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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q229-Q234):

NEW QUESTION # 229
dbt Model Snippet:SQL

A. Your sources.yml file is not configured correctly-
B. There's a custom macro in use that dynamically changes schemas.
C. The stg_orders model uses a schema config in its own model SQL
D. Your dbt_projectyml file has a model-level schema override.

Answer: D

Explanation:
Model-level schema configurations override other settings. Check your project file for this model. The others are possible but less common patterns

NEW QUESTION # 230
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. Set up usage alerts in your data warehouse that notify the team if costs exceed certain thresholds.
C. Implement automated scripts to shut down the development environment during non-working hours.
D. Use dbt seeds to load smaller datasets for testing, reducing overall data processing costs.

Answer: B

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 # 231

Answer:

Explanation:

Explanation:
(branch)
(commit)
(pull request)
In dbt development workflows, version control using Git is essential for ensuring collaborative, safe, and trackable changes to analytics code. The correct first step when making updates-such as adding new models-is to create a new Git branch. This isolates development work from the production (main) branch, preventing incomplete or experimental logic from affecting deployed transformations. Branching supports dbt' s modular development approach and aligns with best practices for analytics engineering.
Once the branch is created, the developer modifies SQL models, tests, macros, or documentation as required.
To permanently record these modifications in Git, the developer must commit the changes. A commit serves as a snapshot of progress and creates an auditable history of transformations made to the project, enabling rollbacks, diffs, and peer review.
After development is complete, the developer submits a pull request (PR). The pull request triggers CI checks-often including dbt build, schema tests, and contract validations-to ensure code quality and identify impacts on downstream models. PRs allow team members to review and comment before changes merge into the main branch, enforcing governance, consistency, and reliability. This workflow embodies the engineering rigor dbt encourages: modular development, testing, versioning, and peer review.

NEW QUESTION # 232
Performance Bottleneck

A. Run EXPLAIN or equivalent on the compiled query to pinpoint the most expensive operations.
B. Add -full-refresh to dbt run on the model to ensure downstream models don't cause errors.
C. Introduce CTEs to pre-calculate aggregated data or common subexpressions, especially if reused repeatedly.
D. Implement database-level indexes on columns involved in the joins, if appropriate.

Answer: A,C

Explanation:
EXPLAIN diagnoses the query plan, while CTEs can restructure for optimization. Indexes are database-level changes. A full refresh is unnecessary and disregards incremental logic.

NEW QUESTION # 233
You need to run a dbt job that performs a long-running data cleansing operation. The job occasionally times out. Which of the following are potential solutions?

A. Schedule the job to run concurrently with other dbt models to maximize resource usage-
B. Break the cleansing logic into multiple models, chaining them with dependencies-
C. Use a dbt macro to retry the cleansing operation with a backoff strategy within the model itself-
D. Increase the timeout setting in your CI/CD tool or dbt job configuration.

Answer: B,C,D

Explanation:
Each tackles the timeout issue differently:A reduces the work done in a single step. B provides more time directly . C adds retry resilience within the logic. D would likely worsen performance due to resource contention.

NEW QUESTION # 234
......

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