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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q136-Q141):

NEW QUESTION # 136
You need to set up a production-like environment for load testing and performance tuning of dbt models. How might you approach the resource allocation and provisioning of this environment?

A. Write dbt macros that artificially increase dataset sizes in development for simulating load.
B. Run performance tests directly against production during a designated off-peak period.
C. Partner with your data infrastructure team to get guidance based on their experience.
D. Create a CI/CD job that dynamically scales a cloned production environment for the test, then scales it back down.

Answer: C,D

NEW QUESTION # 137
You're using dbt Cloud and want to tighten control over when production deployments can occur. What dbt Cloud feature, in conjunction with your CI/CD configuration, would allow you to enforce this?

A. dbt Cloud's built-in source freshness checks.
B. Deployment jobs and schedules.
C. Webhooks for triggering jobs by external events.
D. Environment variables for sensitive credentials.

Answer: B

Explanation:
B enables you to define specific times or conditions for production deployments. Others are less directly related to controlling the timing.

NEW QUESTION # 138
Performance Bottleneck

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

Answer: A,D

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 # 139
In development, you want to avoid having to re-run all upstream models when refactoring part of your project.
What could you do to save time rebuilding models without spending warehouse credits in your next command?

A. Replace your {{ ref() }} functions with hard-coded references.
B. Leverage artifacts from a prior invocation by passing only the --state flag.
C. Refer to a manifest and utilize the --defer and --state flags.
D. Clone your upstream models from the production schema to the development schema.

Answer: C

Explanation:
The correct answer is B: Refer to a manifest and utilize the --defer and --state flags.
According to dbt's official documentation, the --defer flag enables developers to defer the resolution of model references (ref(), source()) to an existing manifest-commonly the one generated from a production run.
When this flag is used along with --state, dbt reads the prior project state and uses the already-built production models instead of rebuilding upstream dependencies. This is essential because production models are typically large and compute-intensive. By deferring to production artifacts, developers can test only the models they have modified, dramatically reducing warehouse costs and speeding up development cycles.
This approach is foundational to dbt's recommended development workflow, especially when working with CI
/CD or large DAGs. It preserves the integrity of the dependency graph while avoiding unnecessary recomputation.
Option A violates dbt best practices by removing dependency awareness. Option C is unnecessary operational overhead and does not integrate with dbt's state management. Option D is incomplete because --state alone does not prevent upstream model execution---defer is required to substitute those models with existing artifacts.

NEW QUESTION # 140
You accidentally pushed a large file containing sensitive data to a public remote repository. What immediate actions should you take?

A. Permanently remove the sensitive file from Git history using tools like git filter-branch.
B. Both B and C.
C. Contact the repository hosting service for assistance.
D. Use git reset -hard to revert the commit and remove the file locally.

Answer: A,C

Explanation:
B: You must ensure the sensitive data is completely removed from the history of the repository. C: The hosting service might have additional security measures or be able to assist in preventing the sensitive data from being accessed

NEW QUESTION # 141
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