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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: 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
Topic 2: 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. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows
Topic 3: 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 4: Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships)
Topic 5: Documentation and Lineage	- dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site - Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG)

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which two are true for a dbt clone command?
Choose 2 options.

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

Answer: B,E

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 # 61
Which explanation describes how dbt infers dependencies between models?
Choose 1 option.

A. .yml configurations for sources and refs are parsed for dependency information.
B. The underlying SQL code is parsed and relationships are created from explicit table references.
C. Information is gathered from the use of source and ref macros.
D. All source and ref macros are resolved to database objects and dbt queries them for dependencies.

Answer: C

Explanation:
The correct answer is A: Information is gathered from the use of source and ref macros.
dbt determines the dependency graph - the DAG - by analyzing calls to ref() and source() inside model SQL files. These macros explicitly declare relationships between models. When a developer writes ref ('orders'), dbt interprets this as: "the current model depends on the orders model." Similarly, source() indicates dependencies on upstream raw data sources. This declarative approach allows dbt to build a structured and deterministic DAG without scanning SQL for implicit table references.
Option B is incorrect because dbt does not query database objects to infer dependencies; it resolves dependencies at compile time through metadata generated from model files. Option C is incorrect because dbt intentionally does not parse SQL to detect table names-this would be brittle and error-prone across warehouses. Instead, dbt requires explicit references to maintain reliability. Option D is incorrect because YAML files define metadata about models and sources but do not create dependency relationships between them.
Thus, the dependency graph is built exclusively by reading ref() and source() macro calls, which ensures clarity, correctness, and maintainability within the analytics engineering workflow.

NEW QUESTION # 62
You're using the dbt-utils.get_column_values macro to list out the unique values from a specific source column in the documentation. However, the generated docs don't display any results. Which of the following reasons are most likely?

A. The macro itself might have a bug or an issue with how it's being used
B. There is a limit on how many unique values the macro can display within the documentation.

Answer: A,B

Explanation:
A: Some macros are designed specifically for model columns and may not function as expected with source columns. B: Macros might depend on data from a recent dbt run operation to generate their output. C: It's always possible that the macro has a bug or your implementation is not quite correct.

NEW QUESTION # 63
While working on a pull request, you realize a much better approach exists. Which actions could you take?

A. Add new commits to the existing branch, explaining the change of direction in the comments on the pull request.
B. Close the original pull request, create a new branch, and open a new pull request.
C. Avoid introducing major changes during a pull request, as it hinders effective code review.
D. Make the changes on your branch and use git push -force to overwrite the existing remote branch.

Answer: A,B

Explanation:
B: Provides the cleanest history for the revised approach. C: Maintains transparency on the evolution of your thought process while allowing for review of the new changes.

NEW QUESTION # 64
You encounter an unexpected dbt run error. Rather than re-running all models, you want to resume from the point of failure. Which of the following, if any, might allow for this?

A. dbt unfortunately lacks native support for resumable jobs.
B. dbt Cloud's job retry features for automatically resuming failed job executions.
C. The --fail-fast flag instructs dbt to only execute successful models, making restarts unnecessary
D. Using dbt snapshots to track model states, allowing you to rollback changes as needed

Answer: B

Explanation:
C can retry in event of transient errors, but not fundamental code problems. The rest are incorrect dbt typically isn't resumable mid-job.

NEW QUESTION # 65
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