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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You discover a flaw in a data cleaning process applied early in your raw data transformation. What implications does this have for the rest of your dbt pipeline and the distinction between production vs. development?

A) All of the above.
B) You might need to re-run models downstream of the fix across both development and production to ensure data integrity.
C) There's a risk that incorrect data exists in both development and production, leading to unreliable modeling.
D) It highlights the importance of testing and validation at each transformation stage, not just in the final reporting layer.

2. You establish a process using dbt tests and snapshots to track data quality changes over time. To maximize the utility of this historical record for troubleshooting, what additional step would be beneficial?

A) Integrate metadata about dbt model versions and schema changes alongside the snapshots.
B) Automatically purge snapshots older than a certain date to save storage resources.
C) Schedule regular alerts whenever a test result deviates significantly from previous runs.
D) Convert the dbt test results and snapshots into time-series tables for easier trend analysis.

3. A team member has opened a pull request, but conflicts arise when you attempt to merge it. Which of the following are essential steps in the conflict resolution process?

A) Communicate with your team member to understand the intent behind the conflicting changes.
B) Manually edit the affected files in your favorite text editor to resolve the differences.
C) All of the above.
D) Utilize Git's built-in merge conflict markers to identify areas of disagreement

4. Several models perform similar filtering and aggregation on a frequently used source table. What's the most effective way to streamline this process and improve maintainability?

A) Create a snapshot to capture the pre-processed state of the source table.
B) Create a table materialization to hold the pre-processed data.
C) Replicate the filtering and aggregation logic directly within each model.
D) Create an ephemeral model to prepare the data in a common format, referenced by downstream models.

5. (Multiple Select)

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

Solutions: