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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Testing and Data Quality	- Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships) - Data validation strategies 1. CI-based validation workflows 2. Schema testing practices
Topic 2: Documentation and Lineage	- dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata - Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG)
Topic 3: Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering - SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions
Topic 4: Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Development vs production environments 2. Version control with Git
Topic 5: dbt Core Concepts	- Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies - Models and materializations 1. Views, tables, incremental models 2. Ref and source functions

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q235-Q240):

NEW QUESTION # 235
How can you overwrite configurations for models within a package?
Choose 1 option.

A. After installing the package, you can apply configurations on the models in the ./target folder.
B. By adding the configurations in dbt_project.yml within the package's namespace.
C. By specifying the configurations within the packages.yml file under the relevant package entry.
D. By navigating into the /dbt_packages folder and adding the configurations directly to the models.

Answer: B

Explanation:
The correct answer is B: By adding the configurations in dbt_project.yml within the package's namespace.
dbt packages are installed as read-only code, meaning you should never modify files inside /dbt_packages, nor should you attempt to overwrite generated artifacts inside /target. Instead, dbt provides a clean and officially supported method for overriding or extending configurations on models from packages: using the dbt_project.yml file.
Each package comes with its own namespace, and users can override model-level configurations by referencing that namespace inside the models: section of dbt_project.yml. dbt merges project-level configurations with package-level defaults, and configurations defined in dbt_project.yml take precedence.
This is the only correct and recommended way to override settings such as materializations, tags, schema, or custom config blocks on models imported through a package.
Option A is incorrect because packages.yml is used only to declare package dependencies-not to configure package models. Option C is incorrect because the ./target folder contains compiled and ephemeral artifacts that should never be edited. Option D is incorrect because modifying code in /dbt_packages violates dbt best practices and will be overwritten each time packages are reinstalled.
Therefore, overriding configurations must be done exclusively through dbt_project.yml.

NEW QUESTION # 236
Is this materialization supported by Python models in dbt?
Ephemeral

A. Yes
B. No

Answer: B

Explanation:
dbt Python models support a limited set of materializations because they rely on execution within the data platform's Python compute engine (such as Snowpark for Snowflake, Dataproc for BigQuery, or Spark).
These engines require models to materialize into actual relations-tables or views-in order to persist the results of Python-based transformations.
The ephemeral materialization, however, is fundamentally incompatible with this behavior. Ephemeral models do not create relations in the warehouse; instead, dbt inlines their SQL logic directly into downstream models. Since Python models cannot be inlined (they execute Python code, not SQL), dbt does not allow ephemeral Python models. dbt requires Python model outputs to be materialized as either:
* table
* view
* incremental
Therefore, ephemeral is not supported for Python models, and attempting to configure a Python model as ephemeral will result in a compilation error.
The reason is straightforward: ephemeral logic depends on SQL compilation, while Python models depend on executing Python code in the data platform. Because these mechanisms are incompatible, dbt restricts Python models to relational materializations only.
Thus, the correct answer is No - ephemeral is not supported for Python models.

NEW QUESTION # 237
Consider these SQL and YAML files for the model model_a:
models/staging/model_a.sql
{{ config(
materialized = "view"
) }}
with customers as (
...
)
dbt_project.yml
models:
my_new_project:
+materialized: table
staging:
+materialized: ephemeral
Which is true about model_a? Choose 1 option.
Options:

A. Select statements made from the database on top of model_a will be quicker, but the data will only be as up to date as the last dbt run.
(Note: A and D are duplicates - typical exam formatting.)
B. Select statements made from the database on top of model_a will result in an error.
C. Select statements made from the database on top of model_a will be slower, but the data will always be up to date.
D. Select statements made from the database on top of model_a and transformation processing within model_a will be quicker, but the data will only be as up to date as the last dbt run.

Answer: C

Explanation:
model_a contains an in-model config explicitly setting:
{{ config(materialized = "view") }}
In dbt, in-model config overrides project-level config, including folder-level defaults. Even though the staging/ folder is configured as:
staging:
+materialized: ephemeral
...this does not apply because the in-file config has higher precedence.
So model_a is materialized as a view.
A view in dbt is not persisted as data; instead, it stores the SQL definition. When a downstream query selects from that view, the database executes the underlying SQL at query time. As a result:
* Queries run slower than selecting from a table, because the underlying computation happens each time.
* But the data is always up to date, because views read directly from the current underlying tables.
This matches Option C.
Why the other options are wrong
* A & D describe table behavior (faster queries, data only as fresh as last run). model_a is not materialized as a table.
* B is incorrect because views are fully queryable; no error occurs.

NEW QUESTION # 238
You've created a pull request, and a discussion in the code review reveals issues with the approach you used in your feature branch. Rather than continuously updating the original branch, what's a strategy to address the feedback while keeping a clean pull request history?

A. Add the changes as a new commit to your feature branch, explaining the revisions in the pull request comments.
B. Request that your collaborators edit the code directly within the pull request interface.
C. Directly modify the commits on your existing feature branch using git rebase -i.
D. Close the original pull request and create a new branch with the revised implementation.

Answer: D

Explanation:
A: A new branch often provides the cleanest history for the reworked feature. B: Altering existing commits should be used judiciously, as it 'rewrites' history and can cause problems for collaborators.

NEW QUESTION # 239
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 revert to undo your previous commit, make the change, and then re-commit.
B. Use git commit -amend to modify your most recent commit.
C. Explain to your collaborator that modifying committed code is not recommended, and the change should wait for a future update.
D. Create a new branch extending from your feature branch, make the change, and open a separate pull request.

Answer: B

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

NEW QUESTION # 240
......

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