Databricks-ML-Pro ML Ops Practice Question
A team is preparing to promote a new model version to production in the MLflow Model Registry. They must ensure the model can be served with a consistent environment across staging and production and that dependency drift is detected before promotion. Which TWO practices should they follow? (Choose two.)
⚠ Common exam trap
The trap here is thinking that a serialized model file alone guarantees reproducible serving, when the surrounding dependency environment must also be captured and verified.
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
✓
Log the model with its conda environment and requirements files so the exact dependency versions are captured as part of the model version.
Capturing the exact dependency environment with the model version and then validating that environment against the target serving environment before promotion together ensure consistent serving and catch drift early. Floating to latest versions, mismatching runtimes, or relying on bare pickles all break reproducibility or hide drift.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store only the model's pickle file and reconstruct dependencies manually on each environment, since pickle serialization is environment-independent.
Why it's wrong here
Pickle files are not environment-independent; they depend on compatible library versions and Python versions, and manual reconstruction is error-prone. Storing only the pickle discards the recorded environment that makes serving reproducible, so this approach neither ensures consistency nor helps detect dependency drift between staging and production.
- ✓
Log the model with its conda environment and requirements files so the exact dependency versions are captured as part of the model version.
Why this is correct
Logging the conda environment and requirements files records the exact library versions used at training time as part of the model artifact. When the same model version is served in staging and production, the environment can be reconstructed from these files, which is the foundation for consistent, reproducible serving and for detecting any drift from the recorded versions.
- ✗
Rely on the serving endpoint to install the latest available versions of each library at deployment time so the environment stays current.
Why it's wrong here
Installing the latest library versions at deployment time defeats reproducibility, because the environment changes independently of the model version and can break inference. It also makes drift undetectable, since there is no fixed reference to compare against. Consistency requires capturing and honoring the recorded dependency versions, not floating to newest.
- ✓
Verify the logged environment files against the target serving environment before promotion, resolving any version mismatches in the model's dependency specification.
Why this is correct
Comparing the logged environment files with what the target serving environment provides surfaces version mismatches before promotion, so drift is caught early. Resolving mismatches in the model's dependency specification ensures both staging and production reconstruct the same environment, which is the second half of a consistent-environment strategy.
- ✗
Pin the serving cluster's runtime to a different Databricks Runtime version than training used, to validate cross-version compatibility during staging.
Why it's wrong here
Deliberately using a different runtime than training is a compatibility test, not a consistency practice, and it increases the chance of dependency drift between staging and production. The requirement is a consistent environment across both environments, so introducing a runtime mismatch undermines the goal rather than supporting it.
About these practice questions
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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.