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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. (Multiple Select)

A) Ensuring a primary key column has no null values.
B) Confirming that the total count of orders in a day matches a rollup value in a pre-calculated summary table.
C) Checking if values in a specific column fall within a valid range based on business logic.
D) Verifying that a paid_at timestamp column always occurs after a signup_at timestamp column.

2. You've been tasked with improving the discoverability of dbt models within your project. Which of the following changes would be the MOST effective in achieving this goal?

A) Adding a tags list to all models, even if the tags are somewhat generic.
B) Writing longer, more verbose descriptions for each column in your models.
C) Ensuring models have clear and concise descriptions that accurately reflect their purpose.
D) Using custom macros to dynamically generate model descriptions based on metadata.

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

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

4. (Multiple Select)

A) Implementing more rigorous access controls and auditing procedures.
B) Potentially running on a different data warehouse or with different database settings.
C) Scheduling models to run outside normal business hours to reduce resource contention.
D) Using credentials with appropriate permissions (likely read-only) to access production data sources.

5. You debug a production issue by running a model in a testing environment with additional logging output. Which dbt features or command-line additions would facilitate this?

A) dbt hooks to redirect model-related logs to a centralized logging platform.
B) Custom dbt flags (using -vars) to control the logging level of the SQL execution.
C) Environment variables to conditionally enable verbose logging macros inside the model.
D) The -warn-error flag to treat warnings as errors, making your run fail louder on potential issues.

Solutions: