dbt Labs Updated dbt-Analytics-Engineering Exam Questions and Answers by tomi

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.

The correct answer is C: Model groups can limit references by other models which aren’t in the same group.

According to the dbt documentation, dbt provides a formal access-control mechanism for models through model groups and the access property. Models can be assigned to groups, and each model can be marked as public, protected, or private. The purpose of this system is to create clear boundaries within a project, making it possible to control which models are allowed to reference others. Specifically, dbt enforces rules where models in a group can restrict references from models outside that group, which helps maintain modularity and prevents accidental coupling of unrelated data layers. This is extremely valuable in large analytics engineering projects where teams manage different domains or data products.

Option A is incorrect because dbt does not use Git user groups or any form of identity-based access control for model usage. Option B is incorrect because dbt does not integrate with warehouse roles for controlling model references—access is handled strictly within the dbt project’s metadata. Option D is incorrect because user emails or platform identities have no role in determining which models can reference others. dbt enforces usage rules only through its metadata-driven grouping and access configuration system.

Here is the correct sequence:

Correct Order

sources:

- name: blue (source definition)

tables:

- name: yellow (table definition)

columns:

- name: red (column definition + tests)

In dbt, source documentation follows a strict YAML hierarchy. At the top level, the sources: key declares that you are defining source metadata. Inside this block, each source is listed as an item beginning with - name:. Therefore, the snippet describing the source (blue) must come immediately after sources:.

Next, dbt requires a tables: block inside each source to define the raw tables belonging to that source. Thus, the snippet containing tables: must follow the source definition. The table description (yellow) is then placed directly under this key because it represents a table within that source.

Finally, dbt allows column-level metadata under a columns: block inside a table definition. Therefore, columns: appears next, followed by the snippet defining a specific column (red) which includes both a description and tests (unique, not_null).

Arranging the YAML in this hierarchical order ensures dbt can correctly assign documentation to the source object, the table object, and the individual column. Misordering these snippets would break the structure and prevent dbt from generating documentation properly.

The correct answers are A: run-operation and E: snapshot.

According to dbt’s retry documentation, the dbt retry command works by examining the artifacts of a previous invocation (manifest, run results) and then re-running failed nodes for commands that generate node executions. Only commands that write run results and maintain execution state can be retried. These include dbt run, dbt test, dbt seed, dbt snapshot, and dbt run-operation when the operation executes nodes or macros that generate runtime artifacts.

Option A is correct because run-operation supports retry when the macro being run triggers execution tracked in run results. Option E is correct because snapshots execute SQL against the warehouse and record stateful results, meaning dbt can retry failed snapshots using the retry mechanism.

Options B (parse), C (debug), and D (deps) do not create runnable nodes or execution results; they simply validate or install project resources. These commands do not produce retryable artifacts, so dbt retry cannot operate on them.

Thus, the only options that work with dbt retry from the list provided are run-operation and snapshot.