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
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.
C
Explanation:
Comprehensive and Detailed in Depth
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.
Reference: Dataform: https://cloud.google.com/dataform/docs
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.
C
Explanation:
Comprehensive and Detailed in Depth
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.
Reference: Dataform: https://cloud.google.com/dataform/docs
Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department.
What should you do?
- A . Create a custom quota for each analyst in BigQuery.
- B . Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
- C . Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
- D . Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
D
Explanation:
Assigning each analyst to a separate project associated with their department and creating a single reservation for each department using BigQuery editions allows for precise cost management. By assigning each project to its department’s reservation, you can allocate fixed compute resources and budgets for each department, ensuring that their query costs are predictable and controlled. This approach aligns with your organization’s goal of creating a fixed budget for query costs while maintaining departmental separation and accountability.
Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department.
What should you do?
- A . Create a custom quota for each analyst in BigQuery.
- B . Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
- C . Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
- D . Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
D
Explanation:
Assigning each analyst to a separate project associated with their department and creating a single reservation for each department using BigQuery editions allows for precise cost management. By assigning each project to its department’s reservation, you can allocate fixed compute resources and budgets for each department, ensuring that their query costs are predictable and controlled. This approach aligns with your organization’s goal of creating a fixed budget for query costs while maintaining departmental separation and accountability.
Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google-recommended approach.
What should you do?
- A . Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
- B . Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
- C . Embed the Looker dashboard in a custom web application, and use the application’s scheduling features to send the report with personalized filters.
- D . Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.
D
Explanation:
Using the Looker Scheduler with user attribute filters is the Google-recommended approach to efficiently automate the delivery of a customized dashboard. User attribute filters allow you to dynamically customize the dashboard’s content based on the recipient’s attributes, ensuring each stakeholder sees data relevant to them. This approach is scalable, does not require creating separate models or custom scripts, and leverages Looker’s built-in functionality to automate recurring deliveries effectively.
You work for an online retail company. Your company collects customer purchase data in CSV files and pushes them to Cloud Storage every 10 minutes. The data needs to be transformed and loaded into BigQuery for analysis. The transformation involves cleaning the data, removing duplicates, and enriching it with product information from a separate table in BigQuery. You need to implement a low-overhead solution that initiates data processing as soon as the files are loaded into Cloud Storage.
What should you do?
- A . Use Cloud Composer sensors to detect files loading in Cloud Storage. Create a Dataproc cluster, and use a Composer task to execute a job on the cluster to process and load the data into BigQuery.
- B . Schedule a direct acyclic graph (DAG) in Cloud Composer to run hourly to batch load the data from Cloud Storage to BigQuery, and process the data in BigQuery using SQL.
- C . Use Dataflow to implement a streaming pipeline using an OBJECT_FINALIZE notification from Pub/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.
- D . Create a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create an OBJECT_FINALI ZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.
C
Explanation:
Using Dataflow to implement a streaming pipeline triggered by an OBJECT_FINALIZE notification from Pub/Sub is the best solution. This approach automatically starts the data processing as soon as new files are uploaded to Cloud Storage, ensuring low latency. Dataflow can handle the data cleaning, deduplication, and enrichment with product information from the BigQuery table in a scalable and efficient manner. This solution minimizes overhead, as Dataflow is a fully managed service, and it is well-suited for real-time or near-real-time data pipelines.
Extract from Google Documentation: From "Dataflow: Streaming from Cloud Storage"
(https://cloud.google.com/dataflow/docs/guides/streaming-data): "Use Dataflow with Pub/Sub notifications (e.g., object_finalize) to process files from Cloud Storage in near real-time, applying transformations and loading results into BigQuery with low operational overhead."
Reference: Google Cloud Documentation – "Dataflow Streaming"
(https://cloud.google.com/dataflow/docs).
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.
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.
You need to create a data pipeline for a new application. Your application will stream data that needs to be enriched and cleaned. Eventually, the data will be used to train machine learning models. You need to determine the appropriate data manipulation methodology and which Google Cloud services to use in this pipeline.
What should you choose?
- A . ETL; Dataflow -> BigQuery
- B . ETL; Cloud Data Fusion -> Cloud Storage
- C . ELT; Cloud Storage -> Bigtable
- D . ELT; Cloud SQL -> Analytics Hub
A
Explanation:
Comprehensive and Detailed In-Depth
Streaming data requiring enrichment and cleaning before ML training suggests an ETL (Extract, Transform, Load) approach, with a focus on real-time processing and a data warehouse for ML.
Option A: ETL with Dataflow (streaming transformations) and BigQuery (storage/ML training) is Google’s recommended pattern for streaming pipelines. Dataflow handles enrichment/cleaning, and BigQuery supports ML model training (BigQuery ML).
Option B: ETL with Cloud Data Fusion to Cloud Storage is batch-oriented and lacks streaming focus.
Cloud Storage isn’t ideal for ML training directly.
Option C: ELT (load then transform) with Cloud Storage to Bigtable is misaligned―Bigtable is for NoSQL, not ML training or post-load transformation.
Option D: ELT with Cloud SQL to Analytics Hub is for relational data and data sharing, not streaming or ML.
Reference: Google Cloud Documentation – "Dataflow: ETL Patterns"
(https://cloud.google.com/dataflow/docs/guides), "BigQuery ML"
(https://cloud.google.com/bigquery-ml).
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.
B
Explanation:
Comprehensive and Detailed in Depth
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.
Reference: Cloud Data Fusion: https://cloud.google.com/data-fusion/docs
You have an existing weekly Storage Transfer Service transfer job from Amazon S3 to a Nearline Cloud Storage bucket in Google Cloud. Each week, the job moves a large number of relatively small files. As the number of files to be transferred each week has grown over time, you are at risk of no longer completing the transfer in the allocated time frame. You need to decrease the total transfer time by replacing the process. Your solution should minimize costs where possible.
What should you do?
- A . Create a transfer job using the Google Cloud CLI, and specify the Standard storage class with the ― custom-storage-class flag.
- B . Create parallel transfer jobs using include and exclude prefixes.
- C . Create a batch Dataflow job that is scheduled weekly to migrate the data from Amazon S3 to Cloud Storage.
- D . Create an agent-based transfer job that utilizes multiple transfer agents on Compute Engine instances.
B
Explanation:
Comprehensive and Detailed in Depth
Why B is correct:Creating parallel transfer jobs by using include and exclude prefixes allows you to split the data into smaller chunks and transfer them in parallel.
This can significantly increase throughput and reduce the overall transfer time.
Why other options are incorrect:A: Changing the storage class to Standard will not improve transfer speed.
C: Dataflow is a complex solution for a simple file transfer task.
D: Agent-based transfer is suitable for large files or network limitations, but not for a large number of small files.