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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. (Multiple Select)

A) Nesting multiple correlated subqueries within a single query_
B) Writing custom macros to encapsulate frequently used SQL logic.
C) Using SELECT * to include all columns dynamically
D) Leveraging window functions to calculate values across rows or groups.

2. A critical model fails with a non-descriptive error. You suspect a third-party package used in the model. How might you narrow down the problem?

A) Add -log-format json to your dbt run command to get more structured error output.
B) Examine recent commits or releases of the package for changes that might have introduced a bug.
C) Run the package's models in isolation to see if the error reproduces.
D) Temporarily disable the package and try running the model.

3. (Multiple Select)

A) Utilize a YAML validator tool independently of dbt to check for general formatting errors.
B) If available, compare your schema.yml to a previous working version to spot problematic changes.
C) Refer to the dbt documentation for a comprehensive list of valid properties within schema files.
D) Introduce a global persist_docs setting within your dbt_project.yml file to resolve schema inconsistencies.

4. You're evaluating whether to adopt an external tool that offers a visual, drag-and-drop interface for building data models. When considering this alongside dbt, what's a KEY factor to weigh?

A) If the visual tool has advanced machine learning capabilities for data cleaning.
B) Whether the tool can handle the full complexity of your data transformations.
C) The ease of migrating existing dbt projects into the visual tool.
D) The ability of the tool to generate dbt-compatible SQL and integrate into your version control workflow.

5. A model passes its tests during development, but later fails when deployed on a less powerful production database. Which of the following might be the reason for this discrepancy?

A) There's a bug in the version of dbt that runs in production.
B) Performance-related test thresholds are too lenient in development.
C) Source data distributions differ significantly between environments.
D) The test was accidentally disabled in the production dbt project.

Solutions: