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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Deployment and Orchestration	15%	- Jobs and scheduling in dbt Cloud - Git version control integration - Environments (dev, staging, prod) - CI/CD with dbt Cloud
Topic 2: Models	25%	- Materializations (table, view, ephemeral, incremental) - Sources and references - Snapshots - Writing and managing SQL models - Seeds
Topic 3: Testing and Documentation	20%	- dbt docs and DAG visualization - Documentation generation - Custom data tests - Schema tests (unique, not_null, accepted_values, relationships)
Topic 4: Data Transformation Techniques	25%	- Common table expressions and subqueries - Jinja templating - Refactoring and incremental models - Macros and packages
Topic 5: dbt Fundamentals	15%	- dbt project structure - dbt Core vs dbt Cloud - dbt workflow and best practices

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q100-Q105):

NEW QUESTION # 100
(Multiple Select)

A. Run dbt docs generate and dbt docs serve to inspect the lineage graph.
B. Add -full-refresh flag to dbt run to ensure all models are rebuilt from scratch.
C. Write custom macros to log intermediate data for each model.
D. IJse the -select flag with dbt run to execute models incrementally, starting with the most upstream dependency.

Answer: A,D

Explanation:
Visualization and incremental execution aid debugging. Custom logging might be necessary but is more time-consuming to implement. A full refresh isn't targeted for debugging.

NEW QUESTION # 101
A model fails with an error similar to:Numeric overflow converting to data type numeric(18,2)

A. Missing permissions to write data to the target table in the warehouse.
B. A timeout issue caused by the model exceeding resource limits.
C. Incorrect ref() usage causing cyclical dependencies to occur-
D. Data within a column exceeding the precision or scale of the defined data type

Answer: D

Explanation:
This error indicates data type limitations, causing computed values to fall outside the allowed range.

NEW QUESTION # 102
You maintain a large dbt project with various reporting models that source data from a set of core business tables. These core tables often include columns with sensitive information. What technique could ensure compliance and maintainability when restricting access to this data?

A. Use views to create different projections of the core tables, exposing only the necessary columns.
B. Apply row-level security within the data warehouse itself for fine-grained control.
C. Write complex WHERE clauses in each reporting model to exclude rows containing sensitive data.
D. Create incremental models that directly remove the sensitive columns before reporting models utilize them.

Answer: A

Explanation:
Views provide a modular and maintainable way to define access restrictions. This approach centralizes the filtering logic, preventing repeated and potentially inefficient WHERE clauses across models.

NEW QUESTION # 103
A new dbt model is created directly in the development environment without a corresponding raw data source. Which of these can be potential ramifications of this approach?

A. The model might be based on assumptions about data that dont hold true when applied in production.
B. All of the above-
C. You might lack a clear reference and lineage back to the original data source.
D. There might be limited ability to reproduce or troubleshoot the model if the development data is modified or deleted.

Answer: B

Explanation:
A: Direct development modeling risks bypassing important raw data checks. B: Data lineage becomes unclear and could lead to misinterpretations. C: Without a reliable source, fixing issues or adapting the model becomes harder

NEW QUESTION # 104
You're reviewing a pull request and notice a subtle logic error that wasn't caught in automated tests. How do you best approach this within the code review?

A. Leave a comment explaining the error and provide a suggestion for how to fix it.
B. Simply fix the error directly in the pull request without leaving a comment.
C. Create a separate issue in the issue tracker, detail the bug, and reference it in the pull request.
D. Reject the pull request outright and ask the author to fix the error and resubmit.

Answer: A

Explanation:
Providing constructive feedback guides the PR author toward a solution, facilitating improvement and knowledge sharing.

NEW QUESTION # 105
......

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