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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Management | 25% | - Storage and data organization
|
| Topic 2: Data Pipeline Orchestration | 18% | - Pipeline design and automation
|
| Topic 3: Data Preparation and Ingestion | 30% | - Data ingestion into Google Cloud services
|
| Topic 4: Data Analysis and Presentation | 27% | - Data visualization
|
Google Associate Data Practitioner Sample Questions:
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?
- A. Compare the two different models.
- B. Evaluate the data skewness.
- C. Compare the confusion matrix.
- D. Evaluate data drift.
Correct Answer: D 🗳️
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?
- A. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
- B. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
- C. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
- D. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
Correct Answer: B 🗳️
Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?
- A. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
- B. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
- C. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
- D. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
Correct Answer: B 🗳️
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 Admin role.
- B. Request the Dataflow Viewer role.
- C. Request the Dataflow Worker role.
- D. Request the Dataflow Developer role.
Correct Answer: D 🗳️
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 🗳️
