dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You have a computationally expensive dbt model that creates a wide table with many columns. You realize that downstream analysis typically only needs a small subset of these columns. How might you optimize this for both

A) Increase memory and CPU resources allocated to the data warehouse to power through the large build-
B) Implement a post-hook that immediately deletes unused columns from the model's output table.
C) Refactor the model to use a view materialization, selecting only necessary columns.
D) Configure a dbt job to run as a lower priority task, accepting slower processing.

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

3. (Multiple Select)

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

4. You suspect discrepancies due to potential changes in upstream source tables. What's a practical first step to investigate?

A) Review the dbt project's manifest.json for related changes.
B) Contact the team responsible for managing the sources to inquire about recent updates.
C) Compare the current state of the source tables against their historical snapshots.
D) Run all dbt models and examine the logs for unusual entries.

5. You notice that long descriptions within sources.yml cause formatting issues when viewing the docs, making them hard to read. What strategies could you employ to mitigate this?

A) Use a third-party dbt package that enhances the presentation of source descriptions in documentation.
B) Utilize Markdown within your descriptions to improve readability with headings and line breaks-
C) Split your source description into multiple properties (e.g., description_short and description_long)
D) Enforce a strict character limit on source descriptions-

Solutions: