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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata
Topic 2: dbt Core Concepts	- Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration - Models and materializations 1. Views, tables, incremental models 2. Ref and source functions
Topic 3: Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering - SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL
Topic 4: Deployment and Orchestration	- Environments and workflows 1. Development vs production environments 2. Version control with Git - Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns
Topic 5: Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
Your team establishes a policy for handling different levels of test failures (e.g., warnings vs. critical errors). How could you implement this policy effectively within your dbt workflows?

A. Use custom severity levels in your test definitions and a post-hook to manage actions accordingly.
B. Rely on your data observability platform to differentiate and alert based on dbt test results.
C. Use dbt tags for classifying tests, then selectively execute them.
D. Run dbt test multiple times with varying filters based on desired severity levels.

Answer: A,B,C

Explanation:
Each provides a mechanism to act upon varying severity levelsA leverages dbt's built-in features- C integrates with external monitoring tools. D offers flexible execution control.

NEW QUESTION # 24
You're working with highly sensitive production data that contains personally identifiable information (PII). How would this impact the design of your dbt project environments?

A. Potentially create separate, lower-level environments with obfuscated, anonymized, or synthetic datasets for broader development.
B. Employ data masking or tokenization techniques within dbt models that process sensitive data.
C. Implement strict access controls ensuring only authorized individuals can access production environments.
D. All of the above.

Answer: D

Explanation:
A: Access management is essential for sensitive data. B: Lower environments should enable development without exposing PII unnecessarily. C: Masking/tokemzation within models adds an extra layer of data protection.

NEW QUESTION # 25
When merging code on a remote repository hosting platform (like GitHub), you notice options like "squash and merge" and "rebase and merge." Which factors would influence your choice between these options?

A. Your team's established conventions and preferences for maintaining the project history.
B. All of the above.
C. The size and complexity of the changes within the pull request.
D. Whether you want to preserve the feature branch commit history in detail.

Answer: B

Explanation:
A: Squash and merge simplifies history; rebase and merge preserve details. B: Large pull requests might benefit from squashing for a cleaner main branch view. C: Teams should have consistent ways to manage history for ease of collaboration.

NEW QUESTION # 26
You want to implement a macro that displays a warning icon next to models in the DAG that haven't been tested. Which of the following would be necessary to make this work?

A. Configuration in your dbt_projectyml file to associate models with the relevant tests.
B. A custom CSS class for the warning icon and a way to apply it dynamically using the macro.
C. The ability for the macro to query the results of test executions from the dbt run-
D. All of the above.

Answer: D

Explanation:
A: You'll need CSS to style the warning icon- B. The macro needs to determine if the associated tests for each model have passed or failed. C: dbt needs a way to understand which tests are related to which models for the macro to function correctly.

NEW QUESTION # 27
You're experimenting with new transformations. You want to iterate on a model without affecting data in the production warehouse. Which dbt command should you use along with appropriate flags?

A. dbt seed with -select to load data into production.
B. dbt test with -defer to temporarily bypass upstream dependencies.
C. dbt compile to ensure changes are valid without executing models.
D. dbt run with -target to specify a development environment.

Answer: D

Explanation:
The -target flag allows you to isolate development work within different environments preventing accidental changes to production data.

NEW QUESTION # 28
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