Quick Answer
The correct step is to customize alert rules based on known good behavior. While Cisco Secure Cloud Analytics (CSCA) uses machine learning to establish a baseline of normal traffic, legitimate traffic that deviates from that baseline—such as a trusted admin IP or a routine API call—can still trigger false positives. Fine-tuning the ML models adjusts the baseline sensitivity, but it does not explicitly whitelist benign activity; custom alert rules fill that gap by allowing you to define exceptions for trusted IP ranges or specific application flows, directly reducing false positives in Cisco Secure Cloud Analytics without lowering overall detection. On the Cisco SCOR 350-701 exam, this scenario tests your understanding of the layered approach to tuning CSCA: first adjust ML models, then apply custom rules for known good behavior. A common trap is assuming lowering sensitivity is the only fix, but that risks missing real threats. Remember the mnemonic: “Model first, rules for trust”—fine-tune the model, then whitelist the known good.
This 350-701 practice question tests your understanding of cloud security. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A cloud operations team reports that after enabling Cisco Secure Cloud Analytics (CSCA) for an AWS account, some legitimate traffic is being flagged as suspicious. The team has fine-tuned the ML models but false positives persist. Which additional step should they take?
Disable ML-based detection
Why wrong: Removes detection entirely.
Increase the severity threshold
Why wrong: May miss real threats.
Customize alert rules based on known good behavior
Whitelists known good traffic to reduce false positives.
Deploy additional sensors in VPC subnets
Why wrong: Increases visibility but not directly reduce false positives.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Customize alert rules based on known good behavior
C is correct because Cisco Secure Cloud Analytics (CSCA) uses machine learning to establish a baseline of normal traffic behavior. When false positives persist despite fine-tuning ML models, the next logical step is to customize alert rules to explicitly whitelist known good behavior, such as trusted IP ranges or specific application flows. This reduces noise without disabling detection or lowering sensitivity, and it directly addresses the root cause: legitimate traffic that deviates from the baseline but is actually benign.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Disable ML-based detection
Why it's wrong here
Removes detection entirely.
Increase the severity threshold
Why it's wrong here
May miss real threats.
Customize alert rules based on known good behavior
Why this is correct
Whitelists known good traffic to reduce false positives.
Related concept
Read the scenario before looking for a memorised answer.
Deploy additional sensors in VPC subnets
Why it's wrong here
Increases visibility but not directly reduce false positives.
Common exam traps
The trap here is that candidates often confuse 'fine-tuning ML models' with 'adjusting alert thresholds' or 'adding more sensors,' when the correct approach is to use explicit whitelisting via custom alert rules to suppress false positives without compromising detection fidelity.
Detailed technical explanation
CSCA builds a behavioral baseline using NetFlow/IPFIX data and AWS VPC Flow Logs, applying unsupervised ML to detect outliers. Custom alert rules allow operators to define exceptions based on attributes like source/destination IP, port, protocol, or application, effectively creating a whitelist that overrides the ML model's suspicion for known-good traffic. In practice, this is often used to exclude internal monitoring tools or automated backup traffic that might otherwise trigger anomaly alerts due to their regular, high-volume patterns.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A network engineer segments a warehouse floor into three subnets: 20 scanners, 5 printers, and 2 management hosts. Picking the wrong mask wastes addresses or leaves too few usable hosts. Exam questions test whether you can apply CIDR notation, calculate block size, and identify the correct usable-host range for a given prefix.
What to study next
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Cloud Security — study guide chapter
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FAQ
Cloud Security — This question tests Cloud Security — Read the scenario before looking for a memorised answer..
The correct answer is: Customize alert rules based on known good behavior — C is correct because Cisco Secure Cloud Analytics (CSCA) uses machine learning to establish a baseline of normal traffic behavior. When false positives persist despite fine-tuning ML models, the next logical step is to customize alert rules to explicitly whitelist known good behavior, such as trusted IP ranges or specific application flows. This reduces noise without disabling detection or lowering sensitivity, and it directly addresses the root cause: legitimate traffic that deviates from the baseline but is actually benign.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
This 350-701 practice question is part of Courseiva's free Cisco certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 350-701 exam.
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