GCP Prep
ProfessionalDataAI/MLArchitect

Professional Data Engineer

Professional Data Engineer covers designing data processing systems, building and operationalising pipelines, and making data usable for analysis and machine learning. It is the strongest certification for anyone whose work centres on moving, storing and modelling data at scale.

Study GuidePractice Test

Overview

This exam is dominated by one skill: choosing correctly between storage and processing options under stated constraints. Latency, volume, structure, access pattern, cost and consistency all appear as constraints, and only one option satisfies all of them.

BigQuery is the centre of gravity. You need more than query syntax — partitioning, clustering, slot behaviour, storage versus compute pricing, materialised views and how those choices change cost at scale.

Streaming is the second heavy area. Pub/Sub delivery guarantees, windowing, watermarks and late-arriving data appear repeatedly, and they are the topics people most often under-prepare.

Data governance has grown steadily in weight. Cataloguing, lineage, classification and access control at column level are all fair game.

Level
Professional
Exam length
120 minutes
Questions
50–60 multiple choice and multiple select
Registration fee
USD $200 (plus tax where applicable)
Valid for
2 years
Delivery
Online proctored, or onsite at a test centre
Recommended experience (official)
Google recommends 3+ years of industry experience, including 1+ year designing and managing solutions on Google Cloud.

Our difficulty rating

4/5

Our recommended preparation

8–14weeks

Difficulty and preparation time are GCP Prep's own editorial assessments, not official figures.

Who Should Take This Certification?

  • Data engineers building production pipelines
  • Analytics engineers moving deeper into infrastructure
  • Backend engineers who own data platforms
  • ETL and data warehouse specialists modernising their skills

Skills Covered

  • Designing batch and streaming data processing systems
  • Building pipelines with managed processing services
  • Data warehouse modelling, partitioning and clustering
  • Choosing storage systems by access pattern and consistency need
  • Data quality, lineage, cataloguing and governance
  • Cost and performance tuning for analytical workloads
  • Operationalising machine learning on top of a data platform

Exam Topics

Weightings are shown only where they are officially published — we do not estimate them.

1.Designing data processing systems

~22%
  • Selecting storage systems by access pattern, volume and consistency need
  • Designing pipelines for batch and streaming workloads
  • Data modelling for analytical and operational use
  • Designing for reliability, fault tolerance and reprocessing
  • Migration planning from existing warehouses and data lakes

2.Ingesting and processing the data

~25%
  • Building pipelines with managed batch and streaming processing
  • Streaming semantics: windowing, watermarks and late data
  • Transformation, enrichment and schema evolution
  • Orchestration, scheduling and dependency management
  • Handling errors, retries and dead-letter paths

3.Storing the data

~20%
  • Choosing between object storage, relational, wide-column and analytical stores
  • BigQuery partitioning, clustering and table design
  • Storage lifecycle, retention and archival strategy
  • Balancing cost against query performance

4.Preparing and using data for analysis

~15%
  • Making data discoverable, documented and trustworthy
  • Sharing data securely across teams and organisations
  • Building for business intelligence consumption
  • Machine learning on warehouse data

5.Maintaining and automating data workloads

~18%
  • Monitoring pipeline health and data freshness
  • Automating repeatable workloads and infrastructure
  • Troubleshooting performance and cost regressions
  • Access control, governance and compliance for data

Preparation Roadmap

Our suggested order of study. Tick steps as you complete them — progress is saved in this browser.

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  1. Data platform fundamentals

    The data lifecycle, batch versus streaming, and the difference between a lake, a warehouse and an operational store.

  2. BigQuery in depth

    The largest single topic. Partitioning, clustering, slots, storage versus compute pricing, and query optimisation.

  3. Streaming ingestion

    Pub/Sub topics, subscriptions, delivery guarantees, ordering keys and dead-letter topics.

  4. Pipeline processing

    Windowing, watermarks, late data and the unified batch/streaming model. Consistently under-prepared.

  5. Operational data stores

    Cloud SQL, Spanner, Bigtable and Firestore — and the access patterns that make each the right answer.

  6. Governance and quality

    Cataloguing, lineage, classification, column-level access and data quality checks in a pipeline.

  7. Machine learning on data platforms

    Feature engineering, in-warehouse ML, and where a managed ML platform takes over.

  8. Cost and performance tuning

    Where analytical cost actually accumulates and the levers that reduce it without hurting query latency.

  9. Practice questions and mock exams

    Focus on selection questions — the ones giving four workable products and one set of constraints.

Certification ready

Study Resources

Learning-hub topics that cover this certification's material, written by us.

Official documentation and training remain the authoritative source for exam content. Our material explains concepts in our own words and is designed to sit alongside it, not replace it.

Practice Questions & Mock Exam

We have 14 original questions relevant to this certification, each with an explanation of why the correct answer is correct and why every distractor is not.

Practice test

Work through questions at your own pace with immediate explanations.

Timed mock exam

Full-length, timed, with results and per-topic breakdown at the end.

Career Opportunities

Data Engineer

Build and operate the pipelines and warehouses analytics depends on.

Analytics Engineer

Model warehouse data for consumption; strong overlap with the storage and analysis domains.

ML Engineer

Most production ML problems are data problems. This certification pairs naturally with the ML Engineer exam.

Data Platform Architect

Design the organisation-wide data estate and its governance model.

Explore career resources

Frequently Asked Questions

Do I need to know SQL well?
Yes. You will not write long queries under exam conditions, but you need to read SQL comfortably and reason about what makes a query expensive. Partitioning and clustering questions assume you understand how a query scans data.
How much programming is involved?
Less than people expect. You need to understand pipeline concepts — transforms, windows, watermarks — rather than write pipeline code. Reading a code snippet and predicting its behaviour is the level required.
Is Associate Cloud Engineer a useful prerequisite?
Helpful but not required. The IAM and networking fundamentals it teaches show up here, but the data-specific material is largely independent. Many data engineers take this exam first.
What is the most under-prepared topic?
Streaming semantics. Windowing, watermarks and late-arriving data are conceptually subtle, appear repeatedly, and are the area where guessing is least likely to work.