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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. Your company wants to standardize pull request templates. Which information would be beneficial to include in such a template?

A) Specific instructions for the reviewer, if certain aspects need special attention.
B) All of the above.
C) A section to link to relevant design documents or specifications.
D) Checkboxes for confirming that tests have been run and documentation has been updated if necessary.

2. While explaining dbt concepts to a colleague, they ask, "Don't macros just add an extra layer of complexity?" How would you address their concern?

A) "Macros don't introduce additional complexity but are mainly used for performance tuning."
B) "Macros are primarily needed for legacy projects; new projects should avoid them."
C) "Yes, they do, but their maintainability benefits outweigh the initial complexity."
D) "Macros simplify debugging because they break down your dbt model logic."

3. You get a warning during dbt run:WARNlNG: Found 100 rows in 'stg_customers' that will become duplicates in the 'dim_customers' target.

A) Update the stg_customers source definition to include filtering logic to remove duplicate rows.
B) Increase the memory allocated for the database to handle the large number of duplicates.
C) Investigate your dim_customers modeling logic to ensure a unique key is correctly used to prevent duplicates.
D) It's safe to ignore this warning, as de-duplication is handled automatically by dbt.

4. You have a dbt project with models defined as views. After deploying to a production database, the view objects are present, but no data is displayed when querying them. What might be the root cause?

A) There's a bug in dbt's SQL generation that specifically affects views in certain environments.
B) Dbt view materializations behave differently in production.
C) Permissions granted in production don't allow reading data from the underlying tables.
D) Your dbt job didn't run successfully, so the views were created empty.

5. You establish a process using dbt tests and snapshots to track data quality changes over time. To maximize the utility of this historical record for troubleshooting, what additional step would be beneficial?

A) Automatically purge snapshots older than a certain date to save storage resources.
B) Convert the dbt test results and snapshots into time-series tables for easier trend analysis.
C) Schedule regular alerts whenever a test result deviates significantly from previous runs.
D) Integrate metadata about dbt model versions and schema changes alongside the snapshots.

Solutions: