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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. Several models perform similar filtering and aggregation on a frequently used source table. What's the most effective way to streamline this process and improve maintainability?

A) Create a table materialization to hold the pre-processed data.
B) Create a snapshot to capture the pre-processed state of the source table.
C) Create an ephemeral model to prepare the data in a common format, referenced by downstream models.
D) Replicate the filtering and aggregation logic directly within each model.

2. You need to run a custom Python script after your dbt models execute. Where and how can you configure this in your dbt_project.yml file?

A) Create an 'integration test' and use the PythonFunction class-
B) Define it as a 'snapshot' to ensure it runs after all models.
C) Include the script as a 'seed' file with a specific execution order
D) Utilize the on-run-end hook configuration.

3. You want to host your dbt project documentation on an internal company server that's behind a firewall. Which approach would be best suited for this scenario?

A) Utilize the dbt docs serve command, making the local web server accessible within your private network.
B) Deploy the docs using dbt Cloud's built-in hosting features.
C) Upload the static files generated by dbt docs generate to your internal web server.
D) Directly modify the HTML and CSS of the generated documentation site to add firewall-specific rules.

4. You're implementing singular tests (accepted values). After providing a list of valid values, you notice some strange behavior: the test is passing even with records that contain unexpected dat a. What's the most likely issue?

A) A downstream process is overwriting the results after the model is built.
B) The accepted_values test configuration has the wrong column specified.
C) Your test case is accidentally permitting null values, which bypass the check.
D) The model's data type isn't compatible with the accepted_values test.

5. You're building tests for a dbt model that performs a join across multiple large datasets. You notice performance issues - tests are taking a very long time to run. What might be optimization strategies?

A) Focus on tests that can validate a subset of the data rather than the entire dataset.
B) Create snapshots of the source tables and test on those snapshots instead.
C) Accept that tests on complex models will always be slow.
D) Refactor model SQL to use incremental logic to avoid full table scans.

Solutions: