- A
Deploy a predictive analytics model to forecast future traffic volumes and adjust thresholds accordingly.
Why wrong: Predictive analytics focuses on forecasting future trends rather than identifying anomalies, so it would not directly reduce false positives from current traffic patterns.
- B
Implement an anomaly detection system that uses machine learning to establish baseline behavior and flag deviations.
Anomaly detection with ML learns normal traffic baselines and adapts to changes, which significantly reduces false positives by only alerting on genuine anomalies.
- C
Apply intent-based networking to automatically enforce security policies based on high-level business intent.
Why wrong: Intent-based networking automates policy deployment and verification, but it does not inherently reduce false positive alerts from monitoring systems.
- D
Use deep packet inspection to examine all traffic and create static rules for known threats.
Why wrong: Deep packet inspection can detect known threats but does not adapt to new patterns, so it would not reduce false positives from normal traffic variations.
Quick Answer
The correct choice is to implement an anomaly detection system that uses machine learning to establish baseline behavior and flag deviations. This approach directly addresses the problem of false positives because machine learning models dynamically learn what constitutes normal traffic patterns during business hours, rather than relying on static thresholds that cannot adapt to routine variations. By continuously updating the baseline, the system can distinguish between benign fluctuations and genuine security threats, which is the core of effective anomaly detection in network monitoring. On the CCNA 200-301 v2 exam, this concept tests your understanding of how AI/ML operations improve network reliability and security, often appearing in questions about reducing alert fatigue. A common trap is choosing static threshold-based alerting, which fails to account for changing network conditions. Remember the memory tip: “Baseline beats baseline—dynamic learning cuts the false alarm churning.”
CCNA AI and Network Operations Practice Question
This 200-301 practice question tests your understanding of ai and network operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 network administrator at a large enterprise notices that the network monitoring system frequently generates false positive alerts for unusual traffic patterns during normal business hours. The administrator wants to reduce these false positives while still detecting genuine security threats. Which AI/ML concept would best address this requirement?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Implement an anomaly detection system that uses machine learning to establish baseline behavior and flag deviations.
Option B is correct because anomaly detection using machine learning establishes a dynamic baseline of normal network behavior, allowing the system to flag only significant deviations. This reduces false positives during normal business hours while still detecting genuine threats that deviate from the learned baseline, unlike static thresholds that trigger alerts on routine traffic variations.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy a predictive analytics model to forecast future traffic volumes and adjust thresholds accordingly.
Why it's wrong here
Predictive analytics focuses on forecasting future trends rather than identifying anomalies, so it would not directly reduce false positives from current traffic patterns.
- ✓
Implement an anomaly detection system that uses machine learning to establish baseline behavior and flag deviations.
Why this is correct
Anomaly detection with ML learns normal traffic baselines and adapts to changes, which significantly reduces false positives by only alerting on genuine anomalies.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Apply intent-based networking to automatically enforce security policies based on high-level business intent.
Why it's wrong here
Intent-based networking automates policy deployment and verification, but it does not inherently reduce false positive alerts from monitoring systems.
- ✗
Use deep packet inspection to examine all traffic and create static rules for known threats.
Why it's wrong here
Deep packet inspection can detect known threats but does not adapt to new patterns, so it would not reduce false positives from normal traffic variations.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The 200-301 exam frequently reuses these exact scenarios with slightly different constraints.
✓Implement an anomaly detection system that uses machine learning to establish baseline behavior and flag deviations.Correct answer▾
Why this is correct
Anomaly detection with ML learns normal traffic baselines and adapts to changes, which significantly reduces false positives by only alerting on genuine anomalies.
✗Deploy a predictive analytics model to forecast future traffic volumes and adjust thresholds accordingly.Wrong answer — click to see why▾
Why this is wrong here
Predictive analytics forecasts future traffic volumes but does not establish a dynamic baseline for normal behavior; thus, it cannot adapt to daily variations and would not reduce false positives from current traffic patterns.
Why candidates choose this
Students may think that forecasting future traffic helps adjust thresholds, but this approach is reactive and does not learn normal patterns, making it ineffective for reducing false positives.
✗Apply intent-based networking to automatically enforce security policies based on high-level business intent.Wrong answer — click to see why▾
Why this is wrong here
Intent-based networking automates policy deployment and verification based on business intent, but it does not analyze traffic patterns or adapt alert thresholds; therefore, it does not directly reduce false positive alerts from monitoring systems.
Why candidates choose this
Students may confuse intent-based networking with adaptive security, but its primary function is policy automation, not anomaly detection or false positive reduction.
✗Use deep packet inspection to examine all traffic and create static rules for known threats.Wrong answer — click to see why▾
Why this is wrong here
Deep packet inspection with static rules can detect known threats but cannot adapt to new or evolving traffic patterns; thus, it would not reduce false positives from normal traffic variations and may even increase them due to rigid rules.
Why candidates choose this
Students might think that deep packet inspection provides thorough analysis, but without machine learning to establish baselines, it lacks the adaptability needed to minimize false positives.
Analysis generated from the official 200-301blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between predictive analytics (forecasting volume) and anomaly detection (learning behavior), trapping candidates who confuse adjusting thresholds with establishing a behavioral baseline.
Detailed technical explanation
How to think about this question
Anomaly detection models, such as those using unsupervised learning (e.g., k-means clustering or autoencoders), profile features like packet size, inter-arrival time, and protocol distribution over sliding windows. For example, a sudden spike in DNS queries to a rarely contacted domain might be flagged as anomalous, while a routine bulk file transfer during business hours is ignored because it matches the baseline. Real-world implementations often use a combination of time-series decomposition and statistical thresholds (e.g., z-score or moving average) to adapt to daily and weekly cycles.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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AI and Network Operations — study guide chapter
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FAQ
Questions learners often ask
What does this 200-301 question test?
AI and Network Operations — This question tests AI and Network Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement an anomaly detection system that uses machine learning to establish baseline behavior and flag deviations. — Option B is correct because anomaly detection using machine learning establishes a dynamic baseline of normal network behavior, allowing the system to flag only significant deviations. This reduces false positives during normal business hours while still detecting genuine threats that deviate from the learned baseline, unlike static thresholds that trigger alerts on routine traffic variations.
What should I do if I get this 200-301 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 11, 2026
This 200-301 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 200-301 exam.
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