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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q326-Q331):

NEW QUESTION # 326
You've been tasked with customizing the appearance of the generated documentation. Which of the following would be the most efficient starting point?

A. Search for third-party dbt packages designed for documentation customization.
B. Investigate overriding the default CSS and JavaScript files used by the documentation site.
C. Write a macro that programmatically generates a new documentation structure based on your requirements_
D. Edit the json files created during the dbt docs generate process to adjust the display of information.

Answer: A,B

Explanation:
A: Overriding the CSS and potentially JavaScript provides customization flexibility without completely rebuilding the documentation structure. D: Extending dbt's functionality with packages often offers pre-built solutions for more extensive customization of the documentation site.

NEW QUESTION # 327
Your fct_orders model depends on both raw_sales and stg_customers. Due to upstream changes, the customer_id column in raw_sales is now named cust_id. How could you manage this change with minimal disruption?

A. Update the fct_orders model to reference cust_id.
B. Utilize dbt's ref() function to dynamically adapt to the new column name.
C. Create an alias for customer_id as cust_id in the stg_customers model.
D. Use a dbt source to rename the column in raw sales.

Answer: B

Explanation:
The ref() function is designed for these situations. It makes your models adaptable to upstream changes. Option A would work but creates maintenance overhead.

NEW QUESTION # 328
You're setting up a dbt project with a model that generates a large intermediate result. You're concerned about performance. Which approaches could be helpful?

A. Deploy the dbt project to a smaller development environment to reduce processing overhead
B. Investigate database-specific features (e.g., temporary tables ) and refactor dbt logic to leverage them.
C. Schedule the model to run only during off-peak hours.
D. Use a staging schema for the intermediate model and adjust the job's materialization settings.

Answer: B,D

Explanation:
A allows customizing how the dataset is handled. B can potentially lead to performance gains. C may help, but isnt a direct solution. D is unrelated to the core issue.

NEW QUESTION # 329
(Multiple Select)

A. Implement a custom data test to specifically check the column for null values.
B. Examine the source data for the model to verify the presence of expected values.
C. Add intermediate CTEs within the model's SQL to inspect data at different transformation stages.
D. Switch the model materialization from 'view' to 'table'.

Answer: B,C

Explanation:
Verifying source data and stepwise debugging are key. Tests help but come later. Changing the materialization is unlikely to directly address the null-value issue.

NEW QUESTION # 330
A colleague asks, "Every time I make changes to my dbt_project.yml I have to wait for errors during dbt compile. Is there a better way?" What would you suggest?

A. Install a YAML linter within their code editor to catch formatting and basic schema errors.
B. Thoroughly review dbts documentation for a complete list of valid properties to memorize.
C. Have them write dbt schema tests for their configuration files.
D. Manually validate the dbt_project.yml against a fixed template file after each edit

Answer: A

Explanation:
Linters provide real-time feedback. Schema tests are useful but don't prevent all YAML issues. Memorization and manual checks are tedious-

NEW QUESTION # 331
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