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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basic AI Concepts Related to Cybersecurity | 17% | - AI-driven threats and risks
|
| Topic 2: AI-assisted Security | 24% | - AI for threat detection and response
|
| Topic 3: Securing AI Systems | 40% | - Secure AI development and operations
|
| Topic 4: AI Governance, Risk and Compliance | 19% | - Governance frameworks and policies
|
仕事に取り掛かって顧客とやり取りする前に厳密に訓練された責任ある忍耐強いスタッフ。 CY0-001試験の準備の質を実践し、経験すると、それらの保守性と有用性を思い出すでしょう。 CY0-001練習教材が試験受験者の98％以上が夢の証明書を取得するのに役立った理由を説明しています。あなたもそれを手に入れることができると信じてください。
質問 # 151
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?
正解：C
解説：
Convolutional neural networks (CNNs) are highly effective at detecting patterns in unstructured data such as images, audio, or text embeddings. Their ability to automatically learn spatial or hierarchical features makes them the best choice for pattern recognition on unstructured datasets.
質問 # 152
A penetration tester is assessing the controls of a deployed AI system that is designed to search and return the contents of files. The tester runs the following:
Which of the following is the best control to prevent abuse of the system?
正解：D
解説：
The penetration test shows that the system accepts arbitrary commands like deleteBuckets or listPermissions, which could lead to privilege abuse. The most effective control is least privilege, ensuring the service account only has access to what is strictly necessary (e.g., reading files) and not sensitive operations like deleting buckets or altering permissions.
質問 # 153
During an update, an AI system flags some potential compatibility issues and provides recommendations. An administrator reviews the recommendations before addressing the issues.
Which of the following processes describes this scenario?
正解：C
解説：
Basic Concept: Human-in-the-loop is a design pattern where AI systems generate recommendations or decisions but require human review and approval before those recommendations are acted upon. This approach maintains human oversight and accountability in AI-assisted workflows. CompTIA SecAI+ Study Guide covers human-in-the-loop as a key responsible AI principle and operational pattern.
Why C is Correct: The scenario precisely describes the human-in-the-loop pattern: the AI system identifies potential issues and provides recommendations, but an administrator must review those recommendations before any action is taken. This deliberate inclusion of human judgment in the AI ' s decision or recommendation workflow ensures human oversight is maintained, which is the defining characteristic of the human-in-the-loop process.
Why A is Wrong: Data validation verifies that data meets expected quality standards and formats before being used in AI processing. It is a data quality control activity, not a workflow pattern describing human review of AI recommendations.
Why B is Wrong: Data preparation involves transforming raw data into a format suitable for AI model training or inference. It encompasses cleaning, normalizing, and formatting data, not the process of human review of AI-generated recommendations during system updates.
Why D is Wrong: Model evaluation assesses a model ' s performance against metrics such as accuracy, precision, and recall on test datasets. It is a technical assessment of model quality, not a workflow process where humans review AI-generated recommendations before acting on them.
質問 # 154
Part 1: Use drop-down menu to select the most appropriate protocol or cipher for each system component.
Part 2: Use the drop-down menu to select the most appropriate technique to apply to the modified data.
An engineer is analyzing findings from a penetration test that indicate insufficient data encryption. The engineer must implement data security.
正解：
解説：
Explanation:
See Explanation
Basic Concept: This is a Performance-Based Question (PBQ) - a simulation requiring interactive protocol and cipher selection in the actual exam. It tests the candidate ' s ability to select appropriate encryption protocols for different AI system components and apply proper data security techniques for sensitive AI data.
Key Concept - Encryption by Component: For data in transit between AI components, TLS 1.3 is the current standard protocol. For API communications, HTTPS with TLS 1.3 and certificate pinning is appropriate. For database connections used by AI systems, TLS with strong cipher suites such as AES-256-GCM should be applied. For data at rest in model stores and training data repositories, AES-256 encryption is standard. For authentication tokens and keys, RSA-2048 or ECC P-256 are appropriate asymmetric options.
Key Concept - Data Techniques: For sensitive AI training data that has been modified, tokenization replaces sensitive values with non-sensitive tokens. Masking obscures sensitive fields in outputs. Hashing provides one-way verification for data integrity.
Reference: CompTIA SecAI+ Study Guide Domain 2 covers encryption requirements for AI system data security. Candidates should know standard encryption protocols (TLS 1.3, AES-256), when to apply each cipher type, and which data security techniques such as masking, tokenization, and encryption at rest are appropriate for different AI system data categories and sensitivity levels.
質問 # 155
A security team is using an AI-based tool to try to bypass organizational boundaries. The team uses AI to look at the current state and suggest different attack vectors based on the outcome of the previous ones. Which of the following techniques is the team most likely using?
正解：B
解説：
The described behavior - iteratively assessing the environment and adapting attack paths based on prior outcomes - matches automated penetration testing, where AI-driven tools simulate attack chains and adjust tactics dynamically.
質問 # 156
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