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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
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
Data Transformation Techniques	25%	- Common table expressions and subqueries - Macros and packages - Jinja templating - Refactoring and incremental models
Models	25%	- Sources and references - Seeds - Snapshots - Materializations (table, view, ephemeral, incremental) - Writing and managing SQL models
Testing and Documentation	20%	- dbt docs and DAG visualization - Custom data tests - Schema tests (unique, not_null, accepted_values, relationships) - Documentation generation

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q354-Q359):

NEW QUESTION # 354
(Multiple Select)

A. Loading configuration data for production environments that differs from development settings.
B. Inserting large volumes of historical transactional data for backfilling purposes.
C. Defining generic tests that apply to multiple data sources.
D. Populating a lookup table with country codes.

Answer: A,D

Explanation:
seeds are ideal for static reference data and environment-specific configurations. Large data backfills are usually handled outside of 'seeds'. Generic tests would be defined in the tests section.

NEW QUESTION # 355
You are working with git to version control the dbt logic.
Order these steps to add or modify transformations to your dbt project.

Answer:

Explanation:

* Create a new branch in git and switch to itB. Update the dbt code according to the new logic
C. Create a Pull / Merge RequestD. Run some automated CI checks and/or manual review of the updated codeE. Merge the updated code to the main git branch
* Version control best practices in dbt follow the same engineering workflow used in modern software development. The first step is always to create a new git branch and switch into it, ensuring all work is isolated from production-ready code and allowing your team to develop safely without affecting others.
Once inside the feature branch, you update the dbt code according to the new logic, which may include modifying models, tests, macros, or documentation.
* Next, you create a Pull Request (PR), also known as a Merge Request, to propose integrating your changes into the main branch. This is important because dbt projects are collaborative, and PRs facilitate peer review, enforce project standards, and prevent regressions. Once the PR is created, automated CI pipelines-such as running dbt build, schema tests, data quality checks, and code-style checks-are executed. Reviewers may also manually inspect code for logic correctness, naming conventions, and modeling consistency.
* After all checks have passed and reviewers approve the PR, the final step is to merge the updated code into the main branch, making the new transformations part of the production dbt project. This workflow ensures governance, reliability, and auditable development practices, all of which are core principles in analytics engineering.

NEW QUESTION # 356
You have a dbt project with models defined as views. After deploying to a production database, the view objects are present, but no data is displayed when querying them. What might be the root cause?

A. There's a bug in dbt's SQL generation that specifically affects views in certain environments.
B. Dbt view materializations behave differently in production.
C. Permissions granted in production don't allow reading data from the underlying tables.
D. Your dbt job didn't run successfully, so the views were created empty.

Answer: C

Explanation:
B is a common security-related cause of 'missing' data. The others are less likely; views, logs, and dbt core logic tend to be consistent.

NEW QUESTION # 357
You want to automatically deploy updates to your documentation site hosted on a cloud service whenever changes to your dbt project are committed to your main branch. Which of the following is essential for this setup?

A. Implementing a CI/CD pipeline that includes dbt build steps.
B. Configuring dbt Cloud to trigger documentation deploys.
C. Enabling webhooks in your Git repository provider.
D. Write a custom script to check for project changes and handle deployment.

Answer: A,C,D

Explanation:
A). A CI/CD pipeline is needed to automate running dbt commands and the deployment process. B: Webhooks allow your CIICD system to get notified when changes are pushed to your repository, triggering the pipeline. D: A custom script could provide flexibility for check conditions and deployment logic-

NEW QUESTION # 358
In what situations might it be acceptable to use raw data directly for reporting or analytics purposes?

A. In environments where strict data governance and transformation processes are in place.
B. Generally, using raw data directly for reporting should be avoided due to potential quality and consistency issues.
C. When dealing with simple analyses, and there's high confidence in the quality of the raw data source.
D. When performance is a top priority, and the overhead of additional data transformations is undesirable.

Answer: B

Explanation:
Raw data is often unpredictable- However, under specific conditions like those listed in A, B, or C, it could be considered if the risks and trade-offs are well understood.

NEW QUESTION # 359
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