Google Associate Data Practitioner Practice Exams
Last updated on Oct 22,2026- Exam Code: Associate Data Practitioner
- Exam Name: Google Cloud Associate Data Practitioner ( ADP Exam )
- Certification Provider: Google
- Latest update: Oct 22,2026
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code.
What should you do?
- A . Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily.
Monitor the pipeline’s execution using the Apache Airflow web interface and Cloud Monitoring. - B . Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
- C . Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job’s progress using the Dataproc job web interface and Cloud Monitoring.
- D . Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
A
Explanation:
Comprehensive and Detailed in Depth
Why A is correct:Cloud Composer is a managed Apache Airflow service, which is a popular open-source workflow orchestration tool.
DAGs in Airflow can be used to automate ETL pipelines.
Airflow’s web interface and Cloud Monitoring provide comprehensive monitoring capabilities.
It also allows you to run existing shell scripts.
Why other options are incorrect:B: Dataflow requires rewriting the ETL pipeline using its SDK.
C: Dataproc is for big data processing, not orchestration.
D: Cloud Run functions are for stateless applications, not long-running ETL pipelines.
Reference: Cloud Composer: https://cloud.google.com/composer/docs
Apache Airflow: https://airflow.apache.org/
You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed
during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost.
What should you do?
- A . Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.
- B . Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
- C . Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
- D . Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
B
Explanation:
Comprehensive and Detailed In-Depth
Cost minimization requires matching storage classes to access patterns using lifecycle rules.
Let’s assess:
Option A: Nearline during training (frequent access) incurs high retrieval costs and latency, unsuitable for ML workloads. Coldline after 30 days and Archive after 90 days are reasonable but misaligned initially.
Option B: Standard storage (no retrieval fees, low latency) is ideal for frequent access during training. Transitioning to Nearline (30-day minimum, low access) after 30 days and Coldline (90-day minimum, rare access) after 90 days matches the pattern and minimizes costs effectively.
Option C: Nearline during training is costly for frequent access, and Archive to Coldline is illogical (Archive is cheaper than Coldline).
Option D: DRA storage doesn’t exist in Google Cloud (legacy AWS term); the progression should be Standard -> Nearline -> Coldline.
Why B is Best: Standard ensures training efficiency, while Nearline and Coldline reduce costs as access drops, all manageable via lifecycle rules (e.g., SetStorageClass actions). This is Google’s recommended tiering strategy.
Extract from Google Documentation: From "Cloud Storage Classes"
(https://cloud.google.com/storage/docs/storage-classes): "Use Standard storage for frequently accessed data, such as during active ML training. Transition to Nearline after 30 days for infrequent
access, and Coldline after 90 days for rare access, optimizing costs with lifecycle management."
Reference: Google Cloud Documentation – "Storage Classes" (https://cloud.google.com/storage/docs/storage-classes).
You work for a gaming company that collects real-time player activity data. This data is streamed into Pub/Sub and needs to be processed and loaded into BigQuery for analysis. The processing involves filtering, enriching, and aggregating the data before loading it into partitioned BigQuery tables. You need to design a pipeline that ensures low latency and high throughput while following a Google-recommended approach.
What should you do?
- A . Use Cloud Composer to orchestrate a workflow that reads the data from Pub/Sub, processes the data using a Python script, and writes it to BigQuery.
- B . Use Dataproc to create an Apache Spark streaming job that reads the data from Pub/Sub, processes the data, and writes it to BigQuery.
- C . Use Dataflow to create a streaming pipeline that reads the data from Pub/Sub, processes the data, and writes it to BigQuery using the streaming API.
- D . Use Cloud Run functions to subscribe to the Pub/Sub topic, process the data, and write it to BigQuery using the streaming API.
C
Explanation:
Comprehensive and Detailed in Depth
Why C is correct:Dataflow is the recommended service for real-time stream processing on Google Cloud.
It provides scalable and reliable processing with low latency and high throughput.
Dataflow’s streaming API is optimized for Pub/Sub integration and BigQuery streaming inserts.
Why other options are incorrect:A: Cloud Composer is for batch orchestration, not real-time streaming.
B: Dataproc and Spark streaming are more complex and not as efficient as Dataflow for this task.
D: Cloud Run functions are for stateless, event-driven applications, not continuous stream processing.
Reference: Dataflow Streaming: https://cloud.google.com/dataflow/docs/concepts/streaming-pipelines
Pub/Sub to BigQuery with Dataflow: https://cloud.google.com/dataflow/docs/tutorials/pubsub-to-bigquery
Your organization stores highly personal data in BigQuery and needs to comply with strict data privacy regulations. You need to ensure that sensitive data values are rendered unreadable whenever an employee leaves the organization.
What should you do?
- A . Use AEAD functions and delete keys when employees leave the organization.
- B . Use dynamic data masking and revoke viewer permissions when employees leave the organization.
- C . Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.
- D . Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.
C
Explanation:
Using customer-managed encryption keys (CMEK) allows you to encrypt highly sensitive data in BigQuery with encryption keys managed by your organization. When an employee leaves the organization, you can render the data unreadable by deleting or revoking access to the encryption keys associated with the data. This approach ensures compliance with strict data privacy regulations by making the data inaccessible without the encryption keys, providing strong control over data access and security.
Your organization stores highly personal data in BigQuery and needs to comply with strict data privacy regulations. You need to ensure that sensitive data values are rendered unreadable whenever an employee leaves the organization.
What should you do?
- A . Use AEAD functions and delete keys when employees leave the organization.
- B . Use dynamic data masking and revoke viewer permissions when employees leave the organization.
- C . Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.
- D . Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.
C
Explanation:
Using customer-managed encryption keys (CMEK) allows you to encrypt highly sensitive data in BigQuery with encryption keys managed by your organization. When an employee leaves the organization, you can render the data unreadable by deleting or revoking access to the encryption keys associated with the data. This approach ensures compliance with strict data privacy regulations by making the data inaccessible without the encryption keys, providing strong control over data access and security.
BigQuery
Explanation:
To build a serverless data pipeline that processes data in real-time from Pub/Sub, transforms it, and stores it for SQL-based analysis using Looker, the best solution is to use Dataflow and BigQuery. Dataflow is a fully managed service for real-time data processing and transformation, while BigQuery is a serverless data warehouse that supports SQL-based querying and integrates seamlessly with Looker for data analysis and visualization. This combination meets the requirements for real-time streaming, transformation, and efficient storage for analytical queries.
BigQuery
Explanation:
To build a serverless data pipeline that processes data in real-time from Pub/Sub, transforms it, and stores it for SQL-based analysis using Looker, the best solution is to use Dataflow and BigQuery. Dataflow is a fully managed service for real-time data processing and transformation, while BigQuery is a serverless data warehouse that supports SQL-based querying and integrates seamlessly with Looker for data analysis and visualization. This combination meets the requirements for real-time streaming, transformation, and efficient storage for analytical queries.
Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages.
What should you do?
- A . Retry messages until they are acknowledged.
- B . Implement flow control on the subscribers
- C . Forward unacknowledged messages to a dead-letter topic.
- D . Seek back to the last acknowledged message.
B
Explanation:
Implementing flow control on the subscribers allows the subscriber applications to manage message processing during activity spikes by controlling the rate at which messages are pulled and processed. This prevents overwhelming the subscribers and ensures that messages are acknowledged within the deadline. Flow control helps maintain the stability of your pipeline during high-traffic periods without dropping or delaying messages unnecessarily.
Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages.
What should you do?
- A . Retry messages until they are acknowledged.
- B . Implement flow control on the subscribers
- C . Forward unacknowledged messages to a dead-letter topic.
- D . Seek back to the last acknowledged message.
B
Explanation:
Implementing flow control on the subscribers allows the subscriber applications to manage message processing during activity spikes by controlling the rate at which messages are pulled and processed. This prevents overwhelming the subscribers and ensures that messages are acknowledged within the deadline. Flow control helps maintain the stability of your pipeline during high-traffic periods without dropping or delaying messages unnecessarily.
You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege.
What should you do?
- A . Enable access control by using IAM roles.
- B . Update dataset privileges by using the SQL GRANT statement.
- C . Export the data to Cloud Storage, and use signed URLs to authorize access.
- D . Encrypt the data by using customer-managed encryption keys (CMEK).
A
Explanation:
Comprehensive and Detailed In-Depth
BigQuery uses IAM for access control, adhering to least privilege by granting only necessary permissions.
Option A: IAM roles (e.g., roles/bigquery.dataViewer for read-only) restrict query access to authorized users, aligning with Google’s security best practices.
Option B: BigQuery doesn’t support SQL GRANT for dataset privileges; access is managed via IAM or authorized views.
Option C: Exporting to Cloud Storage with signed URLs bypasses BigQuery’s native controls and adds complexity.
Option D: CMEK encrypts data at rest but doesn’t control query access.
Extract from Google Documentation: From "Controlling Access to BigQuery"
(https://cloud.google.com/bigquery/docs/access-control): "Use IAM to manage access to BigQuery resources. Predefined roles like roles/bigquery.dataViewer allow you to grant query access while following the principle of least privilege."
Reference: Google Cloud Documentation – "BigQuery IAM" (https://cloud.google.com/bigquery/docs/access-control).