Google ADP : Associate Data Practitioner
- Exam Code: ADP
- Exam Name: Associate Data Practitioner
- Updated: Sep 15, 2026
- Q & A: 66 Questions and Answers
About Google ADP Exam
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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Visualization and Insights | 20-30% | - Choose appropriate visualization types - Create dashboards and reports - Build visualizations using Looker Studio - Interpret and communicate findings - Present data insights to stakeholders |
| Topic 2: Data Preparation and Exploration | 20-30% | - Transform and prepare data for analysis - Ingest and acquire data - Identify data quality issues - Explore data through visualization and queries - Perform exploratory data analysis (EDA) |
| Topic 3: Data Processing and Analytics | 20-30% | - Use BigQuery and SQL for analytics - Aggregate and summarize data - Query and analyze datasets - Apply statistical methods for analysis - Build and maintain data pipelines |
| Topic 4: Data-Driven Decision Making | 10-20% | - Translate business requirements into data solutions - Identify stakeholders and requirements - Assess data quality and completeness - Define success metrics |
Google Associate Data Practitioner Sample Questions:
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. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
- B. 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.
- 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: B 🗳️
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?
- A. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
- B. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
- C. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
- D. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
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 Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
- B. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
- C. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
- D. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
Correct Answer: C 🗳️
Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?
- A. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
- B. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
- C. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
- D. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
Correct Answer: A 🗳️
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?
- A. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
- B. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
- C. Use Cloud Run functions to trigger the Spark job and email the report.
- D. Use Cloud Composer to orchestrate the Spark job and email the report.
Correct Answer: B 🗳️
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