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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformation Techniques	25%	- Common table expressions and subqueries - Macros and packages - Jinja templating - Refactoring and incremental models
Topic 2: Models	25%	- Sources and references - Seeds - Snapshots - Writing and managing SQL models - Materializations (table, view, ephemeral, incremental)
Topic 3: dbt Fundamentals	15%	- dbt project structure - dbt workflow and best practices - dbt Core vs dbt Cloud
Topic 4: Testing and Documentation	20%	- Custom data tests - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Documentation generation
Topic 5: Deployment and Orchestration	15%	- Git version control integration - Environments (dev, staging, prod) - Jobs and scheduling in dbt Cloud - CI/CD with dbt Cloud

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q150-Q155):

NEW QUESTION # 150
You are using seeds in your dbt project for static data lookups. During a dbt seed run, you encounter an error indicating a duplicate primary key value. What are the likely consequences?

A. Seed data is considered non-essential, and the dbt seed command will complete with a warning.
B. Models directly referencing the problematic seed will fail during the next dbt run.
C. Existing seed data will remain, but records with the conflict will not be inserted.
D. The entire dbt seedoperation will roll back, leaving no seed data updated.

Answer: B,D

Explanation:
A and C are correct. Most databases enforce unique primary keys, so the whole dbt seed operation would fail to preserve data integrity. B is incorrect. Seed operations are typically "all or nothing". D is incorrect. dbt treats seeds as important as other model types.

NEW QUESTION # 151
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 run followed by dbt test on both the feature branch and the main branch.
B. dbt deps followed by dbt compile
C. dbt docs generate on the new feature branch.
D. dbt seed to populate any new source tables.

Answer: A

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 # 152
After implementing thorough dbt tests, you confidently deploy changes. Yet, downstream Bl dashboards break, and stakeholders complain about inaccurate dat a. What could have gone wrong?

A. There are logic errors in your dbt models that the tests didn't catch.
B. Your tests were correct, but a bug in dbt prevented them from running successfully.
C. The tests you wrote were too strict, causing false positives.
D. Critical assumptions were made about the data in the Bl layer, independent of dbt models.

Answer: A,D

Explanation:
This emphasizes that models can be internally correct yet still produce unusable output if the source data changes. Tests only cover what they're designed for; the Bl layer may have its own data quality requirements.

NEW QUESTION # 153
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. Partner with your data infrastructure team to get guidance based on their experience.
B. Run performance tests directly against production during a designated off-peak period.
C. Write dbt macros that artificially increase dataset sizes in development for simulating load.
D. Create a CI/CD job that dynamically scales a cloned production environment for the test, then scales it back down.

Answer: A,D

NEW QUESTION # 154
A model intermittently fails. Sometimes it runs without errors. Which category of issue should you suspect FIRST?

A. Race conditions due to models in your DAG running concurrently without proper synchronization.
B. Data quality issues in the source that are not consistently present.
C. dbt-specific configuration errors causing unpredictable behavior at the engine level.
D. Errors within complex macros used by the model, triggered only under specific data conditions.

Answer: B

Explanation:
Inconsistent failures often suggest data dependency problems. While the others could occur, start with potential data quality concerns.

NEW QUESTION # 155
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