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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Creating and Maintaining Documentation	10%	- Documentation standards - Generating documentation - Descriptions and metadata
Topic 2: Implementing dbt Tests	10%	- Test configuration and execution - Custom tests - Built-in tests
Topic 3: dbt Models Governance	15%	- Project organization and structure - Naming conventions and standards - Version control integration
Topic 4: Managing Data Pipelines	15%	- Pipeline orchestration - Deployment strategies - CI/CD integration
Topic 5: Developing dbt Models	20%	- Model optimization 1. Model contracts 2. Performance tuning - Model design and structure 1. Ref and source functions 2. Materialization types 3. Incremental models
Topic 6: External Dependencies	10%	- External sources integration - Using packages - Managing snapshots
Topic 7: Debugging and Error Resolution	15%	- Resolving data quality issues - Debugging techniques - Identifying modeling errors
Topic 8: Leveraging dbt State	5%	- State-aware operations - State management

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q315-Q320):

NEW QUESTION # 315
You're collaborating on a project where team members have varying levels of dbt experience. How can you use the dbt_project.yml to help standardize practices?

A. Include a docs: section with detailed descriptions of core models and transformations.
B. Configure project-level tests to ensure adherence to data quality standards.
C. Define custom macros in the project file to encapsulate commonly used SQL patterns.
D. Set up pre-commit hooks within the project file to enforce linting and formatting rules.

Answer: B

Explanation:
Project-level tests in dbt_project.yml provide a centralized way to maintain data quality expectations across the team.

NEW QUESTION # 316
You've made several commits to your feature branch. Before merging into the main branch, you want to consolidate the changes into a single, clear commit. What Git technique would help achieve this?

A. Run git rebase main followed by git commit -amend.
B. Use git rebase -i (interactive rebase) to squash commits together.
C. git cherry-pick specific commits from your branch and reapply them.
D. Manually copy your files into a new branch, commit, and issue a pull request.

Answer: B

Explanation:
Interactive rebasing (git rebase -i) provides fine-grained control to squash, edit, or rearrange commits, creating a cleaner commit history

NEW QUESTION # 317
You want to include a glossary of terms in your dbt project docs so that users can easily reference definitions. How could you achieve this?

A. Utilize a custom macro in your models to automatically generate a glossary section from designated column descriptions.
B. Manually add a separate "Glossary" page to the generated docs after running dbt docs generate_
C. Create a separate YAML file for your glossary, and use a Jinja macro to incorporate it into the documentation template.
D. Use the meta configuration in your dbt_project_yml file to define the glossary terms and their meanings.

Answer: A,C

Explanation:
A: A flexible approach with Jinja macros and a glossary file allows you to dynamically include your glossary in the documentation_ D. Custom macros can provide automation by extracting glossary terms from specifically marked column descriptions.

NEW QUESTION # 318
You have a dbt project with models defined as views. After deploying to a production database, the view objects are present, but no data is displayed when querying them. What might be the root cause?

A. There's a bug in dbt's SQL generation that specifically affects views in certain environments.
B. Dbt view materializations behave differently in production.
C. Permissions granted in production don't allow reading data from the underlying tables.
D. Your dbt job didn't run successfully, so the views were created empty.

Answer: C

Explanation:
B is a common security-related cause of 'missing' data. The others are less likely; views, logs, and dbt core logic tend to be consistent.

NEW QUESTION # 319
You've included some custom visualizations within your dbt project documentation (e.g., network graphs) but notice they don't render correctly when the site is generated. What might be the cause?

A. The dbt docs generate command has a strict limit on the file size of visualizations it can process-
B. Custom visualizations are not directly supported by dbt's built-in documentation.
C. Custom visualizations usually require additional JavaScript libraries to be included.
D. Your visualization definitions haven't been configured correctly in the project YAML files

Answer: C,D

Explanation:
A: Custom visualizations often rely on external libraries that need to be included in the generated HTML for them to function. B: Some visualizations might require specific settings in your dbt_project.yml or model files for correct rendering.

NEW QUESTION # 320
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