dbt Labs dbt-Analytics-Engineering Flexible Testing Engine | dbt-Analytics-Engineering Test Dates

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Data Transformation Techniques	25%	- Macros and packages - Jinja templating - Common table expressions and subqueries - Refactoring and incremental models
Deployment and Orchestration	15%	- Git version control integration - CI/CD with dbt Cloud - Environments (dev, staging, prod) - Jobs and scheduling in dbt Cloud
Models	25%	- Materializations (table, view, ephemeral, incremental) - Snapshots - Sources and references - Writing and managing SQL models - Seeds
Testing and Documentation	20%	- Documentation generation - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Custom data tests
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 (Q222-Q227):

NEW QUESTION # 222
You're collaborating with others on a dbt project. A new feature branch introduces unexpected errors. Which command combination helps analyze potential issues before merging into the main branch?

A. dbt deps followed by dbt compile
B. dbt docs generate on the new feature branch.
C. dbt seed to populate any new source tables.
D. dbt run followed by dbt test on both the feature branch and the main branch.

Answer: D

Explanation:
Running models and tests on both branches aids comparison and debugging. While 'docs generate' is helpful, it isn't sufficient for pinpointing runtime errors.

NEW QUESTION # 223
"Sometimes columns that should have data are showing null values!" exclaims a colleague. What initial debugging steps would you suggest beyond examining the model logic itself?

A. Verify that any where clauses or filters in the model are not inadvertently too restrictive.
B. Check any recent changes to source definitions or transformations on upstream data.
C. Review if there are snapshots or merge strategies that might impact how the table is updated.
D. All of the above.

Answer: D

Explanation:
Don't limit yourself to the single model in question! Data flows throughout the project, and changes elsewhere might have cascading effects.

NEW QUESTION # 224
During a code review, a collaborator suggests modifying a small portion of code you had already committed to your feature branch. What's the most efficient way to address this without creating a completely new commit?

A. Use git commit -amend to modify your most recent commit.
B. Explain to your collaborator that modifying committed code is not recommended, and the change should wait for a future update.
C. Create a new branch extending from your feature branch, make the change, and open a separate pull request.
D. Use git revert to undo your previous commit, make the change, and then re-commit.

Answer: A

Explanation:
Git commit -amend allows seamless modification of your most recent commit, preserving a clean commit history.

NEW QUESTION # 225
You're building tests for a dbt model that performs a join across multiple large datasets. You notice performance issues - tests are taking a very long time to run. What might be optimization strategies?

A. Refactor model SQL to use incremental logic to avoid full table scans.
B. Accept that tests on complex models will always be slow.
C. Create snapshots of the source tables and test on those snapshots instead.
D. Focus on tests that can validate a subset of the data rather than the entire dataset.

Answer: A,D

Explanation:
B and C target reducing the test's workload with sampling or optimizing the model itself. A might help under specific circumstances, but not always. D is a defeatist approach!

NEW QUESTION # 226
You need a materialization that automatically adapts to upstream schema changes while still preserving query performance. Which dbt materialization offers this kind of flexibility?

A. Incremental
B. View
C. Ephemeral
D. Snapshot

Answer: B

Explanation:
Views are dynamically built at query time, inheriting their schema from the underlying sources, thus providing built-in adaptation.

NEW QUESTION # 227
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