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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q317-Q322):

NEW QUESTION # 317
(Multiple Select)

A. Anomaly detection platforms.
B. Continuous Integration (Cl) tools.
C. Tools specializing in schema change detection.
D. Data integration testing frameworks (e.g., Great Expectations)

Answer: A,C,D

Explanation:
All of these address different aspects of advanced data validation that complement the basic tests within dbt. Cl tools (D) are involved in running these checks but are not the data validation tools themselves.

NEW QUESTION # 318
You discover that a heavily used model is taking significantly longer to materialize. After investigation, you determine that the complexity of the model's logic is a contributing factor. Which of the following actions is LEAST likely to help improve performance?

A. Consider applying an incremental materialization strategy if suitable.
B. Investigate potential optimizations in the underlying SQL code.
C. Break down the model into smaller, intermediate models using CTEs.
D. Change the materialization from a table to a view.

Answer: D

Explanation:
Switching from a table to a view alone doesn't address the core performance issue of complex logic. Other options prioritize breaking down the transformations or directly optimizing the SQL itself.

NEW QUESTION # 319
You've finished a new feature but realize some related models now have minor formatting inconsistencies (e.g., spacing, indentation). What's the best way to handle this alongside your feature changes?

A. Address the formatting issues in the same commits as the feature to avoid extra work.
B. Briefly explain in your pull request description that some incidental formatting changes were included
C. Use git add -p to stage only the feature-related changes, and leave formatting for a future task.
D. Create a separate branch and commit addressing only the formatting.

Answer: D

Explanation:
Separating formatting fixes improves code review and history clarity. It allows reviewers to focus on the feature's functionality first and address formatting in a dedicated way.

NEW QUESTION # 320
You introduce a minor formatting change to a large SQL model. Afterwards, the results change unexpectedly. Which dbt-related issue might explain this, even if the SQL itself appears correct?

A. You've used a reserved keyword as a column alias without properly quoting it.
B. A subtle indentation change in a CTE accidentally modifies the execution order within the query.
C. There's a version mismatch between the dbt library used during development and in production.
D. An upstream data issue only surfaces due to the formatting change making that part of the query execute differently.

Answer: B

Explanation:
Whitespace matters in SQL, and CTE order is often significant even if the query might still be validly formatted.

NEW QUESTION # 321
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. Rely purely on thorough code reviews to prevent execution of these commands in production.
B. Write a custom macro that only executes such operations on non-production targets.
C. Implement a CI/CD process that includes checks to prevent specific dbt commands from running in the production environment
D. Create a separate profiles.yml file for production that disables dangerous materialization strategies.

Answer: B,C

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 # 322
......

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