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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Analysis and Presentation | 27% | - Data exploration and analysis
|
| Data Management and Governance | 25% | - Data security and access control
|
| Data Preparation and Ingestion | 30% | - Data formats and classification
|
| Data Pipeline Orchestration | 18% | - Transformation tools selection
|
Google Associate Data Practitioner Sample Questions:
You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?
- A. Use Dataform to build, orchestrate, and monitor the pipelines.
- B. Use Cloud Composer to orchestrate and run data workflows.
- C. Use Cloud Data Fusion to visually design and manage the pipelines.
- D. Use Dataproc to run MapReduce jobs for distributed data processing.
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. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
- B. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
- C. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
- D. 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.
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?
- A. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
- B. Migrate the Spark jobs to Dataproc on Compute Engine.
- C. Migrate the Spark jobs to Dataproc Serverless.
- D. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
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.
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?
- A. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
- B. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
- C. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
- D. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
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