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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

A) Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
B) Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
C) Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
D) Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

2. Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A) Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
B) Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
C) Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
D) Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I

3. 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?

A) 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.
B) 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.
C) Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
D) 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.

4. Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google- recommended approach. What should you do?

A) Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
B) Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.
C) Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
D) Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.

5. 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?

A) Use Storage Transfer Service.
B) Use a Transfer Appliance.
C) Use gcloud storage commands.
D) Use Cloud Storage FUSE.

Solutions: