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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. (Multiple Select)

A) Downstream models relying on the staging model will continue to function.
B) dbt will issue a warning but continue processing.
C) The dbt run will succeed, but the staging model will have null values due to the missing data.
D) The staging model run will fail.

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) The fct_orders table might lack an appropriate index for the warehouse_code column.
B) The nested CASE statement with repeated conditional checks is causing redundant calculations.
C) Dynamically generating column names in a loop is inefficient for most databases.
D) The Jinja templating itself introduces a significant processing overhead.

4. A raw source table undergoes periodic bulk updates that temporarily disrupt dependent models when they are running. Which of the following strategies might help prevent errors in these models? (Choose two)

A) Utilize snapshots to capture the state of the raw table before the bulk updates.
B) Implement incremental materializations if possible.
C) Schedule dbt jobs to run after the bulk updates are typically complete.
D) Add retries with a delay to any 'ref calls referencing this table.

5. Logic Error

A) Adjust the model's SQL query and re-run it to observe the changes in output.
B) Deploy the modified model to a development or staging environment.
C) Write new unit tests that specifically target the faulty calculation logic with various input scenarios.
D) Create a separate branch specifically for addressing this issue.

Solutions: