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Google Associate Data Practitioner Sample Questions:

1. You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

A) Export the data to Cloud Storage, and use signed URLs to authorize access.
B) Enable access control by using IAM roles.
C) Update dataset privileges by using the SQL GRANT statement.
D) Encrypt the data by using customer-managed encryption keys (CMEK).

2. You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A) Use Dataform to define the transformations in SQLX.
B) Use Cloud Data Fusion to visually design and manage the pipeline.
C) Use Dataproc to run the transformations on a cluster.
D) Use Dataflow with a custom template for the transformation logic.

3. 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) Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
C) 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
D) Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.

4. Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

A) Create an object table.
B) Create a native table.
C) Create an external table.
D) Create a temporary table.

5. Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A) Use Cloud Scheduler to schedule the jobs to run.
B) Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
C) Use Cloud Tasks to schedule and run the jobs asynchronously.
D) Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

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