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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Testing and Documentation	20%	- dbt docs and DAG visualization - Custom data tests - Documentation generation - Schema tests (unique, not_null, accepted_values, relationships)
Topic 2: dbt Fundamentals	15%	- dbt project structure - dbt Core vs dbt Cloud - dbt workflow and best practices
Topic 3: Data Transformation Techniques	25%	- Refactoring and incremental models - Macros and packages - Common table expressions and subqueries - Jinja templating
Topic 4: Models	25%	- Writing and managing SQL models - Seeds - Materializations (table, view, ephemeral, incremental) - Snapshots - Sources and references
Topic 5: Deployment and Orchestration	15%	- Jobs and scheduling in dbt Cloud - Git version control integration - CI/CD with dbt Cloud - Environments (dev, staging, prod)

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q310-Q315):

NEW QUESTION # 310
You're debugging a runtime error. Analyzing the compiled SQL alone doesn't provide enough clues to find the root cause. What's your next debugging step?

A. Examine the dbt logs for more context about the model's execution and dependencies.
B. Disable custom macros to see if one of them is interfering-
C. Compare compiled SQL from a previous successful run to the current failing version-
D. Modify the compiled SQL directly and re-run the query against your warehouse.

Answer: A

Explanation:
Logs often provide additional error messages and context Comparing SQL versions can be helpful but might not illuminate the root cause. Never modify compiled SQL directly

NEW QUESTION # 311
You have a time-sensitive incremental model that must run as quickly as possible whenever new data arrives. What dbt feature or configuration might be crucial for optimization?

A. Use the dbt run -select flag to execute only that specific model.
B. Configure the model to run with maximum possible concurrency.
C. Set the model to refresh on a frequent, time-based schedule.
D. Define appropriate materializations (table, view, incremental) for the model.

Answer: A,D

Explanation:
A materially affects how the model is built. C avoids the overhead of running other models. B depends on warehouse concurrency limits. D might be needed, but the choice in A is more fundamental to performance.

NEW QUESTION # 312
You're setting up a new dbt project on a recently provisioned data warehouse. What actions are essential before your initial development begins?

A. Establishing access controls and user roles specific to the dbt project's requirements.
B. Defining and implementing a schema and naming conventions for your dbt models within the data warehouse.
C. All of the above
D. Configuring monitoring and alerting to track data warehouse usage and events related to dbt jobs

Answer: C

Explanation:
A: Organization is key for maintainability and collaboration. B: Secure access is fundamental from the outset C: Visibility into job execution and resource usage is crucial.

NEW QUESTION # 313
You want to use a YAML anchor to reuse a standard block of descriptive text within multiple models. How would you implement this?

A. Create a YAML anchor within the relevant model file where the text should be reused.
B. Define a Jinja macro for the text block, and then call the macro within your descriptions.
C. Insert a YAML anchor (&) in your dbt_projectyml file and reference it in your model files.
D. YAML anchors are not directly supported in the context of dbt model and source descriptions-

Answer: A

Explanation:
YAML anchors (&) and aliases (*) allow you to define reusable blocks of text within the same YAML file, such as model files or sources.yml.

NEW QUESTION # 314
A model intermittently fails. Sometimes it runs without errors. Which category of issue should you suspect FIRST?

A. Errors within complex macros used by the model, triggered only under specific data conditions.
B. Data quality issues in the source that are not consistently present.
C. dbt-specific configuration errors causing unpredictable behavior at the engine level.
D. Race conditions due to models in your DAG running concurrently without proper synchronization.

Answer: B

Explanation:
Inconsistent failures often suggest data dependency problems. While the others could occur, start with potential data quality concerns.

NEW QUESTION # 315
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