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The Google Cloud Associate Data Practitioner (ADP Exam) (Associate-Data-Practitioner)

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Associate-Data-Practitioner Exam Dumps
  • Exam Code: Associate-Data-Practitioner
  • Vendor: Google
  • Certifications: Google Cloud Platform
  • Exam Name: Google Cloud Associate Data Practitioner (ADP Exam)
  • Updated: Mar 26, 2026 Free Updates: 90 days Total Questions: 106 Try Free Demo

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Google Associate-Data-Practitioner Exam Domains Q&A

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Question 1 Google Associate-Data-Practitioner
QUESTION DESCRIPTION:

Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

  • A.

    Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.

  • B.

    Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.

  • C.

    Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.

  • D.

    Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.

Correct Answer & Rationale:

Answer: B

Explanation:

Comprehensive and Detailed in Depth Explanation:

Why B is correct:Database Migration Service (DMS) is designed for migrating databases to Cloud SQL with minimal downtime and administrative overhead.

Cloud SQL for MySQL is a fully managed MySQL service, which aligns with the requirement to minimize administrative overhead.

Why other options are incorrect:A: Installing MySQL on Compute Engine requires manual management of the database instance, which increases administrative overhead.

C: BigQuery is not a direct replacement for a relational MySQL database. It's an analytical data warehouse.

D: Spanner is a globally distributed, scalable database, but it requires schema conversion and is not a direct replacement for MySQL, and it is also much more complex than cloud SQL.

[References:, , Database Migration Service: https://cloud.google.com/database-migration, Cloud SQL for MySQL: https://cloud.google.com/sql/docs/mysql]

Question 2 Google Associate-Data-Practitioner
QUESTION DESCRIPTION:

Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse. You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

  • A.

    Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

  • B.

    Create BigQuery scheduled queries to define the transformations in SQL.

  • C.

    Use Dataform to define the transformations in SQLX.

  • D.

    Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.

Correct Answer & Rationale:

Answer: C

Explanation:

Comprehensive and Detailed in Depth Explanation:

Why C is correct:Dataform is a managed data transformation service that allows you to define data pipelines using SQL and JavaScript.

It provides version control, modular code development, and data quality checks.

Why other options are incorrect:A: Cloud Composer is an orchestration tool, not a data transformation tool.

B: Scheduled queries are not suitable for complex ETL pipelines.

D: Dataproc requires setting up a Spark cluster and writing code, which is more complex than using Dataform.

[References:, Dataform: https://cloud.google.com/dataform/docs]

Question 3 Google Associate-Data-Practitioner
QUESTION DESCRIPTION:

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 materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.

  • 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 BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.

  • D.

    Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.

Correct Answer & Rationale:

Answer: A

Explanation:

Creating a materialized view in BigQuery with the SUM() function and the DATE_SUB() function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using the DATE_SUB() function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.

Question 4 Google Associate-Data-Practitioner
QUESTION DESCRIPTION:

You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

  • A.

    Use Cloud Client Libraries to transfer the data over the internet.

  • B.

    Use the gcloud storage command to transfer the data over the internet.

  • C.

    Compress the data, upload it to multiple cloud storage providers, and then transfer the data to Cloud Storage.

  • D.

    Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

Correct Answer & Rationale:

Answer: D

Explanation:

Comprehensive and Detailed In-Depth Explanation:

Transferring 300 TB over a 100 Mbps connection would take an impractical amount of time (over 300 days at theoretical maximum speed, ignoring real-world constraints like latency). Google Cloud provides the Transfer Appliance for large-scale, time-sensitive transfers.

    Option A : Cloud Client Libraries over the internet would be slow and unreliable for 300 TB due to bandwidth limitations.

    Option B : The gcloud storage command is similarly constrained by internet speed and not designed for such large transfers.

    Option C : Compressing and splitting across multiple providers adds complexity and isn’t a Google-supported method for Cloud Storage ingestion.

[: Google Cloud Documentation - "Transfer Appliance Overview" (https://cloud.google.com/transfer-appliance)., ]

Question 5 Google Associate-Data-Practitioner
QUESTION DESCRIPTION:

You are a data analyst at your organization. You have been given a BigQuery dataset that includes customer information. The dataset contains inconsistencies and errors, such as missing values, duplicates, and formatting issues. You need to effectively and quickly clean the data. What should you do?

  • A.

    Develop a Dataflow pipeline to read the data from BigQuery, perform data quality rules and transformations, and write the cleaned data back to BigQuery.

  • B.

    Use Cloud Data Fusion to create a data pipeline to read the data from BigQuery, perform data quality transformations, and write the clean data back to BigQuery.

  • C.

    Export the data from BigQuery to CSV files. Resolve the errors using a spreadsheet editor, and re-import the cleaned data into BigQuery.

  • D.

    Use BigQuery's built-in functions to perform data quality transformations.

Correct Answer & Rationale:

Answer: D

Explanation:

Using BigQuery's built-in functions is the most effective and efficient way to clean the dataset directly within BigQuery. BigQuery provides powerful SQL capabilities to handle missing values, remove duplicates, and resolve formatting issues without needing to export data or create complex pipelines. This approach minimizes overhead and leverages the scalability of BigQuery for large datasets, making it an ideal solution for quickly addressing data quality issues.

Question 6 Google Associate-Data-Practitioner
QUESTION DESCRIPTION:

Your company is adopting BigQuery as their data warehouse platform. Your team has experienced Python developers. You need to recommend a fully-managed tool to build batch ETL processes that extract data from various source systems, transform the data using a variety of Google Cloud services, and load the transformed data into BigQuery. You want this tool to leverage your team’s Python skills. What should you do?

  • A.

    Use Dataform with assertions.

  • B.

    Deploy Cloud Data Fusion and included plugins.

  • C.

    Use Cloud Composer with pre-built operators.

  • D.

    Use Dataflow and pre-built templates.

Correct Answer & Rationale:

Answer: C

Explanation:

Comprehensive and Detailed In-Depth Explanation:

The tool must be fully managed, support batch ETL, integrate with multiple Google Cloud services, and leverage Python skills.

    Option A : Dataform is SQL-focused for ELT within BigQuery, not Python-centric, and lacks broad service integration for extraction.

    Option B : Cloud Data Fusion is a visual ETL tool, not Python-focused, and requires more UI-based configuration than coding.

    Option C : Cloud Composer (managed Apache Airflow) is fully managed, supports batch ETL via DAGs, integrates with various Google Cloud services (e.g., BigQuery, GCS) through operators, and allows custom Python code in tasks. It’s ideal for Python developers per the "Cloud Composer" documentation.

[: Google Cloud Documentation - "Cloud Composer Overview" (https://cloud.google.com/composer/docs)., , , ]

Question 7 Google Associate-Data-Practitioner
QUESTION DESCRIPTION:

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.

    Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

  • 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.

    Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

  • D.

    Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

Correct Answer & Rationale:

Answer: C

Explanation:

To troubleshoot a failed Dataflow job as quickly as possible, you should navigate to the Dataflow Jobs page in the Google Cloud console . The console provides access to detailed job logs and worker logs, which can help you identify the cause of the failure. The graphical interface also allows you to visualize pipeline stages, monitor performance metrics, and pinpoint where the error occurred, making it the most efficient way to diagnose and resolve the issue promptly.

Extract from Google Documentation : From "Monitoring Dataflow Jobs" ( https://cloud.google.com/dataflow/docs/guides/monitoring-jobs ): "To troubleshoot a failed Dataflow job quickly, go to the Dataflow Jobs page in the Google Cloud Console, where you can view job logs and worker logs to identify errors and their root causes."

[: Google Cloud Documentation - "Dataflow Monitoring" (https://cloud.google.com/dataflow/docs/guides/monitoring-jobs)., , ]

Question 8 Google Associate-Data-Practitioner
QUESTION DESCRIPTION:

You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud. What should you do?

  • A.

    Use Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.

  • B.

    Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.

  • C.

    Load the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.

  • D.

    Use Apache Beam to read the data and perform the necessary cleaning and transformation operations. Store the cleaned data in BigQuery.

Correct Answer & Rationale:

Answer: B

Explanation:

Using Cloud Data Fusion is the best solution for this scenario because:

    Standardized managed solution : Cloud Data Fusion provides a visual interface for building data pipelines and includes prebuilt connectors and transformations for data cleaning and de-identification.

    Compliance : It ensures sensitive data such as PII is de-identified prior to ingestion into Google Cloud, adhering to regulatory requirements for healthcare data.

    Ease of use : Cloud Data Fusion is designed for transforming and preparing data, making it a managed and user-friendly tool for this purpose.

      It's a fully managed, cloud-native data integration service for building ETL/ELT data pipelines visually.  

      It offers built-in transformations and connectors, including those suitable for data masking and de-identification.  

      It provides a standardized, visual interface, making it easier to create and manage data pipelines across various data sources.  

      It's designed for data integration and transformation, making it ideal for this scenario.  

      It helps to achieve a standardized managed solution.

 

Question 9 Google Associate-Data-Practitioner
QUESTION DESCRIPTION:

You are building a batch data pipeline to process 100 GB of structured data from multiple sources for daily reporting. You need to transform and standardize the data prior to loading the data to ensure that it is stored in a single dataset. You want to use a low-code solution that can be easily built and managed. What should you do?

  • A.

    Use Cloud Data Fusion to ingest data and load the data into BigQuery. Use Looker Studio to perform data cleaning and transformation.

  • B.

    Use Cloud Data Fusion to ingest the data, perform data cleaning and transformation, and load the data into BigQuery.

  • C.

    Use Cloud Data Fusion to ingest the data, perform data cleaning and transformation, and load the data into Cloud SQL for PostgreSQL.

  • D.

    Use Cloud Storage to store the data. Use Cloud Run functions to perform data cleaning and transformation, and load the data into BigQuery.

Correct Answer & Rationale:

Answer: B

Explanation:

Comprehensive and Detailed in Depth Explanation:

Why B is correct:Cloud Data Fusion is a fully managed, cloud-native data integration service for building and managing ETL/ELT data pipelines.

It provides a graphical interface for building pipelines without coding, making it a low-code solution.

Cloud data fusion is perfect for the ingestion, transformation and loading of data into BigQuery.

Why other options are incorrect:A: Looker studio is for visualization, not data transformation.

C: Cloud SQL is a relational database, not ideal for large-scale analytical data.

D: Cloud run is for stateless applications, not batch data processing.

[References:, , Cloud Data Fusion: https://cloud.google.com/data-fusion/docs]

Question 10 Google Associate-Data-Practitioner
QUESTION DESCRIPTION:

Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

  • A.

    Use Cloud Data Fusion pipelines.

  • B.

    Use Dataform workflows.

  • C.

    Use Dataflow pipelines.

  • D.

    Use Cloud Composer operators.

Correct Answer & Rationale:

Answer: B

Explanation:

Dataform workflows are the ideal solution for migrating batch transformation pipelines to Google Cloud when you want to perform programmatic transformations using only SQL. Dataform allows you to define SQL-based workflows for data transformations and supports Git integration for version control, enabling collaboration and version tracking of your pipelines. This approach is purpose-built for SQL-driven data pipeline management and aligns perfectly with your requirements.

The solution must use SQL for transformations and integrate with Git for version control, focusing on batch pipelines. Let’s evaluate:

    Option A : Cloud Data Fusion uses a visual UI with plugins, not SQL-only transformations. It lacks native Git integration (requires external tools), missing a key requirement.

    Option B : Dataform is a SQL-based workflow tool for BigQuery transformations, defining pipelines as SQLX scripts. It integrates natively with Git for version control, supporting batch ELT processes with minimal overhead.

    Option C : Cloud Composer uses Python DAGs and operators, not SQL-only transformations. Git is possible but not intrinsic to its workflow design.

[: Google Cloud Documentation - "Dataform" (https://cloud.google.com/dataform)., , , ]

Google Associate-Data-Practitioner verified by Hilary Beaumont
Verified by Certified Instructors

This Google Associate-Data-Practitioner study pack was audited and verified on March 26, 2026 by Hilary Beaumont,. We ensure every technical rationale aligns with real-world enterprise standards.

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