Palo Alto Networks Exam PSE-Strata Topic 1 Question 75 Discussion

Actual exam question for Palo Alto Networks's PSE-Strata exam

Question #: 75
Topic #: 1

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

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 without disrupting business productivity

CIt is always able to give more accurate verdicts than the cloud ML analysis reducing false positives and false negatives

DIt improves the CPU performance of content inspection

Show Suggested Answer

Suggested Answer: B

by Tiffiny at Dec 11, 2023, 06:31 AM

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Tijuana

11 months ago

Hmm, I was stuck between B and D. Improved CPU performance for content inspection does sound nice, but I think the business impact of blocking malware in real-time wins out. Nice to know the firewall can handle the load though!

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Michal

11 months ago

You know, I was leaning towards option C at first, thinking the local ML would be more accurate. But you both make a good point - real-time protection is probably more important than absolute accuracy in this case.

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Ocie

11 months ago

I agree, option B seems to be the most comprehensive answer. Inline ML on the firewall means we don't have to send files to the cloud for analysis, which can introduce delays. This way, the firewall can act on threats instantly.

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Silvana

10 months ago

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Fausto

10 months ago

C) It is always able to give more accurate verdicts than the cloud ML analysis reducing false positives and false negatives

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Tammi

10 months ago

B) It enables the firewall to block unknown malicious files in real time and prevent patient zero without disrupting business productivity

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Laura

11 months ago

This question is definitely testing our understanding of WildFire's capabilities. I think the key advantage here is the real-time blocking of malicious files without disrupting productivity, which is what option B is talking about.

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