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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q275-Q280):

NEW QUESTION # 275
"The compiled SQL looks correct, but I'm still getting wrong results in my model!" What might you suggest to a colleague having this issue?

A. Review the logic in 'on-run-start' or 'on-run-end' hooks, as they could be modifying data-
B. Examine the source tables for data quality issues like duplicates or missing values.
C. All of the above.
D. Test the model in isolation using the dbt run -select flag to narrow down dependencies.

Answer: C

Explanation:
Sometimes, even with correct SQL, the issue can lie in data itself, external interactions, or hidden dependency effects.

NEW QUESTION # 276
(Multiple Select)

A. Scheduling less frequent jobs using dbt Cloud's built-in job scheduler
B. Integrating dbt with your CI/CD tool and using job-specific triggers or filters.
C. Implementing pre-hooks and post-hooks to perform tasks before and after dbt model execution.
D. Using separate dbt profiles for production and development environments

Answer: A,B,D

Explanation:
A enables environment-specific commands. B provides a way to launch different jobs. C handles scheduled tasks within dbt Cloud.

NEW QUESTION # 277
You want to add new columns to an existing model with a new version.
What will trigger warnings for removal of older model versions?
Choose 1 option.

A. Configure the old model as enabled: false
B. Provide a configuration for deprecation_date
C. Include "deprecated" in the description of the model
D. Create a new version of the model

Answer: B

Explanation:
dbt includes built-in support for model versioning and controlled deprecation. When you introduce a new version of a model, dbt will only warn about the old version being removed if the old version explicitly includes a deprecation_date configuration. This parameter signals to dbt that the model is scheduled for removal after a certain date, allowing dbt to produce structured warnings to users and downstream systems.
These warnings serve as a formal notice that the old version should no longer be referenced and will eventually be removed.
Option A, simply creating a new version, does not trigger warnings by itself. dbt allows multiple versions to coexist without any deprecation messaging unless you explicitly configure it. Option C (enabled: false) disables the model entirely, which is not a graceful deprecation strategy and does not result in version- removal warnings. Option D (adding "deprecated" in the description) is only informational text and has no functional effect on dbt's deprecation system.
Thus, only B-setting deprecation_date on the old version-activates dbt's official deprecation warning mechanism, making it the correct answer.

NEW QUESTION # 278
You frequently need to spin up short-lived development environments on demand for testing and experimentation. Which dbt feature would streamline this process, especially when it involves custom configuration?

A. Using dbt tags to selectively execute parts of your project.
B. dbt profiles defined in your dbt_project.yml file.
C. dbt's -vars flag to override variable values.
D. Creating multiple deployment environments in dbt Cloud.

Answer: B,C

Explanation:
A encapsulates environment settings in reusable blocks- B allows for ad-hoc adjustments when running dbt- C is relevant for cloud-based setups, but less flexible for on-the-fly changes- D relates to model selection, not environment setup.

NEW QUESTION # 279
You're evaluating whether to adopt an external tool that offers a visual, drag-and-drop interface for building data models. When considering this alongside dbt, what's a KEY factor to weigh?

A. Whether the tool can handle the full complexity of your data transformations.
B. If the visual tool has advanced machine learning capabilities for data cleaning.
C. The ability of the tool to generate dbt-compatible SQL and integrate into your version control workflow.
D. The ease of migrating existing dbt projects into the visual tool.

Answer: A,C

Explanation:
A and B touch on the crucial issues of how such a tool fits into your established workflow with dbt as the orchestrator, and whether it really matches the sophistication of your needs.

NEW QUESTION # 280
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