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Google Associate Data Practitioner Sample Questions:

1. Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A) Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
B) Create a single Explore with all sales metrics. Build the dashboard using this Explore.
C) Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
D) Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

2. 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 Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
C) 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.
D) Use Cloud Tasks to schedule and run the jobs asynchronously.

3. You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A) Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
B) Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
C) Create a Cloud Run function that runs daily and deletes files older than seven days.
D) Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.

4. Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

A) Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
B) Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
C) Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
D) Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.

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

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