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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You're working in a dbt Cloud development environment. You make a change to a model definition that accidentally introduces an invalid column reference. What's the likely sequence of events when you trigger a dbt run?

A) The job will fail during compilation, and a clear error message will point to the invalid reference.
B) The job execution state will depend on the specific SQL dialect of your data warehouse.
C) The job will succeed, as dbt only validates schema changes at deployment time.
D) The job will succeed but produce incorrect output due to the invalid reference.

2. (Multiple Select)

A) You detect a frequently-used, non-trivial SQL transformation pattern across multiple models.
B) Your data warehouse has a non-standard SQL function used in various reporting dashboards.
C) You have a very simple filter that is applied to just a small subset of models.
D) You need to perform data type conversions for a specific set of columns.

3. You're experimenting with new transformations. You want to iterate on a model without affecting data in the production warehouse. Which dbt command should you use along with appropriate flags?

A) dbt compile to ensure changes are valid without executing models.
B) dbt run with -target to specify a development environment.
C) dbt test with -defer to temporarily bypass upstream dependencies.
D) dbt seed with -select to load data into production.

4. (Multiple Select)

A) Implement a custom data test to specifically check the column for null values.
B) Add intermediate CTEs within the model's SQL to inspect data at different transformation stages.
C) Switch the model materialization from 'view' to 'table'.
D) Examine the source data for the model to verify the presence of expected values.

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

Solutions: