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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Pipeline Orchestration | 18% | - Transformation tools selection
|
| Topic 2: Data Analysis and Presentation | 27% | - Data exploration and analysis
|
| Topic 3: Data Management and Governance | 25% | - Data security and access control
|
| Topic 4: Data Preparation and Ingestion | 30% | - Data formats and classification
|
Google Associate Data Practitioner Sample Questions:
You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?
- A. Request the Dataflow Worker role.
- B. Request the Dataflow Admin role.
- C. Request the Dataflow Developer role.
- D. Request the Dataflow Viewer role.
Correct Answer: C 🗳️
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 cross join across all of the regional sales tables. Use the rank() window function to query the new table.
- B. 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.
- C. 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.
- D. 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.
Correct Answer: B 🗳️
Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google- recommended approach. What should you do?
- A. Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.
- B. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
- C. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
- D. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
Correct Answer: A 🗳️
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?
- A. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
- B. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
- C. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
- D. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
Correct Answer: D 🗳️
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?
- A. Dataflow BigQuery
- B. BigQuery Analytics Hub
- C. Dataproc Serverless Bigtable
- D. Cloud Composer Cloud SQL for MySQL
Correct Answer: A 🗳️
