Exam prep for
Google Cloud Certified Associate Data Practitioner
Exam set and awarded by Google Cloud On demand
For data practitioners preparing for the Google Cloud Certified Associate Data Practitioner exam: build working-level skills to pursue the credential and apply Google Cloud data services. The plan targets 2027 using the undated v1.0 guide; its applicability to the 2027 exam is unconfirmed.
Included with Elandi Pro: practice on all 4 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
Assesses practical use of Google Cloud services to prepare, analyze, orchestrate, and manage data. Study time of 40–80 hours is a speculative planning estimate, not an official figure, and varies with experience. Candidate numbers and pass rate are unknown; zero defaults are not estimates. The mock pass mark of 70% is our practice target, not a Google pass mark. Your mock exams use this learning plan's question count, time limit and pass mark.
- Questions
- 55
- Time limit
- 120min
- Mock pass mark
- 70%
- 39 correct
- Exam fee (paid to Google Cloud)
- US$125
- When you book the exam
- Sitting
- On demand
- Single select (Radio): 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 Associate Data Practitioner covers
4 modules · 11 units · ~11h of structured prep. Each exam domain with the weight the exam gives it, and the modules that prepare you for it.
- 01Data Preparation and Ingestion 30%
- 1.1 Prepare and process data
- 1.2 Extract and load data into appropriate Google Cloud storage systems
- 02Data Analysis and Presentation 27%
- 2.1 Identify data trends, patterns, and insights by using BigQuery and Jupyter notebooks
- 2.2 Visualize data and create dashboards in Looker given business requirements
- 2.3 Define, train, evaluate, and use ML models
- 03Data Pipeline Orchestration 18%
- 3.1 Design and implement simple data pipelines
- 3.2 Schedule, automate, and monitor basic data processing tasks
- 04Data Management 25%
- 4.1 Configure access control and governance
- 4.2 Configure lifecycle management
- 4.3 Identify high availability and disaster recovery strategies for data in Cloud Storage and Cloud SQL
- 4.4 Apply security measures and ensure compliance with data privacy regulations
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
- 55
- Time limit
- 120 minutes
- Pass score
- 70% · 39 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
- 80% 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
- Data Management 25% of the exam 54%
- Data Pipeline Orchestration 18% of the exam 71%
- Data Preparation and Ingestion 30% of the exam 88%
- Data Analysis and Presentation 27% of the exam 100%
How the content is written
Practical, precise exam preparation for experienced Google Cloud data practitioners, using concise scenarios and working-level service decisions.
- 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.
An analyst wants to train a classification model on data already in BigQuery using GoogleSQL. Which capability should the analyst use? One answer
A team must preprocess a custom stream before writing high-throughput events to BigQuery. Which service is the best fit? One answer
A team wants version-controlled SQL transformations and data-quality tests for raw data already loaded into BigQuery. Which service should it choose? One answer
A storage administrator wants bucket access controlled through IAM policies without object-level ACLs. Which setting should the administrator enable? One answer
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