About the Associate Data Practitioner exam

The Google Cloud Associate Data Practitioner certification validates that you can secure and manage data on Google Cloud and turn it into insight. The exam covers four Domains: data preparation and ingestion; data analysis and presentation, with tools such as BigQuery and Looker; data pipeline orchestration; and data management, including access control with Identity and Access Management (IAM). It suits analysts and data practitioners with hands-on experience working with data on Google Cloud.

Associate Data Practitioner exam domains

Exam Study Guide

1.2 Extract and load data into appropriate Google Cloud storage systems

Distinguishing data formats means matching structure, schema handling, and read pattern to the right ingestion and analysis task. Text formats favor exchange and flexibility, binary formats favor speed and size, and managed tables favor enforced structure and repeated SQL analysis. Data typically lands first as files in Cloud Storage and then moves into a table system for querying.

Continue with this study guide: 1.2 Extract and load data into appropriate Google Cloud storage systems

1.1 Prepare and process data

Extract, transform, and load (ETL) is a pipeline where data is cleaned and reshaped outside the warehouse before it is loaded. Data flows from source systems such as relational databases into a middle processing layer, then into BigQuery as the warehouse sink. The ordered steps are first extract data from sources, then transform it by cleaning, applying business rules, checking integrity, and creating aggregates, then load the finished result into the warehouse. Because heavy work runs outside the warehouse, warehouse compute stays free for concurrent queries. The tradeoff is extra tooling, since teams must use languages and tools beyond SQL, and each added source adds another possible failure point.

Continue with this study guide: 1.1 Prepare and process data

Exam Study Guide

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

Defining and executing SQL queries in BigQuery means writing GoogleSQL statements that filter, join, and aggregate datasets into structured reports and business insights. BigQuery uses GoogleSQL as its preferred ANSI-standard dialect, which supports joins, aggregation and analytic functions, operators, and conditional expressions. The older legacy SQL dialect remains only for existing queries that already use it. A query reads rows from tables, keeps only the rows needed, combines related tables, and summarizes the result into counts, sums, and averages.

Continue with this study guide: 2.1 Identify data trends, patterns, and insights by using BigQuery and Jupyter notebooks

2.3 Define, train, evaluate, and use ML models

Identify ML use cases for developing models by using BigQuery ML and AutoML means matching the data shape and team skills to the right Google Cloud training path. BigQuery ML trains and predicts with SQL where large structured tables already live, while AutoML trains on labeled images, text, and other unstructured data without custom model code. Data flows from BigQuery tables directly to a BigQuery ML model and back to BigQuery tables, while data flows from labeled examples to an AutoML model that learns only from those examples.

Continue with this study guide: 2.3 Define, train, evaluate, and use ML models

2.2 Visualize data and create dashboards in Looker given business requirements

Create, modify, and share dashboards to answer business questions is the workflow that turns saved queries into interactive pages for stakeholders. Data flows from an Explore query into a saved Look or tile, and from one or more tiles into a Looker dashboard where viewers filter and explore. Dashboards combine charts, text, filters, and layout so one page can answer the same business question from different angles.

Continue with this study guide: 2.2 Visualize data and create dashboards in Looker given business requirements

Exam Study Guide

Design and implement simple data pipelines

Selecting a data transformation tool means matching business needs for volume, speed, skills, and budget to the Google Cloud service that fits best. The five main options are Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, and Dataform. Each tool has a different architecture for where compute runs and what the service controls.

Continue with this study guide: 3.1 Design and implement simple data pipelines

Schedule, automate, and monitor basic data processing tasks

Creating and managing scheduled queries means running BigQuery SQL automatically on a timetable and saving the results to a table. A scheduled query is managed by the BigQuery Data Transfer Service as a transfer configuration that stores the query text, schedule, destination table, and credentials. Cloud Composer is used when the work grows beyond a single timed query into a multi-step workflow with dependencies.

Continue with this study guide: 3.2 Schedule, automate, and monitor basic data processing tasks

Exam Study Guide

Configure access control and governance

Identity and Access Management (IAM) is the Google Cloud system that controls who can act on which resources. A principal such as a user, Google group, or service account receives a role on a resource, and that role contains the permissions needed to call the resource. Least privilege means granting each principal only the narrowest role on the narrowest resource that still allows the work.

Continue with this study guide: 4.1 Configure access control and governance

4.4 Apply security measures and ensure compliance with data privacy regulations

Use cases for encryption keys in Google Cloud are decided by who owns the key encryption key (KEK) that protects data. Google-managed encryption keys (GMEK), also called Google default encryption, are owned and managed by Google with no customer action. Customer-managed encryption keys (CMEK) are created and managed by the customer in Cloud Key Management Service (Cloud KMS), while customer-supplied encryption keys (CSEK) are key material supplied directly by the customer.

Continue with this study guide: 4.4 Apply security measures and ensure compliance with data privacy regulations

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

Google-managed backup and recovery for Cloud Storage and Cloud SQL separates whole-instance restores from file-level copies. Cloud SQL provides incremental backups of an entire database instance, while Cloud Storage acts as the durable destination for exports and backup files that you manage. Data flows from the live instance or application to a backup copy stored separately from the instance, so deletion, corruption, or a regional outage does not remove the only copy.

Continue with this study guide: 4.3 Identify high availability and disaster recovery strategies for data in Cloud Storage and Cloud SQL

4.2 Configure lifecycle management

Determining the appropriate storage class means matching how often data is read and how long it must be kept to the lowest-cost class that still meets access needs. Cloud Storage provides Standard, Nearline, Coldline, and Archive storage, ordered from warmest to coldest. Access measurement, automatic tiering, and retention rules work together so frequently read data stays fast and rarely read data stays cheap.

Continue with this study guide: 4.2 Configure lifecycle management

About the Associate Data Practitioner exam

Associate Data Practitioner exam domains

Exam Study Guide

Google Cloud Associate Data Practitioner

About the Associate Data Practitioner exam

Associate Data Practitioner at a glance

Associate Data Practitioner exam domains

1.2 Extract and load data into appropriate Google Cloud storage systems

1.1 Prepare and process data

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

2.3 Define, train, evaluate, and use ML models

2.2 Visualize data and create dashboards in Looker given business requirements

Design and implement simple data pipelines

Schedule, automate, and monitor basic data processing tasks

Configure access control and governance

4.4 Apply security measures and ensure compliance with data privacy regulations

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

4.2 Configure lifecycle management

Exams to take after Associate Data Practitioner

Google Cloud Associate Data Practitioner

About the Associate Data Practitioner exam

Associate Data Practitioner at a glance

Associate Data Practitioner exam domains

1.2 Extract and load data into appropriate Google Cloud storage systems

1.1 Prepare and process data

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

2.3 Define, train, evaluate, and use ML models

2.2 Visualize data and create dashboards in Looker given business requirements

Design and implement simple data pipelines

Schedule, automate, and monitor basic data processing tasks

Configure access control and governance

4.4 Apply security measures and ensure compliance with data privacy regulations

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

4.2 Configure lifecycle management

Exams to take after Associate Data Practitioner

Google Cloud Associate Data Practitioner

About the Associate Data Practitioner exam

Associate Data Practitioner at a glance

Associate Data Practitioner exam domains

1Data Preparation and Ingestion

1.11.2 Extract and load data into appropriate Google Cloud storage systems

1.21.1 Prepare and process data

2Data Analysis and Presentation

2.1Identify data trends, patterns, and insights by using BigQuery and Jupyter notebooks

2.22.3 Define, train, evaluate, and use ML models

2.32.2 Visualize data and create dashboards in Looker given business requirements

3Data Pipeline Orchestration

3.1Design and implement simple data pipelines

3.2Schedule, automate, and monitor basic data processing tasks

4Data Management

4.1Configure access control and governance

4.24.4 Apply security measures and ensure compliance with data privacy regulations

4.3Identify high availability and disaster recovery strategies for data in Cloud Storage and Cloud SQL

4.44.2 Configure lifecycle management

Exams to take after Associate Data Practitioner

Google Cloud Associate Data Practitioner

About the Associate Data Practitioner exam

Associate Data Practitioner at a glance

Associate Data Practitioner exam domains

1Data Preparation and Ingestion

1.11.2 Extract and load data into appropriate Google Cloud storage systems

1.21.1 Prepare and process data

2Data Analysis and Presentation

2.1Identify data trends, patterns, and insights by using BigQuery and Jupyter notebooks

2.22.3 Define, train, evaluate, and use ML models

2.32.2 Visualize data and create dashboards in Looker given business requirements

3Data Pipeline Orchestration

3.1Design and implement simple data pipelines

3.2Schedule, automate, and monitor basic data processing tasks

4Data Management

4.1Configure access control and governance

4.24.4 Apply security measures and ensure compliance with data privacy regulations

4.3Identify high availability and disaster recovery strategies for data in Cloud Storage and Cloud SQL

4.44.2 Configure lifecycle management

Exams to take after Associate Data Practitioner

1.2 Extract and load data into appropriate Google Cloud storage systems

1.1 Prepare and process data

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

2.3 Define, train, evaluate, and use ML models

2.2 Visualize data and create dashboards in Looker given business requirements

Design and implement simple data pipelines

Schedule, automate, and monitor basic data processing tasks

Configure access control and governance

4.4 Apply security measures and ensure compliance with data privacy regulations

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

4.2 Configure lifecycle management

1.2 Extract and load data into appropriate Google Cloud storage systems

1.1 Prepare and process data

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

2.3 Define, train, evaluate, and use ML models

2.2 Visualize data and create dashboards in Looker given business requirements

Design and implement simple data pipelines

Schedule, automate, and monitor basic data processing tasks

Configure access control and governance

4.4 Apply security measures and ensure compliance with data privacy regulations

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

4.2 Configure lifecycle management