Staging, cataloging, and profiling data so it can be found and governed
GCP data governance for staging, cataloging, profiling, and discovery centers on Dataplex as the unified control plane, with BigQuery, Cloud Storage, and Data Catalog metadata capabilities integrated underneath. Exam questions test whether you know which service automatically discovers, classifies, and secures data assets across a lake/warehouse without moving or duplicating data. Understanding Dataplex's lake/zone/asset model and its relationship to legacy Data Catalog is key.
1 · Learn the must-know
- Dataplex organizes data into Lakes (business domains), Zones (raw/curated), and Assets (Cloud Storage buckets or BigQuery datasets), enabling logical organization without physically moving data.
- Dataplex automatically discovers new files/tables in registered assets, infers schema, and publishes metadata to a unified catalog searchable via BigQuery and Data Catalog UI/API.
- Data profiling in Dataplex generates statistical summaries (null %, distinct counts, min/max, top values) on BigQuery tables to help assess data quality and understand structure before use.
- Data quality tasks in Dataplex let you define rules (e.g., uniqueness, freshness, range checks) as managed, scheduled jobs, producing quality scores and alerting on failures.
- Dataplex enforces fine-grained access via IAM at lake/zone/asset level, and tags/policies propagate consistently to underlying BigQuery and Cloud Storage resources for centralized governance.
- Legacy Data Catalog is being consolidated into Dataplex's unified catalog experience, so exam scenarios favor Dataplex as the current recommended service for cataloging and discovery, though Data Catalog APIs/tag templates still underlie metadata storage.
2 · Check your understanding
A data engineering team ingests CSV files from several business units into a staging bucket before loading them into BigQuery. Before the tables are made available for company-wide discovery, the team wants to automatically identify which columns across all incoming files contain sensitive information such as credit card numbers and email addresses, and record that classification as searchable metadata. Which approach should they implement?
What you have tried across GCP PDE's objectives, not a readiness score.
Designing data processing systems~22% of the exam0 of 17 tried
Ingesting and processing the data~25% of the exam0 of 11 tried
Storing the data~20% of the exam0 of 13 tried
Preparing and using data for analysis~15% of the exam0 of 11 tried
Maintaining and automating data workloads~18% of the exam0 of 15 tried
3 · Keep going
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