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CompTIA SecAI+ Certification Exam Sample Questions (Q111-Q116):

NEW QUESTION # 111
A company introduces a large language model (LLM) in an application in order to monitor for a potential denial-of-service attack.
Which of the following should the company use to measure the utilization of the LLM?

Answer: B

Explanation:
Basic Concept: LLMs process and generate text as discrete units called tokens, which represent words or word pieces. The number of tokens consumed is the primary metric for LLM resource utilization and billing.
Understanding token consumption is essential for both performance monitoring and cost management.
CompTIA SecAI+ Study Guide covers token-based measurement under AI system management.
Why A is Correct: Tokens are the fundamental unit of measurement for LLM utilization. Both input tokens sent in prompts and output tokens generated in responses are counted. Token consumption directly correlates with computational resource usage, response time, and API cost. Monitoring token counts enables the company to establish usage baselines and detect the abnormal token consumption spikes associated with DoS attacks or denial-of-wallet attacks against the LLM.
Why B is Wrong: A transformer is the neural network architecture that underlies modern LLMs. It is an architectural description, not a measurable utilization metric. You cannot quantify LLM resource consumption in " transformers. " Why C is Wrong: Chain of thought is a prompting technique that encourages models to reason step-by-step before providing answers. It is an inference strategy for improving response quality, not a unit of measurement for monitoring LLM utilization or resource consumption.
Why D is Wrong: A prompt is the input text submitted to the LLM. While monitoring prompt volumes provides request count information, tokens provide a more granular and meaningful utilization metric because they capture both the complexity of inputs and the length of generated outputs.


NEW QUESTION # 112
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: A

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 # 113
A SOC analyst notices a sudden spike in outbound traffic from a server. The traffic is being sent continuously to an unknown external IP address. Which of the following BEST describes this behavior?

Answer: C

Explanation:
A sudden and sustained outbound transfer to an unknown IP is a common sign of data exfiltration.


NEW QUESTION # 114
Which of the following would most likely be used to prove that an image is AI generated?

Answer: A

Explanation:
Watermarking embeds hidden, verifiable markers into AI-generated images. These markers can later be detected to prove the image originated from an AI system, making it the most reliable method for verification.


NEW QUESTION # 115
A security alert triggers an agentic system. An analyst notices the following payload in the logs. The alert includes multiple shell commands that are not typically run as part of any hardening:

Which of the following is the most effective control to implement?

Answer: B

Explanation:
Basic Concept: Agentic AI systems that execute shell commands based on model-generated output are vulnerable to prompt injection attacks where malicious actors craft inputs that cause the agent to run unauthorized commands. Input validation using allowlists is a critical defense mechanism. CompTIA SecAI+ Study Guide covers agentic AI security controls.
Why A is Correct: Adding logic that validates shell commands against an approved allowlist before execution is the most direct and effective defense. This ensures only pre-approved, safe commands can be executed regardless of what the agentic system ' s model generates, preventing malicious command injection from reaching the operating system. This principle of allowlist-based input validation is a foundational secure agentic AI control.
Why B is Wrong: Deprecating and retraining the model is a lengthy process that addresses root cause training issues but does not provide immediate protection against ongoing injection attacks in the current deployed system.
Why C is Wrong: Modifying the application to ignore a specific tag merely removes one attack surface while leaving the system vulnerable to other injection vectors. It is not a comprehensive defense.
Why D is Wrong: Using only approved libraries controls which code libraries the agentic system can call, but does not validate or restrict the shell commands generated by the model at runtime based on arbitrary user input.


NEW QUESTION # 116
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