dbt-Analytics-Engineering Leak Questions

The behavior described—dbt running the orders model as a view despite being explicitly configured as a table—indicates that dbt is not correctly detecting or applying the model-level configuration during compilation. dbt relies heavily on the target-path directory to write compiled SQL, manifest files, and run artifacts. If the target-path is misconfigured, pointing to a location that dbt does not handle correctly or that overlaps with another folder used internally, dbt may fail to load the correct configuration from the merged project settings.

When dbt cannot locate the compiled configuration for a model, it defaults to its standard materialization type, which is view. This explains why the logs show:

“Finished running 1 view model”

even though the dbt_project.yml clearly declares:

materialized: table.

Additionally, the logs indicate no warnings or parsing errors, meaning dbt ran successfully but with incorrect settings—another indicator of configuration metadata being overridden or misplaced due to an incorrect target-path.

By resolving the target-path issue, dbt will successfully load the model configuration and materialize the orders model as a table as intended.

The correct answers are B and E.

The dbt retry command was introduced to help developers quickly re-run only what failed in the prior dbt invocation. According to dbt documentation, dbt retry examines metadata from the previous execution, including the run results and the manifest.json, to determine which nodes failed. This aligns with Option E, as dbt uses the manifest to identify the models, tests, and other nodes that errored or were not executed successfully.

Option B is also correct because dbt retry works only when the previous run failed due to execution errors (e.g., a warehouse failure or a logic error in SQL). If the failure was due to a syntax error that prevented dbt from compiling, there will be no valid manifest produced, so retry cannot execute. Thus, dbt retry can only re-run models when the previous failure occurred after successful compilation.

Options A and C are incorrect because retry does not run all nodes nor does it automatically pick up exactly where an execution stopped — instead, it runs failed and downstream unbuilt nodes as determined by the manifest. Option D is also incorrect; retry uses the execution metadata, not selectors, to determine what should run.

1. There is a config block at the top of the model with materialized = 'table'

➡️ Answer: False

2. The corresponding table already exists in the data warehouse

➡️ Answer: True

3. The incremental model was executed with the --full-refresh flag

➡️ Answer: False

4. The corresponding table does not already exist in the data warehouse

➡️ Answer: False

The is_incremental() macro in dbt is used within an incremental model to determine whether the current invocation should perform an incremental update or a full rebuild. It evaluates to True only during an incremental run when the target table already exists in the data warehouse and dbt is not performing a full refresh.

For is_incremental() to return True, two conditions must be met:

The model must be materialized as incremental, not table or view.Therefore, if the config specifies materialized = 'table', the model is not incremental, and the function evaluates to False.

The table already exists in the target schema.In this case, dbt will run the model in incremental mode, making is_incremental() return **True`.

If the incremental model is run with --full-refresh, dbt intentionally rebuilds the entire table, meaning is_incremental() evaluates to False, even if the table exists.

Lastly, if the target table does not exist yet, dbt must create it from scratch, so the run is treated as a full rebuild, not an incremental update, causing is_incremental() to evaluate to False.

Thus, the only scenario where is_incremental() returns True is when the table exists and the model is actually incremental.

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.