dbt Labs dbt Analytics Engineering Certification : dbt-Analytics-Engineering Exam Questions

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question 1

You introduce a minor formatting change to a large SQL model. Afterwards, the results change unexpectedly. Which dbt-related issue might explain this, even if the SQL itself appears correct?

A. There's a version mismatch between the dbt library used during development and in production.
B. An upstream data issue only surfaces due to the formatting change making that part of the query execute differently.
C. You've used a reserved keyword as a column alias without properly quoting it.
D. A subtle indentation change in a CTE accidentally modifies the execution order within the query.

Question 2

(Multiple Select)

A. The target database schema where models are materialized.
B. Availability of specific dbt packages used in your project.
C. The specific SQL dialect used when generating model code.
D. Credentials used to authenticate to the data warehouse.

Question 3

A team member proposes directly joining multiple raw source tables into a single large model for convenience.
Which of the following is a potential long-term consequence of this approach?

A. Maintenance complexity increases as business logic or source tables change.
B. The dbt test command might run slower due to the complex join logic.
C. Incremental materialization strategies would no longer be applicable.
D. Your DAG might inadvertently develop circular dependencies.

Question 4

You've included the popular 'dbt-utils' package in your project. Where should you primarily expect to find the models and macros it offers?

A. Within your 'models' directory namespaced to distinguish them from your own models.
B. Within the 'seeds' directory of your dbt project.
C. In the 'dbt_project.yml' file under a 'packages' section.
D. Automatically materialized as new tables in your data warehouse.

Question 5

A CTE within a dbt model is incorrectly casting a string column to a numeric type.

A. A snapshot is incorrectly merging new data with incompatible types into an existing table.
B. A Jinja macro meant to filter out non-numeric strings has a logic flaw.
C. A source table has inconsistent data, mixing true numeric values with strings.

Solutions: