Directory / Google Cloud

Professional Data Engineer

A data platform exam. AI and ML make up under a tenth of the blueprint, and that part is about preparing data rather than building models.

What it signals

Practical assessment: Scenario-based multiple choice. Proctored scenario-based multiple choice.
Identity assurance: Proctored with identity verification. Proctored: online-proctored (Pearson VUE OnVUE) or onsite at a Pearson VUE test centre. Registration via CertMetrics (cp.certmetrics.com/google). Third-party source says delivery moved from Kryterion to Pearson VUE on 2026-03-02.
Depth: Professional. Professional level.
Experience expected: 2 to 3 years. Google recommends 3+ years industry experience including 1+ on Google Cloud.
Portability: Mixed. Data engineering concepts transfer, but questions are framed on Google Cloud products.
Verifiability: Public badge or registry. Public Credly badge and searchable directory.

For hiring managers

A data platform credential, not an AI credential: ML/AI is under 10% of the blueprint and is about preparing data for ML/RAG, not building models.
Older versions (pre-2023) had a substantial ML section; holders may overstate the ML content.

Verifying it

Digital badge issued via Credly (Open Badges); employer opens the candidate's live Credly badge URL (shows name, credential, issue and expiry dates). Opt-in searchable Google Cloud Skills Directory on Credly: https://www.credly.com/organizations/google-cloud/directory

What it covers

Published exam outline

Designing data processing systems 22%
Ingesting and processing the data 25%
Storing the data 20%
Preparing and using data for analysis 15%
Maintaining and automating data workloads 18%

By topic

GenAI apps: Touched on (under 10%). Only preparing unstructured data for embeddings and RAG is mentioned.
Agents: Not covered. Not in the blueprint.
Classical ML: Touched on (under 10%). Limited to preparing data for feature engineering/training with BigQuery ML in objective 4.2.
Operations: Touched on (under 10%). Pipeline automation and monitoring is for data workloads, not ML models.
Data: Core focus (over 35%). The whole exam is data engineering; AI-specific data preparation is a small part.
Responsible AI: Touched on (under 10%). Data security, governance and compliance considerations appear but are not AI-specific.

Exam details

Who the provider says it is for. Data engineers who design, build, secure and operate data processing systems on Google Cloud.

Format: multiple choice; multiple select
Delivery: Proctored: online-proctored (Pearson VUE OnVUE) or onsite at a Pearson VUE test centre. Registration via CertMetrics (cp.certmetrics.com/google). Third-party source says delivery moved from Kryterion to Pearson VUE on 2026-03-02.
Code or hands-on work: No hands-on tasks; multiple choice/multiple select only. Whether code snippets appear is not stated in the material read.
Prerequisites: None
Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud
Renewal: Three routes: (1) standard exam USD 200; (2) renewal exam, 1 hour, 20 questions, USD 100, renewal window opens 60 days before expiry; (3) continuing education via designated Google Skills courses/skill badges, adds 1 year (third-party says this route opened July 2026).

Preparing

Not confirmed

These details were not available from an official source at the time of research.

passingScore
date of current exam guide version
start date of continuing-education renewal (July 2026 is third-party)
examCode

Sources (4)