Purchase Google : ADP Exam Materials and then pass exam easily

Last Updated: Oct 27, 2026

No. of Questions: 66 Questions & Answers with Testing Engine

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Google ADP Practice Q&A's

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Google ADP Exam Syllabus Topics:

SectionWeightObjectives
Data Visualization and Insights20-30%- Interpret and communicate findings
- Choose appropriate visualization types
- Create dashboards and reports
- Build visualizations using Looker Studio
- Present data insights to stakeholders
Data-Driven Decision Making10-20%- Assess data quality and completeness
- Identify stakeholders and requirements
- Translate business requirements into data solutions
- Define success metrics
Data Preparation and Exploration20-30%- Perform exploratory data analysis (EDA)
- Transform and prepare data for analysis
- Explore data through visualization and queries
- Identify data quality issues
- Ingest and acquire data
Data Processing and Analytics20-30%- Build and maintain data pipelines
- Aggregate and summarize data
- Query and analyze datasets
- Apply statistical methods for analysis
- Use BigQuery and SQL for analytics

Google Associate Data Practitioner Sample Questions:

Question #1

Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

  • A. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
  • B. Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
  • C. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
  • D. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #2

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. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
  • B. 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.
  • C. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
  • D. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #3

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. Evaluate the data skewness.
  • B. Compare the confusion matrix.
  • C. Compare the two different models.
  • D. Evaluate data drift.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #4

Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

  • A. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
  • B. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
  • C. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
  • D. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #5

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 Cloud Data Fusion to visually design and manage the pipelines.
  • B. Use Dataproc to run MapReduce jobs for distributed data processing.
  • C. Use Dataform to build, orchestrate, and monitor the pipelines.
  • D. Use Cloud Composer to orchestrate and run data workflows.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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