Google Cloud Associate Data Practitioner Roadmap

Start from data shape, movement constraints, access pattern, consistency, and location—not from a favorite service.

• Compare ETL, ELT, and iterative combinations; identify where cleaning and governance occur.
• Use quality dimensions and quarantine rather than silently dropping invalid records.
• Distinguish CSV, JSON, Parquet, Avro, and structured tables by schema and processing needs.
• Choose Transfer Appliance, Storage Transfer Service, BigQuery Data Transfer Service, Database Migration Service, Dataflow, or Cloud Data Fusion from source and delivery requirements.
• Match Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, and Spanner to object, analytical, transactional, document, wide-column, and distributed relational patterns.
• Verify regional, dual-region, multi-region, and zonal implications across the complete data path.

Turn loaded data into trustworthy answers by preserving grain, semantics, and query efficiency.

• Practice filtering, grouping, joins, window functions, arrays, null behavior, and date-time handling in GoogleSQL.
• Explain how join fanout can duplicate measures and how to align grains before aggregation.
• Use partition filters, selected columns, and query estimates to control bytes processed.
• Explore and visualize synthetic data in Colab Enterprise while preserving IAM boundaries.
• Translate a business question into a metric definition, query, validation total, and limitation.
• Compare a statistically visible pattern with a defensible business conclusion.

Build a semantic path from source truth to dashboards and supported machine-learning workflows.

• Compare Looker governed semantic modeling with Looker Studio report authoring.
• Use simple LookML dimensions, measures, joins, and explores without hiding source grain.
• Create, train, evaluate, and predict with supported BigQuery ML models.
• Use precision, recall, confusion matrices, thresholds, and business cost rather than accuracy alone.
• Recognize appropriate Vertex AI AutoML and BigQuery remote-model use cases.
• Organize approved model versions in Vertex AI Model Registry while separating registration from production approval.

Select the smallest orchestration and transformation service that satisfies dependencies, latency, and operational needs.

• Use Dataform for BigQuery SQL dependency graphs and Dataproc for Spark-oriented processing.
• Use Dataflow for Apache Beam batch or streaming transformations and monitor job progress.
• Choose a scheduled query for one recurring SQL task and Cloud Composer for richer Airflow DAGs.
• Use Workflows for service/API step orchestration and Cloud Scheduler for cron-like invocation.
• Design Pub/Sub ingestion for redelivery, schemas, dead letters, and idempotent BigQuery writes.
• Use Eventarc to route supported events to Cloud Run, functions, Dataflow, Dataform, or orchestration targets.
• Read Cloud Logging and Monitoring evidence for backlog, freshness, errors, throughput, and blind spots.

Close the lifecycle with access, sharing, retention, availability, recovery, encryption, cost, and exam review.

• Apply IAM least privilege and distinguish broad basic roles from service-specific predefined roles.
• Use uniform bucket-level access and public access prevention for appropriate Cloud Storage boundaries.
• Share governed data products through Analytics Hub rather than unmanaged exports.
• Design storage classes, object lifecycle rules, table or partition expiration, and archival from access and retention needs.
• Distinguish Cloud SQL high availability, replicas, backups, and point-in-time recovery.
• Compare Google-managed encryption, CMEK, and CSEK responsibilities; explain Cloud KMS and transit versus at-rest protection.
• Complete all three projects, review all 40 flashcards, and answer the independent 50-question bank by the exact 15/14/9/12 distribution.