dbt Labs 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 add a new seed file. Here's a snippet of your dbt_project.yml:YAML

A. The "+" is incorrect for nested elements in this context
B. Specifying column_types directly under seeds is invalid in a dbt_project_yml.
C. The syntax within the column_types section is incorrect
D. You cannot enable a seed file using the enabled property.

Question #2
A package you depend on has released a major update with breaking changes. How might you approach the migration?

A. Fork the package and maintain your own custom version indefinitely.
B. Update your packages.yml to point to the new version and immediately refactor all your dependent models.
C. Pin the package to the older version in your packages.yml file and plan a controlled migration.
D. Review the release notes and the migration guide provided by the package maintainers.

Question #3
You want to automatically deploy updates to your documentation site hosted on a cloud service whenever changes to your dbt project are committed to your main branch. Which of the following is essential for this setup?

A. Write a custom script to check for project changes and handle deployment.
B. Configuring dbt Cloud to trigger documentation deploys.
C. Implementing a CI/CD pipeline that includes dbt build steps.
D. Enabling webhooks in your Git repository provider.

Question #4
You have a dbt model that performs complex aggregations. During development, you notice some models take excessively long to run, but they ultimately complete without errors. Which of the following might be true?

A. This behavior automatically triggers an optimization feature within dbt.
B. Downstream models will inherit the performance issues.
C. These models likely have issues with joins or CTE structure impacting performance.
D. The models are at risk of failing if the source data grows substantially.

Question #5
You want to write a custom test to check for potential anomalies in a time-series dataset. Which of the following would be the MOST important considerations when designing this test?

A. Identifying the metrics (e.g., average, standard deviation) relevant to your data.
B. Ensuring the test integrates seamlessly with dbt's docs feature.
C. Understanding the expected seasonality or patterns in the data.
D. Defining thresholds for what constitutes an anomaly in your business context.

Solutions: