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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
(Multiple Select)

A. You made a typo when defining the column name in your model.
B. A ref function referencing another model is incorrect.
C. An upstream source table schema was updated without updating the dbt model.
D. Your dbt tests are invalid.

Answer: A,B,C

Explanation:
All but option D could lead to this specific error. Tests wouldn't directly cause a column mismatch, but they might help you catch the error.

NEW QUESTION # 73
You have a dbt model that performs complex aggregations. During development, you notice some models take excessively long to run, but they ultimately complete without errors. Which of the following might be true?

A. Downstream models will inherit the performance issues.
B. The models are at risk of failing if the source data grows substantially.
C. These models likely have issues with joins or CTE structure impacting performance.
D. This behavior automatically triggers an optimization feature within dbt.

Answer: B,C

Explanation:
A and B are likely true. Models without errors can still signify potential scaling issues and inefficient logic. C is not necessarily true. Downstream models won't inherit performance issues unless they directly rely on the output of these slow models. D is false. dbt has optimization features, but they often require manual configuration, not automatic triggering.

NEW QUESTION # 74
Your data warehouse environment undergoes a major version upgrade, introducing changes in SQL syntax and supported functions. How would you handle the migration of dbt projects to ensure compatibility?

A. Thoroughly test all dbt models across your staging and development environments to identify necessary updates.
B. All of the above.
C. Utilize custom macros to introduce conditional logic or abstract away syntax differences between versions, if feasible.
D. Create temporary branches in your dbt projects to isolate upgrade-related code changes for each environment

Answer: B

Explanation:
A: Branches facilitate staged testing and rollback, if necessary. B: Testing in lower environments uncovers potential issues before production deployment. C Macros can in some cases smooth over compatibility issues.

NEW QUESTION # 75
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. Add a WHERE clause to the model to filter out unnecessary rows during the join.
B. Rewrite the join logic by nesting conditions within a CTE.
C. Investigate database-specific techniques like hash joins or distribution keys for optimizing the join.
D. If the dimension data rarely changes, convert it to an incremental model.

Answer: C

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 # 76
Your baseline comparison tests start failing intermittently. Data distributions change slightly from day to day, sometimes triggering the thresholds. Which strategy could make your tests more robust?

A. Removing the baseline comparison tests entirely, as they provide unreliable signals.
B. Scheduling the tests to run less frequently (e.g., weekly rather than daily).
C. Rewriting your SQL to compare averages or medians instead of exact value counts-
D. Introducing configurable tolerances into your test definitions.

Answer: C,D

Explanation:
A makes tests less sensitive to minor noise- B allows for controlled flexibility. C is a workaround, sacrificing freshness. D gives up on a valuable check_

NEW QUESTION # 77
......

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