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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question 1

You've identified the same complex date calculations repeated across multiple models. Which approach aligns with modularity and DRY (Don't Repeat Yourself) principles?

A. Continue copying the date calculation logic into each relevant model.
B. Introduce a new intermediate table to store the result of the date calculations.
C. Write a custom Python function and import it into your dbt project.
D. Create a macro, encapsulating the date calculations, and reference it in the affected models.

Question 2

You create tests to validate counts and timestamps against a baseline dataset. The tests fail unexpectedly. What might be possible root causes?

A. The baseline dataset itself has become corrupted
B. Your upstream ETL process has a logic error, leading to incorrect data.
C. Your tests are too strict and need more generous tolerances.
D. There are differences in data types or time zone handling between the model and baseline.

Question 3

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

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

Question 4

You've made several commits to your feature branch. Before merging into the main branch, you want to consolidate the changes into a single, clear commit. What Git technique would help achieve this?

A. Use git rebase -i (interactive rebase) to squash commits together.
B. git cherry-pick specific commits from your branch and reapply them.
C. Run git rebase main followed by git commit -amend.
D. Manually copy your files into a new branch, commit, and issue a pull request.

Question 5

(Multiple Select)

A. dbt test
B. Inspect the manifest.json file.
C. Jinja templating
D. Use the dbt debug command.
E. Manually examine the compiled SQL files.
F. Write a custom Python script to parse dbt models.
G. dbt docs
H. Snapshots
I. ref() and source() functions

Solutions: