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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. "Sometimes columns that should have data are showing null values!" exclaims a colleague. What initial debugging steps would you suggest beyond examining the model logic itself?

A) Review if there are snapshots or merge strategies that might impact how the table is updated.
B) Check any recent changes to source definitions or transformations on upstream data.
C) All of the above.
D) Verify that any where clauses or filters in the model are not inadvertently too restrictive.

2. While onboarding new data sources to your dbt project, you discover some have unexpectedly high null rates in key columns. How would you integrate tests for this into your dbt workflow?

A) Include a data quality monitoring step in your upstream ETL process before data reaches dbt
B) Write tests within the initial staging models that calculate and check the null percentage.
C) Create not_null tests on the raw source tables in a separate dbt project dedicated to source validation.
D) Designate some nulls as acceptable by extending the not_null test with the OR condition.

3. dbt Model Snippet:SQL

A) Ensuring the grains variable is defined in dbt_project.yml or a macro.
B) Validating that the column names generated by the Jinja loop would be valid SQL identifiers.
C) Making sure the source table fct_orders contains the expected columns.
D) Checking that the date_part() function is compatible with your database-

4. A colleague asks, "Why go through the trouble of using dbt? Can't we just write SQL scripts and schedule them?" Which of the following is the most compelling argument for using dbt?

A) dbt offers native support for advanced scheduling and orchestration features.
B) dbt promotes modularity, reusable code, and testing within data transformation pipelines.
C) dbt automatically generates user-friendly data lineage visualizations.
D) dbt models are typically more performant than hand-written SQL scripts.

5. You've added new tests to a dbt model but are unsure how to determine the test coverage. What's a practical first step?

A) Review the compiled SQL files generated by dbt.
B) Examine the output of dbt docs generate and dbt docs serve.
C) Execute dbt test -select failing to identify any currently failing tests.
D) Employ a code coverage tool specifically designed for SQL.

Solutions: