dbt Labs dbt Analytics Engineering Certification - dbt-Analytics-Engineering certification

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question 1

(Multiple Select)

A. Implement unit tests specifically for the macro to validate its output under different inputs
B. Run dbt Is to confirm that all dependencies for the macro and the model are resolved correctly.
C. Check the generated column names in the compiled SQL to ensure they match existing columns in the data.
D. Add {{ log(macros, info=true) statements within the macro to track how it executes-

Question 2

You're loading fresh data from an external API. How would you implement dbt tests to ensure this feed is consistently providing up-to-date data?

A. Use a dbt source freshness test to define acceptable thresholds for the staleness of the source.
B. Write a custom test comparing the API response payload with previously captured data
C. Create a dbt model that directly queries the API in real-time as part of the dbt test run-
D. Test the maximum recorded timestamp in your model against the current timestamp, allowing for a small lag

Question 3

Your data warehouse is undergoing a migration to a different vendor (e.g., Snowflake to BigQuery). Which category of tools might play a critical role in ensuring a smooth transition of your dbt project?

A. Cloud infrastructure management tools
B. SQL dialect translation tools
C. dbt adapters
D. Data virtualization tools

Question 4

You discover a model sorting large amounts of data using an ORDER BY clause with several columns. Performance needs to be increased. What's the FIRST step in your optimization process?

A. Add a LIMIT clause to restrict the number of records sorted.
B. Change the materialization from a 'table' to a 'view'.
C. Remove unnecessary columns from the ORDER BY clause.
D. Examine query execution plans to identify bottlenecks and potential index usage.

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. Defining thresholds for what constitutes an anomaly in your business context.
C. Ensuring the test integrates seamlessly with dbt's docs feature.
D. Understanding the expected seasonality or patterns in the data.

Solutions: