What is an advantage of having WildFire machine learning (ML) capability Inline on the firewall?

Question

Options

• AIt eliminates of the necessity for dynamic analysis in the cloud
• BIt enables the firewall to block unknown malicious files in real time and prevent patient zero
• CIt is always able to give more accurate verdicts than the cloud ML analysis reducing false
• DIt improves the CPU performance of content inspection

• A
  8% (4)
• B
  73% (36)
• C
  14% (7)
• D
  4% (2)

Explanation

Inline ML on the firewall intercepts and analyzes files at the point of entry - before they are allowed through - enabling a real-time block decision without waiting for a cloud round-trip. This is the "patient zero" prevention scenario: the very first user who encounters an unknown malicious file is protected immediately, rather than being infected while the cloud verdict is still being fetched.

Why the distractors are wrong:

• A is incorrect because Inline ML complements cloud-based dynamic analysis (sandboxing), it does not replace it. Unknown files that require deeper behavioral analysis are still forwarded to the WildFire cloud.
• C is incorrect because Inline ML prioritizes speed, not necessarily higher accuracy. Cloud ML and dynamic analysis have access to greater compute resources and can produce more thorough verdicts; Inline ML trades some depth for real-time decisioning.
• D is incorrect and actually backward - adding ML inference directly on the firewall consumes CPU resources for content inspection rather than improving performance.

Memory tip: Think of "Inline" as a bouncer at the door. The bouncer (Inline ML) stops obvious troublemakers before they enter, in real time. The cloud is the forensics lab - more thorough, but too slow to stop someone already inside. The key exam phrase to lock in: "block in real time + prevent patient zero."

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