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Google Cloud · Data & Analytics · Professional

Google Cloud Professional Data Engineer exam guide

Exam Professional Data Engineer

Professional Data Engineer validates that you can design, build, secure and operate data processing systems on Google Cloud, from ingestion through storage, analysis and automation. It is among the most sought-after data credentials for cloud data roles.

Questions40-50
Time limit120 minutes
Passing scoreSee official page
Exam fee$200 USD
Valid for2 years
DeliveryOnline proctored or onsite at a Pearson VUE test center

Format: Multiple choice and multiple select. Fee note: Plus tax where applicable. A 1-hour, 20-question renewal exam costs $100..

Who it's for

Data engineers with 3+ years of industry experience, including a year or more designing and managing data solutions on Google Cloud.

What the exam covers

Designing data processing systems22%
Ingesting and processing the data25%
Storing the data20%
Preparing and using data for analysis15%
Maintaining and automating data workloads18%

Eligibility & prerequisites

Required: None

Recommended: 3+ years of industry experience, including 1+ years designing and managing solutions on Google Cloud.

Keeping it active

Valid for 2 years. Renew by retaking the standard exam, the 1-hour, 20-question renewal exam ($100), or the Google Skills renewal path (extends validity by 1 year).

Skills & careers

Skills it validates

  • Designing batch and streaming pipelines with Dataflow and Pub/Sub
  • Modeling, partitioning and optimizing BigQuery
  • Choosing between Bigtable, Spanner, Cloud SQL and Cloud Storage
  • Orchestrating with Cloud Composer and Dataform
  • Applying data security, DLP and governance with Dataplex
  • Monitoring, automating and cost-optimizing data workloads

Job roles

Data EngineerAnalytics EngineerBig Data EngineerCloud Data ArchitectETL Developer

Where it fits in your path

Planning a degree too? See how this cert earns college credit in the College Credit Navigator.

Build my study plan

  1. Week 1-2: Data Engineer learning path; storage decision trees for BigQuery, Bigtable, Spanner, Cloud SQL and Cloud Storage.
  2. Week 3-4: Build a streaming pipeline (Pub/Sub to Dataflow to BigQuery) and a batch pipeline orchestrated in Cloud Composer.
  3. Week 5: BigQuery optimization: partitioning, clustering, materialized views, slots and pricing; security and governance with Dataplex and DLP.
  4. Week 6: Sample questions and practice exams; review weak sections and sit the exam.

Make it yours: pick your exam date and weekly hours. We'll split your time by domain weight and give you a printable plan.

Exam-day tips

  • Ingestion and processing is the largest section at about 25%; master Dataflow windowing, triggers and late data.
  • Know storage selection by access pattern: Bigtable for low-latency wide-column, Spanner for global relational, BigQuery for analytics.
  • Expect BigQuery cost-control questions: partition pruning, clustering, editions and reservations.
  • Know Dataproc vs Dataflow trade-offs for migrating existing Spark/Hadoop jobs.
  • Only 40-50 questions in 2 hours, so you have time to read long scenarios carefully.

Key terms for this exam

All 77 terms Study with thinQcards™

Commonly confused on this exam

Frequently asked questions

How much does Professional Data Engineer cost?

$200 plus tax; the renewal exam is $100.

How long is it valid?

2 years, renewable by standard exam, renewal exam or Google Skills path.

How hard is it?

Hard for those without production data pipeline experience; plan 6-10 weeks.

Do I need Associate Data Practitioner first?

No. It is a helpful stepping stone but not a prerequisite.

Data Engineer vs ML Engineer?

Data Engineer focuses on pipelines and storage; ML Engineer focuses on building, serving and monitoring models.

Official page: cloud.google.com. Reviewed November 2026. Vendors change fees and objectives, so confirm with the vendor before you book. Spot something out of date? Tell us.