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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You've configured your project to include both schema and data tests in the generated documentation. A user reports the tests are visible but don't show any execution results (pass/fail status). Why might this be happening?

A) Your data pipeline hasn't been executed recently, so there are no recent test results.
B) The project settings are explicitly configured to hide test results in the documentation.
C) The documentation generator has a known limitation on how test results are displayed.
D) The type of test you're using is not supported for displaying results in the documentation.

2. dbt Model Snippet:SQL

A) Incorrect Jinja usage within a macro used for templating.
B) The source() function retrieving data from the incorrect schema.
C) The SUM(order_amount) aggregation logic.
D) The GROUP BY month clause leading to duplicate rows.

3. You discover that dbt models create objects with the default database user's permissions. However, a policy mandates that analytics objects must be owned by a dedicated, less privileged account. How could you adapt your dbt workflow?

A) Implement pre-hooks or post-hooks to execute ALTER OWNER statements after model creation.
B) Write dbt macros that contain permission management logic as part of the model deployment.
C) Modify your dbt profile to use the less privileged account's credentials during job execution.
D) Configure the target database to automatically assign desired ownership to new objects in the schema

4. Your fct_orders model depends on both raw_sales and stg_customers. Due to upstream changes, the customer_id column in raw_sales is now named cust_id. How could you manage this change with minimal disruption?

A) Utilize dbt's ref() function to dynamically adapt to the new column name.
B) Use a dbt source to rename the column in raw sales.
C) Create an alias for customer_id as cust_id in the stg_customers model.
D) Update the fct_orders model to reference cust_id.

5. You're experimenting with new transformations. You want to iterate on a model without affecting data in the production warehouse. Which dbt command should you use along with appropriate flags?

A) dbt run with -target to specify a development environment.
B) dbt test with -defer to temporarily bypass upstream dependencies.
C) dbt compile to ensure changes are valid without executing models.
D) dbt seed with -select to load data into production.

Solutions: