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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question 1

A project stakeholder requests access to an intermediate stage of a dbt transformation pipeline to validate specific calculations.
You need to weigh this request carefully because:

A. Raw data, development data, and production data often have different levels of data quality, completeness, and timeliness.
B. Granting access to intermediate data may confuse stakeholders who are not familiar with the full transformation process-
C. Exposing intermediate results can lead to premature analysis based on incomplete data.
D. All of the above-

Question 2

dbt Model Snippet

A. The presence of filters on the source fct_orders or dim_customers.
B. The logic contained within a custom macro.
C. The ref() functions arent correctly resolving dependencies.
D. The underlying data types of o.customer_id and c.customer_id mismatch.

Question 3

A new stakeholder needs to receive an email report generated from the results of a specific dbt model. Unfortunately, your Bl tool doesn't directly integrate with dbt. How could you address this?

A. Within the model's SQLi write logic to insert the output data into a dedicated reporting table-
B. Modify the model to output a CSV file and schedule a task to email the file as an attachment
C. Develop a separate script, triggered after the dbt job, to handle report generation and emailing-
D. Write a post-hook that queries the model, formats the results, and sends the email.

Question 4

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

Question 5

The dataset you work with contains personally identifiable information (PII). Which types of additional tooling might be necessary for your project beyond a core dbt implementation?

A. Advanced statistical modeling tools.
B. Automated ETL pipelines
C. Enhanced data access governance and auditing tools.
D. Data masking or tokenization solutions.

Solutions: