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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
You're onboarding a new dbt project to an existing production database. You find tables that might conflict with naming conventions used by dbt. What's a proactive way to handle this?

A. Add configuration settings to your database connection profile instructing dbt to use a specific schema prefix.
B. Update your dbt models to use aliases that avoid the name collisions.
C. Manually rename the conflicting tables in the database prior to running any dbt jobs.
D. Customize the schema property in your dbt_project.yml to namespace dbt-generated objects.

Answer: D

Explanation:
A isolates dbt's output in a dedicated way. B is less common, C risks breaking existing applications, D is more tedious.

NEW QUESTION # 65
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. Replace dbt_get_column_values with a custom macro that only fetches the first few sample values.
B. Increase the compute resources allocated to your dbt documentation generation process.
C. Utilize a dbt snapshot to materialize the macro results instead of computing them on the fly.
D. Modify the macro to only retrieve sample values during development and not for production documentation.

Answer: A,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 # 66
You need to run a custom Python script after your dbt models execute. Where and how can you configure this in your dbt_project.yml file?

A. Create an 'integration test' and use the PythonFunction class-
B. Include the script as a 'seed' file with a specific execution order
C. Define it as a 'snapshot' to ensure it runs after all models.
D. Utilize the on-run-end hook configuration.

Answer: D

Explanation:
on-run-end hooks allow you to execute arbitrary code at the end of a dbt run.

NEW QUESTION # 67
You want to include a multi-paragraph description for a source. Which of the following approaches is the most suitable?

A. Utilize YAML's block scalar syntax (using l) to maintain formatting for multi-line descriptions.
B. dbt documentation does not support multi-paragraph descriptions for sources.
C. Reference an external Markdown file within the source description.
D. Write a longer description directly within the YAML file, breaking it across multiple lines.

Answer: A

Explanation:
The block scalar syntax in YAML allows for writing multi-line text blocks that preserve the original formatting

NEW QUESTION # 68
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. Employ data masking or tokenization techniques within dbt models that process sensitive data.
B. All of the above.
C. Implement strict access controls ensuring only authorized individuals can access production environments.
D. Potentially create separate, lower-level environments with obfuscated, anonymized, or synthetic datasets for broader development.

Answer: B

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 # 69
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