1. Question 1

What is continuous monitoring in vulnerability management?

❌ Collaboration with system administrators
❌ Categorization of vulnerabilities
❌ Implementing patches
✅ Routine scans and intrusion detection systems

Explanation:
Continuous monitoring uses automated scanning, IDS, and real-time alerts to detect new vulnerabilities continuously.

---

2. Question 2

How do AI-based vulnerability management systems improve efficiency?

❌ By relying on manual intervention
✅ By automating scanning, assessment, and planning
❌ By introducing more human errors
❌ By limiting scale and speed

Explanation:
AI removes manual bottlenecks by automating repetitive tasks, increasing speed and accuracy.

---

3. Question 3

What role does predictive analytics play in machine learning models within SIEM?

❌ Predictive analytics relies solely on external threat intelligence sources.
❌ Predictive analytics has no role in threat intelligence.
✅ Predictive analytics helps identify potential future threats.
❌ Predictive analytics focuses on analyzing past incidents only.

Explanation:
Predictive analytics forecasts likely threats by learning from past patterns and current signals.

---

4. Question 4

What is the focus of behavioral analysis facilitated by machine learning in SIEM?

❌ Relying solely on signature-based detection methods
❌ Ignoring changes in user, system, and network behavior
❌ Identifying common security threats based on predefined patterns
✅ Learning typical behavior to identify deviations and anomalies

Explanation:
Behavioral ML models learn “normal” patterns and flag unusual deviations as threats.

---

5. Question 5

What is the Unified Analyst Experience (UAX) in the QRadar Suite?

❌ Automated investigation tool
❌ A tool for automating incident response workflows
❌ Cloud-based tool for managing security logs
✅ Modern and unified interface consolidating capabilities

Explanation:
UAX provides a centralized and seamless interface for threat detection, investigation, and response.

---

6. Question 6

How do machine learning algorithms contribute to analyzing extensive historical security event data sets in SIEM?

❌ By creating predefined rules based on historical data
❌ By relying solely on static rules for threat detection
❌ By ignoring insights from historical data
✅ By facilitating the understanding of normal behavior within an organization’s IT environment

Explanation:
ML models use large historical datasets to learn baseline activity and detect abnormalities.

---

7. Question 7

What is the limitation of generative AI models in anomaly detection?

❌ Dependence on labeled objects for anomalies
❌ Reducing computing power and expertise requirements
❌ Increasing accuracy with fewer false positives/negatives
❌ Providing interpretability for model outputs
✅ (Correct option) Dependence on labeled objects for anomalies

Explanation:
Generative models struggle when there is limited labeled anomalous data, which reduces detection accuracy.

---

8. Question 8

What is a key application of anomaly detection using generative AI models?

✅ Detecting abnormalities in cybersecurity and fraud detection
❌ Overcoming class imbalance in labeled data
❌ Broadening the scope of labeled objects
❌ Identifying regular patterns in the data set

Explanation:
Generative AI is widely used to detect unusual events in cybersecurity, finance, and fraud detection.

---

9. Question 9

In “Bias in Threat Detection,” what is the primary ethical concern raised?

❌ Potential collateral damage caused by AI deception tactics
❌ Ethical dilemma of balancing security with privacy
❌ Lack of transparency in incident response
✅ Discrimination and bias in threat detection

Explanation:
AI systems may unfairly target specific groups or behaviors due to biased training data.

---

10. Question 10

What is the primary strategy suggested for responsible AI deployment in cybersecurity?

❌ Emphasizing advanced threat simulation
❌ Application of adaptive security policies
❌ Increasing vulnerability to adversarial attacks
✅ Continuous monitoring and iterative improvement

Explanation:
Responsible AI requires constant evaluation, feedback loops, and refinement to stay safe and effective.

---

🧾 Summary Table

---