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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q106-Q111):

NEW QUESTION # 106
(Multiple Select)

A. Tests must be explicitly included in a model's test property for them to block model creation.
B. A 'post-hook' in your project configuration automatically fixes the issues flagged by tests.
C. Your dbt run command includes flags that override failing tests.
D. Your tests are likely configured to raise warnings rather than cause true errors.

Answer: C,D

Explanation:
A is a common pattern to allow non-critical tests to provide information without necessarily halting the process. C Certain dbt flags can force execution even when tests fail. B is incorrect; tests apply without model-specific references. D is unlikely (unless you have very sophisticated automation')

NEW QUESTION # 107
(Multiple Select)

A. Implementing more rigorous access controls and auditing procedures.
B. Using credentials with appropriate permissions (likely read-only) to access production data sources.
C. Scheduling models to run outside normal business hours to reduce resource contention.
D. Potentially running on a different data warehouse or with different database settings.

Answer: A,B,D

Explanation:
A reflects the increased security needs of production environments. B allows scaling or optimization based on workload. C prevents accidental changes in production. D is a strategy, but not a fundamental difference.

NEW QUESTION # 108
Which of the following scenarios might warrant you using the --no-compile flag with dbt docs generate? When you have syntax errors in your dbt models that would prevent compilation.

A. When you're deploying documentation updates to a production environment and want to minimize risks associated with model
B. When you want to quickly preview documentation changes without triggering a full model build.
C. recompilation.
D. When your CI/CD pipeline requires you to generate documentation as a separate step from model compilation.

Answer: A,C,D

Explanation:
B: The -no-compile flag speeds up docs generation when a full model build is unnecessary- C: It isolates documentation updates in production environments to reduce risk.
D: This flag provides control within CIICD pipelines when needed

NEW QUESTION # 109
A model intermittently fails. Sometimes it runs without errors. Which category of issue should you suspect FIRST?

A. Data quality issues in the source that are not consistently present.
B. Errors within complex macros used by the model, triggered only under specific data conditions.
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: A

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

NEW QUESTION # 110
You're evaluating whether to adopt an external tool that offers a visual, drag-and-drop interface for building data models. When considering this alongside dbt, what's a KEY factor to weigh?

A. The ease of migrating existing dbt projects into the visual tool.
B. Whether the tool can handle the full complexity of your data transformations.
C. The ability of the tool to generate dbt-compatible SQL and integrate into your version control workflow.
D. If the visual tool has advanced machine learning capabilities for data cleaning.

Answer: B,C

Explanation:
A and B touch on the crucial issues of how such a tool fits into your established workflow with dbt as the orchestrator, and whether it really matches the sophistication of your needs.

NEW QUESTION # 111
......

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