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CompTIA CY0-001 Exam Syllabus Topics:

Section	Weight	Objectives
Securing AI Systems	40%	- AI System Protection 1. Secure AI pipelines and deployment environments 2. Data protection and model security - Adversarial Defense 1. Data poisoning mitigation 2. Model extraction and inference attack defense
AI Governance, Risk, and Compliance	19%	- AI Governance Frameworks 1. NIST AI RMF concepts 2. ISO/IEC AI governance alignment - Risk and Compliance 1. Ethical AI and responsible usage policies 2. Regulatory compliance for AI systems
Basic AI Concepts Related to Cybersecurity	17%	- AI Threat Landscape 1. Adversarial AI and model manipulation 2. AI-driven cyber threats (phishing, malware automation) - Generative AI Concepts 1. LLMs and generative AI basics 2. Prompting and AI interaction fundamentals - AI and Machine Learning Fundamentals 1. Neural networks and deep learning basics 2. Supervised, unsupervised, reinforcement learning
AI-Assisted Security	24%	- Operational Use of AI 1. AI-enabled threat intelligence analysis 2. Incident response acceleration - Security Operations Enhancement 1. SOC automation and alert correlation 2. AI-driven threat detection and anomaly detection

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CY0-001 Study Test | Exam CY0-001 Guide Materials

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CompTIA SecAI+ Certification Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
A loan company compares a machine learning model that was found on the dark web with the company ' s proprietary model. Using proprietary information, the company generates the following:
Input
Dark web model
Company ' s proprietary model
Jane A.
Approve
Approve
John B.
Deny
Deny
Ann J.
Deny
Deny
Joe H.
Approve
Approve
Which of the following should the AI red team recommend to mitigate risks for future deployments?

A. Input validation
B. Prompt firewall
C. Model encryption
D. Parameter anonymization

Answer: C

Explanation:
The matching behavior between the unauthorized dark-web model and the company ' s proprietary model strongly indicates a model-theft or unauthorized model-copying risk. Model encryption is the most appropriate control among the available choices because it directly protects proprietary model artifacts, including model weights and other sensitive intellectual property, from unauthorized access or extraction when stored or transferred. CompTIA SecAI+ specifically identifies model theft as an AI-system attack and lists encryption among the compensating controls used to protect AI systems. Parameter anonymization is not the primary protection for proprietary model weights and does not prevent unauthorized possession of the model itself. A prompt firewall analyzes prompts for malicious or prohibited input but does not protect model files from theft. Input validation similarly protects an application from malformed or malicious inputs rather than safeguarding the model ' s intellectual property. Therefore, strengthening protection of the model artifact through encryption is the most directly applicable mitigation for future deployments.

NEW QUESTION # 98
A cybersecurity analyst must use pattern recognition on a data set containing unstructured data.
Which of the following models is the best for this task?

A. Logistic regression
B. Decision tree
C. Convolutional neural network
D. Long short-term memory

Answer: C

Explanation:
Basic Concept: Different ML model architectures are optimized for different data types and tasks.
Unstructured data such as images, raw network packet captures, and visual content requires models capable of automatically extracting hierarchical spatial features. CompTIA SecAI+ covers ML model selection for security tasks under basic AI concepts.
Why B is Correct: Convolutional Neural Networks (CNNs) are specifically designed for pattern recognition in unstructured data, particularly image and grid-structured data. CNNs use convolutional layers to automatically extract local and hierarchical features without requiring manual feature engineering. They excel at recognizing patterns in raw, unstructured inputs, making them the optimal choice for pattern recognition on unstructured datasets in cybersecurity contexts such as image-based malware analysis or visual traffic pattern recognition.
Why A is Wrong: Long Short-Term Memory (LSTM) networks are recurrent neural networks optimized for sequential and time-series data such as network traffic flows over time or log sequences. While they handle unstructured sequential data, they are not specifically designed for spatial pattern recognition in general unstructured data.
Why C is Wrong: Decision trees work on structured, tabular data with defined features. They require feature extraction and engineering before processing unstructured data and are not designed for raw pattern recognition in unstructured inputs.
Why D is Wrong: Logistic regression is a linear classification algorithm that requires structured, numerical input features. It cannot directly process unstructured data and requires extensive preprocessing and feature extraction, making it unsuitable for pattern recognition on raw unstructured datasets.

NEW QUESTION # 99
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?

A. Modifying the application to ignore the SECURITY_UPDATE tag
B. Deprecating model usage and retaining the model with safer parameters
C. Using only approved libraries when interacting with agentic systems
D. Adding logic that includes approved strings before running the shell commands

Answer: D

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 # 100
Which of the following helps end users within an organization the most in safeguarding against the risk of AI- related non-compliance?

A. Policies and procedures
B. Enabling multifactor authentication (MFA) for access
C. Implementing data loss prevention
D. AI center of excellence

Answer: A

Explanation:
Basic Concept: End users are the employees who interact with AI systems daily and may inadvertently create compliance risks through their AI usage behaviors. Equipping users with clear guidance on acceptable and compliant AI use is the most effective way to reduce compliance violations at the user level. CompTIA SecAI+ Study Guide emphasizes policies and procedures as the foundational compliance tool for end users.
Why B is Correct: Policies and procedures directly inform end users of what AI-related behaviors are compliant, what is prohibited, and how to use AI tools safely and legally. Comprehensive AI usage policies covering acceptable use, data handling requirements, prohibited data inputs, and reporting obligations give users the knowledge they need to avoid compliance violations. Without clear policies, users cannot reliably identify compliant from non-compliant behavior.
Why A is Wrong: An AI center of excellence governs AI adoption at the organizational level, developing standards and approving use cases. While it benefits the organization overall, its governance activities are directed at organizational processes and technical standards rather than providing direct day-to-day compliance guidance to individual end users.
Why C is Wrong: Data loss prevention (DLP) technology automatically prevents the transmission of sensitive data through monitoring and blocking capabilities. While effective at preventing certain compliance violations technically, it cannot guide users on why certain behaviors are non-compliant or how to make compliant choices in situations DLP doesn ' t cover.
Why D is Wrong: MFA secures user authentication and prevents unauthorized account access. It is an identity security control that protects accounts, not a mechanism that helps users understand or comply with AI governance requirements.

NEW QUESTION # 101
Which of the following technologies is used in deepfake?

A. Prompt engineering
B. Multi-shot prompting
C. Generative adversarial network (GAN)
D. Transfer learning

Answer: C

Explanation:
Deepfakes are primarily created using GANs, where two neural networks (a generator and a discriminator) compete to produce highly realistic synthetic media, such as manipulated videos or images.

NEW QUESTION # 102
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