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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Models	25%	- Sources and references - Seeds - Snapshots - Materializations (table, view, ephemeral, incremental) - Writing and managing SQL models
Data Transformation Techniques	25%	- Common table expressions and subqueries - Jinja templating - Refactoring and incremental models - Macros and packages
Testing and Documentation	20%	- dbt docs and DAG visualization - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests - Documentation generation
Deployment and Orchestration	15%	- Git version control integration - Environments (dev, staging, prod) - CI/CD with dbt Cloud - Jobs and scheduling in dbt Cloud
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 (Q17-Q22):

NEW QUESTION # 17
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. Existing seed data will remain, but records with the conflict will not be inserted.
B. Models directly referencing the problematic seed will fail during the next dbt run.
C. The entire dbt seedoperation will roll back, leaving no seed data updated.
D. Seed data is considered non-essential, and the dbt seed command will complete with a warning.

Answer: B,C

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 # 18
You're loading fresh data from an external API. How would you implement dbt tests to ensure this feed is consistently providing up-to-date data?

A. Write a custom test comparing the API response payload with previously captured data
B. Test the maximum recorded timestamp in your model against the current timestamp, allowing for a small lag
C. Create a dbt model that directly queries the API in real-time as part of the dbt test run-
D. Use a dbt source freshness test to define acceptable thresholds for the staleness of the source.

Answer: B,C

NEW QUESTION # 19
Your project has models calculating order metrics. Some models aggregate data daily; others do monthly roll-ups. What's a likely performance issue if all models are materialized as tables?

A. The DAG visualization would be too complex to understand.
B. Testing would become impractical due to increased runtimes.
C. Conflicts might arise due to models overwriting each other's data.
D. Unnecessary computations, as monthly models would rebuild daily data redundantly.

Answer: D

Explanation:
Rebuilding pre-aggregated data is inefficient. While testing becomes slower (D), the primary concern is computational waste.

NEW QUESTION # 20
You define a new generic test on model customers in a YAML file:
version: 2
models:
- name: customers
columns:
- name: customer_id
tests:
- unique
- not_null
The next time your project compiles you get this error:
Raw Error:
mapping values are not allowed in this context
in "<unicode string>", line 7, column 21
What is the cause of this error?

A. tests should be a dictionary key, not a list
B. unique and not_null should be indented at the same level as tests
C. tests should be wrapped in double quotes (")
D. unique and not_null should not be elements in a list

Answer: B

Explanation:
This error occurs because the YAML structure is incorrectly indented, causing dbt's parser (and YAML itself) to misinterpret the test definitions. In dbt, generic tests must be declared as a list under the tests: key, but YAML is extremely sensitive to indentation levels. In the faulty YAML, unique and not_null are indented incorrectly relative to the tests: key, which produces the error: "mapping values are not allowed in this context." According to the dbt Testing documentation, valid generic test syntax follows this exact pattern:
columns:
- name: id
tests:
- unique
- not_null
The indentation under tests: must be consistent and aligned so that YAML interprets the items as list elements, not as malformed mappings. When indentation is wrong, YAML attempts to parse list entries as key-value mappings, which leads to the error seen during compilation.
dbt does not require generic test names to be quoted, nor does it expect tests to be a dictionary. The test list format is correct-only the indentation is wrong. Therefore, the root cause is incorrect YAML indentation, making Option D correct.

NEW QUESTION # 21
You implement dbt tests to monitor data quality over time. Which of the following scenarios could these tests potentially help you detect?

A. Performance issues in your data warehouse impacting the speed of dbt model updates.
B. Gradual degradation in source data completeness due to an API rate limit being reached
C. An unexpected spike in web traffic leading to invalid data entries in your source system-
D. An accidental schema change by someone working outside of your dbt project-

Answer: B,C,D

Explanation:
A, C, and D relate to the changing quality of the data itself, which tests can surface. B (warehouse performance) is a separate issue.

NEW QUESTION # 22
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