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| Section | Weight | Objectives |
|---|---|---|
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Explain penetration testing concepts - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with network attacks - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks |
| Governance, Risk, and Compliance | 14% | - Given a scenario, follow organizational security policies and procedures - Explain risk management processes and concepts - Compare and contrast various types of security controls - Explain privacy and sensitive data concepts in relation to security - Summarize regulations, standards, and frameworks that impact organizations |
| Operations and Incident Response | 16% | - 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 - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, apply mitigation techniques or controls to secure an environment |
| Implementation | 25% | - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure host settings - Given a scenario, implement secure network architecture concepts - Given a scenario, implement identity and account management controls - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure systems design - Given a scenario, implement secure mobile device policies |
| Architecture and Design | 21% | - Summarize basics of cryptographic 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 importance of physical security controls - Explain the security implications of embedded and specialized systems - Summarize virtualization and cloud security concepts - Summarize authentication and authorization design concepts |
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NEW QUESTION # 40
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: A
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 # 41
A customer-facing, AI-powered chatbot has been jailbroken through prompt injections. As a result, the AI model is offering a 99% discount on the purchase of a new vehicle. Which of the following should be implemented to enhance the model's robustness against such attacks?
Answer: C
Explanation:
Guardrails enforce strict rules on what the AI model can and cannot do, preventing malicious prompt injections from overriding intended behavior. In this case, guardrails would stop the chatbot from generating unauthorized offers such as extreme discounts.
NEW QUESTION # 42
A company develops an AI model to diagnose patients. Hospitals access the model through an integrated application programming interface (API). The security team performs a denial-of-service (DoS) attack via brute force on the model.
Which of the following controls would have prevented this issue?
Answer: B
Explanation:
Basic Concept: API-based AI systems are susceptible to DoS attacks where excessive requests overwhelm the system ' s ability to respond to legitimate users. Rate limiting is the standard control for preventing both intentional and unintentional API abuse. CompTIA SecAI+ Study Guide covers rate limiting as a key availability control for AI APIs.
Why C is Correct: Rate limiting restricts the number of requests a client can make to an API within a defined time window. In this scenario, a brute-force DoS attack works by sending a massive volume of requests to exhaust the model ' s resources. Rate limiting would have automatically throttled or blocked the excessive request volume, preventing the attack from succeeding and preserving service availability for legitimate hospital users.
Why A is Wrong: Tokenization replaces sensitive data values with non-sensitive placeholders. It is a data security control for protecting sensitive information such as patient identifiers, not a control for managing API request volumes or preventing DoS attacks.
Why B is Wrong: Model guardrails filter and constrain model inputs and outputs for safety and policy compliance. They inspect content quality, not request volume, and cannot prevent a volume-based DoS attack.
Why D is Wrong: A prompt firewall inspects the content of prompts for malicious patterns or policy violations. Like guardrails, it analyzes content rather than controlling request frequency and cannot prevent resource exhaustion from a high-volume brute-force attack.
NEW QUESTION # 43
Which of the following is the most appropriate reason for model validation?
Answer: D
Explanation:
Model validation is intended to determine systematically whether an AI or machine learning model performs according to its defined objectives and produces acceptable results. This requires a formal methodology, appropriate validation data, measurable criteria, and comparison of actual model behavior against expected outcomes. CompTIA SecAI+ explicitly includes model validation among model-training techniques and also places validation, model evaluation, monitoring, and feedback within the AI lifecycle. Option C therefore represents the actual purpose of validation: objectively establishing whether the model satisfies predetermined performance and quality requirements. Providing feedback between interactions is closer to feedback and iteration or model adaptation rather than validation itself. Checking an exported file is merely an output or serialization check and does not evaluate model quality comprehensively. CI/CD integration checks establish whether software components build, integrate, or deploy correctly; a model could successfully pass those technical checks while still producing inaccurate, biased, unsafe, or otherwise unacceptable predictions.
Formal measurement against expected outcomes is therefore the distinguishing characteristic of proper model validation.
NEW QUESTION # 44
Which of the following improves the observability and auditing of an AI system?
Answer: A
Explanation:
MLOps provides structured monitoring, logging, and version control for AI models. This improves observability and ensures detailed auditing of model behavior, data pipelines, and changes throughout the AI lifecycle.
NEW QUESTION # 45
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