Google Associate Data Practitioner PRACTICE EXAM

This immersive course teaches how to ingest, prepare, transform, analyze, secure, and operationalize data on Google Cloud. You’ll learn to choose appropriate storage services, design ETL/ELT pipelines (batch and streaming), write performant BigQuery SQL, create dashboards in Looker Studio (and basic LookML), apply IAM and encryption best practices, and use built-in ML capabilities (BigQuery ML / AutoML) to deliver actionable insights.

Lessons are project-based: each module includes a mini project (sample datasets provided) so you practice end-to-end—from data acquisition through transformation, analysis, visualization and governance. Frequent quizzes, a full practice exam, and instructor feedback ensure readiness for the Associate Data Practitioner certification. v1.0_associate_data_practitione…

Course structure & module titles

1. Module 1 — Selecting Cloud Storage & Ingestion Solutions (2.5 hours)

   • Overview of Cloud Storage, BigQuery, Cloud SQL, Bigtable, Firestore, Spanner.

   • When to use CSV/JSON/Parquet/Avro.

   • Batch vs streaming ingestion: Storage Transfer Service, Transfer Appliance, Pub/Sub.

   • Lab: Load mixed CSV/JSON datasets into Cloud Storage and import into BigQuery.

2. Module 2 — Data Preparation & Transformation Techniques

   • Data quality checks, schema design, cleaning strategies, common ETL/ELT patterns.

   • Tools: BigQuery SQL, Dataflow, Cloud Data Fusion, Dataform.

   • Performance patterns: partitioning, clustering, denormalization.

3. Module 3 — Designing & Orchestrating Data Pipelines

   • Pipeline patterns (batch/streaming), orchestration options: Cloud Composer, Cloud Scheduler, Workflows.

   • Monitoring, retries, SLAs, logging and alerting (Cloud Monitoring & Logging).

   • Event-driven ingestion (Pub/Sub → Dataflow → BigQuery).

4. Module 4 — Analysis & Dashboarding with BigQuery and Looker Studio

   • Writing performant BigQuery SQL queries, analytical functions and windowing.

   • Looker Studio fundamentals and dashboard design best practices; basic LookML concepts.

   • Storytelling with data and stakeholder-focused visualizations.

5. Module 5 — Data Security, Governance & Lifecycle Management

   • IAM roles & least privilege, dataset and table-level access controls.

   • Encryption options (GMEK, CMEK), data residency, retention policies, Object lifecycle rules.

   • Backups, replication, Analytics Hub sharing patterns.

6. Module 6 — Integrating Basic ML into Analytics Workflows

   • BigQuery ML basics: training, evaluating, exporting predictions.

   • When to use AutoML or pretrained models; basic model performance metrics.

Learning objectives (titles)

1. Selecting Cloud Storage & Ingestion Solutions

2. Data Preparation & Transformation Techniques

3. Designing & Orchestrating Data Pipelines

4. Analysis & Dashboarding with BigQuery and Looker Studio

5. Data Security, Governance & Lifecycle Management

6. Integrating Basic ML into Analytics Workflows

Prerequisites

• Basic SQL (SELECT, JOINs, GROUP BY).

• Comfortable with spreadsheets and basic statistics.

• Google account (recommended: access to a Google Cloud project).

• Basic web browser and command-line familiarity.

• Recommended: Python familiarity and prior exposure to BigQuery or a Cloud Foundations course.

Who this course is for

• Aspiring data practitioners preparing for the Google Associate Data Practitioner exam.

• Analysts & engineers who need practical skills for ingestion, transformation, analytics, and governance on Google Cloud.

• Managers who want a grounded understanding of analytics pipelines to partner with technical teams.

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