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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Developing dbt Models	20%	- Model optimization 1. Performance tuning 2. Model contracts - Model design and structure 1. Ref and source functions 2. Incremental models 3. Materialization types
dbt Models Governance	15%	- Version control integration - Project organization and structure - Naming conventions and standards
External Dependencies	10%	- Using packages - Managing snapshots - External sources integration
Debugging and Error Resolution	15%	- Identifying modeling errors - Debugging techniques - Resolving data quality issues
Leveraging dbt State	5%	- State management - State-aware operations
Creating and Maintaining Documentation	10%	- Descriptions and metadata - Generating documentation - Documentation standards
Implementing dbt Tests	10%	- Built-in tests - Custom tests - Test configuration and execution
Managing Data Pipelines	15%	- CI/CD integration - Deployment strategies - Pipeline orchestration

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q285-Q290):

NEW QUESTION # 285
You've updated a model's description in a YAML file to reflect a new business requirement. You've also added additional context to the column descriptions. After regenerating the documentation, you notice the model description has updated, but the column descriptions have not. What could be the most likely reasons?

A. The model's YAML file has incorrect syntax preventing column descriptions from rendering.
B. You are viewing an older cached version of the documentatiom
C. You forgot to run dbt docs generate after modifying the YAML file.
D. The documentation was not configured correctly to display column descriptions.

Answer: B,C

Explanation:
Explanation: B:
The dbt docs generate command is essential for updating the documentation website with changes made in the project files. D: Web browsers often cache content; you might need to clear your cache or force a refresh to see the latest changes.

NEW QUESTION # 286
You want to include a multi-paragraph description for a source. Which of the following approaches is the most suitable?

A. dbt documentation does not support multi-paragraph descriptions for sources.
B. Write a longer description directly within the YAML file, breaking it across multiple lines.
C. Reference an external Markdown file within the source description.
D. Utilize YAML's block scalar syntax (using l) to maintain formatting for multi-line descriptions.

Answer: D

Explanation:
The block scalar syntax in YAML allows for writing multi-line text blocks that preserve the original formatting

NEW QUESTION # 287
(Multiple Select)

A. Integrating dbt's logging with your existing monitoring and alerting platforms.
B. Require thorough code reviews with a focus on testability before deploying model changes to production.
C. Using dbt Cloud's built-in job monitoring and notification features, if applicable.
D. Implement custom metrics tracking the duration and resource usage of individual dbt models.

Answer: A,C,D

Explanation:
These provide active monitoring of your dbt production environment. D is a good practice, but not about real-time observability

NEW QUESTION # 288
Business requirements call for a report ranking products based on year-over-year (YoY) revenue growth. Historical data must be preserved. What's a potential issue in implementing this directly in a dbt model?

A. Incremental materializations aren't suitable for trend analysis.
B. ref() functions can hinder performance when working with historical data.
C. Data warehouses might not be optimized for period-over-period comparisons.
D. Views cannot perform complex date manipulation required for YoY calculations.

Answer: C

Explanation:
YoY calculations can strain database performance. Consider strategies like pre-aggregating data at the month level. Others issues are less likely to be a bottleneck.

NEW QUESTION # 289
You run this command:
dbt build --select "source_status:fresher+" --state path/to/prod/artifacts Which two need to happen before it can be executed successfully?
Choose 2 options.

A. Invoke either dbt run or dbt build.
B. Add generic tests to your sources.
C. Test your sources with dbt test.
D. Define a freshness block on your source(s).
E. Invoke the command: dbt source freshness.

Answer: D,E

Explanation:
The selector source_status:fresher+ depends entirely on freshness metadata created by a previous dbt source freshness run. Source freshness results are stored in artifacts, and the --state flag points dbt to the location where these artifacts were previously generated. Without first running dbt source freshness, dbt has no stored freshness status and therefore cannot filter sources by "fresher" status. This makes option A required.
Additionally, source freshness checks only work when sources include a freshness block in their YAML configuration. This block specifies the loaded_at_field and the warn_after / error_after thresholds. If no freshness block exists, dbt does not compute freshness at all, meaning such sources cannot appear in the source_status:fresher+ selector. Therefore D is also required.
Options B and E are unrelated to source freshness artifacts and do not contribute to the preconditions for this selector. Option C does not impact freshness logic, as generic tests have no role in generating freshness metadata.
Thus, the two prerequisites are:
* Running dbt source freshness (to create freshness metadata).
* Defining a freshness configuration on the relevant sources.

NEW QUESTION # 290
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