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| Section | Weight | Objectives |
|---|---|---|
| Architecture and Design | 21% | - Explain the importance of physical security controls - Given a scenario, implement cybersecurity resilience - Explain the importance of security concepts in an enterprise environment - Explain secure application development, deployment, and automation concepts - Summarize virtualization and cloud security concepts - Explain the security implications of embedded and specialized systems - Summarize basics of cryptographic concepts - Summarize authentication and authorization design concepts |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with network attacks - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with application attacks - Compare and contrast types of social engineering attacks - Explain threat actor types and attributes - Explain penetration testing concepts |
| Governance, Risk, and Compliance | 14% | - Given a scenario, follow organizational security policies and procedures - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations - Compare and contrast various types of security controls - Explain privacy and sensitive data concepts in relation to security |
| Operations and Incident Response | 16% | - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, apply mitigation techniques or controls to secure an environment - Summarize the importance of policies, processes, and procedures for incident response - Explain key aspects of digital forensics - Given a scenario, use data sources to support an investigation |
| Implementation | 25% | - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement identity and account management controls - Given a scenario, implement secure systems design - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure host settings - Given a scenario, implement authentication and authorization solutions |
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NEW QUESTION # 120
An attacker successfully completes a denial-of-service (DoS) attack through the context window of an AI system. Thousands of characters are obfuscated and hidden behind an emoji.
Which of the following techniques best mitigates this type of attack?
Answer: B
Explanation:
Basic Concept: Context window DoS attacks flood an LLM ' s context with obfuscated content to exhaust processing resources or manipulate model behavior. Attackers may hide large amounts of text behind Unicode characters like emojis. CompTIA SecAI+ Study Guide identifies prompt filtering as the primary defense against input-based attacks on LLMs.
Why D is Correct: A prompt filter inspects incoming inputs before they reach the LLM, detecting and blocking malicious content including obfuscated text hidden behind Unicode characters or emojis. By analyzing input structure, character counts, hidden content, and encoding anomalies, prompt filters can identify and reject attacks that attempt to abuse the context window, preventing resource exhaustion.
Why A is Wrong: Fraud detection systems are designed to identify fraudulent transactions or activities in structured data contexts. They are not designed to inspect LLM prompt structures for obfuscated content attacks on context windows.
Why B is Wrong: LLM-as-a-judge uses a secondary LLM to evaluate the quality or safety of another model ' s outputs. It operates post-generation and cannot prevent a DoS attack that occurs during input processing before output is generated.
Why C is Wrong: Pattern recognition can identify known attack patterns but requires the attack to match pre- learned patterns. Novel obfuscation techniques using Unicode or emoji hiding may evade pattern-based detection without dedicated prompt filtering logic.
NEW QUESTION # 121
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:
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.
NEW QUESTION # 122
A group of security engineers is developing a SIEM system that will be able to ingest data from multiple structured and unstructured sources, have a chatbot integrated with an LLM that the security analyst can interact with, and 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: D
Explanation:
Basic Concept: Before ingesting data from multiple sources - both structured and unstructured - into an AI- powered SIEM system, the data must be prepared to ensure quality, consistency, and usability. Data from diverse sources often contains noise, errors, duplicates, and formatting inconsistencies that will degrade AI performance if not addressed. CompTIA SecAI+ covers data preparation as a prerequisite for AI system effectiveness.
Why C is Correct: Data cleansing is the process of detecting and correcting or removing corrupt, inaccurate, incomplete, and duplicate data. Before collecting data from multiple structured and unstructured SIEM sources, engineers must cleanse the data to standardize formats, remove duplicates, fill missing values, and eliminate noise. Clean input data is fundamental to producing accurate AI-generated insights and reliable LLM interactions in the SIEM context.
Why A is Wrong: Balancing addresses class distribution imbalance in labeled training data for classification models. While relevant when training ML detection models, it is not the primary consideration before initial data collection from diverse SIEM sources.
Why B is Wrong: Verification confirms that data meets expected quality standards and validates its accuracy against trusted sources. It is a post-collection quality check performed after cleansing, not the first step before data collection.
Why D is Wrong: Vector storage refers to databases that store embeddings for semantic search, relevant for RAG systems. It is a storage architecture decision made after data is collected, processed, and prepared, not a pre-collection technique.
NEW QUESTION # 123
A security engineer needs to monitor an AI-based system for runtime operations. The engineer is mostly concerned about the visibility of internal activity. Which of the following is the most appropriate monitoring solution?
Answer: A
Explanation:
For runtime visibility into internal activity of an AI system, the most suitable control is enabling stack calls and debugging-level traces. This provides granular insights into function-level execution, dependencies, and operations, which directly supports monitoring of runtime behavior.
NEW QUESTION # 124
Which techniques belong to the MITRE ATT&CK Command-and-Control phase? (Choose two.)
Answer: A,B
Explanation:
Beaconing and covert channels maintain communication with the attacker.
NEW QUESTION # 125
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