dbt Labs Analytics Engineers dbt-Analytics-Engineering

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question #1
You assume two columns of type 'numeric' will always align in terms of precision and scale (number of decimal places). What's a key way to design your models and tests to be resilient even if this assumption changes?

A. Always store all numeric values as strings to prevent these issues.
B. Avoid relying on exact equality when comparing values from these columns.
C. Write tests to compare the column metadata rather than their contents.
D. Use explicit casting functions to enforce matching precision during calculations.

Question #2
You need to create a model that combines data from a large fact table with smaller dimension tables. Performance is paramount, and the data in the fact table updates incrementally but frequently. Which materialization strategy is likely to provide the optimal balance of efficiency and freshness?

A. Materialize the fact table as an incremental model and the dimension tables as views.
B. Materialize all tables (fact and dimensions) as tables.
C. Materialize all tables (fact and dimensions) as views.
D. Materialize the fact table as a table and the dimension tables as incremental models.

Question #3
You're onboarding a new dbt project to an existing production database. You find tables that might conflict with naming conventions used by dbt. What's a proactive way to handle this?

A. Update your dbt models to use aliases that avoid the name collisions.
B. Add configuration settings to your database connection profile instructing dbt to use a specific schema prefix.
C. Manually rename the conflicting tables in the database prior to running any dbt jobs.
D. Customize the schema property in your dbt_project.yml to namespace dbt-generated objects.

Question #4
You notice that long descriptions within sources.yml cause formatting issues when viewing the docs, making them hard to read. What strategies could you employ to mitigate this?

A. Use a third-party dbt package that enhances the presentation of source descriptions in documentation.
B. Utilize Markdown within your descriptions to improve readability with headings and line breaks-
C. Split your source description into multiple properties (e.g., description_short and description_long)
D. Enforce a strict character limit on source descriptions-

Question #5
You've added a new exposure to your project, but it doesn't appear in dbt docs. Here's part of your dbt_project.yml:YAML

A. Exposures of type 'notebook' are not automatically detected by dbt docs generate.
B. You haven't run dbt docs serve after making changes to your project file.
C. The maturity property is missing within your exposure's configuration.
D. The owner property is not correctly formatted as an email address.

Solutions: