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Google Associate Data Practitioner Sample Questions:

1. You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types.
You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?

A) Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
B) Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
C) Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.
D) Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.

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

3. You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

A) Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
B) Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
C) Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
D) Grant each partner read access to the BigQuery dataset by using 1AM roles.

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

5. You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A) Use Cloud Run functions to trigger the Spark job and email the report.
B) Use Cloud Composer to orchestrate the Spark job and email the report.
C) Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
D) Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.

Solutions: