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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q173-Q178):

NEW QUESTION # 173
(Multiple Select)

A. You need to temporarily disable a computationally expensive model that isnt critical for daily reporting.
B. A critical upstream data source has been delayed, and you only want to refresh dependent models once the data arrives.
C. You've refactored a core model and need to quickly validate its output and downstream dependencies.
D. There's an urgent request to update a single dashboard, and you know the exact subset of models to re-run.

Answer: B,C,D

Explanation:
Each involves a need for targeted, not blanket, execution. D is more about configuration than selective runs.

NEW QUESTION # 174
(Multiple Select)

A. Pre-calculate frequently used periods (e.g., current month, previous month) and store them as intermediate CTEs.
B. Introduce redundant columns in source tables to store pre-calculated percentage changes.
C. Ensure relevant columns in underlying tables have appropriate indexes.
D. Utilize window functions to efficiently compute the difference without self-joins.

Answer: C,D

Explanation:
Window functions excel at period-over-period calculations. Indexes are crucial for accelerating queries. Options A and B could help but introduce potential maintenance and storage tradeoffs.

NEW QUESTION # 175
Your team uses a custom macro to generate model descriptions dynamically. However, these descriptions don't appear in the DAG visualization within the documentation. Which of the following might cause this issue?

A. Custom macros always require explicit configuration in your dbt_project.yml file to be rendered in the DAG-
B. The macro might not be returning the description in the standard format that dbt documentation expects.
C. The generated documentation site might be cached, preventing the updated descriptions from appearing.
D. Dynamically generated descriptions are incompatible with DAG visualizations in dbt

Answer: A,B,C

Explanation:
A Some macros might require additional settings to be integrated correctly into the documentation. B- dbt expects descriptions in specific formats for DAG rendering. Your macro output needs to adhere to these. C: Caching can sometimes mask the latest changes in the documentation.

NEW QUESTION # 176
You get a compilation error: "Configuration key 'enabled' not found". Where in your dbt_project.yml would you start looking to fix this, and why?

A. In a seeds.yml file or the seeds section, where enabled is a valid property.
B. Within the models section, as models often use the enabled property.
C. In the top-level profiles.yml, as the enabled key is specific to setting up project profiles.
D. Under a specific source definition, as sources can also sometimes be conditionally enabled.

Answer: B

Explanation:
Since it's a generic error, start with the most common location - model configurations. The others might be valid in certain scenarios.

NEW QUESTION # 177
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. Use custom severity levels in your test definitions and a post-hook to manage actions accordingly.
B. Rely on your data observability platform to differentiate and alert based on dbt test results.
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 # 178
......

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