Special Summer Sale Limited Time 70% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: cramtick70

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

Questions 4

You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

Options:

A.

Enable access control by using IAM roles.

B.

Update dataset privileges by using the SQL GRANT statement.

C.

Export the data to Cloud Storage, and use signed URLs to authorize access.

D.

Encrypt the data by using customer-managed encryption keys (CMEK).

Buy Now
Questions 5

Your company is adopting BigQuery as their data warehouse platform. Your team has experienced Python developers. You need to recommend a fully-managed tool to build batch ETL processes that extract data from various source systems, transform the data using a variety of Google Cloud services, and load the transformed data into BigQuery. You want this tool to leverage your team’s Python skills. What should you do?

Options:

A.

Use Dataform with assertions.

B.

Deploy Cloud Data Fusion and included plugins.

C.

Use Cloud Composer with pre-built operators.

D.

Use Dataflow and pre-built templates.

Buy Now
Questions 6

Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

Options:

A.

Use a Transfer Appliance.

B.

Use Cloud Storage FUSE.

C.

Use Storage Transfer Service.

D.

Use gcloud storage commands.

Buy Now
Questions 7

You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

Options:

A.

Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.

B.

Use Cloud Logging to create a chart displaying the pipeline’s error logs. Use Metrics Explorer to validate the findings from the chart.

C.

Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.

D.

Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline’s metrics, such as CPU utilization and memory usage.

Buy Now
Questions 8

You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

Options:

A.

Export the data from BigQuery to a local machine. Use scikit-learn in a Jupyter notebook to build the churn prediction model.

B.

Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.

C.

Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.

D.

Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.

Buy Now
Questions 9

You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:

1. Audio files from phone interactions with support agents that will be accessed during trainings.

2. CSV files of users’ personally identifiable information (Pll) that will be analyzed with SQL.

3. A large volume of small document files that will power other applications.

You need to select the appropriate tool for each data type given the required use case, while following Google-recommended practices. Which should you choose?

Options:

A.

1. Cloud Storage

2. CloudSQL for PostgreSQL

3. Bigtable

B.

1. Filestore

2. Cloud SQL for PostgreSQL

3. Datastore

C.

1. Cloud Storage

2. BigQuery

3. Firestore

D.

1. Filestore

2. Bigtable

3. BigQuery

Buy Now
Questions 10

Another team in your organization is requesting access to a BigQuery dataset. You need to share the dataset with the team while minimizing the risk of unauthorized copying of data. You also want tocreate a reusable framework in case you need to share this data with other teams in the future. What should you do?

Options:

A.

Create authorized views in the team’s Google Cloud project that is only accessible by the team.

B.

Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.

C.

Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.

D.

Export the dataset to a Cloud Storage bucket in the team’s Google Cloud project that is only accessible by the team.

Buy Now
Questions 11

You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

Options:

A.

Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

B.

Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

C.

Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.

D.

Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.

Buy Now
Questions 12

Your company uses Looker to visualize and analyze sales data. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

Options:

A.

Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

B.

Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.

C.

Create a single Explore with all sales metrics. Build the dashboard using this Explore.

D.

Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.

Buy Now
Questions 13

Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

Options:

A.

Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

B.

Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.

C.

Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.

D.

Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.

Buy Now
Questions 14

You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?

Options:

A.

Add a policy tag in BigQuery.

B.

Create a row-level access policy.

C.

Create a data masking rule.

D.

Grant the appropriate 1AM permissions on the dataset.

Buy Now
Questions 15

Your company’s ecommerce website collects product reviews from customers. The reviews are loaded as CSV files daily to a Cloud Storage bucket. The reviews are in multiple languages and need to be translated to Spanish. You need to configure a pipeline that is serverless, efficient, and requires minimal maintenance. What should you do?

Options:

A.

Load the data into BigQuery using Dataproc. Use Apache Spark to translate the reviews by invoking the Cloud Translation API. Set BigQuery as the sink.U

B.

Use a Dataflow templates pipeline to translate the reviews using the Cloud Translation API. Set BigQuery as the sink.

C.

Load the data into BigQuery using a Cloud Run function. Use the BigQuery ML create model statement to train a translation model. Use the model to translate the product reviews within BigQuery.

D.

Load the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews.

Buy Now
Questions 16

Your company’s customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files’ metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

Options:

A.

Create an external table.

B.

Create a temporary table.

C.

Create a native table.

D.

Create an object table.

Buy Now
Questions 17

You work for an online retail company. Your company collects customer purchase data in CSV files and pushes them to Cloud Storage every 10 minutes. The data needs to be transformed and loaded into BigQuery for analysis. The transformation involves cleaning the data, removing duplicates, and enriching it with product information from a separate table in BigQuery. You need to implement a low-overhead solution that initiates data processing as soon as the files are loaded into Cloud Storage. What should you do?

Options:

A.

Use Cloud Composer sensors to detect files loading in Cloud Storage. Create a Dataproc cluster, and use a Composer task to execute a job on the cluster to process and load the data into BigQuery.

B.

Schedule a direct acyclic graph (DAG) in Cloud Composer to run hourly to batch load the data from Cloud Storage to BigQuery, and process the data in BigQuery using SQL.

C.

Use Dataflow to implement a streaming pipeline using anOBJECT_FINALIZEnotification from Pub/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.

D.

Create a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create anOBJECT_FINALIZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.

Buy Now
Questions 18

You have a BigQuery dataset containing sales data. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

Options:

A.

Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.

B.

Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.

C.

Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.

D.

Store all data in a single BigQuery table without partitioning or lifecycle policies.

Buy Now
Questions 19

You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud. What should you do?

Options:

A.

Use Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.

B.

Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.

C.

Load the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.

D.

Use Apache Beam to read the data and perform the necessary cleaning and transformation operations. Store the cleaned data in BigQuery.

Buy Now
Questions 20

You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

Options:

A.

Use Cloud Composer to orchestrate the Spark job and email the report.

B.

Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.

C.

Use Cloud Run functions to trigger the Spark job and email the report.

D.

Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.

Buy Now
Questions 21

You are designing an application that will interact with several BigQuery datasets. You need to grant the application’s service account permissions that allow it to query and update tables within the datasets, and list all datasets in a project within your application. You want to follow the principle of least privilege. Which pre-defined IAM role(s) should you apply to the service account?

Options:

A.

roles/bigquery.jobUser and roles/bigquery.dataOwner

B.

roles/bigquery.connectionUser and roles/bigquery.dataViewer

C.

roles/bigquery.admin

D.

roles/bigquery.user and roles/bigquery.filteredDataViewer

Buy Now
Questions 22

You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

Options:

A.

Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.

B.

Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.

C.

Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

D.

Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.

Buy Now
Questions 23

Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive data. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

Options:

A.

Use authorized views to share query results with the payroll specialist.

B.

Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.

C.

Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.

D.

Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.

Buy Now
Questions 24

Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company’s products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

Options:

A.

Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

B.

Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

C.

Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

D.

Apply a filter to only show products with a positive profit margin.

Buy Now
Questions 25

You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach. What should you do?

Options:

A.

Query the BigQuery table from within a Python notebook, use the Gemini API to summarize the data within the notebook, and store the summaries in BigQuery.

B.

Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the Gemini API to summarize the pre- processed data.

C.

Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.

D.

Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.

Buy Now
Questions 26

You work for a financial organization that stores transaction data in BigQuery. Your organization has a regulatory requirement to retain data for a minimum of seven years for auditing purposes. You need to ensure that the data is retained for seven years using an efficient and cost-optimized approach. What should you do?

Options:

A.

Create a partition by transaction date, and set the partition expiration policy to seven years.

B.

Set the table-level retention policy in BigQuery to seven years.

C.

Set the dataset-level retention policy in BigQuery to seven years.

D.

Export the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.

Buy Now
Exam Name: Google Cloud Associate Data Practitioner (ADP Exam)
Last Update: Apr 1, 2025
Questions: 106
Associate-Data-Practitioner pdf

Associate-Data-Practitioner PDF

$25.5  $84.99
Associate-Data-Practitioner Engine

Associate-Data-Practitioner Testing Engine

$30  $99.99
Associate-Data-Practitioner PDF + Engine

Associate-Data-Practitioner PDF + Testing Engine

$40.5  $134.99