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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Analysis and Presentation | 27% | - Data visualization
|
| Data Management | 25% | - Storage and data organization
|
| Data Pipeline Orchestration | 18% | - Pipeline design and automation
|
| Data Preparation and Ingestion | 30% | - Data ingestion into Google Cloud services
|
Google Associate Data Practitioner Sample Questions:
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. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
- C. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
- D. Create a Cloud Run function that runs daily and deletes files older than seven days.
Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?
- A. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
- B. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.
- C. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
- D. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
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. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
- D. 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.
Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?
- A. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
- B. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
- C. Create a view that filters out rows that are older than one year.
- D. Require users to specify a partition filter using the alter table statement in SQL.
Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?
- A. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
- B. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
- C. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
- D. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
