Free Practice Questions for Google Associate Data Practitioner Certification
- Guide checked for updates:
- 18 Sep 2026
- Question bank created:
- 15 May 2026
- Question bank last updated:
- 11 Aug 2026
Study with 351 exam-style practice questions designed to help you prepare for the Google Associate Data Practitioner.
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Exam Details
Key information about Google Associate Data Practitioner
- Multiple choice
Associate
Individuals with experience securing and managing data on Google Cloud, performing tasks like data ingestion, transformation, pipeline management, analysis, machine learning, and visualization. Candidates should have a basic understanding of cloud computing concepts (IaaS, PaaS, SaaS).
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: Data Preparation and Ingestion
1.1: Prepare and process data.
Considerations include:
- Differentiate between different data manipulation methodologies (e.g., ETL, ELT, ETLT) - Choose the appropriate data transfer tool (e.g., Storage Transfer Service, Transfer Appliance) - Assess data quality - Conduct data cleaning (e.g., Cloud Data Fusion, BigQuery, SQL, Dataflow)
1.2: Extract and load data into appropriate Google Cloud storage systems.
Considerations include:
- Distinguish the format of the data (e.g., CSV, JSON, Apache Parquet, Apache Avro, structured database tables) - Choose the appropriate extraction tool (e.g., Dataflow, BigQuery Data Transfer Service, Database Migration Service, Cloud Data Fusion) - Select the appropriate storage solution (e.g., Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner, AlloyDB) - Choose the appropriate data storage location type (e.g., regional, dual-regional, multi-regional, zonal) - Classify use cases into having structured, unstructured, or semi-structured data requirements - Load data into Google Cloud storage systems using the appropriate tool (e.g., gcloud and BQ CLI, Storage Transfer Service, BigQuery Data Transfer Service, client libraries)
2: Data Analysis and Presentation
2.1: Identify data trends, patterns, and insights by using BigQuery and Jupyter notebooks.
Considerations include:
- Define and execute SQL queries in BigQuery to generate reports and extract key insights - Use Jupyter notebooks to analyze and visualize data (e.g., Colab Enterprise) - Analyze data to answer business questions
2.2: Visualize data and create dashboards in Looker given business requirements.
Considerations include:
- Create, modify, and share dashboards to answer business questions - Compare Looker and Looker Studio for different analytics use cases - Manipulate simple LookML parameters to modify a data model
2.3: Define, train, evaluate, and use ML models.
Considerations include:
- Identify ML use cases for developing models by using BigQuery ML and AutoML - Use pretrained Google large language models (LLMs) using remote connection in BigQuery - Plan a standard ML project (e.g., data collection, model training, model evaluation, prediction) - Execute SQL to create, train, and evaluate models using BigQuery ML - Perform inference using BigQuery ML models - Organize models in Model Registry
3: Data Pipeline Orchestration
3.1: Design and implement simple data pipelines.
Considerations include:
- Select a data transformation tool (e.g., Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform) based on business requirements - Evaluate use cases for ELT and ETL - Choose products required to implement basic transformation pipelines
3.2: Schedule, automate, and monitor basic data processing tasks.
Considerations include:
- Create and manage scheduled queries (e.g., BigQuery, Cloud Scheduler, Cloud Composer) - Monitor Dataflow pipeline progress using the Dataflow job UI - Review and analyze logs in Cloud Logging and Cloud Monitoring - Select a data orchestration solution (e.g., Cloud Composer, scheduled queries, Dataproc Workflow Templates, Workflows) based on business requirements - Identify use cases for event-driven data ingestion from Pub/Sub to BigQuery - Use Eventarc triggers in event-driven pipelines (Dataform, Dataflow, Cloud Functions, Cloud Run, Cloud Composer)
4: Data Management
4.1: Configure access control and governance.
Considerations include:
- Establish the principles of least privileged access by using Identity and Access Management (IAM) - Differentiate between basic roles, predefined roles, and permissions for data services (e.g., BigQuery, Cloud Storage) - Compare methods of access control for Cloud Storage (e.g., public or private access, uniform access) - Determine when to share data using Analytics Hub
4.2: Configure lifecycle management.
Considerations include:
- Determine the appropriate Cloud Storage classes based on the frequency of data access and retention requirements - Configure rules to delete objects after a specified period to automatically remove unnecessary data and reduce storage expenses (e.g., BigQuery, Cloud Storage) - Evaluate Google Cloud services for archiving data given business requirements
4.3: Identify high availability and disaster recovery strategies for data in Cloud Storage and Cloud SQL.
Considerations include:
- Compare backup and recovery solutions offered as Google-managed services - Determine when to use replication - Distinguish between primary and secondary data storage location type (e.g., regions, dual-regions, multi-regions, zones) for data redundancy
4.4: Apply security measures and ensure compliance with data privacy regulations.
Considerations include:
- Identify use cases for customer-managed encryption keys (CMEK), customer-supplied encryption keys (CSEK), and Google-managed encryption keys (GMEK) - Understand the role of Cloud Key Management Service (Cloud KMS) to manage encryption keys - Identify the difference between encryption in transit and encryption at rest
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