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Google Associate Data Practitioner Sample Questions:

1. Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A) Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
B) Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
C) Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
D) Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.

2. Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A) Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
B) Create BigQuery scheduled queries to define the transformations in SQL.
C) Use Dataform to define the transformations in SQLX.
D) Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

3. 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 --no- clobber option.
B) Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
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 -- daisy-chain option.

4. 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.

5. Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

A) Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
B) Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
C) Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
D) Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.

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