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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
dbt Models Governance	15%	- Project organization and structure - Naming conventions and standards - Version control integration
Debugging and Error Resolution	15%	- Resolving data quality issues - Debugging techniques - Identifying modeling errors
Leveraging dbt State	5%	- State management - State-aware operations
External Dependencies	10%	- Using packages - Managing snapshots - External sources integration
Implementing dbt Tests	10%	- Test configuration and execution - Custom tests - Built-in tests
Developing dbt Models	20%	- Model design and structure 1. Materialization types 2. Incremental models 3. Ref and source functions - Model optimization 1. Performance tuning 2. Model contracts
Creating and Maintaining Documentation	10%	- Documentation standards - Descriptions and metadata - Generating documentation
Managing Data Pipelines	15%	- Pipeline orchestration - Deployment strategies - CI/CD integration

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q232-Q237):

NEW QUESTION # 232
You've built a macro to implement a common custom test pattern. When using this macro in multiple models, you realize the tests are running much slower than expected. What might be the cause?

A. The macro is accidentally triggering downstream model rebuilds.
B. The macro's SQL logic is inefficiently querying the model or other data.
C. Too many tests are being defined on individual models.
D. This is expected behavior, and custom tests always incur a performance penalty.

Answer: B,C

Explanation:
Inefficient macro logic would be amplified with wide reuse. Having numerous tests, even simple ones, can add up. C is unlikely unless the macro directly references downstream models.

NEW QUESTION # 233
You must exclude a specific model from being included in the generated documentation entirely. How would you achieve this?

A. Add a dæs: {enabled: false} entry to the model's YAML configuration.
B. Use the enabled flag in your dbt_projectyml file to control model inclusion.
C. Its not possible to exclude individual models; you can only exclude entire folders.
D. Remove all descriptions and tags associated with the model.

Answer: B

Explanation:
The enabled flag in dbt_project.yml provides control over whether a model (or other resources) are includedin dbt operations, including doc generation. Check your dbt version documentation for the latest syntax

NEW QUESTION # 234
Examine the configuration for the source:
sources:
- name: jaffle_shop
schema: jaffle_shop_raw_current
tables:
- name: orders
identifier: customer_orders
Which reference to the source is correct?

A. {{ source('jaffle_shop_raw_current', 'orders') }}
B. {{ source('jaffle_shop', 'orders') }}
C. {{ source('jaffle_shop', 'customer_orders') }}
D. {{ source('jaffle_shop_raw_current', 'customer_orders') }}

Answer: B

Explanation:
In dbt, the source() function resolves a source by its declared source name and table name, not by the physical schema or identifier in the warehouse. The YAML block defines a source named jaffle_shop, and under that source, a table named orders. The identifier: customer_orders field tells dbt that although the logical table name is orders, the actual physical object in the warehouse is named customer_orders.
dbt always expects the syntax:
{{ source(source_name, table_name) }}
Here, the correct reference uses jaffle_shop as the source name and orders as the table name because these are the logical names assigned in the YAML. dbt internally resolves the physical table name via the identifier field, so the model should not reference customer_orders directly.
Option A and B are incorrect because the first argument is not the schema; dbt does not use schemas in the source() call. Option D is incorrect because customer_orders is the warehouse identifier, not the logical table name recognized by dbt.
Therefore, the correct reference is:
{{ source('jaffle_shop', 'orders') }}
This ensures consistent modeling, dependency tracking, and accurate documentation.

NEW QUESTION # 235
You notice that long descriptions within sources.yml cause formatting issues when viewing the docs, making them hard to read. What strategies could you employ to mitigate this?

A. Use a third-party dbt package that enhances the presentation of source descriptions in documentation.
B. Enforce a strict character limit on source descriptions-
C. Utilize Markdown within your descriptions to improve readability with headings and line breaks-
D. Split your source description into multiple properties (e.g., description_short and description_long)

Answer: A,C,D

Explanation:
B: Markdown syntax can improve long description readability within the generated docs. C: Third-party packages may offer customized formatting for source descriptions. D: Creating separate properties can provide an alternative way to manage both short and longer versions of a description.

NEW QUESTION # 236
A CTE within a dbt model is incorrectly casting a string column to a numeric type.

A. A source table has inconsistent data, mixing true numeric values with strings.
B. A Jinja macro meant to filter out non-numeric strings has a logic flaw.
C. A snapshot is incorrectly merging new data with incompatible types into an existing table.

Answer: A,B

Explanation:
This error usually originates from mismatched data types at the database level. CTE logic or source data quality are frequent culprits.

NEW QUESTION # 237
......

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