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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q324-Q329):

NEW QUESTION # 324
The dataset raw user activity is critical to several models. You've been informed that a major schema change is scheduled for this source. Select the best way to proactively mitigate the potential impact of this change on your dbt project.

A. Communicate with the relevant data team to create a temporary staging table mirroring the old schema.
B. Run dbt test after the schema changes and address any failures.
C. Create custom data tests that validate the critical columns in raw_user_activity.
D. Pause all dbt jobs until the schema change risk is fully assessed.

Answer: C

Explanation:
Custom data tests proactively ensure that even if the schema changes, the critical data in raw_user_activity remains valid and consistent for your models. Other options are reactive or might introduce unnecessary downtime.

NEW QUESTION # 325
You define a source and later try to reference it. Compilation fails with: "Model depends on a node named 'my source' which was not found". What are some likely causes?

A. All of the above.
B. Your dbt_project.yml configuration has the incorrect source-paths setting.
C. The source definition is missing or incorrect in your dbt_project.yml or sources file.
D. There's a typo in how you're referencing the source name using source().

Answer: A

Explanation:
Dependency issues can manifest like this. Check your source configuration, the source() usage, and even your project's file paths.

NEW QUESTION # 326
Your dbt project contains a series of interlinked models (A, B, C, D) where some models have downstream dependencies on upstream models. While running dbt run, model C fails due to a data validation test you added. Which of the following statements are potentially true?

A. Model C has stale data but can still be queried.
B. The failure of Model C triggers an automatic rollback of Model A and Model B.
C. Model D cannot be built until the issue with Model C is resolved.
D. Model A and Model B are unaffected and have the most up-to-date data.

Answer: A,C,D

Explanation:
A is true because upstream models that successfully complete are unaffected by downstream failures. B is true due to the dependency structure of the DAG. Model D cannot be created if its prerequisite, Model C, fails. C is false. dbt models don't automatically roll back upon failure unless you explicitly program this. D is true. Even though the model wasn't created successfully, the prior version (if it existed) remains in the warehouse

NEW QUESTION # 327
You discover a vulnerability in a third-party lineage-enhancing dbt package you use. Where might you first look for information and potential fixes?

A. Directly in the manifest.json or packages.yml files within your project
B. Only the authors of the dbt package would be able to address the vulnerability.
C. The GitHub repository or documentation of the dbt package.
D. Centralized dbt documentation or a discussion forum.

Answer: C

Explanation:
The package maintainer is the primary source for updates and vulnerability fixes. However, other resources like A & C might provide additional information or community solutions.

NEW QUESTION # 328
A large dimension table is joined frequently in your dbt models. Due to its size, these joins significantly impact performance. Which strategy might offer improvement?

A. Investigate database-specific techniques like hash joins or distribution keys for optimizing the join.
B. If the dimension data rarely changes, convert it to an incremental model.
C. Rewrite the join logic by nesting conditions within a CTE.
D. Add a WHERE clause to the model to filter out unnecessary rows during the join.

Answer: A

Explanation:
While other options could help marginally, they don't address the large-table join issue head- on. Database-level join optimizations are crucial for this scenario.

NEW QUESTION # 329
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