Google Associate Data Practitioner Practice Exams
Last updated on Oct 21,2026- Exam Code: Associate Data Practitioner
- Exam Name: Google Cloud Associate Data Practitioner ( ADP Exam )
- Certification Provider: Google
- Latest update: Oct 21,2026
Question #31
You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re-run risk reports when new data becomes available.
What should you do?
- A . Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
- B . Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
- C . Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.
- D . Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.
Correct Answer: B
B
Explanation:
Copying the storm event dataset into your BigQuery project and using BigQuery Studio to query and visualize the data in Looker Studio is the best approach. This solution allows you to create reusable and automated workflows for generating risk reports. BigQuery handles the querying efficiently, and Looker Studio provides powerful tools for creating and sharing dynamic charts and dashboards. This setup ensures that reports can be easily re-run with updated data, minimizing manual effort and providing a scalable, interactive solution for visualizing risk reports.
B
Explanation:
Copying the storm event dataset into your BigQuery project and using BigQuery Studio to query and visualize the data in Looker Studio is the best approach. This solution allows you to create reusable and automated workflows for generating risk reports. BigQuery handles the querying efficiently, and Looker Studio provides powerful tools for creating and sharing dynamic charts and dashboards. This setup ensures that reports can be easily re-run with updated data, minimizing manual effort and providing a scalable, interactive solution for visualizing risk reports.
Question #32
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?
- 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.
Correct Answer: D
D
Explanation:
Using the BigQuery Python client library to query and preprocess data directly in BigQuery and then leveraging BigQueryML to 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.
D
Explanation:
Using the BigQuery Python client library to query and preprocess data directly in BigQuery and then leveraging BigQueryML to 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.
Question #33
You work for a financial services company that handles highly sensitive data. Due to regulatory requirements, your company is required to have complete and manual control of data encryption.
Which type of keys should you recommend to use for data storage?
- A . Use customer-supplied encryption keys (CSEK).
- B . Use a dedicated third-party key management system (KMS) chosen by the company.
- C . Use Google-managed encryption keys (GMEK).
- D . Use customer-managed encryption keys (CMEK).
Correct Answer: A
A
Explanation:
For regulatory requirements that mandate complete and manual control of data encryption, you should use customer-supplied encryption keys (CSEK). With CSEK, your company provides the encryption keys for data storage, and Google Cloud does not store or manage these keys. This approach ensures that your organization retains full control and responsibility over the encryption process, meeting strict regulatory compliance requirements.
A
Explanation:
For regulatory requirements that mandate complete and manual control of data encryption, you should use customer-supplied encryption keys (CSEK). With CSEK, your company provides the encryption keys for data storage, and Google Cloud does not store or manage these keys. This approach ensures that your organization retains full control and responsibility over the encryption process, meeting strict regulatory compliance requirements.
Question #34
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?
- A . Apply a filter to only show products with a positive profit margin.
- 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 . Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
Correct Answer: B
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:Defining a new measure in LookML is the most efficient and direct way to calculate and visualize aggregated metrics like profit margin.
Measures are designed for calculations based on existing fields.
Why other options are incorrect:A: Filtering doesn’t calculate or visualize the profit margin itself.
C: Dimensions are for categorizing data, not calculating aggregated metrics.
D: Derived tables are more complex and unnecessary for a simple calculation like profit margin, which can be done using a measure.
Reference: Looker Measures: https://cloud.google.com/looker/docs/reference/field-params/measure
Looker Dimensions: https://cloud.google.com/looker/docs/reference/field-params/dimension
Looker Derived Tables: https://cloud.google.com/looker/docs/data-modeling/derived-tables
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:Defining a new measure in LookML is the most efficient and direct way to calculate and visualize aggregated metrics like profit margin.
Measures are designed for calculations based on existing fields.
Why other options are incorrect:A: Filtering doesn’t calculate or visualize the profit margin itself.
C: Dimensions are for categorizing data, not calculating aggregated metrics.
D: Derived tables are more complex and unnecessary for a simple calculation like profit margin, which can be done using a measure.
Reference: Looker Measures: https://cloud.google.com/looker/docs/reference/field-params/measure
Looker Dimensions: https://cloud.google.com/looker/docs/reference/field-params/dimension
Looker Derived Tables: https://cloud.google.com/looker/docs/data-modeling/derived-tables
Question #35
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text.
What should you do?
- A . Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
- B . Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
- C . Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
- D . Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
Correct Answer: B
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:AutoML Natural Language is designed for text classification tasks, including sentiment analysis, and can handle diverse language patterns without extensive preprocessing.
AutoML can train a custom model with minimal coding.
Why other options are incorrect:A: Unnecessary extra preprocessing. AutoML can handle the raw data.
C: Dataproc and Spark are overkill for this task. AutoML is more efficient and easier to use.
D: Developing a custom TensorFlow model requires significant expertise and time, which is not efficient for this scenario.
Reference: AutoML Natural Language: https://cloud.google.com/natural-language/automl/docs
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:AutoML Natural Language is designed for text classification tasks, including sentiment analysis, and can handle diverse language patterns without extensive preprocessing.
AutoML can train a custom model with minimal coding.
Why other options are incorrect:A: Unnecessary extra preprocessing. AutoML can handle the raw data.
C: Dataproc and Spark are overkill for this task. AutoML is more efficient and easier to use.
D: Developing a custom TensorFlow model requires significant expertise and time, which is not efficient for this scenario.
Reference: AutoML Natural Language: https://cloud.google.com/natural-language/automl/docs
Question #35
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text.
What should you do?
- A . Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
- B . Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
- C . Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
- D . Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
Correct Answer: B
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:AutoML Natural Language is designed for text classification tasks, including sentiment analysis, and can handle diverse language patterns without extensive preprocessing.
AutoML can train a custom model with minimal coding.
Why other options are incorrect:A: Unnecessary extra preprocessing. AutoML can handle the raw data.
C: Dataproc and Spark are overkill for this task. AutoML is more efficient and easier to use.
D: Developing a custom TensorFlow model requires significant expertise and time, which is not efficient for this scenario.
Reference: AutoML Natural Language: https://cloud.google.com/natural-language/automl/docs
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:AutoML Natural Language is designed for text classification tasks, including sentiment analysis, and can handle diverse language patterns without extensive preprocessing.
AutoML can train a custom model with minimal coding.
Why other options are incorrect:A: Unnecessary extra preprocessing. AutoML can handle the raw data.
C: Dataproc and Spark are overkill for this task. AutoML is more efficient and easier to use.
D: Developing a custom TensorFlow model requires significant expertise and time, which is not efficient for this scenario.
Reference: AutoML Natural Language: https://cloud.google.com/natural-language/automl/docs
Question #37
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 to clean the data to ensure that it is accurate and consistent before using it for analysis.
What should you do?
- 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.
Correct Answer: B
B
Explanation:
Using BigQuery to batch load the data and perform cleaning and analysis with SQL is the best approach for this scenario. BigQuery provides powerful SQL capabilities to handle missing values, enforce correct data types, and remove duplicates efficiently. This method simplifies the pipeline by leveraging BigQuery’s built-in processing power for both cleaning and analysis, reducing the need for additional tools or services and minimizing complexity.
B
Explanation:
Using BigQuery to batch load the data and perform cleaning and analysis with SQL is the best approach for this scenario. BigQuery provides powerful SQL capabilities to handle missing values, enforce correct data types, and remove duplicates efficiently. This method simplifies the pipeline by leveraging BigQuery’s built-in processing power for both cleaning and analysis, reducing the need for additional tools or services and minimizing complexity.
Question #38
Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible.
What should you do?
- A . Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
- B . Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
- C . Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
- D . Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
Correct Answer: C
C
Explanation:
To troubleshoot a failed Dataflow job as quickly as possible, you should navigate to the Dataflow 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.
Extract from Google Documentation: From "Monitoring Dataflow Jobs"
(https://cloud.google.com/dataflow/docs/guides/monitoring-jobs): "To troubleshoot a failed Dataflow job quickly, go to the Dataflow Jobs page in the Google Cloud Console, where you can view job logs and worker logs to identify errors and their root causes."
Reference: Google Cloud Documentation – "Dataflow Monitoring" (https://cloud.google.com/dataflow/docs/guides/monitoring-jobs).
C
Explanation:
To troubleshoot a failed Dataflow job as quickly as possible, you should navigate to the Dataflow 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.
Extract from Google Documentation: From "Monitoring Dataflow Jobs"
(https://cloud.google.com/dataflow/docs/guides/monitoring-jobs): "To troubleshoot a failed Dataflow job quickly, go to the Dataflow Jobs page in the Google Cloud Console, where you can view job logs and worker logs to identify errors and their root causes."
Reference: Google Cloud Documentation – "Dataflow Monitoring" (https://cloud.google.com/dataflow/docs/guides/monitoring-jobs).
Question #39
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?
- 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
Correct Answer: A
A
Explanation:
roles/bigquery.jobUser:
This role allows a user or service account to run BigQuery jobs, including queries. This is necessary for the application to interact with and query the tables.
From Google Cloud documentation: "BigQuery Job User can run BigQuery jobs, including queries, load jobs, export jobs, and copy jobs."
roles/bigquery.dataOwner:
This role grants full control over BigQuery datasets and tables. It allows the service account to update tables, which is a requirement of the application.
From Google Cloud documentation: "BigQuery Data Owner can create, delete, and modify BigQuery datasets and tables. BigQuery Data Owner can also view data and run queries."
Why other options are incorrect:
B. roles/bigquery.connectionUser and roles/bigquery.dataViewer:
roles/bigquery.connectionUser is used for external connections, which is not required for this task.
roles/bigquery.dataViewer only allows viewing data, not updating it.
C. roles/bigquery.admin:
roles/bigquery.admin grants excessive permissions. Following the principle of least privilege, this role is too broad.
D. roles/bigquery.user and roles/bigquery.filteredDataViewer:
roles/bigquery.user grants the ability to run queries, but not the ability to modify data. roles/bigquery.filteredDataViewer only provides permission to view filtered data, which is not sufficient for updating tables.
Principle of Least Privilege:
The principle of least privilege is a security concept that states that a user or service account should be granted only the permissions necessary to perform its intended tasks.
By assigning roles/bigquery.jobUser and roles/bigquery.dataOwner, we provide the application with the exact permissions it needs without granting unnecessary access.
Google Cloud Documentation
Reference: BigQuery IAM roles: https://cloud.google.com/bigquery/docs/access-control-basic-roles
IAM best practices: https://cloud.google.com/iam/docs/best-practices-for-using-iam
A
Explanation:
roles/bigquery.jobUser:
This role allows a user or service account to run BigQuery jobs, including queries. This is necessary for the application to interact with and query the tables.
From Google Cloud documentation: "BigQuery Job User can run BigQuery jobs, including queries, load jobs, export jobs, and copy jobs."
roles/bigquery.dataOwner:
This role grants full control over BigQuery datasets and tables. It allows the service account to update tables, which is a requirement of the application.
From Google Cloud documentation: "BigQuery Data Owner can create, delete, and modify BigQuery datasets and tables. BigQuery Data Owner can also view data and run queries."
Why other options are incorrect:
B. roles/bigquery.connectionUser and roles/bigquery.dataViewer:
roles/bigquery.connectionUser is used for external connections, which is not required for this task.
roles/bigquery.dataViewer only allows viewing data, not updating it.
C. roles/bigquery.admin:
roles/bigquery.admin grants excessive permissions. Following the principle of least privilege, this role is too broad.
D. roles/bigquery.user and roles/bigquery.filteredDataViewer:
roles/bigquery.user grants the ability to run queries, but not the ability to modify data. roles/bigquery.filteredDataViewer only provides permission to view filtered data, which is not sufficient for updating tables.
Principle of Least Privilege:
The principle of least privilege is a security concept that states that a user or service account should be granted only the permissions necessary to perform its intended tasks.
By assigning roles/bigquery.jobUser and roles/bigquery.dataOwner, we provide the application with the exact permissions it needs without granting unnecessary access.
Google Cloud Documentation
Reference: BigQuery IAM roles: https://cloud.google.com/bigquery/docs/access-control-basic-roles
IAM best practices: https://cloud.google.com/iam/docs/best-practices-for-using-iam
Question #40
Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud. The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage.
What should you do?
- A . Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
- B . Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the – -daisy-chain option.
- C . Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
- D . Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the – -no-clobber option.
Correct Answer: A
A
Explanation:
Comprehensive and Detailed in Depth
Why A is correct: Storage Transfer Service with agent-based transfer allows for online migrations and provides the ability to set bandwidth limits.
Agents are installed on-premises and can be configured to respect network constraints.
Why other options are incorrect: B: The –daisy-chain option is not related to bandwidth control.
C: Transfer Appliance is for offline migrations and is not suitable for online transfers with bandwidth constraints.
D: The –no-clobber option prevents overwriting existing files but does not control bandwidth.
Reference: Storage Transfer Service: https://cloud.google.com/storage-transfer-service/docs
Storage Transfer Service Agents: https://cloud.google.com/storage-transfer-service/docs/agent-overview
gcloud storage cp: https://cloud.google.com/storage/docs/gsutil/commands/cp
A
Explanation:
Comprehensive and Detailed in Depth
Why A is correct: Storage Transfer Service with agent-based transfer allows for online migrations and provides the ability to set bandwidth limits.
Agents are installed on-premises and can be configured to respect network constraints.
Why other options are incorrect: B: The –daisy-chain option is not related to bandwidth control.
C: Transfer Appliance is for offline migrations and is not suitable for online transfers with bandwidth constraints.
D: The –no-clobber option prevents overwriting existing files but does not control bandwidth.
Reference: Storage Transfer Service: https://cloud.google.com/storage-transfer-service/docs
Storage Transfer Service Agents: https://cloud.google.com/storage-transfer-service/docs/agent-overview
gcloud storage cp: https://cloud.google.com/storage/docs/gsutil/commands/cp