dbt Analytics Engineering Certification Exam: dbt-Analytics-Engineering Exam

"dbt Analytics Engineering Certification Exam", also known as dbt-Analytics-Engineering exam, is a dbt Labs Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 359 Q&As to your dbt-Analytics-Engineering Exam preparation. In the dbt-Analytics-Engineering exam resources, you will cover every field and category in Analytics Engineers Certification helping to ready you for your successful dbt Labs Certification.

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  • Exam Code: dbt-Analytics-Engineering
  • Exam Name: dbt Analytics Engineering Certification Exam
  • Certification Provider: dbt Labs
  • Corresponding Certification: Analytics Engineers
  • Updated: Sep 05, 2026
  • No. of Questions: 359 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionWeightObjectives
Leveraging dbt State5%- State-aware operations
- State management
External Dependencies10%- Using packages
- Managing snapshots
- External sources integration
Implementing dbt Tests10%- Custom tests
- Test configuration and execution
- Built-in tests
Managing Data Pipelines15%- CI/CD integration
- Deployment strategies
- Pipeline orchestration
Creating and Maintaining Documentation10%- Generating documentation
- Descriptions and metadata
- Documentation standards
dbt Models Governance15%- Version control integration
- Naming conventions and standards
- Project organization and structure
Debugging and Error Resolution15%- Resolving data quality issues
- Identifying modeling errors
- Debugging techniques
Developing dbt Models20%- Model optimization
  • 1. Model contracts
  • 2. Performance tuning
- Model design and structure
  • 1. Ref and source functions
  • 2. Incremental models
  • 3. Materialization types

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question 1

A project stakeholder prefers a text-based lineage representation over the graphical DAG. Which of these approaches might be a suitable alternative?

A. Utilize a macro that generates a Markdown list representation of model dependencies.
B. Rely on third-party tools that specialize in parsing dbt projects to produce textual lineage outputs
C. Disable the DAG visualization entirely within the generated documentation.
D. Extend the dbt documentation framework to create a custom text-based lineage view


Question 2

You need to build a model to identify customers who haven't placed orders in the past 90 days. Performance with this model is critical, as it will trigger a re-engagement campaign. Which strategy is likely to yield the best results?

A. Join the customers and orders table, group by customer ID and use a HAVING clause to filter inactive customers.
B. Create a materialized view that contains only active customers and join against the orders table.
C. IJse a window function to rank order dates for each customer and filter those where the most recent order is older than 90 days.
D. Filter the orders table for dates within the past 90 days, then find customers without corresponding order IDs.


Question 3

(Multiple Select)

A. Run dbt Is to confirm that all dependencies for the macro and the model are resolved correctly.
B. Check the generated column names in the compiled SQL to ensure they match existing columns in the data.
C. Add {{ log(macros, info=true) statements within the macro to track how it executes-
D. Implement unit tests specifically for the macro to validate its output under different inputs


Question 4

Your dbt project contains sensitive dat
a. You need to load seed data (e.g., lookup tables) while ensuring it's not accidentally committed to version control. How can you best achieve this?

A. Create separate seed files and add them to your .gitignore or equivalent.
B. Employ a secrets management tool and integrate it with your dbt deployment.
C. Utilize environment variables to store seed data values.
D. Write the seed data into log files, excluding them from version control.


Question 5

A team member suggests building a dbt package for a set of reusable transformations your team frequently uses. When is this MOST likely a good idea?

A. The transformations are complex and involve domain-specific logic that might be beneficial to others.
B. The transformations are relatively simple and could be written as macros.
C. You need to limit access to these transformations to a specific team within your organization.
D. The transformations are project-specific but used across multiple models.


Solutions:

Question 1
Answer: A,B,D
Question 2
Answer: A
Question 3
Answer: B,D
Question 4
Answer: A
Question 5
Answer: A

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