Machine Learning Professional · 44% of the exam

ML Ops: free practice questions

5 sample questions from our 24-question bank for this domain — answers and explanations included. These are the same scenario-based style as the real Databricks exam.

1. Which practice BEST enables CI/CD for ML projects on Databricks, packaging notebooks, jobs, and configuration for deployment across environments (dev/staging/prod)?

  • A. Databricks Asset Bundles (define resources as code and deploy across environments)✓ Correct
  • B. Manually copying notebooks between workspaces
  • C. Emailing .dbc archives
  • D. Editing production directly in the UI
Explanation

Databricks Asset Bundles define jobs, pipelines, and configuration as code (YAML) for versioned, repeatable deployment across dev/staging/prod — the modern MLOps CI/CD approach. Manual copying (B), emailing archives (C), and editing prod directly (D) are error-prone and unrepeatable.

2. Why track model lineage (data version, code version, and run) for a registered model?

  • A. So you can trace exactly how a production model was produced — for reproducibility, debugging, and governance/audit✓ Correct
  • B. To make predictions faster
  • C. To reduce the model's file size
  • D. To split serving traffic
Explanation

Lineage links a registered model back to its training run, data version, and code, enabling reproduction, root-cause debugging, and audit/governance. It doesn't affect inference speed (B), model size (C), or traffic splitting (D).

3. What is a benefit of defining ML jobs and infrastructure as code (e.g., via Asset Bundles) rather than clicking through the UI?

  • A. Deployments become versioned, repeatable, and reviewable across environments, reducing drift and manual error✓ Correct
  • B. It makes models more accurate automatically
  • C. It removes the need for any testing
  • D. It eliminates the need for a model registry
Explanation

Infrastructure/jobs as code gives versioned, repeatable, reviewable deployments across environments, reducing configuration drift and manual mistakes. It doesn't improve model accuracy (B), remove the need for testing (C), or replace the registry (D).

4. In the newer Unity Catalog Model Registry, how are model versions typically designated for a role like 'the production model' in a way that decouples the pointer from a specific version number?

  • A. Using model aliases (e.g., @champion) that point to a chosen version✓ Correct
  • B. Renaming the model file each deployment
  • C. Hard-coding version 1 forever
  • D. Deleting old versions
Explanation

Aliases (like @champion or @production) are named pointers to a specific model version, so deployment code references the alias and you can repoint it without changing consumers. Renaming files (B), hard-coding a version (C), or deleting versions (D) are brittle or destructive.

5. Which environment-management practice supports promoting ML code safely from development to production?

  • A. Separate dev/staging/prod environments (workspaces/catalogs) with deployment via Asset Bundles/CI, not editing prod directly✓ Correct
  • B. Developing and testing directly in the production environment
  • C. Sharing one environment for everything with no separation
  • D. Disabling version control
Explanation

Distinct dev/staging/prod environments with automated, version-controlled promotion (Asset Bundles + CI) reduce risk and enable testing before production. Editing prod directly (B), one shared environment (C), and disabling version control (D) all increase risk.

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ML Ops — Free Machine Learning Professional Practice Questions | DataCertPrep — Certification Prep