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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deployment and Orchestration15%- Environments (dev, staging, prod)
- Jobs and scheduling in dbt Cloud
- Git version control integration
- CI/CD with dbt Cloud
Topic 2: Data Transformation Techniques25%- Common table expressions and subqueries
- Refactoring and incremental models
- Jinja templating
- Macros and packages
Topic 3: Models25%- Materializations (table, view, ephemeral, incremental)
- Writing and managing SQL models
- Sources and references
- Seeds
- Snapshots
Topic 4: Testing and Documentation20%- Documentation generation
- dbt docs and DAG visualization
- Custom data tests
- Schema tests (unique, not_null, accepted_values, relationships)
Topic 5: dbt Fundamentals15%- dbt project structure
- dbt workflow and best practices
- dbt Core vs dbt Cloud

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q41-Q46):

NEW QUESTION # 41
You assume two columns of type 'numeric' will always align in terms of precision and scale (number of decimal places). What's a key way to design your models and tests to be resilient even if this assumption changes?

Answer: B,D

Explanation:
These strategies allow for flexibility with numeric comparisons, making your models less sensitive to changes in precision. C is a good idea but not a full solution. D is generally bad practice.


NEW QUESTION # 42
You've added new tests to a dbt model but are unsure how to determine the test coverage. What's a practical first step?

Answer: D

Explanation:
dbt docs provides visual indicators of which models have tests, aiding in identifying gaps in your coverage strategy


NEW QUESTION # 43
You've added new tests but notice they aren't failing even though you've intentionally introduced errors. What's a LIKELY cause?

Answer: B

Explanation:
Compilation is necessary for new tests to be recognized. Others are less likely, though severity thresholds could be a factor if misconfigured.


NEW QUESTION # 44
You want to display the runtime metrics (e.g., execution time) of each model directly on the DAG node. Which challenges might you face when implementing this?

Answer: B

Explanation:
A: You might need to generate static summaries or find workarounds to display dynamic metrics. B. Collecting runtime metrics across all historical runs might require additional logging or integration with dbt artifacts (manifestjson, run_resultsjson)- C: The DAG visualization needs a balance between information and readability.


NEW QUESTION # 45
You have complex dbt models involving incremental logic based on a timestamp column. To debug a production issue, you want to temporarily force a full rebuild of a specific incremental model. Which dbt command-line options could help?

Answer: B,C

Explanation:
A overrides the incremental setting project-wide. C allows the model to be 'tricked" into a full refresh. B is valid, but more work. D is risky and violates dbts workflow.


NEW QUESTION # 46
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