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MLOps

The bank most graduates are short of. It mixes theory (drift types, CI/CD/CT, model registries) with applied scenario questions written the way real interview case studies are posed — a system is described, something breaks, and you pick the correct diagnosis or remedy. Hard level is almost entirely case-study driven.

300 questions

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Easy

100

questions in this bank

Definitions, vocabulary and single-step recall. Build the base you can answer without thinking.

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Medium

100

questions in this bank

Applied selection and comparison. Two facts have to be combined before you can answer.

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Hard

100

questions in this bank

Scenario and case-study questions with plausible distractors. This is exam-day difficulty.

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What the bank covers

  • ML lifecycle, technical debt, CACE principle
  • Data versioning, lineage, reproducibility (DVC, LakeFS)
  • Feature stores, online/offline parity, training–serving skew
  • Experiment tracking & model registries (MLflow, W&B)
  • CI/CD/CT, pipeline orchestration (Airflow, Kubeflow, SageMaker)
  • Serving: batch, online, streaming, shadow, canary, A/B
  • Drift: covariate, prior, concept; detection tests
  • Monitoring, alerting, ground-truth lag, proxy metrics
  • Model governance, model cards, bias & fairness audits
  • Cost, latency, autoscaling, GPU utilisation
  • Case studies: recommender, fraud, forecasting, LLM/RAG in production