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

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question #1
You're working on a dbt project in a development environment where compute resources are limited. You want to prototype a new model quickly without incurring high materialization overhead. Which materialization would be most suitable for this?

A. Ephemeral
B. Incremental
C. Snapshot
D. Table

Question #2
(Multiple Select)

A. Testing performance under a simulated heavy load.
B. Catching subtle errors or discrepancies that may not cause immediate functional issues.
C. Validating changes during development before deploying a new dbt model version.
D. Ensuring data synchronization after a migration.

Question #3
Your team uses a custom macro to generate model descriptions dynamically. However, these descriptions don't appear in the DAG visualization within the documentation. Which of the following might cause this issue?

A. The macro might not be returning the description in the standard format that dbt documentation expects.
B. Dynamically generated descriptions are incompatible with DAG visualizations in dbt
C. Custom macros always require explicit configuration in your dbt_project.yml file to be rendered in the DAG-
D. The generated documentation site might be cached, preventing the updated descriptions from appearing.

Question #4
You need a way to document relationships between raw source tables, dbt models, and downstream Bl dashboards. What type of tool would be best suited for this task?

A. A data profiling tool
B. dbt's built-in documentation (dbt docs)
C. A version control system
D. A data catalog

Question #5
You maintain a large dbt project with various reporting models that source data from a set of core business tables. These core tables often include columns with sensitive information. What technique could ensure compliance and maintainability when restricting access to this data?

A. Create incremental models that directly remove the sensitive columns before reporting models utilize them.
B. Apply row-level security within the data warehouse itself for fine-grained control.
C. Write complex WHERE clauses in each reporting model to exclude rows containing sensitive data.
D. Use views to create different projections of the core tables, exposing only the necessary columns.

Solutions: