Databricks-ML-Pro ML Ops Practice Question
You are implementing a CI/CD pipeline for a machine learning model on Databricks. The pipeline must automatically run unit tests, train the model, and deploy it to a staging endpoint. Which TWO practices should you follow to ensure the pipeline is reproducible and reliable? (Choose two.)
⚠ Common exam trap
The trap here is assuming that using the latest libraries or manual promotion improves reliability, when in fact they introduce variability and reduce automation.
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
✓
Pin all library dependencies to specific versions in a requirements file or conda environment specification.
For a reproducible and reliable CI/CD pipeline, code should be version-controlled and checked out consistently, and dependencies should be pinned to specific versions. These practices ensure that the pipeline runs the same code in the same environment every time. Autoscaling and manual promotion do not address reproducibility, and using latest libraries can introduce instability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the latest version of all libraries to ensure compatibility and security patches.
Why it's wrong here
Using the latest versions can introduce breaking changes and make the pipeline non-reproducible. A model trained with one version may not perform the same with another. For reliable CI/CD, dependencies should be pinned to avoid unexpected behavior. While security patches are important, they should be applied deliberately and tested, not automatically in a pipeline.
- ✓
Pin all library dependencies to specific versions in a requirements file or conda environment specification.
Why this is correct
Pinning dependencies ensures that the environment is consistent across runs, preventing issues caused by library updates. This is critical for reproducibility in ML pipelines, where even minor version changes can affect model training and inference. Using a requirements file or conda environment specification allows the pipeline to recreate the exact environment.
- ✗
Configure the pipeline to run on a cluster with autoscaling enabled to handle variable workloads.
Why it's wrong here
Autoscaling helps with performance and cost, but it does not directly contribute to reproducibility or reliability of the pipeline logic. The cluster size may vary, but the code and environment remain the same. While useful, it is not a core practice for ensuring reproducible and reliable ML pipelines in the context of CI/CD.
- ✗
Manually promote the model to staging after reviewing the test results to ensure quality.
Why it's wrong here
Manual promotion introduces human intervention and reduces automation, which can lead to inconsistencies and delays. For a reliable CI/CD pipeline, promotion should be automated based on predefined criteria, such as passing tests and meeting performance thresholds. Manual steps are error-prone and not reproducible.
- ✓
Store all code, including notebooks and Python modules, in a Git repository and use Databricks Repos to check out the code in the pipeline.
Why this is correct
Using Git for version control and Databricks Repos for checkout ensures that the exact code version is used in each pipeline run. This is fundamental for reproducibility. It allows you to track changes, collaborate, and roll back if needed. Databricks Repos integrates with Git to provide a consistent codebase for jobs and notebooks.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-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-ML-Pro 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-ML-Pro exam.