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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt-Analytics-Engineering Free Practice Exams & New dbt-Analytics-Engineering Practice Questions

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q25-Q30):

NEW QUESTION # 25
You've defined development, staging, and production targets in your dbt project. You want to prevent accidental execution of destructive model materialization strategies (like drop or truncate) on the production target. How could you achieve this?

A. Implement a CI/CD process that includes checks to prevent specific dbt commands from running in the production environment
B. Create a separate profiles.yml file for production that disables dangerous materialization strategies.
C. Rely purely on thorough code reviews to prevent execution of these commands in production.
D. Write a custom macro that only executes such operations on non-production targets.

Answer: A,D

Explanation:
A Macros can provide conditional logic for model execution based on environments- C: Automated checks in CI/CD pipelines add a layer of protection. D: Relying solely on code reviews is prone to human error.

NEW QUESTION # 26
Which of the following are essential elements of a well-structured pull request description?

A. References to related issues in the project tracker, if applicable.
B. A concise title summarizing the overall changes.
C. Explanations of the reasoning behind major changes and implementation choices.
D. Detailed logs of each Git command used during development.

Answer: A,B,C

Explanation:
A: Clear titles aid quick understanding of the PRs purpose. C: Explanations help reviewers and future developers understand the thought process behind the code. D: Issue references create traceability and context.

NEW QUESTION # 27
"The compiled SQL looks correct, but I'm still getting wrong results in my model!" What might you suggest to a colleague having this issue?

A. All of the above.
B. Review the logic in 'on-run-start' or 'on-run-end' hooks, as they could be modifying data-
C. Test the model in isolation using the dbt run -select flag to narrow down dependencies.
D. Examine the source tables for data quality issues like duplicates or missing values.

Answer: A

Explanation:
Sometimes, even with correct SQL, the issue can lie in data itself, external interactions, or hidden dependency effects.

NEW QUESTION # 28
Your team establishes a policy for handling different levels of test failures (e.g., warnings vs. critical errors). How could you implement this policy effectively within your dbt workflows?

A. Rely on your data observability platform to differentiate and alert based on dbt test results.
B. Use custom severity levels in your test definitions and a post-hook to manage actions accordingly.
C. Run dbt test multiple times with varying filters based on desired severity levels.
D. Use dbt tags for classifying tests, then selectively execute them.

Answer: A,B,D

Explanation:
Each provides a mechanism to act upon varying severity levelsA leverages dbt's built-in features- C integrates with external monitoring tools. D offers flexible execution control.

NEW QUESTION # 29

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 # 30
......

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