Google ADP : Associate Data Practitioner

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 Dataflow with a custom template for the transformation logic.
• D. Use Dataproc to run the transformations on a cluster.

Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?

• A. Create a dual-region bucket, and upload the files to this bucket.
• B. Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.
• C. Create a multi-region bucket, and upload the files to this bucket.
• D. Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.

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. 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.
• C. 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.
• D. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.

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. Update dataset privileges by using the SQL GRANT statement.
• C. Enable access control by using IAM roles.
• D. Encrypt the data by using customer-managed encryption keys (CMEK).

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 view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
• D. 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.