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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Topic 2: Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
| Topic 3: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Topic 4: Securing AI Systems | 40% | - Adversarial Defense
|
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NEW QUESTION # 85
Instructions: Use the drop-down menus to define two appropriate security controls for each component of the AI system. Each control may be used only once.
An engineer is deploying a new AI system and wants to integrate it into the core system through an API.
Answer:
Explanation:
Explanation:
Basic Concept: This is a Performance-Based Question (PBQ) - a HOTSPOT/simulation item requiring interactive selection in the actual exam. It tests the candidate ' s ability to map appropriate security controls to AI system components such as API gateway, model endpoint, data layer, and authentication layer.
Key Concept - Appropriate Controls by Component: For an API gateway connecting an AI system, typical controls include API key authentication, rate limiting, TLS encryption, and input validation. For the model endpoint, controls include IAM role-based access, audit logging, and guardrails. For data access components, encryption at rest and data masking are appropriate. For the authentication layer, MFA and expiring session tokens are relevant.
Why This Matters: The CompTIA SecAI+ Study Guide emphasizes defense-in-depth for AI system integration, ensuring each architectural layer has dedicated, appropriate security controls. The principle of least privilege should guide access control assignments at each component, while availability controls such as rate limiting protect against abuse.
Reference: CompTIA SecAI+ Exam Objectives Domain 2 (Securing AI Systems) covers AI system component security controls. Candidates should study the mapping of controls to infrastructure components including API gateways, model serving endpoints, data stores, and identity management layers. In the live exam, select the most specific and directly relevant control for each component based on the component ' s function and risk profile.
NEW QUESTION # 86
A data scientist is working with unlabeled data and wants to build a clustering model.
Which of the following techniques should a data scientist use?
Answer: D
Explanation:
Basic Concept: Different ML learning paradigms handle different data situations. The availability of labeled versus unlabeled data determines which learning approach is appropriate. Building clustering models specifically requires learning from data without predefined category labels. CompTIA SecAI+ Study Guide covers ML learning paradigms under basic AI concepts.
Why C is Correct: Unsupervised learning works with unlabeled data by discovering inherent patterns, structures, and groupings within the data without predefined categories. Clustering is the canonical unsupervised learning task, where algorithms like k-means, hierarchical clustering, or DBSCAN group similar data points together based on feature similarity. Since the data scientist has unlabeled data and wants to find natural groupings, unsupervised learning is the appropriate and correct technique.
Why A is Wrong: Supervised learning requires labeled training data where each example has a corresponding correct output label. The data scientist explicitly has unlabeled data, making supervised learning inapplicable without first completing the labor-intensive task of manually labeling all examples.
Why B is Wrong: Reinforcement learning trains agents to take actions in an environment to maximize cumulative rewards through trial and error. It is designed for sequential decision-making problems, not for finding groupings in static, unlabeled datasets.
Why D is Wrong: Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data. It requires at least some labels to guide learning. The scenario specifies working with unlabeled data only, making unsupervised learning the pure fit.
NEW QUESTION # 87
An automobile manufacturer implements a chatbot to assist with configuration options for customer automobiles. Given a customer ' s prompt, the chatbot gives offensive responses.
Which of the following describes this behavior?
Answer: D
NEW QUESTION # 88
A detection engineering team wants to use AI to automatically prevent vulnerable code from reaching production.
Which of the following is the most effective way to accomplish this task?
Answer: A
Explanation:
Basic Concept: Preventing vulnerable code from reaching production requires an automated, mandatory gate in the software delivery pipeline. The CI/CD pipeline is the enforcement point where all code must pass before deployment. CompTIA SecAI+ Study Guide covers AI integration in secure development pipelines under AI-assisted security.
Why C is Correct: Implementing an LLM in the CI/CD runner creates a mandatory automated security gate that every code change must pass. The LLM can analyze code for vulnerabilities, insecure patterns, and policy violations, then automatically fail the build if issues are found. This prevents vulnerable code from progressing toward production without human bypass capability, making it the most effective enforcement mechanism.
Why A is Wrong: IDE plug-ins provide warnings to developers during coding, but developers can choose to ignore them and proceed with compilation and commits. Warnings are advisory, not preventive, and cannot guarantee vulnerable code is blocked from the pipeline.
Why B is Wrong: SOAR platforms with ML models are excellent for incident response and security operations automation. However, they are not positioned in the code delivery pipeline and do not gate code from progressing to production.
Why D is Wrong: An agentic penetration testing tool validates vulnerabilities reactively after code is written or deployed. This approach does not intercept code before production deployment and is typically used for post-deployment assessment rather than prevention.
NEW QUESTION # 89
An analyst finds failed login attempts across multiple systems using different usernames but from the same IP. Which attack is MOST likely?
Answer: C
Explanation:
Password spraying uses common passwords across many usernames.
NEW QUESTION # 90
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