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| Section | Weight | Objectives |
|---|---|---|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Securing AI Systems | 40% | - Adversarial Defense
|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
>> CY0-001 Valid Exam Materials <<
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NEW QUESTION # 11
An organization recently developed an AI-powered product and discovers that it is vulnerable to attacks in which malicious actors can alter the input, causing the system to recommend inappropriate information. Which of the following techniques is the most effective way to secure the system against manipulation attacks?
Answer: C
Explanation:
Guardrails restrict and control how an AI model processes and responds to inputs, making them the most effective defense against manipulation attacks such as prompt injection or malicious input alteration.
NEW QUESTION # 12
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 # 13
A group of security engineers is developing a security incident and event management (SIEM) system that will:
- Be able to ingest data from multiple structured and unstructured
sources.
- Have a chatbot integrated with a large language model (LLM) that the
security analyst can interact with.
- Provide insights from the SIEM alert data.
Which of the following techniques should the security engineers consider before collecting the data from the respective sources?
Answer: A
Explanation:
Data cleansing ensures that structured and unstructured data ingested into the SIEM is accurate, consistent, and free from errors or irrelevant information. This step is critical before integrating with an LLM chatbot, as clean data improves the reliability and quality of insights generated.
NEW QUESTION # 14
An automobile manufacturer implements a chatbot to assist with configuration options for customer automobiles. Given a customer ' s prompt, the chatbot gives offensive responses.
Which of the following describes this behavior?
Answer: D
Explanation:
Basic Concept: AI chatbots are designed with safety guidelines and content policies that prevent them from generating harmful, offensive, or inappropriate content. When users find ways to bypass these restrictions through crafted prompts, they have " jailbroken " the model. CompTIA SecAI+ Study Guide covers jailbreaking as a key AI vulnerability category.
Why C is Correct: Jailbreaking is the process of using cleverly crafted prompts to bypass an AI model ' s built- in safety restrictions, content policies, and behavioral guardrails, causing it to produce outputs it was designed to refuse. The scenario describes a chatbot that was designed for automobile configuration assistance but is producing offensive responses following customer prompts, indicating that customers have successfully prompted the model to bypass its safety constraints and generate prohibited content.
Why A is Wrong: Model skewing refers to attacks or biases that cause a model to favor certain outputs or perspectives systematically over time, often through data manipulation. It describes a gradual distortion of model behavior, not a direct user-prompted bypass of safety restrictions in a single interaction.
Why B is Wrong: Model theft involves extracting or replicating a proprietary model ' s functionality or architecture through repeated queries. It is an intellectual property attack aimed at stealing the model ' s knowledge, not an attack that causes the model to produce offensive content.
Why D is Wrong: Insecure output handling occurs when an application fails to properly validate or sanitize AI-generated outputs before using them in ways that could cause harm such as passing AI output directly to a system command or database query. It describes a developer implementation vulnerability, not the act of a user prompting a model to bypass its safety constraints.
NEW QUESTION # 15
A financial organization implements a new AI-based fraud detection system to flag suspicious transactions. A security analyst discovers that it occasionally blocks legitimate transactions.
Which of the following is the best recommendation?
Answer: C
Explanation:
False positives occur when the AI model lacks sufficient or representative training data.
Retraining the model with more diverse and recent transaction patterns improves accuracy, reducing the chance of legitimate transactions being incorrectly flagged.
NEW QUESTION # 16
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