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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You define a source table and include an override:YAML

A) The overrides property needs to be defined at the source level, not the table level.
B) The overrides property is not valid for tables within your dbt_project.yml.
C) The value 'my_project' is not a recognized keyword by dbt.
D) You need to create a macro named 'my_project' that contains the specific overriding logic.

2. (Multiple Select)

A) Enable version control on your dbt_project.yml file and related configuration.
B) Integrate a specialized data lineage tool that tracks dependencies down to the database level..
C) Configure dbt to log all executed SQL statements with sufficient metadata for auditing.
D) Leverage your data warehouse's built-in auditing or change tracking features, if available.

3. (Multiple Select)

A) Configuring tests to run only on models that have changed or have upstream changes.
B) Scheduling less critical tests to run during off-peak hours.
C) Running dbt tests in parallel with dbt model builds whenever possible-
D) Designing tests, where feasible, to examine subsets of data rather than full tables.

4. You need to run a dbt job that performs a long-running data cleansing operation. The job occasionally times out. Which of the following are potential solutions?

A) Use a dbt macro to retry the cleansing operation with a backoff strategy within the model itself-
B) Schedule the job to run concurrently with other dbt models to maximize resource usage-
C) Increase the timeout setting in your CI/CD tool or dbt job configuration.
D) Break the cleansing logic into multiple models, chaining them with dependencies-

5. You have a computationally expensive dbt model that creates a wide table with many columns. You realize that downstream analysis typically only needs a small subset of these columns. How might you optimize this for both

A) Increase memory and CPU resources allocated to the data warehouse to power through the large build-
B) Configure a dbt job to run as a lower priority task, accepting slower processing.
C) Refactor the model to use a view materialization, selecting only necessary columns.
D) Implement a post-hook that immediately deletes unused columns from the model's output table.

Solutions: