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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Testing and Data Quality	- Data validation strategies 1. Schema testing practices 2. CI-based validation workflows - Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests
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
Analytics Engineering Foundations	- SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL - Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering
Deployment and Orchestration	- Environments and workflows 1. Version control with Git 2. Development vs production environments - Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling
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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q112-Q117):

NEW QUESTION # 112
You are using seeds in your dbt project for static data lookups. During a dbt seed run, you encounter an error indicating a duplicate primary key value. What are the likely consequences?

A. Existing seed data will remain, but records with the conflict will not be inserted.
B. Models directly referencing the problematic seed will fail during the next dbt run.
C. Seed data is considered non-essential, and the dbt seed command will complete with a warning.
D. The entire dbt seedoperation will roll back, leaving no seed data updated.

Answer: B,D

Explanation:
A and C are correct. Most databases enforce unique primary keys, so the whole dbt seed operation would fail to preserve data integrity. B is incorrect. Seed operations are typically "all or nothing". D is incorrect. dbt treats seeds as important as other model types.

NEW QUESTION # 113
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. Include "deprecated" in the description of the model
B. Configure the old model as enabled: false
C. Create a new version of the model
D. Provide a configuration for deprecation_date

Answer: D

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 # 114
"Our dbt jobs take hours to run, and it's hard to know why!" says a frustrated colleague. What initial investigation steps would you suggest?

A. Look into dbt's profiling capabilities to pinpoint the slowest models and queries.
B. Review any 'on-run-start' or 'on-run-end' hooks that might add unexpected overhead.
C. Check your dbt_project.yml configuration for any project-wide performance settings to optimize.
D. All of the above.

Answer: B

Explanation:
Each of these could provide valuable insights. Start with profiling to find the most performance-critical areas, but don't neglect hooks and potential config-level optimizations.

NEW QUESTION # 115
A dbt deployment pipeline fails on the production environment, yet everything works as intended in development and staging. Which areas would you investigate when troubleshooting?

A. Verify the credentials and connection details between production and other environments in your profiles-yml.
B. Consider schema differences, if any between the production data warehouse and other environments.
C. Ensure consistency of dependency versions across different environments-
D. Check if all required permissions are granted to the dbt user on the production database.

Answer: C

Explanation:
A: Connection errors lead to deployment failures. B: Mismatched schemas can break models. C: Lack of permissions prevents dbt from performing its job. D: Inconsistent dependency versions across environments can sometimes introduce subtle differences in behavior, leading to production-specific issues.

NEW QUESTION # 116
You want to implement a macro that displays a warning icon next to models in the DAG that haven't been tested. Which of the following would be necessary to make this work?

A. A custom CSS class for the warning icon and a way to apply it dynamically using the macro.
B. The ability for the macro to query the results of test executions from the dbt run-
C. Configuration in your dbt_projectyml file to associate models with the relevant tests.
D. All of the above.

Answer: D

Explanation:
A: You'll need CSS to style the warning icon- B. The macro needs to determine if the associated tests for each model have passed or failed. C: dbt needs a way to understand which tests are related to which models for the macro to function correctly.

NEW QUESTION # 117
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