DBT-Analytics-Engineering Dumps - DBT Analytics Engineering Certification Exam Practice Exam Questions

Check your terminal or log file for the output from the most recent dbt run.

Look for specific error messages associated with the models that failed.

3️⃣ Isolate the problem by dbt run --select model_name to run a single model and confirm whether the issue is localized to that model.

4️⃣ Use dbt run --select model_name+ to run the model and its downstream dependencies, ensuring that your fix works across the DA

G.  

Brief Explanation

First, you always inspect the latest run output (step 1).

Then, identify the exact failing models and error messages (step 2).

Next, you reproduce the issue on the individual model to be sure the fix works locally (step 3).

Finally, you re-run the model plus its downstream dependencies to validate the fix across the DAG (step 4).

Will always point to the latest version of the source schema

Correct Match: ✔ defer

Allows to use objects built in the target schema with any downstream tool

Correct Match: ✔ dbt clone

3️⃣ Allows to safely modify objects built in the target schema

Correct Match: ✔ defer

4️⃣ Is a point-in-time operation

Correct Match: ✔ dbt clone

defer and dbt clone serve very different purposes in dbt, and understanding their behavior is essential for choosing the right command.

The --defer flag tells dbt to use already-built objects from a previous environment (often production) instead of rebuilding them. Because it always references the existing target schema’s most recent objects, it “always points to the latest version of the source schema.” Since no objects are overwritten when using defer, it also “allows safely modifying objects built in the target schema”—your development environment uses production objects without altering them.

By contrast, dbt clone creates a point-in-time copy of existing relations. This cloned schema is static; it does not auto-update when source data changes. Therefore, clone is classified as a “point-in-time operation.” Since clone copies physical tables/views into a new schema, downstream tools (BI dashboards, ML pipelines) can safely query the cloned environment without affecting production, making “allows to use objects built in the target schema with any downstream tool” the correct match.

Thus, defer is used for logic substitution without copies, while clone is used for replicable, point-in-time snapshots.