How does a machine learning enhance WildFire operation?

Question

Options

• Aanalyzes file sample characteristics during static analysis
• Bdetermines which file samples should be forwarded to WildFire
• Cdetermines the minimum CIS Windows version to analyze file samples
• Danalyzes file sample characteristics during dynamic analysis

• A
  4% (1)
• B
  4% (1)
• D
  92% (22)

Explanation

Machine learning in WildFire enhances dynamic analysis by examining file behavior as it executes in a sandbox environment - ML models analyze runtime characteristics like system calls, network behavior, and process activity to detect malicious patterns that static inspection would miss.

Why the distractors are wrong:

• A is incorrect because static analysis examines file structure and code without executing the file - ML isn't the primary enhancer here; rule-based signatures and heuristics dominate static analysis.
• B is incorrect because forwarding decisions (which files go to WildFire) are based on file type, policy rules, and verdict caching - not ML.
• C is incorrect because CIS Windows version selection is a configuration/compatibility concern, not a machine learning function at all.

Memory tip: Think of ML as needing to watch something to learn from it - just like a student learns better by doing (dynamic) than just reading (static). ML in WildFire watches files run, making D the natural fit.

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