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| Section | Weight | Objectives |
|---|---|---|
| Securing AI Systems | 40% | - Security controls for AI systems
|
| AI-assisted Security | 24% | - Security automation and orchestration
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI applications in security
|
| AI Governance, Risk and Compliance | 19% | - Governance frameworks and policies
|
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NEW QUESTION # 59
An organization is developing and implementing AI features into a customer service application.
Which of the following practices should the organization put the place before releasing the application for customer trials?
Answer: A
Explanation:
Before releasing AI features for customer trials, it is critical to protect sensitive information that may be used during testing. Data masking and sanitization ensure customer or corporate data is anonymized or obfuscated, reducing the risk of data exposure while still allowing realistic evaluation of the AI system.
NEW QUESTION # 60
An AI security administrator receives an inquiry about an unusually high monthly bill from the AI solution provider. The administrator thinks the majority of staff might be using the most powerful model available.
Which of the following AI measures should the administrator implement to lower costs?
Answer: C
Explanation:
Basic Concept: LLM API billing is primarily based on token consumption. High costs resulting from staff using powerful, verbose models can be controlled by limiting the maximum tokens processed per interaction and restricting which models staff can access. CompTIA SecAI+ Study Guide covers token management as the primary cost control mechanism for AI deployments.
Why D is Correct: Implementing token limits caps the maximum tokens consumed per API call for both input and output. This directly controls the per-interaction cost by preventing excessively long prompts or overly verbose model responses from generating large token bills. Combined with model tier restrictions, token limits ensure that interactions with powerful models remain within budget constraints regardless of how extensively staff use the service.
Why A is Wrong: Storage monitoring tracks the utilization and performance of data storage systems. Storage costs are separate from LLM API token-based billing and monitoring storage does not address the high API charges resulting from excessive model usage by staff.
Why B is Wrong: Modality types refer to the input formats an AI model accepts such as text, images, audio, or video. While different modalities have different pricing, managing modality types is not the direct cost control lever. Token consumption is the primary cost driver for text-based interactions.
Why C is Wrong: Prompt firewalls inspect and filter prompt content for security and policy compliance.
While they can block certain types of queries, they are designed for security purposes, not as financial controls to limit token consumption or enforce cost budgets across staff usage.
NEW QUESTION # 61
Which of the following job roles in an organizational governance structure develops a model from business use cases?
Answer: C
Explanation:
Basic Concept: In AI governance, each role holds distinct responsibilities. Understanding these roles is core to CompTIA SecAI+ Domain 4 (AI Governance, Risk, and Compliance).
Why D is Correct: The Data Scientist is responsible for translating business use cases into working AI/ML models. They analyze business requirements, identify the appropriate machine learning approach, and develop models that fulfill specific business objectives. According to the CompTIA SecAI+ Study Guide, data scientists bridge raw data and actionable AI solutions by building and validating models derived from business-driven needs.
Why A is Wrong: A Platform Architect designs and manages the infrastructure and technical platforms hosting AI systems. Their focus is architectural design of the environment, not model development from business use cases.
Why B is Wrong: An AI Risk Analyst identifies, evaluates, and mitigates risks associated with AI adoption.
Their role is governance and risk-oriented, not model creation.
Why C is Wrong: An MLOps Engineer operationalizes, deploys, monitors, and maintains AI models in production. They take models already built by data scientists and ensure reliable operation at scale, not develop them from business use cases.
NEW QUESTION # 62
A security operations center (SOC) has a very high volume of logs and alerts. The manager proposes the implementation of a machine learning (ML) system to help with triage.
Which of the following tasks is most suitable?
Answer: B
Explanation:
Basic Concept: ML models excel at classification tasks, learning to assign incoming data points to predefined categories based on patterns in training data. In a SOC context, alert classification is the highest-value triage function ML can perform. CompTIA SecAI+ Exam Objectives address AI-assisted security operations under Domain 3.
Why C is Correct: ML-based alert classification automatically analyzes characteristics of each alert and assigns it to a severity category such as critical, high, medium, or low, or to a threat type such as malware or intrusion attempt. This dramatically reduces analyst workload and speeds triage by prioritizing which alerts demand immediate human attention, directly solving the high-volume problem.
Why A is Wrong: Applying filters on specific alerts is a rule-based operation achievable without ML using simple log management tools. It requires no learning capability and does not adapt to new or evolving threats.
Why B is Wrong: Automatically patching systems is a remediation action requiring validated, controlled processes. Having an ML system autonomously patch production systems without human oversight poses unacceptable operational and security risk.
Why D is Wrong: Summarizing alert content is a useful generative AI function but does not provide prioritization value for triage. Classification tells analysts what to act on first; summarization only rephrases existing information.
NEW QUESTION # 63
After the latest software update, a developer receives reports that the system no longer requires reauthentication to display account balances because this issue was present in a previous release. Which of the following should the developer do to best mitigate the risk of recurrence?
Answer: B
Explanation:
Option B is correct because the defect is a regression: a security behavior that should require reauthentication was previously corrected but reappeared after a software update. Automated AI-assisted regression testing in the CI/CD pipeline can repeatedly verify that protected account-balance functions still demand reauthentication before a release is promoted. The test should reproduce the prior defect, fail the build when authentication is bypassed, and preserve the case as a permanent security test. Option A adds an approval step, but an approver may not detect the reintroduced behavior without an executable test. Option C improves release scheduling and traceability but does not prove that security controls still function. Option D uses SAST to inspect source code for known weakness patterns; it may identify some authentication flaws, but it does not reliably validate the end-to-end runtime behavior described here. Regression testing is the direct control for preventing recurrence. The NIST AI Risk Management Framework emphasizes testing AI systems before deployment and regularly during operation, supporting automated security validation within the delivery pipeline.
NEW QUESTION # 64
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