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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q188-Q193):

NEW QUESTION # 188
31. Your entire DAG looks like the image shown.

(Several stg_ models appear upstream, feeding into int_ and fct_ models.) The question asks:
"Was this modeling rule violated?
Staging models dependent on other staging models"

A. No
B. Yes

Answer: B

Explanation:
In dbt's recommended layered modeling architecture, the staging layer is intended to provide a clean, one- to-one representation of raw source tables. Each stg_ model should depend only on sources, not on other staging models. This ensures staging remains a simple, transparent layer where data is renamed, recast, standardized, and lightly transformed before being passed to intermediate and mart layers.
In the DAG shown, at least one staging model (for example, stg_line_items or stg_tpch_line_items) appears downstream of another staging model, meaning a stg_ model is referencing another stg_ model. This violates dbt's recommended modeling practice, because it creates unnecessary complexity in the staging layer and reduces modularity and transparency. Downstream layers such as intermediate (int_) and marts (fct_) should be used to combine, enrich, or join multiple staging outputs.
When staging models depend on each other, it becomes harder to trace lineage, reduces clarity about where transformations occur, and complicates the entire DAG. The proper pattern is:
* Sources # Staging (stg_) # Intermediate (int_) # Marts (fct_)
Since the DAG shows staging models referencing other staging models, the rules have indeed been violated.

NEW QUESTION # 189
(Multiple Select)

A. If macros themselves have dependencies on other macros, this adds an extra layer to consider.
B. If macros reference raw tables, those dependencies might not be reflected in dbt's lineage graphs.
C. Macros generally make dependency tracking easier by encapsulating logic.
D. Macros can completely obfuscate dependencies, making them untraceable.

Answer: A,B

Explanation:
Macros can introduce "hidden" dependencies. Also, if your macros build upon each other, you must track their internal dependencies as well.

NEW QUESTION # 190
Which of the following scenarios would typically warrant the use of production data in a development environment?

A. Training new members of your team on writing SQL and basic dbt models.
B. Development environments should generally avoid using real production data to minimize risks.
C. Debugging a logic error that only manifests itself under the exact conditions of real-world production data.
D. Testing changes to a critical business reporting model for accuracy before deployment.

Answer: B

Explanation:
While A and C might occasionally have valid use cases for production data in development, it usually should be avoided. Consider these for special circumstances:A Ideally, realistic test data or anonymized production samples should be used. C: Production data snapshots or masked/anonymized versions might be necessary in rare situations.

NEW QUESTION # 191
You've identified the same complex date calculations repeated across multiple models. Which approach aligns with modularity and DRY (Don't Repeat Yourself) principles?

A. Continue copying the date calculation logic into each relevant model.
B. Create a macro, encapsulating the date calculations, and reference it in the affected models.
C. Introduce a new intermediate table to store the result of the date calculations.
D. Write a custom Python function and import it into your dbt project.

Answer: B

Explanation:
Macros excel in encapsulating reusable logic, promoting modularity and avoiding code repetition. While option C could work, it introduces unnecessary materialization.

NEW QUESTION # 192
Which two are true for a dbt retry command?
Choose 2 options.

A. It picks up from the error without running all of the upstream dependencies.
B. It reuses selectors from the previous command.
C. It retries the previous command if it is not a syntax error in a model.
D. It reads a manifest.json file to identify the models and tests that failed in the last run.
E. It reruns all nodes in your previous invocation statement.

Answer: C,D

NEW QUESTION # 193
......

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