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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?
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.
Dataform: https://cloud.google.com/dataform/docs
You need to create a weekly aggregated sales report based on a large volume of dat
a. You want to use Python to design an efficient process for generating this report. What should you do?
Using Dataflow with a Python-coded Directed Acyclic Graph (DAG) is the most efficient solution for generating a weekly aggregated sales report based on a large volume of data. Dataflow is optimized for large-scale data processing and can handle aggregation efficiently. Python allows you to customize the pipeline logic, and Cloud Scheduler enables you to automate the process to run weekly. This approach ensures scalability, efficiency, and the ability to process large datasets in a cost-effective manner.
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?
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).
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).
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud. You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?
Migrating the Spark jobs to Dataproc Serverless is the best approach because it allows you to run Spark workloads without the need to provision or manage clusters. Dataproc Serverless automatically scales resources based on workload requirements, simplifying operations and reducing administrative overhead. This solution is ideal for organizations that want to focus on managing their Spark code without worrying about the underlying infrastructure. It is cost-effective and fully managed, aligning well with the goal of minimizing cluster management.
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?
Since your existing data pipeline tools already support connectors to BigQuery, the most efficient approach is to use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping. This leverages your current tools, reducing migration complexity and setup time, while optimizing migration speed. By reconfiguring the data mapping within the existing pipeline, you can seamlessly direct the data into BigQuery without needing additional services or intermediary steps.
106 questions covering all exam domains, starting from $20
Exam domains verified against: Official Google Associate-Data-Practitioner exam guide, last checked September 2026.
Differentiate between data manipulation methodologies such as ETL, ELT, and ETLT. Choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery.
Define and execute SQL queries to generate reports and analyze data for business questions. Visualize data and create dashboards in Looker based on business requirements.
Select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL. Schedule, automate, and monitor basic data processing tasks using tools like Cloud Composer and BigQuery.
Establish principles of least privilege access using Identity and Access Management (IAM). Compare methods of access control for Cloud Storage and configure lifecycle management rules to manage data retention effectively.
Common questions about the exam itself
