Google : ADP Questions & Answers
Updated: Sep 01, 2026
No. of Questions: 66 Questions & Answers with Testing Engine
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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data-Driven Decision Making | 10-20% | - Identify stakeholders and requirements - Assess data quality and completeness - Translate business requirements into data solutions - Define success metrics |
| Topic 2: Data Visualization and Insights | 20-30% | - Build visualizations using Looker Studio - Interpret and communicate findings - Create dashboards and reports - Present data insights to stakeholders - Choose appropriate visualization types |
| Topic 3: Data Processing and Analytics | 20-30% | - Apply statistical methods for analysis - Build and maintain data pipelines - Query and analyze datasets - Use BigQuery and SQL for analytics - Aggregate and summarize data |
| Topic 4: Data Preparation and Exploration | 20-30% | - Explore data through visualization and queries - Transform and prepare data for analysis - Perform exploratory data analysis (EDA) - Identify data quality issues - Ingest and acquire data |
Google Associate Data Practitioner Sample Questions:
Question 1
You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?
A. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
B. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.
C. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
D. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
Question 2
Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?
A. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
B. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
C. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
D. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
Question 3
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. Cloud Composer Cloud SQL for MySQL
B. BigQuery Analytics Hub
C. Dataproc Serverless Bigtable
D. Dataflow BigQuery
Question 4
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?
A. Use Storage Transfer Service.
B. Use Cloud Storage FUSE.
C. Use gcloud storage commands.
D. Use a Transfer Appliance.
Question 5
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?
A. Use Vertex Al Workbench to develop a custom model for purchase prediction.
B. Use Colab Enterprise to develop a custom model for purchase prediction.
C. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
D. Use BigQuery ML to create a logistic regression model for purchase prediction.
Solutions:
| Question 1 Answer: A | Question 2 Answer: A | Question 3 Answer: D | Question 4 Answer: A | Question 5 Answer: D |
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