Associate-Data-Practitioner Google Cloud Associate Data Practitioner ( ADP Exam ) Questions and Answers

Audio files from phone interactions: Use Cloud Storage. Cloud Storage is ideal for storing large binary objects like audio files, offering scalability and easy accessibility for training purposes.

CSV files of users’ personally identifiable information (PII): Use BigQuery. BigQuery is a serverless data warehouse optimized for analyzing structured data, such as CSV files, using SQL. It ensures compliance with PII handling through access controls and data encryption.

A large volume of small document files: Use Firestore. Firestore is a scalable NoSQL database designed for applications requiring fast, real-time interactions and structured document storage, making it suitable for powering other applications.

Using theStorage Transfer Serviceis the best solution for migrating data from an on-premises NFS to Google Cloud. This service allows you to control bandwidth usage by configuring transfer speed limits and provides detailed reporting on the migration status. Storage Transfer Service is specifically designed for large-scale data migrations and supports scheduling, monitoring, and error handling, making it an efficient and reliable choice for your use case.

Scheduling the creation of asnapshot of the tableweekly ensures that you have a point-in-time backup of the table. In case of accidental deletion, you can re-create the table from the snapshot. Additionally, BigQuery'stime travelfeature allows you to recover data from up to seven days prior to deletion. Combining snapshots with time travel provides a robust solution for preventing data loss and ensuring reporting continuity for critical tables. This approach minimizes risks while offering flexibility for recovery.

UsingBigQuery's built-in functionsis the most effective and efficient way to clean the dataset directly within BigQuery. BigQuery provides powerful SQL capabilities to handle missing values, remove duplicates, and resolve formatting issues without needing to export data or create complex pipelines. This approach minimizes overhead and leverages the scalability of BigQuery for large datasets, making it an ideal solution for quickly addressing data quality issues.

Using a Colab Enterprise notebook connected to BigQuery provides a managed, collaborative environment ideal for complex statistical calculations, Python packages, and visualizations. Colab Enterprise supports Python libraries for advanced analytics and offers seamless integration with BigQuery for querying large datasets. It allows teams to collaboratively develop and share analyses while taking advantage of its visualization capabilities. This approach is particularly suitable for tasks involving sophisticated computations and custom visualizations.

UsingCloud Data Fusionis the best solution for this scenario because:

Standardized managed solution: Cloud Data Fusion provides a visual interface for building data pipelines and includes prebuilt connectors and transformations for data cleaning and de-identification.

Compliance: It ensures sensitive data such as PII is de-identified prior to ingestion into Google Cloud, adhering to regulatory requirements for healthcare data.

Ease of use: Cloud Data Fusion is designed for transforming and preparing data, making it a managed and user-friendly tool for this purpose.

Creating arow-level access policyin BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative's region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.

Using asales_region user attributeis the best solution because it allows you to dynamically filter data based on each manager's assigned region. By adding anaccess_filterExplore filter on theregion_namedimension that references thesales_regionuser attribute, each manager sees only the sales metrics specific to their region. This approach is easy to implement, scalable, and avoids duplicating dashboards or Explores, making it both efficient and maintainable.

Creating amaterialized viewwith the limited data in a new dataset and granting the data analyst theBigQuery Data Viewerrole on the dataset and theBigQuery Job Userrole in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.

Defining a newmeasurein LookML to calculate the profit margin using the existing revenue and cost fields is the most efficient and straightforward solution. This approach allows you to dynamically compute the profit margin directly within your Looker Explores and dashboards without needing to pre-calculate or create additional tables. The measure can be defined using LookML syntax, such as:

measure: profit_margin {

type: number

sql: (revenue - cost) / revenue ;;

value_format: "0.0%"

}

This method is quick to implement and integrates seamlessly into your existing Looker model, enabling accurate visualization of profit margins across your products.

Using theBigQuery Python client libraryto query and preprocess data directly in BigQuery and then leveragingBigQueryMLto train the churn prediction model is the Google-recommended approach for this scenario. BigQueryML allows you to build machine learning models directly within BigQuery using SQL, eliminating the need to export data or manage additional infrastructure. This minimizes overhead, scales effectively for a dataset as large as 50 PB, and simplifies the end-to-end process of building and training the churn prediction model.

Using aGoogle-provided Dataflow templateis the most efficient and development-friendly approach to implement near real-time analytics for Pub/Sub messages. Dataflow templates are pre-built and optimized for processing streaming data, allowing you to quickly configure and deploy a pipeline with minimal development effort. These templates can handle message ingestion from Pub/Sub, perform necessary transformations, and load the processed data into BigQuery, ensuring scalability and low latency for near real-time analytics.

To troubleshoot intermittent failures in a Dataflow pipeline, you should useCloud Loggingto view detailed error messages in the pipeline's logs. These logs provide insights into the specific issues causing failures, such as data format errors or resource limitations. Additionally, you should useCloud Monitoringto analyze the pipeline's metrics, such as CPU utilization, memory usage, and throughput, to identify performance bottlenecks or resource constraints that may contribute to the failures. This approach provides a comprehensive view of the pipeline's health and helps pinpoint the root cause of the intermittent issues.

To troubleshoot a failed Dataflow job as quickly as possible, you should navigate to theDataflow Jobs page in the Google Cloud console. The console provides access to detailed job logs and worker logs, which can help you identify the cause of the failure. The graphical interface also allows you to visualize pipeline stages, monitor performance metrics, and pinpoint where the error occurred, making it the most efficient way to diagnose and resolve the issue promptly.

Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.

Using customer-managed encryption keys (CMEK) allows you to encrypt highly sensitive data in BigQuery with encryption keys managed by your organization. When an employee leaves the organization, you can render the data unreadable by deleting or revoking access to the encryption keys associated with the data. This approach ensures compliance with strict data privacy regulations by making the data inaccessible without the encryption keys, providing strong control over data access and security.

UsingTransfer Appliancesis the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.

Configuring alifecycle management policyon each Cloud Storage bucket allows you to automatically transition objects to lower-cost storage classes (such as Nearline, Coldline, or Archive) based on their age or other criteria. Additionally, the policy can automate the removal of objects once they are no longer needed, ensuring compliance with retention rules and optimizing storage costs. This approach aligns well with well-defined data tiering and retention needs, providing cost efficiency and automation.