Free Practice Questions for DBT Analytics Engineering Certification Exam Certification
Study with 512 exam-style practice questions designed to help you prepare for the DBT Analytics Engineering Certification Exam. All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
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Exam Details
Key information about DBT Analytics Engineering Certification Exam
- Multiple choice
- Ordering
- Matching
- True/False
- 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)
Domain 1: Developing dbt models
Subdomain 1.1: Developing dbt models
- Identifying and verifying any raw object dependencies - Understanding core dbt materializations - Conceptualizing modularity and how to incorporate DRY principles - Converting business logic into performant SQL queries - Using commands such as run, test, docs and seed - Creating a logical flow of models and building clean DAGs - Defining configurations in dbt_project.yml - Configuring sources in dbt - Using dbt Packages - Utilizing git functionality within the development lifecycle - Creating Python models - Providing access to users to models with the “grants” configuration
Domain 2: Understanding dbt models governance
Subdomain 2.1: Understanding dbt models governance
- Adding contracts to models to ensure the shape of models - Creating different versions of our models and deprecating the old ones - Configuring model access
Domain 3: Debugging data modeling errors
Subdomain 3.1: Debugging data modeling errors
- Understanding logged error messages - Troubleshooting using compiled code - Troubleshooting .yml compilation errors - Distinguishing between a pure SQL and a dbt issue that presents itself as a SQL issue - Developing and implementing a fix and testing it prior to merging
Domain 4: Managing data pipelines
Subdomain 4.1: Managing data pipelines
- Troubleshooting and managing failure points in the DAG - Using dbt clone - Troubleshooting errors from integrated tools
Domain 5: Implementing dbt tests
Subdomain 5.1: Implementing dbt tests
- Using generic, singular, custom, and custom generic tests on a wide variety of models and sources - Testing assumptions for dbt models and sources - Implementing various testing steps in the workflow
Domain 6: Creating and Maintaining dbt documentation
Subdomain 6.1: Creating and Maintaining dbt documentation
- Updating dbt docs - Implementing source, table, and column descriptions in .yml files - Using macros to show model and data lineage on the DAG
Domain 7: Implementing and maintaining external dependencies
Subdomain 7.1: Implementing and maintaining external dependencies
- Implementing dbt exposures - Implementing source freshness
Domain 8: Leveraging the dbt state
Subdomain 8.1: Leveraging the dbt state
- Understanding state - Using dbt retry - Combining state and result selectors
Techniques & products