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Exam Information
Official specifications published by Google
Exam Format
120 min
50-60
Pass/Fail (approximately 70%)
Associate
Registration
$125 USD
Kryterion (Online Proctored), Kryterion (Test Center), or online proctoring
English, Japanese
Validity
3 years
Take the renewal exam within the renewal eligibility period (starts 180 days before expiration); Renewal available up to 30 days after certification expiration
GCP-ADP Exam Topics and Domains
GCP-ADP is organized into 4 weighted domains. Expect to work with BigQuery, Cloud Storage, Dataflow, Cloud Data Fusion, and more.
1
Data Preparation and Ingestion
30%
Prepare and process data
Data manipulation methodologiesData transfer toolsData quality assessmentData cleaning
- 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)
Extract and load data into appropriate Google Cloud storage systems
Data formatsExtraction toolsStorage solutionsData storage location typesData classification by structureLoading data into Google Cloud storage systems
- 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)
- 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
27%
Identify data trends, patterns, and insights by using BigQuery and Jupyter notebooks
SQL queries in BigQueryJupyter notebooks for data analysisBusiness question analysis
- 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
Visualize data and create dashboards in Looker given business requirements
Dashboard creation and managementLooker vs Looker StudioLookML parameters
- 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
Define, train, evaluate, and use ML models
ML use cases with BigQuery ML and AutoMLPretrained Google LLMs via BigQueryStandard ML project planningBigQuery ML model creation and evaluationModel Registry
- 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
18%
Design and implement simple data pipelines
Data transformation tool selectionELT and ETL use casesBasic transformation pipeline implementation
- 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
Schedule, automate, and monitor basic data processing tasks
Scheduled queriesDataflow pipeline monitoringLogging and monitoringData orchestration solutionsEvent-driven data ingestionEventarc triggers in event-driven pipelines
- 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
25%
Configure access control and governance
IAM and least privilege accessCloud Storage access controlAnalytics Hub for data sharing
- 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
Configure lifecycle management
Cloud Storage classesObject lifecycle rulesArchiving data
- 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
Identify high availability and disaster recovery strategies for data in Cloud Storage and Cloud SQL
Backup and recovery solutionsReplication strategiesData storage location for redundancy
- 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
Apply security measures and ensure compliance with data privacy regulations
Encryption key managementCloud Key Management ServiceEncryption in transit and at rest
- 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
How do I earn this certification?
Passing GCP-ADP earns the Google Cloud Certified - Associate Data Practitioner certification. It sits in the Data track.
Current Level Exams
Next Level Options
Alternative Paths
- GCP-ACE - Associate Cloud EngineerComplementary infrastructure skills for data professionals who need to manage compute and networking
- GCP-PCSE - Professional Cloud Security EngineerFor data professionals focused on data security, governance, and compliance
- GCP-PCDBE - Professional Cloud Database EngineerFor those specializing in database administration and optimization on Google Cloud
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
The most effective way to prepare for GCP-ADP is by using the PlanetCert Simulator to practice questions and review detailed explanations.
What's changed on this exam?
Current Status
- ACTIVE
- Last content update: 2025-01-01
- Announcement date: 2024-10-01
Updates
- BigQuery Current Most heavily tested service across all domains. BigQuery ML, scheduled queries, data loading, SQL analysis all covered. • Release date: Ongoing
- Looker and Looker Studio Current New exam focus area - many candidates lack experience with Looker and LookML. Understanding the difference between Looker and Looker Studio is critical. • Release date: Ongoing
- Cloud Data Fusion Current Key tool for visual ETL pipeline design. Candidates often lack hands-on experience. CDAP-based interface for data integration. • Release date: Ongoing
- Eventarc Current Event-driven architecture trigger service featured in Section 3. Used with Dataform, Dataflow, Cloud Functions, Cloud Run, Cloud Composer. • Release date: Ongoing
Who should take this exam?
- 6+ months of experience working with data on Google Cloud
- Basic understanding of cloud computing concepts (IaaS, PaaS, SaaS)
- Experience using Google Cloud data services for data ingestion, transformation, pipeline management, analysis, machine learning, and visualization