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Google Associate Data Practitioner Sample Questions:

1. 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 the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.
C) 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.
D) Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.

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

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

4. 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) Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
B) Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
C) 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.
D) Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.

5. Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A) Enable Object Versioning.
B) Set temporary object holds.
C) Set a bucket retention policy.
D) Change the bucket storage class to Archive.

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