Exam prep for
Google Cloud Certified Professional Data Engineer
Exam set and awarded by Google Cloud On demand
For experienced data engineers preparing for this Google Cloud certification exam: strengthen your ability to design, build, secure and operate data solutions on Google Cloud as you pursue the professional credential.
Included with Elandi Pro: practice on all 5 exam domains and unlimited mock exams.
Independent exam prep. Elandi is not affiliated with, endorsed by or approved by Google Cloud, and does not award Google Cloud credentials. Our practice questions and mocks are written by Elandi, not taken from the real exam. Exam names are trademarks of their owners. Google Cloud and related certification names are trademarks of Google LLC.
The exam in numbers
The standard exam assesses how well a data engineer can design secure, reliable data systems; ingest, process and store data; prepare it for analysis; and maintain and automate workloads on Google Cloud. It uses multiple-choice and multiple-select questions and has no formal prerequisites. Candidates can book an online-proctored appointment or an onsite appointment at a test center. Preparation hours are an estimate, not an official figure: the planning range assumes 5–10 hours per week over eight weeks. Google publishes no pass-rate or annual candidate figures. Your mock exams use this learning plan's question count, time limit and pass mark.
- Questions
- 45
- Time limit
- 120min
- Mock pass mark
- 70%
- 32 correct
- Sitting
- On demand
- Single select (Radio): 4 options
- Multiple select (Checkbox): 4 options
Upcoming sittings
Pick a sitting when you start. Your workspace counts down to it and paces your prep from there.
- Any day On demand Book any day
What Google Cloud Certified Professional Data Engineer covers
5 modules · 19 units · ~19h of structured prep. Each exam domain with the weight the exam gives it, and the modules that prepare you for it.
- 01Designing data processing systems 22%
- Designing for security and compliance
- Designing for reliability and fidelity
- Designing for flexibility and portability
- Designing data migrations
- 02Ingesting and processing the data 25%
- Planning the data pipelines
- Building the pipelines
- Deploying and operationalizing the pipelines
- 03Storing the data 20%
- Selecting storage systems
- Planning for using a data warehouse
- Using a data lake
- Designing for a data platform
- 04Preparing and using data for analysis 15%
- Preparing data for visualization
- Preparing data for AI and ML
- Sharing data
- 05Maintaining and automating data workloads 18%
- Optimizing resources
- Designing automation and repeatability
- Organizing workloads based on business requirements
- Monitoring and troubleshooting processes
- Maintaining awareness of failures and mitigating impact
Your exam workspace counts down and keeps score
Set your exam day once. The workspace paces your prep in four phases, weighs your coverage by what each topic is worth, and shows where the marks are still open after every mock.
- Overview
- All attempts
- Review
- Preview questions
- Questions
- 45
- Time limit
- 120 minutes
- Pass score
- 70% · 32 correct
days until your exam
6 weeks
Thursday, 24 December 2026
- Learn 8+ weeks out
- Mock and repair 8 to 2 weeks out
- Simulate Last 2 weeks
- Consolidate Last 3 days
Now: Mock and repair. Sit a full mock every week or two, then practise the topics you missed.
How ready you are
- Exam-weighted coverage
- 78% of the plan, counted by what each topic is worth
- Full mocks taken
- 2 Prep guides suggest three to five before the exam
- Latest mock
- 66% 4 points below the 70% pass mark
Where the marks are still open
- Preparing and using data for analysis 15% of the exam 54%
- Maintaining and automating data workloads 18% of the exam 63%
- Storing the data 20% of the exam 71%
- Designing data processing systems 22% of the exam 88%
- Ingesting and processing the data 25% of the exam 100%
How the content is written
Write for experienced data engineers preparing for the Professional Data Engineer exam: precise, practical explanations and decision-focused practice grounded in Google Cloud documentation.
- Worked examples
- Heavy
- Technical jargon
- Heavy
- Maths & formulas
- Light
- Code
- Light
- Diagrams
- Light
Try a sample question
Elandi wrote these in this learning plan's style. Pick an answer to see whether it's right and why. They are not taken from the exam.
A team needs a current BigQuery replica of an operational PostgreSQL database for analytics. Source changes must arrive with minimal latency, and the team wants to avoid operating replication servers. The initial dataset also needs to be loaded. Which approach best fits? Select one. One answer
A team must consolidate large historical datasets for ad hoc SQL aggregations and joins. Query volumes vary substantially, and the team wants managed analytics without provisioning or manually scaling database infrastructure. Low-latency transactional point reads are not required. Which architecture is the best fit? Select one. One answer
A dashboard queries Cloud Storage data through a BigLake table. It needs lower interactive latency, and the business accepts a refresh once per hour. The team proposes a BI Engine reservation, but the existing queries do not accelerate. Which change best addresses the limitation while retaining the source files? Select one. One answer
A team develops interdependent SQL transformations on data already loaded into BigQuery. Developers must test changes in isolation, and production runs must detect violations of a uniqueness rule. Which two actions best meet these needs using Dataform? Select two. Select all that apply
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