AI & Technical question
Design a system that can detect fraudulent use of Microsoft Word.
- Microsoft
- AI & Technical
- Hard
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What this question tests
Tests technical system design for a fraud/abuse detection problem, covering signal design, the precision-recall trade-off, and how enforcement actually works.
How to approach it
- Clarify what fraudulent use means here: pirated or unlicensed copies, or malicious macro-based malware distributed via Word documents, since detection differs completely.
- Assume the malicious macro/malware case, since it's the more common and higher-stakes interpretation, and state that assumption.
- Define signals to detect: unusual macro behavior (auto-executing on open, obfuscated code, network calls from within a macro), and file metadata anomalies (mismatched file type, suspicious sender).
- Propose a layered detection system: static analysis of macro code for known malicious patterns, combined with sandboxed dynamic execution to observe real behavior before the file reaches the user.
- Address the precision-recall trade-off: aggressive blocking risks false positives on legitimate business macros, so use a warning-and-quarantine flow rather than silent auto-deletion for medium-confidence cases.
- Define success as malicious file catch rate at a fixed false-positive rate on legitimate documents, tracked separately from user-reported issues.
What a strong answer includes
- Explicitly states which interpretation of fraudulent use it's answering, showing the candidate resolved the ambiguity deliberately.
- Combines static and dynamic (sandboxed) analysis, which is how real malware detection systems layer defenses.
- Names the precision-recall trade-off directly and proposes a graduated response (warn versus block) instead of one blunt action.
- Uses illustrative numbers as assumptions: assume a target of 95% malicious catch rate while keeping false positives on legitimate macros under 0.1%.
Common mistakes
- Not clarifying or stating which interpretation of the ambiguous question is being answered.
- Proposing detection with no mention of the false-positive cost to legitimate business users.
- Relying on static analysis alone, missing behaviors only visible when a macro actually executes.
Likely follow-up questions
- How would you handle a novel attack pattern not seen before?
- What's your response when a false positive blocks a legitimate business document?
- How would you keep detection effective as attackers adapt?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture