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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. A model fails with an error similar to:Numeric overflow converting to data type numeric(18,2)

A) Incorrect ref() usage causing cyclical dependencies to occur-
B) A timeout issue caused by the model exceeding resource limits.
C) Missing permissions to write data to the target table in the warehouse.
D) Data within a column exceeding the precision or scale of the defined data type

2. You have strict data retention policies. Which factors might influence how data is managed differently across your production, development, and raw data environments?

A) Development data might have less stringent retention requirements, allowing for more frequent deletion-
B) Regulatory requirements could mandate that production data be retained for longer periods.
C) All of the above.
D) Raw data might be archived or deleted following a shorter retention period than production data

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

4. You discover that a heavily used model is taking significantly longer to materialize. After investigation, you determine that the complexity of the model's logic is a contributing factor. Which of the following actions is LEAST likely to help improve performance?

A) Consider applying an incremental materialization strategy if suitable.
B) Break down the model into smaller, intermediate models using CTEs.
C) Change the materialization from a table to a view.
D) Investigate potential optimizations in the underlying SQL code.

5. You need to run a custom Python script after your dbt models execute. Where and how can you configure this in your dbt_project.yml file?

A) Utilize the on-run-end hook configuration.
B) Create an 'integration test' and use the PythonFunction class-
C) Include the script as a 'seed' file with a specific execution order
D) Define it as a 'snapshot' to ensure it runs after all models.

Solutions: