Free Practice Questions for DBT Analytics Engineering Certification
- Exam guide version:
- 1.11
- Guide checked for updates:
- 9 Oct 2026
- Question bank created:
- 16 Sep 2026
- Question bank last updated:
- 12 Oct 2026
Study with 341 exam-style practice questions designed to help you prepare for the DBT Analytics Engineering. All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
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LessonsNew
31 documentation-grounded lessons, one per exam-guide subdomain — every claim cited to the official docs.Based on the official docs as of 12 Oct 2026
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Exam Details
Key information about DBT Analytics Engineering
- Multiple choice
- Ordering
- Matching
- Fill in the blank
$200
English
Multiple-choice, Fill-in-the-blank, Matching, Hotspot, Build list, Discrete Option Multiple Choice (DOMC)
65% or higher
SQL proficiency, 6+ months of experience with dbt (Core or Cloud), foundational Git skills
Online proctored
120
dbt Core 1.7
65
2 years
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: Developing and optimizing dbt models
1.1: Identifying and verifying any raw object dependencies
Identifying and verifying any raw object dependencies
1.2: Understanding core dbt materializations
Understanding core dbt materializations
1.3: Conceptualizing modularity and how to incorporate DRY principles
Conceptualizing modularity and how to incorporate DRY principles
1.4: Using commands such as build, run, test, docs, show, snapshot, and seed
Using commands such as build, run, test, docs, show, snapshot, and seed
1.5: Creating a logical flow of models and building clean DAGs
Creating a logical flow of models and building clean DAGs
1.6: Defining configurations in dbt_project.yml
Defining configurations in dbt_project.yml
1.7: Using dbt Packages
Using dbt Packages
1.8: Creating Python Models
Creating Python Models
1.9: Providing access to users to models with the "grants" config
Providing access to users to models with the "grants" config
1.10: Creating snapshots in YAML
Creating snapshots in YAML
1.11: Selecting the optimal incremental strategy based on a dataset's characteristics
Selecting the optimal incremental strategy based on a dataset's characteristics
1.12: Validating model logic and schema definitions in dry-runs using the --empty flag
Validating model logic and schema definitions in dry-runs using the --empty flag
1.13: Running models in sample mode using the --sample flag
Running models in sample mode using the --sample flag
1.14: Understanding advanced dbt materializations such as microbatch
Understanding advanced dbt materializations such as microbatch
2: Managing dbt models governance
2.1: Adding contracts to models to ensure the shape of models
Adding contracts to models to ensure the shape of models
2.2: Creating different versions of our models and deprecating the old ones
Creating different versions of our models and deprecating the old ones
2.3: Defining constraints in YAML to enforce data integrity at the platform level
Defining constraints in YAML to enforce data integrity at the platform level
3: Debugging data modeling errors
3.1: Understanding logged error messages
Understanding logged error messages
3.2: Troubleshooting using compiled code
Troubleshooting using compiled code
3.3: Troubleshooting .yml compilation errors
Troubleshooting .yml compilation errors
3.4: Developing and implementing a fix and testing it prior to merging
Developing and implementing a fix and testing it prior to merging
3.5: Managing dbt behavior with flags
Managing dbt behavior with flags
4: Troubleshooting and optimizing dbt pipelines
4.1: Troubleshooting and managing failure points in the DAG
Troubleshooting and managing failure points in the DAG
4.2: Using dbt clone
Using dbt clone
5: Implementing dbt tests
5.1: Using generic, singular, custom, custom generic, and unit tests on a wide variety of models and sources
Using generic, singular, custom, custom generic, and unit tests on a wide variety of models and sources
5.2: Testing assumptions for dbt models and sources
Testing assumptions for dbt models and sources
5.3: Implementing various testing steps in the workflow
Implementing various testing steps in the workflow
6: Implementing and maintaining external dependencies
6.1: Implementing dbt exposures
Implementing dbt exposures
6.2: Implementing source freshness
Implementing source freshness
7: Leveraging the dbt state
7.1: Understanding state and state selection
Understanding state and state selection
7.2: Using dbt retry
Using dbt retry
Techniques & products