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| Section | Weight | Objectives |
|---|---|---|
| Securing AI Systems | 40% | - AI System Protection
|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
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NEW QUESTION # 33
Which of the following helps in managing potential security issues related to model training?
Answer: C
Explanation:
The NIST AI RMF provides structured guidance for identifying, assessing, and managing risks specific to AI systems, including those arising during model training. It addresses issues like bias, security, and data integrity, making it the best framework for managing training-related security concerns.
NEW QUESTION # 34
Which of the following ensures the integrity of data usage in an AI system?
Answer: A
Explanation:
Basic Concept: Data integrity in AI systems requires not only that data is accurate at a point in time, but that its entire history of transformation and usage can be traced and verified. Tracking how data has been used and transformed throughout the AI system lifecycle provides ongoing integrity assurance. CompTIA SecAI+ Study Guide covers data governance controls including lineage for AI integrity.
Why D is Correct: Data lineage tracks and documents the complete journey of data from its origin through every transformation, processing step, and use within an AI system. By recording what happened to the data, when, by whom, and through which processes, data lineage provides the audit trail needed to ensure data integrity throughout the AI system ' s data usage lifecycle. It enables verification that data has been used as intended and has not been improperly modified at any stage.
Why A is Wrong: Data masking replaces sensitive data values with anonymized equivalents to protect privacy. It is a confidentiality control that modifies data values rather than a mechanism for ensuring or tracking data integrity across the system.
Why B is Wrong: Data cleansing removes or corrects errors, inconsistencies, and noise in datasets to improve data quality. It is a data preparation activity that improves data accuracy at a point in time but does not track data usage or provide ongoing integrity assurance throughout the AI system lifecycle.
Why C is Wrong: Data verification confirms that data meets expected quality standards and validates its accuracy at a specific check point. While important for quality assurance, it provides a point-in-time check rather than continuous tracking of data usage and transformations as data lineage does.
NEW QUESTION # 35
An organization recently created a custom model that integrates with a language model (LLM).
The developer notices that the application programming interface (API) costs have increased.
Which of the following is the best control to reduce cost?
Answer: B
Explanation:
API costs for large language model integrations are directly tied to token usage (input + output tokens). By adjusting token limits, the organization can reduce unnecessary processing of overly long prompts or responses, thereby lowering overall API costs without changing model size or infrastructure resources.
NEW QUESTION # 36
An organization wants to reduce vulnerabilities after deployment. The organization decides to incorporate an AI-assisted early detection and vulnerability identification process in its development workflow.
Which of the following AI-assisted functions is the best option?
Answer: C
Explanation:
Basic Concept: Reducing post-deployment vulnerabilities requires catching security issues as early as possible in the development workflow. AI-assisted tools that analyze code during development provide the earliest possible intervention point. CompTIA SecAI+ Study Guide covers AI integration in secure development under AI-assisted security.
Why A is Correct: AI-assisted code linting analyzes source code in real time during development to identify security vulnerabilities, insecure coding patterns, policy violations, and quality issues before code is compiled or committed. By catching vulnerabilities at the coding stage - the earliest possible point in the development workflow - AI code linting prevents vulnerable code from progressing to testing, staging, or production, directly reducing post-deployment vulnerabilities at their source.
Why B is Wrong: Incident management handles security events and incidents after they have occurred in production. It is a reactive capability focused on response and recovery rather than early-stage vulnerability identification in the development workflow.
Why C is Wrong: Automated deployment/rollback automates the process of pushing code to production and reverting to previous versions when issues are detected post-deployment. It is a deployment safety mechanism rather than an early detection tool during the development phase.
Why D is Wrong: System auditing reviews and records system activities and configurations for compliance verification. It is primarily a detective and compliance control for systems that are already deployed, not an early development-phase vulnerability identification tool.
NEW QUESTION # 37
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 # 38
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