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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
(Multiple Select)

Answer: B,C

Explanation:
Renaming or introducing nulls can directly break downstream SQL that references that column. B (adding data) is generally less disruptive. Column order (D) is usually irrelevant for analytics.


NEW QUESTION # 89
You're working with a dataset where a column is expected to be updated frequently with newly arriving events. Which type of test would be MOST suitable to check that this process is working as intended?

Answer: C

Explanation:
It focuses on freshness, which is the core concern in this scenario. The other options, while valid, don't target this specific assumption.


NEW QUESTION # 90
A source table is updated with a full overwrite every night. Your dbt job fails due to freshness checks. What's a likely cause and solution?

Answer: C

Explanation:
Freshness checks designed for incremental updates might fail with full overwrites. Other solutions might be needed in specific scenarios but aligning the check is the first step.


NEW QUESTION # 91
You're onboarding a new dbt project to an existing production database. You find tables that might conflict with naming conventions used by dbt. What's a proactive way to handle this?

Answer: A

Explanation:
A isolates dbt's output in a dedicated way. B is less common, C risks breaking existing applications, D is more tedious.


NEW QUESTION # 92

Answer:

Explanation:

Explanation:
For models not stored under finance:
# reporter and bi
For models inside the finance folder:
# finance, reporter, bi, and public
In dbt, grants configured at the root level apply to all models unless overridden by a more specific folder- or model-level configuration.
The root-level grant is:
+grants:
+select: ['reporter', 'bi']
This means all models by default are selectable by:
* reporter
* bi
Now the finance folder contains its own override:
finance:
+grants:
+select: ['finance']
When dbt merges grants, overrides do not replace the entire list-they add onto inherited grants unless explicitly cleared. Therefore, models inside the finance folder inherit the parent grants and add the finance grant.
So models under finance are accessible to:
* finance
* reporter
* bi
* and public (implicit default in most warehouses unless denied)
Models not inside finance use only the root grants:
* reporter
* bi
Thus the correct dropdown answers are:
* reporter and bi
* finance, reporter, bi, and public


NEW QUESTION # 93
......

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