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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt-Analytics-Engineering Real Questions, Exam dbt-Analytics-Engineering Collection Pdf

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q252-Q257):

NEW QUESTION # 252
(Multiple Select)

A. Leveraging window functions to calculate values across rows or groups.
B. Nesting multiple correlated subqueries within a single query_
C. Writing custom macros to encapsulate frequently used SQL logic.
D. Using SELECT * to include all columns dynamically

Answer: B,D

Explanation:
SELECT * can lead to performance issues and unexpected results. Correlated subqueries often have better-performing joins equivalents. Macros and window functions are good practices.

NEW QUESTION # 253
You have a time-series fact table, and you're building incremental dbt models for efficiency. Model A loads an initial snapshot; subsequent models process new records only. During a dbt run, a model that processes new data fails. Select the possible consequences:

A. The initial snapshot will remain intact, but no new data will be updated.
B. All incremental models will need to be manually rerun after fixing the failed model.
C. dbt will revert the fact table to a state before the project was ever run.
D. The initial snapshot and all successfully processed incremental models will be unaffected.

Answer: A,D

Explanation:
A and D are true. Incremental models and snapshot models are designed to work independently within a well-structured dbt project. Failure of one shouldn't affect another. B is incorrect. Dbt doesn't have automatic rollback across entire tables. C is incorrect. Only downstream dependent incremental models would require a rerun, not all of them.

NEW QUESTION # 254
You've implemented the built-in dbt.get_column_values macro to display sample data values from a source column directly on the DAG. However, you notice performance issues when loading the docs. Which optimization strategy could you consider?

A. Increase the compute resources allocated to your dbt documentation generation process.
B. Modify the macro to only retrieve sample values during development and not for production documentation.
C. Replace dbt_get_column_values with a custom macro that only fetches the first few sample values.
D. Utilize a dbt snapshot to materialize the macro results instead of computing them on the fly.

Answer: B,C,D

Explanation:
B: Disabling expensive computations conditionally can boost performance for production environments. C: Snapshots pre-compute values, potentially improving DAG loading times. D: Reducing the amount of data retrieved by the macro could help with performance.

NEW QUESTION # 255
Logic Error

A. Write new unit tests that specifically target the faulty calculation logic with various input scenarios.
B. Adjust the model's SQL query and re-run it to observe the changes in output.
C. Deploy the modified model to a development or staging environment.
D. Create a separate branch specifically for addressing this issue.

Answer: B

Explanation:
Branching creates an isolated workspace, unit tests proactively define expected results, then you modify the code, and finally, you deploy to a non-production environment for further testing.

NEW QUESTION # 256
Your development environment uses a smaller, sampled dataset compared to production for efficiency. When a new performance-sensitive model is deployed, users report significant slowdowns. What might be some of the potential causes?

A. The smaller dataset in development didn't reveal performance issues that become apparent at the scale of production data
B. All of the above-
C. Performance optimizations made in development might not translate directly to the larger production data volumes.
D. Your data warehouse might have different resource configurations or indexing in the production environment.

Answer: B

Explanation:
A: Sampled data obscures performance problems related to data volume. B: Optimizations designed for small data might not be effective at scale. C: Differences between environments can drastically affect performance.

NEW QUESTION # 257
......

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