize costs by allowing queries to queue up for a short period before adding additional compute resources. References: [COF-C02] SnowPro Core Certification Exam Study Guide

User-Defined Function (UDF) handlers must be written inline for Javascript and SQL. These languages allow the UDF logic to be included directly within the SQL statement that creates the UDF2.

The maximum size limit for a record of a VARIANT data type in Snowflake is 16MB. This allows for storing semi-structured data types like JSON, Avro, ORC, Parquet, or XML within a single VARIANT column. References: Based on general database knowledge as of 2021.

The Snowflake URL type used by directory tables is the scoped URL. This type of URL provides access to files in a stage with metadata, such as the Snowflake file URL, for each file

The parameter that prevents streams on tables from becoming stale is MAXDATAEXTENSIONTIMEINDAYS. This parameter specifies the maximum number of days for which Snowflake can extend the data retention period for the table to prevent streams on the table from becoming stale4.

 To use Partner Connect, the ACCOUNTADMIN role is required. Partner Connect allows account administrators to easily create trial accounts with selected Snowflake business partners and integrate these accounts with Snowflake

 Snowflake reorganizes data into its internal optimized, compressed, columnar format in the Database Storage layer. This process is part of how Snowflake manages data storage, ensuring efficient data retrieval and query performance

To address query concurrency issues, a user can add additional clusters to the virtual warehouse. This allows for the distribution of queries across multiple clusters, reducing the load on any single cluster and improving overall query performance2.

A materialized view is beneficial when the query consumes many compute resources every time it runs (B), and when the results of the query do not change often and are used frequently (E). This is because materialized views store pre-computed data, which can speed up query performance for workloads that are run frequently or are complex

 The kind of Snowflake table that stores file-level metadata for each file in a stage is a directory table. A directory table is an implicit object layered on a stage and stores file-level metadata about the data files in the stage3.

The semi-structured data function that interprets an input string as a JSON document and produces a VARIANT value is PARSE_JSON. This function is used to parse a JSON formatted string and return it as a VARIANT data type, which can then be used for further processing within Snowflake3.

When a database is cloned in Snowflake, it does not retain any privileges that were granted on the source object. The clone will need to have privileges reassigned as necessary for users to access it. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 In a Query Profile, the statistical information that indicates a query is too large to fit in memory includes bytes spilled to local cache and bytes spilled to local storage. These metrics suggest that the working data set of the query exceeded the memory available on the warehouse nodes, causing intermediate results to be written to disk

When file format options are specified in multiple locations, the load operation applies the options in the following order of precedence: first, the COPY INTO TABLE statement; second, the stage definition; and third, the table definition1

 Snowflake’s billing for virtual warehouses is per-second, with a minimum of 60 seconds for each time the warehouse is started or resumed. Therefore, if a warehouse runs for 61 seconds, it is billed for 61 seconds. If it is then shut down and restarted, running for an additional 30 seconds, it is billed for another 60 seconds (the minimum charge for a restart), totaling 121 seconds2

To change the existing file format type from CSV to JSON, the recommended way is to use the ALTER FILE FORMAT command with the SET TYPE=JSON clause. This alters the file format specification to use JSON instead of CSV. References: Based on my internal knowledge as of 2021.

 Snowflake’s secure data sharing feature allows sharing of certain database objects with other Snowflake accounts. Among the options provided, external tables and secure UDFs can be shared

Snowflake supports storing unstructured data and provides native support for semi-structured file formats such as JSON, Avro, Parquet, ORC, and XML1. GeoJSON, being a type of JSON, and XML are among the formats that can be stored in Snowflake. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 For the ALLOWED VALUES tag property, the maximum number of possible string values for a single tag is 256. This allows for a wide range of values to be assigned to a tag when it is set on an object

 When deciding on a clustering key for a table, Snowflake recommends using the columns that are most actively used in the select filters. This is because clustering by these columns can improve the performance of queries that filter on these values, leading to more efficient scans and better overall query performance2. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Snowflake edition that enables data sharing only through Snowflake Support is the Virtual Private Snowflake (VPS). By default, VPS does not permit data sharing outside of the VPS environment, but it can be enabled through Snowflake Support4.

The Activity area of Snowsight, Snowflake's web interface, allows users to perform several important tasks related to query management and performance analysis. Among the options provided, the correct ones are:

• B. Explore each step of an executed query: Snowsight provides detailed insights into query execution, including the ability to explore the execution plan of a query. This helps users understand how a query was processed, identify performance bottlenecks, and optimize query performance.
• C. Monitor queries executed by users in an account: The Activity area enables users to monitor the queries that have been executed by users within the Snowflake account. This includes viewing the history of queries, their execution times, resources consumed, and other relevant metrics.

These features are crucial for effective query performance tuning and ensuring efficient use of Snowflake's resources.

References:

• Snowflake Documentation on Snowsight: Using Snowsight

Parquet is a columnar storage file format that is optimized for efficiency in both storage and processing. It supports compression and encoding schemes that significantly reduce the storage space needed and speed up data retrieval operations, making it ideal for handling large volumes of data. Unlike JSON or TSV, which are row-oriented and typically uncompressed, Parquet is designed specifically for use with big data frameworks, offering advantages in terms of performance and cost when storing and querying semi-structured data.References: Apache Parquet Documentation

In Snowflake, the use of streams impacts how the data retention period for a table is handled, particularly in scenarios where the stream has not been consumed. The key point to understand is that Snowflake's streams are designed to capture data manipulation language (DML) changes such as INSERTS, UPDATES, and DELETES that occur on a source table. Streams maintain a record of these changes until they are consumed by a DML operation or a COPY command that references the stream.

When a stream is created on a table and remains unconsumed, Snowflake extends the data retention period of the table to ensure that the changes captured by the stream are preserved. This extension is specifically up to the point in time represented by the stream's offset, which effectively ensures that the data necessary for consuming the stream's contents is retained. This mechanism is in place to prevent data loss and ensure the integrity of the stream's data, facilitating accurate and reliable data processing and analysis based on the captured DML changes.

This behavior emphasizes the importance of managing streams and their consumption appropriately to balance between data retention needs and storage costs. It's also crucial to understand how this temporary extension of the data retention period impacts the overall management of data within Snowflake, including aspects related to data lifecycle, storage cost implications, and the planning of data consumption strategies.

References:

• Snowflake Documentation on Streams: Using Streams
• Snowflake Documentation on Data Retention: Understanding Data Retention

Fail-safe is a non-configurable feature in Snowflake that provides an additional layer of data protection beyond Time Travel. Time Travel allows users to access historical data within a configurable period (up to 90 days), while Fail-safe provides an additional 7-day period during which Snowflake retains historical data to recover from significant data loss or corruption incidents. This period is not accessible by users but can be used by Snowflake support to assist in data recovery efforts.References: Snowflake Documentation on Fail-safe and Time Travel

The MIN_DATA_RETENTION_TIME_IN_DAYS parameter is set at the Account level in Snowflake. This parameter specifies the minimum number of days Snowflake retains the historical data for time travel, which allows users to access and query data as it existed at previous points in time.

Here's how to understand and adjust this parameter:

• Purpose of MIN_DATA_RETENTION_TIME_IN_DAYS: This parameter is crucial for managing data lifecycle and compliance requirements within Snowflake. It determines the minimum time frame for which you can perform operations like restoring deleted objects or accessing historical versions of data.
• Setting the Parameter: Only account administrators can set or modify this parameter. It is done at the account level, impacting all databases and schemas within the account. The setting can be adjusted based on the organization's data retention policy.
• Adjusting the Parameter:

SHOW PARAMETERS LIKE 'MIN_DATA_RETENTION_TIME_IN_DAYS';

• uk.co.certification.simulator.questionpool.PList@189c9030

ALTER ACCOUNT SET MIN_DATA_RETENTION_TIME_IN_DAYS = ;

The correct SQL statement to list the version of the JDBC (Java Database Connectivity) drivers currently being used is to execute SELECT CURRENT_JDBC_VERSION(); from within SnowSQL or any client application that utilizes JDBC for connecting to Snowflake. Snowflake provides specific functions to query the version of the client drivers or connectors being used, such as JDBC, ODBC, and others. The CURRENT_JDBC_VERSION() function is designed specifically to return the version of the JDBC driver in use.

It's important to note that Snowflake supports various types of drivers and connectors for connecting to different client applications, including ODBC, JDBC, Python, and others. Each of these connectors has its own method or function for querying the current version in use. For JDBC, the appropriate function is CURRENT_JDBC_VERSION(), reflecting the specificity required to obtain version information relevant to the JDBC driver specifically.

References:

• Snowflake Documentation on Client Functions: This information can typically be found in the Snowflake documentation under the section that covers SQL functions, particularly those functions that provide information about the client or session.

The correct function to find the query ID of the second query executed in the current session is SELECT LAST_QUERY_ID(-2). The LAST_QUERY_ID function returns the query ID for the most recent query executed in the session when called with no arguments. When used with an argument, it can retrieve the ID of previous queries within the same session, where -2 would reference the second most recent query executed.

References:

• There's a clarification needed here; Snowflake's documentation indicates LAST_QUERY_ID() function does not accept arguments. It returns the ID of the last query executed in the session. To find the query ID of the second last executed query, users typically need to track query IDs manually or use session history views.

Objects in a data share become unavailable to a consumer account if the objects in the data share are deleted or if the permissions on these objects are altered without re-applying the grant permissions systematically. This is because the sharing mechanism in Snowflake relies on explicit grants of permissions on specific objects (like tables, views, or secure views) to the share. If these objects are deleted or if their permissions change without updating the share accordingly, consumers can lose access.

The DATA_RETENTION_TIME_IN_DAYS parameter does not directly affect the availability of shared objects, as it controls how long Snowflake retains historical data for time travel and does not impact data sharing permissions.

Running the GRANT IMPORTED PRIVILEGES command in the consumer account is not related to the availability of shared objects; this command is used to grant privileges on imported objects within the consumer's account and is not a routine maintenance command that would need to be run regularly.

Acquiring a data share through a private data exchange does not inherently make objects unavailable; issues would only arise if there were problems with the share configuration or if the shared objects were deleted or had their permissions altered without re-granting access to the share.

In Snowflake, configuring a virtual warehouse to be in a "Maximized" mode implies maximizing the resources allocated to the warehouse for its duration. This is done to ensure that the warehouse has a consistent amount of compute resources available, enhancing performance for workloads that require a high level of parallel processing or for handling high query volumes.

To configure a virtual warehouse in maximized mode, you should set the same value for both MIN_CLUSTER_COUNT and MAX_CLUSTER_COUNT. This configuration ensures that the warehouse operates with a fixed number of clusters, thereby providing a stable and maximized level of compute resources.

Reference to Snowflake documentation on warehouse sizing and scaling:

• Warehouse Sizing and Scaling
• Understanding Warehouses

Snowflake's Cloud Services layer is responsible for managing various aspects of the platform that are not directly related to computing or storage. Specifically, it handles authorisation, ensuring that users have appropriate access rights to perform actions or access data. Additionally, it takes care of query parsing and optimization, interpreting SQL queries and optimizing their execution plans for better performance. This layer abstracts much of the platform's complexity, allowing users to focus on their data and queries without managing the underlying infrastructure.References: Snowflake Architecture Documentation

The search optimization service in Snowflake enhances query performance by maintaining a persistent data structure. This structure indexes the values of table columns across micro-partitions, allowing Snowflake to quickly identify which micro-partitions contain relevant data for a query. By efficiently narrowing down the search space, this service reduces the amount of data scanned during query execution, leading to faster response times and more efficient use of resources.References: Snowflake Documentation on Search Optimization Service

The privilege required on a virtual warehouse to abort any existing executing queries is OPERATE. The OPERATE privilege on a virtual warehouse allows a user to perform operational tasks on the warehouse, including starting, stopping, and restarting the warehouse, as well as aborting running queries. This level of control is essential for managing resource utilization and ensuring that the virtual warehouse operates efficiently.

References:

• Snowflake Documentation on Access Control: Access Control Privileges

In Snowflake, permissions for accessing database objects, including tables, are not granted directly to users but rather to roles. A role encapsulates a collection of privileges on various Snowflake objects. Users are then granted roles, and through those roles, they inherit the permissions necessary to read a table or perform other actions. This approach adheres to the principle of least privilege, allowing for granular control over database access and simplifying the management of user permissions.

Snowflake network policies are a security feature that allows administrators to control access to Snowflake by specifying allowed and blocked IP address ranges. These policies apply to all editions of Snowflake, making them widely applicable across different Snowflake environments. They are specifically designed to restrict or enable access based on the originating IP addresses of client requests, adding an extra layer of security.

Network policies are not applied to roles but are set at the account or user level. They are not activated using ALTER DATABASE SQL commands but are managed through ALTER ACCOUNT or ALTER NETWORK POLICY commands. The management of network policies does not exclusively require the ORGADMIN role; instead, they can be managed by users with the necessary privileges on the account.

In Snowflake, the default access level for any securable object (such as a table, view, or schema) is "No access" until explicit access is granted. This means that when an object is created, only the owner of the object and roles with the necessary privileges can access it. Other users or roles will not have any form of access to the object until it is explicitly granted.

This design adheres to the principle of least privilege, ensuring that access to data is tightly controlled and that users and roles only have the access necessary for their functions. To grant access, the owner of the object or a role with the GRANT option can use the GRANT statement to provide specific privileges to other users or roles.

For example, to grant SELECT access on a table to a specific role, you would use a command similar to:

GRANT SELECT ON TABLE my_table TO ROLE my_role;

In addition to performing all the standard steps to share data, the USAGE privilege must be granted on each database referenced by a secure view in order to be shared. When sharing a database or specific objects like secure views, the receiving account needs to have the USAGE privilege on the database and schema to access the shared data. This privilege enables the receiving account to access the database and its schemas but does not allow for any DML operations. It's a prerequisite for accessing any objects within the database.

For a secure view to be part of a share, not only does the view itself need to be shared, but the underlying database (and schema, if applicable) must also be accessible to the recipients. Granting USAGE privilege on the database ensures that the receiving account can access the database in a read-only mode to utilize the shared view.

References:

• Snowflake Documentation on Shares: Creating and Managing Shares

In the execution time summary of a Query Profile in Snowflake, the categories included provide insights into various aspects of query execution. "Pruning" refers to the process by which Snowflake reduces the amount of data scanned by eliminating partitions of data that are not relevant to the query, thus improving performance. "Initialization" represents the time taken for query planning and setup before actual execution begins. These metrics are crucial for understanding and optimizing query performance.

In Snowflake, the consumption of credits by a virtual warehouse is determined by its size and the duration for which it runs. A size 3X-Large virtual warehouse consumes 128 credits if it runs continuously for 2 hours. This consumption rate is based on the principle that larger warehouses, capable of providing greater computational resources and throughput, consume more credits per hour of operation. The specific rate of consumption is defined by Snowflake’s pricing model and the scale of the virtual warehouse.References: Snowflake Pricing Documentation

Snowflake supports a variety of subquery types, including both correlated and uncorrelated subqueries. The correct answers are B and E, which highlight Snowflake's flexibility in handling subqueries within SQL queries.

• Uncorrelated Scalar Subqueries: These are subqueries that can execute independently of the outer query. They return a single value and can be used anywhere a value expression is allowed, offering great flexibility in SQL queries.
• EXISTS, ANY/ALL, and IN Subqueries: These subqueries are used in WHERE clauses to filter the results of the main query based on the presence or absence of matching rows in a subquery. Snowflake supports both correlated and uncorrelated versions of these subqueries, providing powerful tools for complex data analysis scenarios.
• Examples and Usage:

SELECT * FROM employees WHERE salary > (SELECT AVG(salary) FROM employees);

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SELECT * FROM orders o WHERE EXISTS (SELECT 1 FROM customer c WHERE c.id = o.customer_id AND c.region = 'North America');

In Snowflake, the system-defined, read-only view that displays information on column lineage, which specifies how data flows from source to target in a SQL write operation, is ACCESS_HISTORY. This view is instrumental in auditing and analyzing data access patterns, as it provides detailed insights into how and from where the data is being accessed and manipulated within Snowflake.

Reference to Snowflake documentation on ACCESS_HISTORY:

• Using Access History to Audit Data Access

When attempting to load large files, such as a 125 GB file, into Snowflake using SnowSQL, the process might encounter limitations related to the maximum execution time for queries or data loading operations. If the loading process exceeds this time limit (typically around 24 hours), it could be aborted without committing any part of the file to the database. This behavior is designed to prevent indefinite resource consumption and to maintain system stability, emphasizing the need for optimizing data load operations, possibly through file segmentation or parallel loading strategies.References: Snowflake Documentation on Data Loading Considerations

A pre-signed URL provides a way to access objects stored in a stage without requiring authentication to Snowflake. This URL contains all the necessary information for access control, including an expiration time, and it’s signed by the service's credentials. This mechanism allows users or applications to access specific files stored in a cloud storage location (stage) directly, bypassing the need to authenticate into Snowflake, ensuring secure and temporary access to the data.References: Snowflake Documentation on Stages and Data Loading

The PARTITION BY <expression> parameter option in the COPY INTO <location> command is used to split the output into multiple files based on the distinct values of the specified expression. This feature is particularly useful for organizing large datasets into smaller, more manageable files and can help with optimizing downstream processing or consumption of the data. For example, if you are unloading a large dataset of transactions and use PARTITION BY DATE(transactions.transaction_date), Snowflake generates a separate output file for each unique transaction date, facilitating easier data management and access.

This approach to data unloading can significantly improve efficiency when dealing with large volumes of data by enabling parallel processing and simplifying data retrieval based on specific criteria or dimensions.

References:

• Snowflake Documentation on Unloading Data: COPY INTO

In Snowflake, a Share is the object that contains all the information required to share a database with other Snowflake accounts. Shares are used to securely share data stored in Snowflake tables and views, enabling data providers to grant data consumers access to their datasets without duplicating data. When a database is shared, it can include one or more schemas, and each schema can contain tables, views, or both.

References:

• Snowflake Documentation on Shares: Shares

 The Snowflake Cloud Services layer manages various services, including authentication and metadata management. This layer ties together all the different components of Snowflake to process user requests, manage sessions, and control access3.

Data masking policies in Snowflake allow for the obfuscation of specific data within a field, enabling some users to see the full data while limiting others. This feature is particularly useful for handling PII, ensuring that sensitive information is only visible to authorized users1.

The SQL statement SHOW GRANTS TO ROLE MY_ROLE is used to determine which users and roles have access to a role called MY_ROLE. This statement lists all the privileges granted to the role, including which roles and users can assume MY_ROLE. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 By default, the Snowflake role required to create a share is ACCOUNTADMIN (D). This role has the necessary privileges to perform administrative tasks, including creating shares for data sharing purposes

 For loading data into Snowflake via Snowpipe, the recommended compressed file size is between 100-250 MB. This size range is optimal for balancing the performance of parallel processing and minimizing the overhead associated with handling many small files2.

The Query Profile in Snowflake is used to identify performance issues with queries. Common issues that can be found using the Query Profile include identifying inefficient micro-partition pruning (D) and data spilling to a local or remote disk (E). Micro-partition pruning is related to the efficiency of query execution, and data spilling occurs when the memory is insufficient, causing the query to write data to disk, which can slow down the query performance1.