Databricks-DE-Assoc Implementing CI/CD Practice Question
A CI/CD pipeline runs unit tests against transformation logic before deploying to production. The tests must run on a Databricks cluster and produce a pass/fail result that fails the pipeline when assertions do not hold. Which implementation best fits this requirement?
⚠ Common exam trap
The trap here is treating local pytest runs or SQL row-count checks as equivalent to executing assertions on the Databricks runtime.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a Databricks Job that runs a notebook containing assertions, and have the CI/CD pipeline trigger the job and check its run result.
Tests must run where the production code runs. Triggering a Databricks Job that executes assertion notebooks lets the CI/CD pipeline inspect the run result and fail on assertion errors. This validates behavior on the actual Databricks runtime rather than in a disconnected local environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run pytest locally in the CI runner against a sample of production data downloaded from cloud storage.
Why it's wrong here
Running pytest locally on downloaded production data does not exercise the Databricks runtime, Spark version, or cluster configuration used in production. It can pass while the deployed code fails, and it also risks exposing production data outside the governed workspace, so it does not satisfy the requirement.
- ✗
Use the Databricks SQL Statement Execution API to run `SELECT` statements that compare row counts between source and target tables.
Why it's wrong here
Row-count comparisons through the SQL Statement Execution API validate data volumes but do not execute the transformation code's unit tests or assertions. They cannot detect logic errors in notebook or job code, so they do not provide the pass/fail gate the pipeline requires.
- ✗
Configure the cluster's init script to run the test suite automatically whenever the cluster starts.
Why it's wrong here
Init scripts run during cluster startup and are not designed to execute test suites or report pass/fail status to a CI/CD pipeline. They also cannot gate a merge or deployment, so this approach provides no enforceable quality check and is unrelated to test execution.
- ✓
Create a Databricks Job that runs a notebook containing assertions, and have the CI/CD pipeline trigger the job and check its run result.
Why this is correct
A Databricks Job that executes assertion notebooks on a real cluster exercises the production runtime and returns a run result the pipeline can inspect. If the job fails, the pipeline fails, giving the required pass/fail gate with the same Spark environment used in production.
About these practice questions
This Databricks-DE-Assoc question is part of Courseiva's 276-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Assoc exam.