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| Section | Weight | Objectives |
|---|---|---|
| Operations and Incident Response | 16% | - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, use data sources to support an investigation - Summarize the importance of policies, processes, and procedures for incident response |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with network attacks - Given a scenario, analyze potential indicators associated with application attacks - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators to determine the type of attack - Compare and contrast types of social engineering attacks - Explain threat actor types and attributes - Explain penetration testing concepts |
| Architecture and Design | 21% | - Explain the importance of physical security controls - Explain secure application development, deployment, and automation concepts - Explain the importance of security concepts in an enterprise environment - Summarize basics of cryptographic concepts - Summarize virtualization and cloud security concepts - Explain the security implications of embedded and specialized systems - Summarize authentication and authorization design concepts - Given a scenario, implement cybersecurity resilience |
| Governance, Risk, and Compliance | 14% | - Compare and contrast various types of security controls - Given a scenario, follow organizational security policies and procedures - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations - Explain privacy and sensitive data concepts in relation to security |
| Implementation | 25% | - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure host settings - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure network architecture concepts - Given a scenario, implement identity and account management controls - Given a scenario, implement secure systems design - Given a scenario, implement secure mobile device policies |
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NEW QUESTION # 83
Which control BEST prevents attackers from harvesting password hashes during lateral movement?
Answer: B
Explanation:
LSASS-protection solutions prevent hash extraction.
NEW QUESTION # 84
An organization is concerned with the exposure of sensitive data. Which of the following is the most relevant security concern?
Answer: C
Explanation:
Model inversion is a security concern where attackers can reconstruct or infer sensitive training data from the AI model's outputs. This directly threatens data confidentiality, making it the most relevant concern for sensitive data exposure.
NEW QUESTION # 85
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?
Answer: D
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 # 86
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: A
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 # 87
An AI architect reviews AI utilization and wants to improve the user experience.
Which of the following should the architect review within the logs?
Answer: B
Explanation:
Basic Concept: User experience with AI systems is directly correlated to how accurately and relevantly the model responds to user queries. Poor model accuracy manifests as irrelevant, incorrect, or unhelpful responses, which is the primary driver of poor user experience. CompTIA SecAI+ Study Guide covers AI performance monitoring and user experience optimization.
Why B is Correct: Model accuracy metrics in logs reveal how often the model provides correct, relevant, and useful responses. Reviewing accuracy-related log data such as confidence scores, response quality ratings, error rates, and user feedback correlations enables the architect to identify performance gaps causing poor experiences and guides optimization efforts like fine-tuning or retrieval improvements.
Why A is Wrong: Rate monitoring tracks API call frequency and throughput. While important for capacity planning and detecting abuse, it does not directly reflect the quality of model responses that determine user experience.
Why C is Wrong: Access controls manage who can use the system and what permissions they have. They are a security concern rather than a user experience metric. Reviewing access control logs does not reveal information about response quality.
Why D is Wrong: Data storage metrics relate to storage capacity, utilization, and performance of data persistence layers. While these can affect response speed, they do not provide insights into model response quality or accuracy that drive user experience.
NEW QUESTION # 88
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