Free dbt Analytics Engineering Certification Exam Practice 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.

dbt model contracts enforce that the column names, data types, and number of columns defined in the contract exactly match the columns produced by the compiled SQL. If any column appears in one location (the contract or the SQL) but not in the other, dbt raises an enforcement error.

In this scenario, the error message shows:

ORDER_ID is missing in the model definition, meaning the SQL no longer contains a column named order_id, but the contract still expects it.

ORDER_KEY is missing in the contract, meaning the model SQL now contains a new column, but this column has not been added to the contract.

To fix the mismatch, you must remove columns from the contract that no longer exist in the SQL and add to the contract any new columns that now appear in the SQL.

Therefore:

Option A — Remove order_id from the contract — is correct because the column no longer exists in the model SQL.

Option D — Add order_key to the contract — is correct because the SQL now produces this column.

Options B and C incorrectly alter the wrong side of the definition, and Option E would create a mismatch in the opposite direction.

Thus, the correct fixes are A and D.

The correct answers are C and D.

dbt supports two main categories of tests: generic tests and singular tests. Generic tests are defined in YAML and applied to models or columns, while singular tests are written as SQL files placed in the /tests/ directory. This makes Option C correct—singular tests must be created as .sql files inside that folder, and dbt will execute each file as a test query.

Option D is also correct. dbt includes four built-in generic tests: unique, not_null, relationships, and accepted_values. These are considered “native” tests and are available without requiring any additional packages. They cover the most common data quality checks and are applied through YAML configurations on models or columns.

Option A is incorrect because dbt does not automatically apply any tests. All tests—native or custom—must be explicitly defined in YAML or created manually. Nothing is inferred from primary keys.

Option B is incorrect because dbt’s package hub contains community packages, not the list of native tests. Native tests are documented directly within dbt’s core functionality.

Option E is incorrect because the constraints configuration is used to create database-level constraints on supported warehouses, not to run dbt tests. Tests still require YAML test definitions or .sql files.

Thus, only C and D accurately describe dbt's testing behavior.