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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
(Multiple Select)

A. You've refactored a core model and need to quickly validate its output and downstream dependencies.
B. A critical upstream data source has been delayed, and you only want to refresh dependent models once the data arrives.
C. You need to temporarily disable a computationally expensive model that isnt critical for daily reporting.
D. There's an urgent request to update a single dashboard, and you know the exact subset of models to re-run.

Answer: A,B,D

Explanation:
Each involves a need for targeted, not blanket, execution. D is more about configuration than selective runs.

NEW QUESTION # 74
(Multiple Select)

A. Implementing pre-hooks and post-hooks to perform tasks before and after dbt model execution.
B. Using separate dbt profiles for production and development environments
C. Integrating dbt with your CI/CD tool and using job-specific triggers or filters.
D. Scheduling less frequent jobs using dbt Cloud's built-in job scheduler

Answer: B,C,D

Explanation:
A enables environment-specific commands. B provides a way to launch different jobs. C handles scheduled tasks within dbt Cloud.

NEW QUESTION # 75
A colleague asks, "Why do we need dbt docs when we already have good database documentation?" Provide a compelling response.

A. dbt docs automatically tracks model testing results.
B. dbt docs is mandatory for version control with dbt projects.
C. dbt docs primarily serves as a replacement for dbt seed.
D. dbt docs generates dynamic documentation that directly reflects your project's dependencies and current state.

Answer: D

Explanation:
Highlight how dbt docs offers up-to-date, project-specific documentation, while traditional database documentation can easily become outdated.

NEW QUESTION # 76
You create tests to validate counts and timestamps against a baseline dataset. The tests fail unexpectedly. What might be possible root causes?

A. Your upstream ETL process has a logic error, leading to incorrect data.
B. Your tests are too strict and need more generous tolerances.
C. There are differences in data types or time zone handling between the model and baseline.
D. The baseline dataset itself has become corrupted

Answer: A,B,C,D

Explanation:
Each scenario could cause a baseline comparison test to fail, covering issues in the data pipeline, the baseline, and the test logic itself.

NEW QUESTION # 77
After merging a large pull request, you notice a critical issue introduced in one of the commits. What actions might be necessary?

A. All of the above could be needed as part of the fix.
B. Identify the problematic commit using git log or git blame.
C. Use git revert to create a new commit that specifically undoes the problematic changes.
D. Use git cherry-pick to apply specific commits from the reverted change to another branch, effectively restoring the necessary parts.

Answer: A

Explanation:
A: git revert provides a way to 'undo' changes safely. B: Identifying the source of the issue is essential. C: git cherry-pick lets you selectively reintroduce only the necessary portions in a controlled manner.

NEW QUESTION # 78
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