Google Associate Data Practitioner Practice Exams
Last updated on Oct 21,2026- Exam Code: Associate Data Practitioner
- Exam Name: Google Cloud Associate Data Practitioner ( ADP Exam )
- Certification Provider: Google
- Latest update: Oct 21,2026
Question #21
Your organization needs to implement near real-time analytics for thousands of events arriving each second in Pub/Sub. The incoming messages require transformations. You need to configure a pipeline that processes, transforms, and loads the data into BigQuery while minimizing development time.
What should you do?
- A . Use a Google-provided Dataflow template to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
- B . Create a Cloud Data Fusion instance and configure Pub/Sub as a source. Use Data Fusion to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
- C . Load the data from Pub/Sub into Cloud Storage using a Cloud Storage subscription. Create a Dataproc cluster, use PySpark to perform transformations in Cloud Storage, and write the results to BigQuery.
- D . Use Cloud Run functions to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
Correct Answer: A
A
Explanation:
Using a Google-provided Dataflow template is the most efficient and development-friendly approach to implement near real-time analytics for Pub/Sub messages. Dataflow templates are pre-built and optimized for processing streaming data, allowing you to quickly configure and deploy a pipeline with minimal development effort. These templates can handle message ingestion from Pub/Sub, perform necessary transformations, and load the processed data into BigQuery, ensuring scalability and low latency for near real-time analytics.
A
Explanation:
Using a Google-provided Dataflow template is the most efficient and development-friendly approach to implement near real-time analytics for Pub/Sub messages. Dataflow templates are pre-built and optimized for processing streaming data, allowing you to quickly configure and deploy a pipeline with minimal development effort. These templates can handle message ingestion from Pub/Sub, perform necessary transformations, and load the processed data into BigQuery, ensuring scalability and low latency for near real-time analytics.
Question #22
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost.
What should you do?
- A . Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
- B . Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
- C . Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
- D . Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
Correct Answer: B
B
Explanation:
Comprehensive and Detailed In-Depth
For a 25 TB dataset, efficiency and cost require minimizing data movement and leveraging BigQuery’s scalability within Colab Enterprise.
Option A: Exporting 25 TB to Google Drive and loading via Pandas is impractical (size limits, transfer costs) and slow.
Option B: BigQuery magic commands (%%bigquery) in Colab Enterprise allow direct querying of BigQuery data, keeping processing in the cloud, reducing costs, and enabling collaboration.
Option C: Dataproc with Spark adds cluster costs and complexity, unnecessary when BigQuery can handle the workload.
Option D: Copying 25 TB to local storage is infeasible due to size and cost.
Extract from Google Documentation: From "Using BigQuery with Colab Enterprise"
(https://cloud.google.com/colab/docs/bigquery): "You can use BigQuery magic commands (%%bigquery) in Colab Enterprise to execute SQL queries directly against BigQuery datasets, providing efficient access to large-scale data without moving it."
Reference: Google Cloud Documentation – "Colab Enterprise with BigQuery"
(https://cloud.google.com/colab/docs).
B
Explanation:
Comprehensive and Detailed In-Depth
For a 25 TB dataset, efficiency and cost require minimizing data movement and leveraging BigQuery’s scalability within Colab Enterprise.
Option A: Exporting 25 TB to Google Drive and loading via Pandas is impractical (size limits, transfer costs) and slow.
Option B: BigQuery magic commands (%%bigquery) in Colab Enterprise allow direct querying of BigQuery data, keeping processing in the cloud, reducing costs, and enabling collaboration.
Option C: Dataproc with Spark adds cluster costs and complexity, unnecessary when BigQuery can handle the workload.
Option D: Copying 25 TB to local storage is infeasible due to size and cost.
Extract from Google Documentation: From "Using BigQuery with Colab Enterprise"
(https://cloud.google.com/colab/docs/bigquery): "You can use BigQuery magic commands (%%bigquery) in Colab Enterprise to execute SQL queries directly against BigQuery datasets, providing efficient access to large-scale data without moving it."
Reference: Google Cloud Documentation – "Colab Enterprise with BigQuery"
(https://cloud.google.com/colab/docs).
Question #23
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach.
What should you do?
- A . Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
- B . Use BigQuery’s SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
- C . Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
- D . Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
Correct Answer: B
B
Explanation:
Comprehensive and Detailed In-Depth
For a small dataset with simple transformations (filtering, aggregation), Google recommends leveraging BigQuery’s native SQL capabilities to minimize cost and complexity.
Option A: Dataproc with Spark is overkill for a small dataset, incurring cluster management costs and setup time.
Option B: BigQuery can load data directly from Cloud Storage (e.g., CSV, JSON) and perform transformations using SQL in a serverless manner, avoiding additional service costs. This is the most efficient and cost-effective approach.
Option C: Cloud Data Fusion is suited for complex ETL but adds overhead (instance setup, UI design) unnecessary for simple tasks.
Option D: Dataflow is powerful for large-scale or streaming ETL but introduces unnecessary complexity and cost for a small, simple batch job.
Extract from Google Documentation: From "Loading Data into BigQuery from Cloud Storage"
(https://cloud.google.com/bigquery/docs/loading-data-cloud-storage): "You can load data directly from Cloud Storage into BigQuery and use SQL queries to transform it without needing additional processing tools, making it cost-effective for simple transformations."
Reference: Google Cloud Documentation – "BigQuery Data Loading"
(https://cloud.google.com/bigquery/docs/loading-data).
B
Explanation:
Comprehensive and Detailed In-Depth
For a small dataset with simple transformations (filtering, aggregation), Google recommends leveraging BigQuery’s native SQL capabilities to minimize cost and complexity.
Option A: Dataproc with Spark is overkill for a small dataset, incurring cluster management costs and setup time.
Option B: BigQuery can load data directly from Cloud Storage (e.g., CSV, JSON) and perform transformations using SQL in a serverless manner, avoiding additional service costs. This is the most efficient and cost-effective approach.
Option C: Cloud Data Fusion is suited for complex ETL but adds overhead (instance setup, UI design) unnecessary for simple tasks.
Option D: Dataflow is powerful for large-scale or streaming ETL but introduces unnecessary complexity and cost for a small, simple batch job.
Extract from Google Documentation: From "Loading Data into BigQuery from Cloud Storage"
(https://cloud.google.com/bigquery/docs/loading-data-cloud-storage): "You can load data directly from Cloud Storage into BigQuery and use SQL queries to transform it without needing additional processing tools, making it cost-effective for simple transformations."
Reference: Google Cloud Documentation – "BigQuery Data Loading"
(https://cloud.google.com/bigquery/docs/loading-data).
Question #24
You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege.
What should you do?
- A . Request the Dataflow Developer role.
- B . Request the Dataflow Viewer role.
- C . Request the Dataflow Worker role.
- D . Request the Dataflow Admin role.
Correct Answer: A
A
Explanation:
The Dataflow Developer role provides the necessary permissions to manage Dataflow streaming pipelines, including the ability to restart pipelines. This role adheres to the principle of least privilege, as it grants only the permissions required to manage and operate Dataflow jobs without unnecessary administrative access. Other roles, such as Dataflow Admin, would grant broader permissions, which are not needed in this scenario.
A
Explanation:
The Dataflow Developer role provides the necessary permissions to manage Dataflow streaming pipelines, including the ability to restart pipelines. This role adheres to the principle of least privilege, as it grants only the permissions required to manage and operate Dataflow jobs without unnecessary administrative access. Other roles, such as Dataflow Admin, would grant broader permissions, which are not needed in this scenario.
Question #25
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 new Cloud Composer environment and copy DAGs to the Cloud Composer dags/ folder.
- C . Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload.
Migrate all DAGs to the new Airflow environment. - D . Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
Correct Answer: B
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:Cloud Composer is a managed Apache Airflow service that provides a seamless migration path for existing Airflow DAGs.
Simply copying the DAGs to the Cloud Composer folder allows them to run directly on Google Cloud.
Why other options are incorrect:A: Cloud Workflows is a different orchestration tool, not compatible with Airflow DAGs.
C: GKE deployment requires setting up and managing a Kubernetes cluster, which is more complex.
D: Cloud Data Fusion is a data integration tool, not suitable for orchestrating existing pipelines.
Reference: Cloud Composer: https://cloud.google.com/composer/docs
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:Cloud Composer is a managed Apache Airflow service that provides a seamless migration path for existing Airflow DAGs.
Simply copying the DAGs to the Cloud Composer folder allows them to run directly on Google Cloud.
Why other options are incorrect:A: Cloud Workflows is a different orchestration tool, not compatible with Airflow DAGs.
C: GKE deployment requires setting up and managing a Kubernetes cluster, which is more complex.
D: Cloud Data Fusion is a data integration tool, not suitable for orchestrating existing pipelines.
Reference: Cloud Composer: https://cloud.google.com/composer/docs
Question #26
Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys.
What should you do?
- A . Use Google-managed encryption keys (GMEK).
- B . Use customer-managed encryption keys (CMEK).
- C . Use customer-supplied encryption keys (CSEK).
- D . Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.
Correct Answer: A
A
Explanation:
Using Google-managed encryption keys (GMEK) is the best choice when you want to encrypt sensitive data in Cloud Storage without the operational overhead of managing encryption keys. GMEK is the default encryption mechanism in Google Cloud, and it ensures that data is automatically encrypted at rest with no additional setup or maintenance required. It provides strong security while eliminating the need for manual key management.
Google Cloud encrypts all data at rest by default, and the simplest way to avoid key management overhead is to use Google-managed encryption keys (GMEK).
Option A: GMEK is fully managed by Google, requiring no user intervention, and meets the requirement of no operational overhead while ensuring encryption.
Option B: CMEK requires managing keys in Cloud KMS, adding operational overhead.
Option C: CSEK requires users to supply and manage keys externally, increasing complexity significantly.
Option D: Combining CSEK and CMEK adds unnecessary complexity and overhead for a uniform sensitive dataset.
Extract from Google Documentation: From "Encryption at Rest in Google Cloud"
(https://cloud.google.com/security/encryption-at-rest): "By default, Google Cloud automatically encrypts data at rest using Google-managed encryption keys (GMEK), providing strong security without requiring you to manage keys."
Reference: Google Cloud Documentation – "Encryption Options"
(https://cloud.google.com/storage/docs/encryption).
A
Explanation:
Using Google-managed encryption keys (GMEK) is the best choice when you want to encrypt sensitive data in Cloud Storage without the operational overhead of managing encryption keys. GMEK is the default encryption mechanism in Google Cloud, and it ensures that data is automatically encrypted at rest with no additional setup or maintenance required. It provides strong security while eliminating the need for manual key management.
Google Cloud encrypts all data at rest by default, and the simplest way to avoid key management overhead is to use Google-managed encryption keys (GMEK).
Option A: GMEK is fully managed by Google, requiring no user intervention, and meets the requirement of no operational overhead while ensuring encryption.
Option B: CMEK requires managing keys in Cloud KMS, adding operational overhead.
Option C: CSEK requires users to supply and manage keys externally, increasing complexity significantly.
Option D: Combining CSEK and CMEK adds unnecessary complexity and overhead for a uniform sensitive dataset.
Extract from Google Documentation: From "Encryption at Rest in Google Cloud"
(https://cloud.google.com/security/encryption-at-rest): "By default, Google Cloud automatically encrypts data at rest using Google-managed encryption keys (GMEK), providing strong security without requiring you to manage keys."
Reference: Google Cloud Documentation – "Encryption Options"
(https://cloud.google.com/storage/docs/encryption).
Question #27
Your company uses Looker to visualize and analyze sales data. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach.
What should you do?
- A . Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
- B . Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
- C . Create a single Explore with all sales metrics. Build the dashboard using this Explore.
- D . Use Looker’s custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
Correct Answer: C
C
Explanation:
Creating a single Explore with all the sales metrics is the Google-recommended approach. This Explore should be designed to include all relevant attributes and dimensions, enabling users to analyze sales data by region, product category, time period, and other filters like sales representatives. With a well-structured Explore, you can efficiently build a dashboard that supports filtering and drill-down functionality. This approach simplifies maintenance, provides a consistent data model, and ensures users have the flexibility to interact with and analyze the data seamlessly within a unified framework.
Looker’s recommended approach for dashboards is a single, unified Explore for scalability and usability, supporting filters and drill-downs.
Option A: Materialized views in BigQuery optimize queries but bypass Looker’s modeling layer, reducing flexibility.
Option B: Custom visualizations are for specific rendering, not multi-metric dashboards with filtering/drill-down.
Option C: Multiple Explores fragment the data model, complicating dashboard cohesion and maintenance.
Option D: A single Explore joins tables in LookML, enabling all metrics, filters (e.g., sales rep), and drill-downs to transactions, per Looker’s best practices.
Extract from Google Documentation: From "Building Dashboards in Looker"
(https://cloud.google.com/looker/docs/creating-dashboards): "Create a single Explore that models your data relationships, allowing users to filter and drill into details like individual transactions directly within a dashboard."
Reference: Looker Documentation – "Explores and Dashboards"
(https://cloud.google.com/looker/docs).
C
Explanation:
Creating a single Explore with all the sales metrics is the Google-recommended approach. This Explore should be designed to include all relevant attributes and dimensions, enabling users to analyze sales data by region, product category, time period, and other filters like sales representatives. With a well-structured Explore, you can efficiently build a dashboard that supports filtering and drill-down functionality. This approach simplifies maintenance, provides a consistent data model, and ensures users have the flexibility to interact with and analyze the data seamlessly within a unified framework.
Looker’s recommended approach for dashboards is a single, unified Explore for scalability and usability, supporting filters and drill-downs.
Option A: Materialized views in BigQuery optimize queries but bypass Looker’s modeling layer, reducing flexibility.
Option B: Custom visualizations are for specific rendering, not multi-metric dashboards with filtering/drill-down.
Option C: Multiple Explores fragment the data model, complicating dashboard cohesion and maintenance.
Option D: A single Explore joins tables in LookML, enabling all metrics, filters (e.g., sales rep), and drill-downs to transactions, per Looker’s best practices.
Extract from Google Documentation: From "Building Dashboards in Looker"
(https://cloud.google.com/looker/docs/creating-dashboards): "Create a single Explore that models your data relationships, allowing users to filter and drill into details like individual transactions directly within a dashboard."
Reference: Looker Documentation – "Explores and Dashboards"
(https://cloud.google.com/looker/docs).
Question #28
Your company is setting up an enterprise business intelligence platform. You need to limit data access between many different teams while following the Google-recommended approach.
What should you do first?
- A . Create a separate Looker Studio report for each team, and share each report with the individuals within each team.
- B . Create one Looker Studio report with multiple pages, and add each team’s data as a separate data source to the report.
- C . Create a Looker (Google Cloud core) instance, and create a separate dashboard for each team.
- D . Create a Looker (Google Cloud core) instance, and configure different Looker groups for each team.
Correct Answer: D
D
Explanation:
Comprehensive and Detailed In-Depth
For an enterprise BI platform with data access control across teams, Google recommends Looker (Google Cloud core) over Looker Studio for its robust access management. The "first" step focuses on setting up the foundation.
Option A: Looker Studio reports are lightweight but lack granular access control beyond sharing.
Creating separate reports per team is inefficient and unscalable.
Option B: One Looker Studio report with multiple pages and data sources doesn’t enforce team-level access control natively―users could access all pages/data.
Option C: Creating a Looker instance with separate dashboards per team is a step forward but skips the foundational access control setup (groups), reducing scalability.
Option D: Setting up a Looker instance and configuring groups aligns with Google’s recommendation for enterprise BI. Groups allow role-based access control (RBAC) at the model, Explore, or dashboard level, ensuring teams see only their data. This is the scalable, foundational step per Looker’s "Access Control" documentation.
Reference: Looker Documentation – "Managing Users and Groups"
(https://cloud.google.com/looker/docs/admin-users-groups).
D
Explanation:
Comprehensive and Detailed In-Depth
For an enterprise BI platform with data access control across teams, Google recommends Looker (Google Cloud core) over Looker Studio for its robust access management. The "first" step focuses on setting up the foundation.
Option A: Looker Studio reports are lightweight but lack granular access control beyond sharing.
Creating separate reports per team is inefficient and unscalable.
Option B: One Looker Studio report with multiple pages and data sources doesn’t enforce team-level access control natively―users could access all pages/data.
Option C: Creating a Looker instance with separate dashboards per team is a step forward but skips the foundational access control setup (groups), reducing scalability.
Option D: Setting up a Looker instance and configuring groups aligns with Google’s recommendation for enterprise BI. Groups allow role-based access control (RBAC) at the model, Explore, or dashboard level, ensuring teams see only their data. This is the scalable, foundational step per Looker’s "Access Control" documentation.
Reference: Looker Documentation – "Managing Users and Groups"
(https://cloud.google.com/looker/docs/admin-users-groups).
Question #29
Your company’s customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files’ metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files.
What should you do?
- A . Create an external table.
- B . Create a temporary table.
- C . Create a native table.
- D . Create an object table.
Correct Answer: D
D
Explanation:
To analyze audio files stored in a Cloud Storage bucket and represent them in BigQuery, you should create an object table. Object tables in BigQuery are designed to represent objects stored in Cloud Storage, including their metadata. This enables you to query the metadata of audio files directly from BigQuery without duplicating the data. Once the object table is created, you can use it in conjunction with other BigQuery ML workflows for inference and analysis.
D
Explanation:
To analyze audio files stored in a Cloud Storage bucket and represent them in BigQuery, you should create an object table. Object tables in BigQuery are designed to represent objects stored in Cloud Storage, including their metadata. This enables you to query the metadata of audio files directly from BigQuery without duplicating the data. Once the object table is created, you can use it in conjunction with other BigQuery ML workflows for inference and analysis.
Question #30
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional data. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost.
What should you do?
- A . Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
- B . Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
- C . Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
- D . Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
Correct Answer: C
C
Explanation:
Using Database Migration Service (DMS) to replicate the MySQL database to a Cloud SQL for MySQL instance is the best approach. DMS is a fully managed service designed for migrating databases to Google Cloud with minimal downtime and cost. It supports continuous data replication, ensuring data integrity during the migration process, and handles schema and data transfer efficiently. This solution is particularly suited for large tables and transactional data, as it maintains real-time synchronization between the source and target databases, minimizing downtime for the migration.
C
Explanation:
Using Database Migration Service (DMS) to replicate the MySQL database to a Cloud SQL for MySQL instance is the best approach. DMS is a fully managed service designed for migrating databases to Google Cloud with minimal downtime and cost. It supports continuous data replication, ensuring data integrity during the migration process, and handles schema and data transfer efficiently. This solution is particularly suited for large tables and transactional data, as it maintains real-time synchronization between the source and target databases, minimizing downtime for the migration.