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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Auto-generated docs site 2. Model descriptions and metadata
Topic 2: 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 3: 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
Topic 4: Deployment and Orchestration	- Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns - Environments and workflows 1. Version control with Git 2. Development vs production environments
Topic 5: Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows - SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q142-Q147):

NEW QUESTION # 142
You introduce a new data source for a dbt model. Production policies require review and approval of the source's schema before it can be used for reporting. How could you streamline this within your dbt project?

A. Use dbfs ephemeral models feature to temporarily load the data for the approval process.
B. Create a pre-hook that automates schema validation and sends the output to the designated team.
C. Write dbt tests that explicitly fail if the new source schema does not match the approved definition.
D. Run a separate dbt project in production exclusively for source validation, providing access to relevant stakeholders.

Answer: A,B,D

Explanation:
A provides proactive checks integrated into the workflow. B establishes a self-contained validation environment. D is an easy, controlled way for review. C is reactive and could potentially halt valid deployments.

NEW QUESTION # 143
You want to trigger a dbt job immediately upon successful completion of an upstream ETL process. Which methods could achieve this?

A. Scheduling a dbt job with a very frequent interval (e.g., every 5 minutes) to poll for changes in a metadata table.
B. Using the dbt build command to run all dependent models after a successful dbt run.
C. Using dbt Cloud's webhooks to create an endpoint that your ETL process can call.
D. Configuring the ETL process to send a completion notification to your CIICD tool, triggering the dbt job.

Answer: C,D

Explanation:
A and B establish event-driven, active triggering. C is a workaround that may be inefficient and introduce a lag. D is for sequential execution, not external event dependency.

NEW QUESTION # 144
You're working in a large, enterprise-level dbt deployment. Which aspect of using dbt documentation commands could become especially important in this context?

A. All of the above.
B. Developing strict documentation contribution guidelines for multiple teams and collaborators.
C. Version-controlling your documentation changes alongside your project code.
D. Optimizing the dbt docs generate process for very large projects to minimize execution time.

Answer: A

Explanation:
All aspects become increasingly important in larger projects:A: Optimization matters when many models create more complex documentation. B: Tracking documentation history alongside code is crucial for governance. C: Clear guidelines are essential with many contributors.

NEW QUESTION # 145
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 and transformation processing within model_a will be quicker, but the data will only be as up to date as the last dbt run.
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 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.)

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 # 146
Which two configuration items can be defined under models: in your dbt_project.yml file?
Choose 2 options.

A. source
B. schema
C. tags
D. target
E. test

Answer: B,C

NEW QUESTION # 147
......

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