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ML Interview Roadmap
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Day 67 of 133

Privacy & security: PII, DP, federated, attacks + DSA 2-D DP

K-anonymity, ε in DP, model-extraction defenses.

DSA · NeetCode 2-D DP

  • Best Time TO Buy And Sell Stock With CooldownDSA · 2-D DP

    Interview questions to prep

    1. State the 2-D DP: indices, recurrence, base case. What's the order of fill?
    2. Can you reduce 2-D to 1-D by reusing rows or columns? Walk through the dependency direction.
    3. Top-down with memoization vs bottom-up — which is easier to reason about, and which is faster in practice?
  • Coin Change IIDSA · 2-D DP

    Interview questions to prep

    1. State the 2-D DP: indices, recurrence, base case. What's the order of fill?
    2. Can you reduce 2-D to 1-D by reusing rows or columns? Walk through the dependency direction.
    3. Top-down with memoization vs bottom-up — which is easier to reason about, and which is faster in practice?

MLOps · Privacy & security

  • PII handling, anonymization, k-anonymityMLOpsRead

    Interview questions to prep

    1. How would you remove PII from training data without destroying signal?
    2. Compare hashing vs tokenization vs full redaction for PII fields — when does each fit?
  • Differential privacy basicsMLOpsGoogle

    Interview questions to prep

    1. Explain differential privacy in one minute — what does ε mean?
    2. How does DP-SGD differ from regular SGD, and what does it cost in accuracy?
  • Federated learningMLOpsGoogle

    Interview questions to prep

    1. When is federated learning the right choice over centralized training?
    2. What are the systems-side challenges in federated learning beyond the algorithm?
  • Adversarial & model-extraction attacksMLOpsGoogle

    Interview questions to prep

    1. What is a model-extraction attack, and how do you mitigate it?
    2. How would you defend a public ML API from membership-inference attacks?

References & further reading