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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) 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.
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) Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
D) Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.

2. Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

A) Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
B) Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
C) Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
D) Create a custom quota for each analyst in BigQuery.

3. You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

A) Use the gcloud storage command to transfer the data over the internet.
B) Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
C) Use Cloud Client Libraries to transfer the data over the internet.
D) Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

4. 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.

5. You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

A) Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
B) Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
C) Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
D) Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

Solutions: