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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q315-Q320):

NEW QUESTION # 315
You have a computationally expensive dbt model that creates a wide table with many columns. You realize that downstream analysis typically only needs a small subset of these columns. How might you optimize this for both

A. Implement a post-hook that immediately deletes unused columns from the model's output table.
B. Configure a dbt job to run as a lower priority task, accepting slower processing.
C. Refactor the model to use a view materialization, selecting only necessary columns.
D. Increase memory and CPU resources allocated to the data warehouse to power through the large build-

Answer: C

Explanation:
A fundamentally reduces the work the model has to do. B is reactive and still incurs build cost. C helps but doesn't address the core problem. D doesn't change the efficiency of the model itself.

NEW QUESTION # 316
You're setting up CI/CD for your dbt project to automate deployments. Which aspects of your testing strategy might need adaptation for a production-focused CI/CD workflow?

A. Prioritizing tests with strict thresholds to avoid false positives from minor data variations in production.
B. Including smoke tests that run in production to verify core functionality after a deployment.
C. Emphasizing tests with longer run times for thorough checks before deployment.
D. Disabling all tests that rely on creating new tables, to avoid impacting the production database.

Answer: A,B

Explanation:
B ensures the deployed changes actually work in the real environment. C helps maintain CI/CD flow without unnecessary disruptions. A is counterproductive in a time-sensitive CI/CD context. D would overly limit your testing coverage.

NEW QUESTION # 317
(Multiple Select)

A. Tables, by default, are rebuilt completely with each dbt run.
B. Tables persist their results on disk, consuming storage resources.
C. Tables are well-suited for datasets that undergo frequent, wholesale changes.
D. Tables generally offer the fastest query performance for pre-computed models.

Answer: B,D

Explanation:
Tables trade off storage for excellent query performance and are a good fit for stable datasets or ones that can be efficiently updated.

NEW QUESTION # 318
Your team decides to use separate databases within the data warehouse for development, staging, and production. Which are the potential benefits of this approach?

A. Increased isolation of changes between environments, reducing the risk of unintended consequences in production.
B. All of the above.
C. The ability to fine-tune database configurations and permissions specifically for each environment's purpose.
D. Simplified testing and deployment processes due to clear separation of environments.

Answer: B

Explanation:
A: Isolation minimizes risks of production interference. B: Tailored configuration optimizes performance or security per environment. C: Clean separation streamlines operations and workflows.

NEW QUESTION # 319
You introduce a new test, which unexpectedly leads to many models previously deemed 'successful' now failing.
What should your initial troubleshooting steps include?

A. Temporarily raise the test's failure threshold to give you time to investigate.
B. Verify the test logic doesn't contain errors.
C. Check if the test is revealing previously overlooked data quality problems.
D. Review the test's severity configuration (warning vs_ error).

Answer: B,C,D

Explanation:
The focus is to determine if the issue is with the test itself, the data, or how the test is interpreted within your workflow. D is a last resort that risks masking real issues.

NEW QUESTION # 320
......

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