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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implementing dbt Tests	10%	- Test configuration and execution - Built-in tests - Custom tests
Topic 2: Debugging and Error Resolution	15%	- Identifying modeling errors - Debugging techniques - Resolving data quality issues
Topic 3: Leveraging dbt State	5%	- State-aware operations - State management
Topic 4: Creating and Maintaining Documentation	10%	- Generating documentation - Documentation standards - Descriptions and metadata
Topic 5: Managing Data Pipelines	15%	- CI/CD integration - Pipeline orchestration - Deployment strategies
Topic 6: dbt Models Governance	15%	- Version control integration - Naming conventions and standards - Project organization and structure
Topic 7: External Dependencies	10%	- External sources integration - Managing snapshots - Using packages
Topic 8: Developing dbt Models	20%	- Model design and structure 1. Materialization types 2. Incremental models 3. Ref and source functions - Model optimization 1. Model contracts 2. Performance tuning

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
You frequently need to spin up short-lived development environments on demand for testing and experimentation. Which dbt feature would streamline this process, especially when it involves custom configuration?

A. dbt's -vars flag to override variable values.
B. dbt profiles defined in your dbt_project.yml file.
C. Using dbt tags to selectively execute parts of your project.
D. Creating multiple deployment environments in dbt Cloud.

Answer: A,B

Explanation:
A encapsulates environment settings in reusable blocks- B allows for ad-hoc adjustments when running dbt- C is relevant for cloud-based setups, but less flexible for on-the-fly changes- D relates to model selection, not environment setup.

NEW QUESTION # 63
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. Use git add -p to stage only the feature-related changes, and leave formatting for a future task.
B. Address the formatting issues in the same commits as the feature to avoid extra work.
C. Briefly explain in your pull request description that some incidental formatting changes were included
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 # 64
You're planning your production dbt deployment strategy. When deciding between completely separate development and deployment environments vs. using different schemas within a single data warehouse, what are I key factors to weigh?

A. The need to collaborate between developers with varying skill levels.
B. The complexity of your dbt project and the number of models.
C. Your data warehouse's pricing model and how it charges for resource usage.
D. The desired level of isolation and access control granularity.

Answer: A,C,D

Explanation:
B speaks to security and change management. C is important as the cost could differ considerably. D influences the need for stronger isolation. A is less important than the others here.

NEW QUESTION # 65
You typically use dbt run to execute your project. However, you want to trigger a refresh of only models that depend on newly updated source dat a. How could you achieve this?

A. Add dbt run to the post-hook of your upstream ETL process, so it triggers upon data arrival.
B. Configure your sources with the freshness property and run dbt source freshness.
C. Manually determine dependent models, then use dbt run -m
D. Leverage dbt snapshots to identify changed sources and derive the dependent model list.

Answer: B,D

Explanation:
B automates discovery of what's stale. D uses snapshots for the same purpose. A is tedious, C runs all models, not selectively

NEW QUESTION # 66
During development, you frequently used temporary print statements for debugging. Which of the following is the best practice before opening a pull request?

A. Comment out the print statements but leave them in the code.
B. Consolidate the print statements into a separate debugging function that can be toggled on and off
C. Remove the print statements entirely from your commits-
D. Leave the print statements for future reference, in case similar debugging is needed-

Answer: C

Explanation:
Debugging code should generally not be part of committed changes, as it clutters the codebase and offers no value to the core functionality.

NEW QUESTION # 67
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