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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance, Risk, and Compliance | 14% | - Given a scenario, follow organizational security policies and procedures - Explain privacy and sensitive data concepts in relation to security - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Explain risk management processes and concepts |
| Topic 2: Architecture and Design | 21% | - Summarize authentication and authorization design concepts - Explain secure application development, deployment, and automation concepts - Given a scenario, implement cybersecurity resilience - Explain the importance of security concepts in an enterprise environment - Explain the security implications of embedded and specialized systems - Summarize basics of cryptographic concepts - Explain the importance of physical security controls - Summarize virtualization and cloud security concepts |
| Topic 3: Attacks, Threats, and Vulnerabilities | 24% | - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with network attacks - Given a scenario, analyze potential indicators to determine the type of attack - Explain threat actor types and attributes - Explain vulnerability scanning concepts - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with application attacks |
| Topic 4: Operations and Incident Response | 16% | - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use data sources to support an investigation - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, apply mitigation techniques or controls to secure an environment - Explain key aspects of digital forensics |
| Topic 5: Implementation | 25% | - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure systems design - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure mobile device policies - Given a scenario, apply cybersecurity solutions to the cloud |
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NEW QUESTION # 55
A company discovers that attackers exploited an unpatched vulnerability in a web server. Which control BEST prevents this?
Answer: D
Explanation:
Timely patching directly prevents exploitation of known vulnerabilities.
NEW QUESTION # 56
Which of the following is used to train an AI model with unstructured data?
Answer: B
Explanation:
Basic Concept: Unstructured data such as free-form text, images, and audio does not have predefined labels or rigid schema. Training an AI model effectively on unstructured data requires techniques that can leverage patterns within the data itself or adapt a pre-trained model to new data types. CompTIA SecAI+ covers AI training methodologies under basic AI concepts.
Why B is Correct: Fine-tuning takes a pre-trained foundation model that has already learned rich representations from massive unstructured datasets and further trains it on a specific, potentially smaller unstructured dataset. This adapts the model to a new domain, task, or data type without requiring labeled data for every training example. Fine-tuning is the most practical and effective approach for working with unstructured data in modern AI development.
Why A is Wrong: Statistical learning typically refers to classical machine learning approaches that often assume structured, numerical data with defined features. These methods generally struggle with high- dimensional unstructured data without significant preprocessing.
Why C is Wrong: Supervised learning requires labeled training data where each example has an associated correct output label. Applying supervised learning to unstructured data requires extensive manual labeling, which is the opposite of working with raw unstructured data.
Why D is Wrong: Reinforcement learning trains models through reward signals based on actions taken in an environment. It is designed for sequential decision-making tasks and is not the standard approach for learning representations from unstructured data at scale.
NEW QUESTION # 57
A large number of employees receive a video message in which the company ' s CEO states that the company will be filing for bankruptcy. After an investigation, it was discovered that the CEO did not send this message.
Which of the following is this scenario an example of?
Answer: D
Explanation:
Basic Concept: Advances in AI generative technology have enabled the creation of highly realistic synthetic video and audio content that convincingly impersonates real individuals. These AI-generated impersonations are called deepfakes and represent a significant threat for corporate fraud, misinformation, and social engineering. CompTIA SecAI+ covers deepfakes under basic AI concepts and cybersecurity threats.
Why C is Correct: The scenario describes a video in which the CEO ' s likeness was convincingly replicated to deliver a false message about bankruptcy. This is a textbook deepfake attack - AI-generated synthetic media using the target ' s face and potentially voice to make it appear they said something they never said.
Deepfake technology uses GANs and CNNs to create this type of convincing impersonation, and the attack specifically exploited AI generation for deception.
Why A is Wrong: An on-path attack intercepts network communications between two parties to eavesdrop or modify traffic. It requires positioning in the network path, not creating synthetic media of a person ' s likeness.
Why B is Wrong: Phishing is a social engineering attack that uses deceptive messages typically via email to trick recipients into taking harmful actions such as clicking malicious links or providing credentials. While this attack has social engineering elements, the use of AI-generated video to impersonate the CEO specifically categorizes it as a deepfake attack.
Why D is Wrong: Social engineering broadly describes psychological manipulation tactics to deceive individuals. While deepfakes can be used as part of social engineering campaigns, the specific technical technique used - AI-generated synthetic video - makes deepfake the most precise and accurate categorization.
NEW QUESTION # 58
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: A
Explanation:
A secure MDLC is tailored to AI and ML systems, ensuring security is integrated throughout the model's design, training, validation, deployment, and monitoring phases. This directly addresses best practices for securing systems with AI models.
NEW QUESTION # 59
User experience is declining since the launch of a large language model (LLM) in internal networks. Which of the following should be the highest priority for the prompt engineers?
Answer: C
Explanation:
When user experience is declining after an LLM launch, the top priority for prompt engineers is quality control. Ensuring prompts produce accurate, relevant, and safe outputs directly improves usability and restores user trust.
NEW QUESTION # 60
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