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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-assisted Security | 24% | - AI for threat detection and response
|
| Topic 2: Basic AI Concepts Related to Cybersecurity | 17% | - AI-driven threats and risks
|
| Topic 3: Securing AI Systems | 40% | - Security controls for AI systems
|
| Topic 4: AI Governance, Risk and Compliance | 19% | - Governance frameworks and policies
|
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NEW QUESTION # 100
Which of the following improves the observability and auditing of an AI system?
Answer: B
Explanation:
Basic Concept: Observability in AI systems refers to the ability to monitor, log, trace, and audit the behavior of AI models in production. MLOps is the operational discipline that establishes the processes, tooling, and practices for managing AI systems throughout their lifecycle. CompTIA SecAI+ Study Guide covers MLOps as a key mechanism for AI system transparency and auditability.
Why C is Correct: MLOps implements comprehensive monitoring, logging, versioning, and audit pipelines for AI systems. It provides observability through model performance tracking, data drift detection, prediction logging, lineage tracking, and audit trails. MLOps platforms enable organizations to understand what their AI models are doing, why they are making certain decisions, and how their behavior changes over time, directly improving observability and auditing.
Why A is Wrong: Redeploying a model is an operational action taken to restore a previous version or apply updates. It does not improve monitoring infrastructure, logging capabilities, or auditing frameworks for ongoing observability.
Why B is Wrong: Manual detection relies on human observation to identify issues. It is labor-intensive, inconsistent, and not scalable for AI systems processing high volumes of data. It does not provide systematic observability or comprehensive audit trails.
Why D is Wrong: Anomaly detection identifies unusual patterns in data or behavior. While useful as a monitoring component within an observability strategy, it is a single technique and does not encompass the full observability and auditing capabilities provided by a comprehensive MLOps implementation.
NEW QUESTION # 101
Which tool prevents unauthorized system file modifications?
Answer: A
Explanation:
FIM detects changes to critical system files.
NEW QUESTION # 102
A financial services company is being sued for using AI in the loan underwriting process. The lawsuit reveals that the AI model used biased sources to disproportionately deny loans to residents in certain postal codes.
Which of the following employees is the most qualified to remove these biased sources from the model?
Answer: B
Explanation:
The data scientist is the most appropriate role because correcting biased source data requires examining the datasets used to develop, train, validate, or refine the model and then modifying the data preparation or model- development process. Data scientists work directly with training data, feature selection, statistical distributions, model development, and evaluation, which places them in the strongest position to identify biased inputs and retrain or adjust the model using more representative information. CompTIA SecAI+ includes the data scientist as a formal AI governance role and separately identifies fairness, introduction of bias, accuracy, and model performance as key responsible-AI risks. A platform engineer primarily manages the infrastructure supporting AI workloads. A cybersecurity analyst focuses on security threats, monitoring, and defensive controls rather than correcting discriminatory training data. An AI architect designs the broader architecture and integration of AI solutions but normally does not perform the detailed dataset preparation and model-training work required here. Because the problem originates in biased data sources, the data scientist is the role closest to the underlying cause.
NEW QUESTION # 103
A financial organization implements a new AI-based fraud detection system to flag suspicious transactions. A security analyst discovers that it occasionally blocks legitimate transactions.
Which of the following is the best recommendation?
Answer: A
Explanation:
Basic Concept: When an AI fraud detection model produces false positives (blocking legitimate transactions), this indicates the model ' s decision boundary is insufficiently calibrated. The model needs improved training data to better distinguish fraudulent from legitimate transactions. CompTIA SecAI+ covers model performance improvement under AI-assisted security.
Why A is Correct: Retraining the model with more data and recent transaction patterns directly addresses the root cause of false positives. Additional representative legitimate transaction data helps the model learn more accurate decision boundaries, reducing false positives while maintaining detection sensitivity for actual fraud.
This improves model accuracy without abandoning AI-based detection.
Why B is Wrong: Token usage and rate limits are cost and resource management controls for LLM APIs.
They have no relevance to improving the accuracy of a fraud detection ML model that incorrectly classifies legitimate transactions.
Why C is Wrong: Encrypting data and applying access controls are data security measures that protect confidentiality and integrity. They do not address model classification accuracy or the false positive problem in fraud detection.
Why D is Wrong: Rolling back to a traditional system abandons the capabilities of AI-based fraud detection.
The appropriate response to model performance issues is to improve the model through retraining, not to regress to less capable detection approaches.
NEW QUESTION # 104
A security consultant needs to detect attacks across a large language model (LLM) firewall.
Which of the following techniques should the consultant use?
Answer: C
Explanation:
Basic Concept: LLM firewalls inspect prompts and responses to identify malicious content, policy violations, and attack attempts. To detect known attack patterns, these systems apply inspection techniques that compare content against established threat indicators. CompTIA SecAI+ Study Guide covers LLM security monitoring and detection techniques.
Why A is Correct: Signature matching compares incoming prompts and outgoing responses against a library of known attack signatures, including common prompt injection patterns, jailbreaking attempts, data exfiltration queries, and known malicious payload strings. When content matches a known attack signature, the LLM firewall can block or flag it. Signature matching is an efficient, proven detection technique for identifying known attack patterns traversing an LLM firewall.
Why B is Wrong: Distributed denial-of-service is itself a type of attack, not a detection technique. DDoS floods systems with traffic to cause service unavailability and has no role in detecting attacks through an LLM firewall.
Why C is Wrong: Translation analysis involves converting content between languages or formats. While it might be used to detect obfuscated attacks in different encodings, it is not a standard detection technique for identifying attacks crossing an LLM firewall.
Why D is Wrong: Vulnerability enumeration systematically identifies and catalogs vulnerabilities in systems or applications during security assessments. It is an assessment activity used to discover weaknesses, not a real-time detection technique for attacks traversing an LLM firewall.
NEW QUESTION # 105
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