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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Deployment and Orchestration	- Environments and workflows 1. Version control with Git 2. Development vs production environments - Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns
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. Schema testing practices 2. CI-based validation workflows
Topic 3: 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 4: Analytics Engineering Foundations	- SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions - Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering
Topic 5: dbt Core Concepts	- Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies - Models and materializations 1. Ref and source functions 2. Views, tables, incremental models

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
Which two are true for a dbt clone command?
Choose 2 options.

A. It requires the reference to a manifest from a previous dbt invocation.
B. It can be used to replicate data across different data warehouses.
C. It allows testing your code changes on downstream dependencies outside of dbt (such as a BI tool).
D. It creates new versions of your dbt models suffixed with _v#.
E. It allows comparison between manifests of source and target dbt runs, but does not create any objects itself.

Answer: A,C

Explanation:
The correct answers are A and C.
dbt clone is a command introduced to efficiently replicate existing relations (tables/views) from one schema
/environment to another within the same warehouse without fully rebuilding them. It typically leverages warehouse-level "clone" functionality (e.g., Snowflake zero-copy clone). To know what to clone, dbt needs information about previously built models; this is stored in artifacts like manifest.json. Therefore, A is correct:
it requires a reference to a previous manifest/state to know which models and locations to operate on.
C is also correct because cloning a production schema into a development schema enables you to test code changes while pointing downstream tools (like BI dashboards) at the cloned data. That way, BI or ML systems can exercise new dbt logic without impacting real production objects.
Option B describes state:modified / state comparison behavior, but dbt clone does create (or clone) objects, so B is false.
Option D is incorrect because clone works within a warehouse, not across different warehouses.
Option E is wrong: _v# suffixing is for model versioning, not cloning.
Thus, A and C correctly describe the behavior and purpose of dbt clone.

NEW QUESTION # 45
(Multiple Select)

A. Anomaly detection platforms.
B. Data integration testing frameworks (e.g., Great Expectations)
C. Continuous Integration (Cl) tools.
D. Tools specializing in schema change detection.

Answer: A,B,D

Explanation:
All of these address different aspects of advanced data validation that complement the basic tests within dbt. Cl tools (D) are involved in running these checks but are not the data validation tools themselves.

NEW QUESTION # 46
(Multiple Select)

A. Increase the query_comment timeout setting in your dbt_project. yml
B. Use CTEs or views in the source definition to pre-filter columns or apply initial transformations.
C. Introduce a source snapshot to create a point-in-time copy of the data.
D. Apply an incremental materialization strategy to subsequent models if appropriate.

Answer: B,D

Explanation:
Pre-filtering at the source definition and incremental downstream models reduce processing. Timeouts and snapshots address different issues.

NEW QUESTION # 47
You need to gather detailed metrics about how your dbt models are used (who runs them, frequency, common failure points, etc.). What kind of tooling would help with this?

A. Specialized data observability platforms
B. Version control system metadata (e.g., Git history)
C. dbt Cloud's built-in job monitoring features, if applicable
D. Custom logging built into your models

Answer: A,C

Explanation:
B is relevant if you use dbt Cloud. C is suitable, as many observability platforms have features for tracking operational data, including dbt job metrics. A and D, while containing information, are likely too granular or require a lot of manual work.

NEW QUESTION # 48
You're collaborating on a project where team members have varying levels of dbt experience. How can you use the dbt_project.yml to help standardize practices?

A. Set up pre-commit hooks within the project file to enforce linting and formatting rules.
B. Include a docs: section with detailed descriptions of core models and transformations.
C. Define custom macros in the project file to encapsulate commonly used SQL patterns.
D. Configure project-level tests to ensure adherence to data quality standards.

Answer: D

Explanation:
Project-level tests in dbt_project.yml provide a centralized way to maintain data quality expectations across the team.

NEW QUESTION # 49
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