Prepare with Google : ADP exam braindumps as your best preparation materials
Updated: Jul 03, 2026
No. of Questions: 66 Questions & Answers with Testing Engine
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Google Associate Data Practitioner Sample Questions:
1. You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?
A) Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
B) Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
C) Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
D) Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
2. Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?
A) Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
B) Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
C) Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
D) Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
3. 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 the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
B) 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.
C) Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
D) Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
4. 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 BigQuery ML to create a logistic regression model for purchase prediction.
B) Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
C) Use Colab Enterprise to develop a custom model for purchase prediction.
D) Use Vertex Al Workbench to develop a custom model for purchase prediction.
5. 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 Dataflow pipelines.
B) Use Dataform workflows.
C) Use Cloud Data Fusion pipelines.
D) Use Cloud Composer operators.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: B |
Google Associate Data Practitioner Sample Questions:
1. You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?
A) Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
B) Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
C) Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
D) Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
2. Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?
A) Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
B) Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
C) Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
D) Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
3. 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 the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
B) 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.
C) Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
D) Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
4. 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 BigQuery ML to create a logistic regression model for purchase prediction.
B) Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
C) Use Colab Enterprise to develop a custom model for purchase prediction.
D) Use Vertex Al Workbench to develop a custom model for purchase prediction.
5. 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 Dataflow pipelines.
B) Use Dataform workflows.
C) Use Cloud Data Fusion pipelines.
D) Use Cloud Composer operators.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: B |
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