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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q58-Q63):

NEW QUESTION # 58
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. Utilize the on-run-end hook configuration.
B. Create an 'integration test' and use the PythonFunction class-
C. Define it as a 'snapshot' to ensure it runs after all models.
D. Include the script as a 'seed' file with a specific execution order

Answer: A

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

NEW QUESTION # 59
You need to give read-only access to a specific dataset in your production data warehouse to a data analyst on your team. How might you approach this using dbt environments?

A. A combination of the above, depending on the capabilities of your data warehouse.
B. Create a dedicated dbt target for the analyst and utilize grants within your models to control access.
C. Use row-level security features in your data warehouse and dynamically filter data in models using environment variables.
D. Modify the data warehouse permissions directly to grant access to the required dataset.

Answer: A

Explanation:
A: Managing permissions in the data warehouse is often essential. B: dbt grants offer an additional layer of access control within your models. C: Row-level security provides fine-grained control but requires support from your data warehouse.

NEW QUESTION # 60
Which of the following scenarios might warrant a git rebase instead of a standard git merge when integrating your changes into the main branch?

A. You need to resolve complex merge conflicts that arise during a standard merge.
B. You only want to include a subset of your feature branch commits in the main branch.
C. Your branch has diverged significantly from the main branch with unrelated changes.
D. You want to avoid a merge commit and keep a linear project history.

Answer: C,D

Explanation:
A Rebasing creates a more linear history, making it easier to trace changes over time. D. Rebasing allows for a cleaner integration when a feature branch contains intermingled changes that shouldn't all be merged.

NEW QUESTION # 61
You want to compare the current version of a file in your working directory to the most recently committed version. Which Git command would be most helpful?

A. git log
B. git status
C. git diff
D. git show

Answer: C

Explanation:
git diff shows line-by-line differences between versions, letting you compare your working version to the committed one (HEAD).

NEW QUESTION # 62
You're setting up a development environment in a cloud data warehouse with usage-based billing. How might you approach cost management in this context?

A. Create a separate profiles.yml specifically for the development environment that utilizes smaller data warehouse resource classes.
B. Use dbt seeds to load smaller datasets for testing, reducing overall data processing costs.
C. Implement automated scripts to shut down the development environment during non-working hours.
D. Set up usage alerts in your data warehouse that notify the team if costs exceed certain thresholds.

Answer: D

Explanation:
A). Controlling uptime reduces unnecessary costs. B. Right-sizing data warehouse resources helps align usage and cost C: Smaller datasets lower compute and storage costs. D: Proactive alerts prevent unexpected cost spikes.

NEW QUESTION # 63
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