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Google Associate Data Practitioner Sample Questions:

1. Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

A) Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
B) Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
C) Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.
D) Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.

2. 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.

3. You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

A) Use Cloud Run functions to transform and load the data into BigQuery.
B) Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
C) Use Dataflow to transform the data and write the transformed data to BigQuery.
D) Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.

4. 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) 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.
B) Grant the data analyst the BigQuery Data Viewer 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) Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.

5. 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 Dataform workflows.
C) Use Dataflow pipelines.
D) Use Cloud Data Fusion pipelines.

Solutions: