CY0-001 Exam Topics & CY0-001 Examinations Actual Questions

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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Securing AI Systems40%- Security controls for AI systems
  • 1. Data protection: integrity, confidentiality, privacy
  • 2. Deployment environment security
  • 3. Model security: access, integrity, anti-tampering
- Defending against AI-specific attacks
  • 1. Threat modeling for AI lifecycles
  • 2. Adversarial example defense
  • 3. Prompt injection, data poisoning, model inversion
- Secure AI development and operations
  • 1. DevSecOps integration for AI
  • 2. Secure MLOps and AI pipeline design
Topic 2: AI Governance, Risk and Compliance19%- Risk management for AI
  • 1. AI risk identification and assessment
  • 2. Risk mitigation and control strategies
- Compliance and legal requirements
  • 1. Data protection and privacy laws
  • 2. Transparency, accountability and auditability
- Governance frameworks and policies
  • 1. Global standards: NIST AI RMF, EU AI Act
  • 2. Organizational AI governance structures
  • 3. Responsible AI principles and ethics
Topic 3: Basic AI Concepts Related to Cybersecurity17%- AI applications in security
  • 1. Security automation and decision support
  • 2. Threat detection and anomaly analysis
- AI-driven threats and risks
  • 1. Automated phishing, polymorphic malware
  • 2. Malicious use of generative AI
  • 3. Adversarial machine learning attacks
- Core AI principles and terminology
  • 1. Generative AI concepts and capabilities
  • 2. Machine learning, deep learning, NLP, automation
Topic 4: AI-assisted Security24%- AI for threat detection and response
  • 1. Automated incident triage and correlation
  • 2. Anomaly detection and behavioral analysis
  • 3. Accelerated threat hunting
- AI in security strategy and operations
  • 1. Threat modeling and risk assessment
  • 2. Compliance monitoring and auditing
- Security automation and orchestration
  • 1. Workflow automation and response playbooks
  • 2. Vulnerability management and assessment

>> CY0-001 Exam Topics <<

CY0-001 Examinations Actual Questions, Training CY0-001 Pdf

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CompTIA SecAI+ Certification Exam Sample Questions (Q147-Q152):

NEW QUESTION # 147
When should containment occur during the incident response lifecycle?

Answer: C

Explanation:
Containment follows identification and precedes eradication.


NEW QUESTION # 148
During the selection of a machine learning (ML)-based threat classification model, a cybersecurity administrator verifies that label distribution is highly unbalanced.
Which of the following processing techniques should the engineer use to balance the model?

Answer: A

Explanation:
Basic Concept: Class imbalance in training data - where some categories have significantly more examples than others - causes ML models to be biased toward the majority class, producing poor detection of minority class threats. Addressing this imbalance before training is critical for threat classification accuracy. CompTIA SecAI+ covers data preparation techniques under basic AI concepts.
Why B is Correct: Data augmentation addresses class imbalance by artificially increasing the number of training samples in under-represented classes. Techniques include oversampling minority classes by creating synthetic examples using methods like SMOTE (Synthetic Minority Over-sampling Technique), or undersampling majority classes. This balances label distribution and enables the model to learn decision boundaries that accurately classify all threat categories, not just the dominant ones.
Why A is Wrong: Data lineage documents the origin, movement, and transformation of data throughout its lifecycle. It provides traceability and auditability but does not address class imbalance in training data distribution.
Why C is Wrong: Data provenance records the history and context of data origins. Like lineage, it is a governance and tracking concept that does not alter data distribution for model training balance.
Why D is Wrong: Data verification confirms that data is correct and consistent with expected formats and values. It checks data quality and integrity but does not address the statistical distribution imbalance between threat classes in training datasets.


NEW QUESTION # 149
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 # 150
A machine learning (ML) engineer is working with a security engineer to identify the best practices for securing a system with various AI models.
Which of the following actions should the engineers suggest?

Answer: C

Explanation:
Basic Concept: Securing AI systems requires a structured, end-to-end approach that addresses security at every phase of the AI model ' s lifecycle from data collection through training, testing, deployment, and ongoing monitoring. CompTIA SecAI+ Study Guide identifies the Model Development Life Cycle as the foundational framework for AI system security.
Why B is Correct: A secure Model Development Life Cycle (MDLC) integrates security practices at every stage of AI model development specifically tailored to ML workflows. It encompasses secure data handling, training data validation, model testing for adversarial robustness, secure deployment practices, and ongoing monitoring. Unlike generic software development lifecycles, the MDLC addresses ML-specific risks such as data poisoning, model drift, and adversarial attacks.
Why A is Wrong: Guardrail testing and security validation are important components of the MDLC but represent only the testing phase. They do not encompass the full lifecycle of security practices needed from data acquisition through production monitoring.
Why C is Wrong: Implementing comprehensive security architecture is a broad statement that describes an outcome rather than a specific actionable practice. It does not provide the structured, ML-specific guidance of an MDLC.
Why D is Wrong: A secure SDLC is designed for traditional software development and covers code security, testing, and deployment. While relevant to AI application development, it does not specifically address ML model-specific risks such as training data security, model integrity, and inference-time attacks.


NEW QUESTION # 151
What control reduces the impact radius when a single host is compromised?

Answer: A

Explanation:
Segmentation isolates systems and limits lateral movement.


NEW QUESTION # 152
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