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Google ADP Exam Overview:

Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

Question #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 Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute 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 Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.

Question #2
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. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
B. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
C. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
D. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.

Question #3
Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A. Use Cloud Composer operators.
B. Use Cloud Data Fusion pipelines.
C. Use Dataflow pipelines.
D. Use Dataform workflows.

Question #4
Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

A. Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.
B. Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.
C. Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
D. Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.

Question #5
You have a BigQuery dataset containing sales dat
a. 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?

A. 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.
B. 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.
C. Store all data in a single BigQuery table without partitioning or lifecycle policies.
D. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.

Solutions: