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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q78-Q83):

NEW QUESTION # 78
(Multiple Select)

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

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 # 79
During a code review, a collaborator suggests modifying a small portion of code you had already committed to your feature branch. What's the most efficient way to address this without creating a completely new commit?

A. Create a new branch extending from your feature branch, make the change, and open a separate pull request.
B. Use git commit -amend to modify your most recent commit.
C. Explain to your collaborator that modifying committed code is not recommended, and the change should wait for a future update.
D. Use git revert to undo your previous commit, make the change, and then re-commit.

Answer: B

Explanation:
Git commit -amend allows seamless modification of your most recent commit, preserving a clean commit history.

NEW QUESTION # 80
While reviewing a dbt project, you notice a pattern where ref() calls are chained together:
Model A references Model B, which in turn references Model C.
What's a potential downside of this pattern?

A. Decreased performance due to unnecessary materializations.
B. Model dependencies become harder to track and understand.
C. Test results might become less reliable.
D. Version control conflicts become more likely.

Answer: B

Explanation:
While chaining ref() calls works technically, it can quickly make dependency tracking overly complex, especially in larger projects.

NEW QUESTION # 81
You want to include a detailed lineage diagram in your project documentation. Which of these approaches would you likely consider?

A. Write a custom macro that generates a lineage diagram and include it in relevant model descriptions.
B. Manually create a diagram using a separate tool and add an image reference in a model's description.
C. Use the built-in lineage functionality within dbt's standard documentation generation.
D. Configure a third-party dbt package designed for enhanced lineage and visualization.

Answer: A,B,D

Explanation:
A: For basic needs, a manual approach might be sufficient C: Custom macros provide flexibility in how the lineage is presented. D: Specialized packages offer advanced lineage visualization capabilities-

NEW QUESTION # 82
You notice that your dbt jobs take longer than expected. You suspect potential issues with models referencing large raw tables unnecessarily. Which of the following strategies might help optimize performance? (Choose two)

A. Convert your most-used models to incremental materializations.
B. Add more indexes to all the raw tables in your warehouse.
C. Introduce CTEs within your models to pre-filter raw data where possible.
D. Move the raw table into a separate staging schema and grant access only to necessary models.

Answer: A,C

Explanation:
Using CTEs to pre-filter and incremental materializations (where applicable) directly reduce the data processed by your models. Options A and C might help in specific cases but don't directly address the core issue.

NEW QUESTION # 83
......

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