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| Section | Weight | Objectives |
|---|---|---|
| Operations and Incident Response | 16% | - Given a scenario, apply mitigation techniques or controls to secure an environment - 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 - Summarize the importance of policies, processes, and procedures for incident response |
| Attacks, Threats, and Vulnerabilities | 24% | - Explain threat actor types and attributes - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators associated with network attacks - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators to determine the type of attack - Explain penetration testing concepts |
| Implementation | 25% | - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement identity and account management controls - Given a scenario, implement secure mobile device policies - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure host settings - Given a scenario, implement secure systems design |
| 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 |
| Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Explain secure application development, deployment, and automation concepts - Summarize basics of cryptographic concepts - Summarize authentication and authorization design concepts - Explain the security implications of embedded and specialized systems - Summarize virtualization and cloud security concepts - Explain the importance of security concepts in an enterprise environment - Explain the importance of physical security controls |
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NEW QUESTION # 138
A company deploys an internet-facing chatbot using RAG. Logs show that an administrator can retrieve employee names and usernames while an employee receives ' information not available. ' Which of the following is reducing the risk of sensitive data exposure in this scenario?
Answer: C
Explanation:
Basic Concept: RAG-based AI systems retrieve information from knowledge bases to augment their responses. The differential access to sensitive employee data based on user role demonstrates that role-based data access controls are functioning correctly, restricting what data different users can retrieve through the AI interface. CompTIA SecAI+ Study Guide covers data access controls as the primary mechanism for preventing sensitive data exposure in RAG systems.
Why A is Correct: Data access controls define what information each user role is permitted to retrieve from the knowledge base. In this scenario, administrator-level users can access employee directory information while employee-level users cannot. The RAG system enforces these permissions when retrieving data for the AI ' s responses, preventing unauthorized users from accessing sensitive employee data through the chatbot interface regardless of how they phrase their queries.
Why B is Wrong: Model-specific guardrails filter responses based on content policies. While they can prevent certain categories of sensitive information from being disclosed, the scenario specifically shows differential access based on user role, which is the characteristic of access control enforcement, not content-based guardrail filtering.
Why C is Wrong: Rate limiting restricts request frequency. It does not differentiate what data different users can access; it only controls how often they can make requests. Both the administrator and employee could be subject to the same rate limit while still receiving different data based on their access controls.
Why D is Wrong: Prompt templates standardize how queries are structured. They do not implement user role- based data access restrictions or prevent specific user types from accessing sensitive information in the underlying knowledge base.
NEW QUESTION # 139
Which of the following technologies is used in deepfake?
Answer: D
Explanation:
Basic Concept: Deepfakes are AI-generated synthetic media that convincingly replace or manipulate a person
' s likeness, voice, or actions in images and videos. Creating realistic deepfakes requires generative AI techniques capable of learning and reproducing complex data distributions. CompTIA SecAI+ Exam Objectives cover deepfake technology under basic AI concepts.
Why A is Correct: Generative Adversarial Networks (GANs) are the primary technology behind deepfakes. A GAN consists of two competing neural networks: a generator that creates synthetic content and a discriminator that evaluates whether content is real or fake. Through adversarial training, the generator continuously improves at creating convincing synthetic media such as realistic human faces, voice clones, and video manipulations indistinguishable from authentic recordings.
Why B is Wrong: Multi-shot prompting is a prompting technique where multiple examples are provided to an LLM to guide its responses. It is an inference technique for language models and has no role in generating synthetic video or image deepfake content.
Why C is Wrong: Prompt engineering is the practice of crafting effective prompts to guide LLM outputs. It is a communication strategy for working with text-based AI systems, not a technology for generating synthetic media.
Why D is Wrong: Transfer learning is a training technique that repurposes knowledge from one domain to another, improving model performance with limited data. While it can be used in model training pipelines, it is not the core technology that enables deepfake generation.
NEW QUESTION # 140
A web server shows signs of SQL injection. Which control BEST prevents this?
Answer: C
Explanation:
Validating and sanitizing user input is the core SQLi defense.
NEW QUESTION # 141
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: B
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 # 142
Which of the following improves the observability and auditing of an AI system?
Answer: B
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 # 143
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