Google Cloud Associate Data Practitioner Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Data Preparation and Ingestion (30% of the exam)

Prepare and process data.

• 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)

Summary: This section equips you with the understanding of how to prepare and process data for cloud environments. You will learn the differences between data transformation patterns such as ETL, ELT, and ETLT, and how each approach enables flexibility depending on the workload. Additionally, importance is placed on selecting the right Google Cloud transfer tools for tasks such as large-scale migrations or continuous synchronization.

Assessing and improving data quality is also emphasized, as reliable data is the foundation for effective analysis. You will explore practical tools like SQL queries, BigQuery, Cloud Data Fusion, and Dataflow to clean and refine raw data. By mastering preparation and processing skills, you will ensure data is trustworthy, available in the right location, and ready for use across analytics and machine learning workflows.

Extract and load data into appropriate 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)
• Select the appropriate storage solution (e.g., Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner) — 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)

Summary: This section focuses on the ability to extract data from different sources and load it into Google Cloud environments efficiently. You will practice recognizing data formats such as CSV, JSON, Parquet, and Avro, and understand their impact on processing and storage needs. Choosing the right extraction tool is key, whether migrating a relational database with Database Migration Service or pulling periodic reports with BigQuery Data Transfer Service.

Just as important is selecting the right storage system, such as Cloud Storage for unstructured data, BigQuery for analytical queries, or Spanner for global operational workloads. This section also covers location strategy, including regional and multi-regional storage options to align with performance and compliance requirements. Ultimately, you will build confidence in matching the correct tools, locations, and formats so that data is highly accessible and optimally stored.

Domain 2: Data Analysis and Presentation (27% of the exam)

Identify data trends, patterns, and insights by using BigQuery and Jupyter notebooks.

• 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

Summary: This section emphasizes the role of querying tools and notebooks in uncovering value from data. You will practice writing SQL queries in BigQuery to extract insights that inform decision-making. These queries allow you to summarize, aggregate, and highlight patterns that connect directly to business objectives.

Jupyter notebooks, including Colab Enterprise, bring visualization and flexible analysis into the process, giving you an environment where code, data, and insights converge. You will develop skills in generating reports and explaining findings, ensuring that data patterns translate into actionable business knowledge.

Visualize data and create dashboards in Looker given business requirements.

• 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

Summary: This section highlights how dashboards and reports support collaboration and communication of insights. You will gain experience with Looker and Looker Studio, learning to match them to different user needs. For example, Looker offers powerful model-driven architecture with LookML, while Looker Studio is exceptional for lightweight, shareable dashboards.

You will practice developing dashboards that align with specific business goals, whether to monitor KPIs or track operational metrics. A key component is understanding LookML parameters, which allow you to tailor the data model to better fit the analysis. By completing this section, you will be prepared to transform queries into visually compelling dashboards that tell the story of your data.

Define, train, evaluate, and use ML models.

• 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

Summary: This section introduces how machine learning integrates with Google Cloud analytics to enrich insights. You will learn how to identify workloads suitable for BigQuery ML and AutoML and understand when to use pretrained large language models for advanced use cases. The focus is on building familiarity with each stage of an ML workflow, from collecting data through generating predictions.

With hands-on SQL-driven ML in BigQuery ML, you will see how approachable training, evaluation, and predictions can be, even for data practitioners without deep coding backgrounds. Organizing and managing these models in the Model Registry ensures that models remain discoverable and usable in collaboration scenarios. This section bridges analytics and machine learning, empowering you to extend your skill set into predictive modeling.

Domain 3: Data Pipeline Orchestration (18% of the exam)

Design and implement simple data pipelines.

• 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

Summary: This section builds your understanding of how to design pipelines that move and transform data effectively. You will compare transformation tools such as Dataproc for Hadoop/Spark workloads, Dataflow for stream and batch processing, and Cloud Composer for orchestration. Each tool fits different business requirements, making your ability to select the best option an essential skill.

The section also explains the difference between ELT and ETL workflows and when to apply each. By choosing suitable products and services for a transformation pipeline, you will gain practical know-how in building streamlined, efficient pipelines that deliver clean and ready-to-use data where and when it is needed.

Schedule, automate, and monitor basic data processing tasks.

• 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)

Summary: This section ensures you have the tools to automate and monitor ongoing pipeline operations. You will learn to create scheduled queries and manage pipelines through Cloud Composer, as well as monitor data movement using the Dataflow job UI. Logging and monitoring are integral, and tools like Cloud Logging and Cloud Monitoring enable visibility into workflow performance.

Event-driven data ingestion is a key focus, showing how you can design streaming pipelines that bring real-time responsiveness to data workflows. Tools like Pub/Sub and Eventarc enable developers to connect events with services such as Dataflow or Cloud Functions, making automation seamless and adaptive. This section connects orchestration, reliability, and efficiency into pipeline management.

Domain 4: Data Management (25% of the exam)

Configure access control and governance.

• 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

Summary: This section explains how access controls safeguard data environments while maintaining collaboration. You will learn about IAM roles and permissions, comparing when to apply broad versus fine-grained roles across services like BigQuery and Cloud Storage. Assessing which access control model to use, such as uniform or object-level, ensures appropriate governance.

Sharing data effectively is another key point. Analytics Hub makes it easy to share datasets securely across organizations while still keeping boundaries clear. These practices strengthen governance, aligning technical access methods with organizational policies and collaboration goals.

Configure lifecycle management.

• 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

Summary: This section highlights how lifecycle rules help optimize storage costs and retention. You will learn how to map data to storage classes such as Nearline or Archive, ensuring data is stored cost-effectively while meeting performance needs. By using lifecycle configurations, you can automatically delete or transition data at the right time.

Archiving solutions are also explored, ensuring that historic or compliance-driven data remains preserved with minimal expense. Lifecycle management skills combine efficiency with governance, enabling data practitioners to align cloud storage with both technical and financial goals.

Identify high availability and disaster recovery strategies for data in Cloud Storage and Cloud SQL.

• 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

Summary: This section empowers you to ensure that cloud data remains durable and accessible even in the face of disruption. You will evaluate backup and restore capabilities built into services like Cloud SQL and Cloud Storage and learn where replication plays a role in creating redundancy.

The section also covers the differences between regional, dual-regional, and multi-regional storage, teaching you when to apply each option for reliability. These practices ensure high availability, business continuity, and recovery readiness in any data architecture.

Apply security measures and ensure compliance with data privacy regulations.

• 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

Summary: This section centers on protecting sensitive data through strong encryption practices. You will learn how to differentiate between Google-managed, customer-managed, and customer-supplied keys, and why each might be chosen for compliance or business needs. Cloud KMS provides a centralized, efficient way to handle keys.

Both encryption in transit and at rest are explored, providing clarity on how data is secured as it moves or when it is stored. Together, these strategies create a foundation for compliance with privacy regulations and trust in Google Cloud environments.