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| Section | Weight | Objectives |
|---|---|---|
| Attacks, Threats, and Vulnerabilities | 24% | - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with application attacks |
| Governance, Risk, and Compliance | 14% | - Compare and contrast various types of security controls - Explain risk management processes and concepts - Given a scenario, follow organizational security policies and procedures - Explain privacy and sensitive data concepts in relation to security - Summarize regulations, standards, and frameworks that impact organizations |
| Operations and Incident Response | 16% | - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, use data sources to support an investigation - 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 |
| Implementation | 25% | - Given a scenario, implement secure host settings - Given a scenario, implement secure systems design - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement identity and account management controls - Given a scenario, implement secure network architecture concepts - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure mobile device policies |
| Architecture and Design | 21% | - Explain the security implications of embedded and specialized systems - Given a scenario, implement cybersecurity resilience - Summarize authentication and authorization design concepts - 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 |
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NEW QUESTION # 159
An AI security administrator notices that the information referenced by the model is incorrectly formatted and missing values.
Which of the following job roles would most likely be responsible for correcting this error?
Answer: D
Explanation:
Basic Concept: In AI teams, different roles hold distinct responsibilities for specific aspects of the AI system.
Data quality issues such as incorrect formatting and missing values fall within the domain of data engineering, which is responsible for designing, building, and maintaining the data pipelines and datasets that feed AI models. CompTIA SecAI+ Study Guide covers AI team role definitions under governance and basic AI concepts.
Why C is Correct: A Data Engineer is responsible for building and maintaining the data pipelines, transformation processes, and data quality controls that supply AI models with properly formatted, complete data. Addressing incorrectly formatted data and missing values is a core data engineering responsibility, as these professionals own the ETL (Extract, Transform, Load) processes, data validation rules, and data quality frameworks that ensure the model receives clean, usable input.
Why A is Wrong: A Platform Engineer designs and maintains the computing infrastructure and platforms that host AI systems. Their focus is the technical environment and deployment infrastructure, not the quality or formatting of the data consumed by models.
Why B is Wrong: An MLOps Engineer manages the deployment, monitoring, and operational lifecycle of AI models in production. While they may detect data quality issues through monitoring, resolving data formatting and missing value problems is the responsibility of data engineers who own the data pipelines.
Why D is Wrong: An AI Architect designs the overall AI system architecture, component interactions, and technical strategy. While they define data requirements, the hands-on work of correcting data formatting errors and filling missing values belongs to the data engineering function.
NEW QUESTION # 160
During the selection of a machine learning (ML)-based threat classification model, a cybersecurity administrator verifies that label distribution is highly unbalanced. Which of the following processing techniques should the engineer use to balance the model?
Answer: C
Explanation:
When label distribution is highly unbalanced, data augmentation generates additional synthetic samples for the underrepresented classes. This balances the dataset, improving the ML model's ability to classify threats accurately across all categories.
NEW QUESTION # 161
An organization deploys a browser-based AI plug-in to detect malicious websites and phishing links in corporate email.
Which of the following techniques is used in this AI plug-in?
Answer: C
Explanation:
Basic Concept: AI-based security tools for detecting malicious websites and phishing links operate by analyzing URLs, page content, and link characteristics against known malicious patterns and behavioral signatures. CompTIA SecAI+ Study Guide covers pattern recognition and signature matching as fundamental AI-assisted threat detection techniques.
Why B is Correct: Pattern recognition and signature matching are the core techniques used in malicious website and phishing link detection. The AI plug-in uses pattern recognition to identify characteristics of phishing pages such as login form structures mimicking legitimate sites, suspicious domain patterns, and redirect behaviors. Signature matching compares URLs and page content against databases of known malicious sites and phishing infrastructure. Together these techniques enable accurate detection of threats in email links before users click them.
Why A is Wrong: Code quality testing analyzes source code for bugs, vulnerabilities, and adherence to coding standards during software development. It has no application for detecting malicious websites or phishing links in real-time email scanning.
Why C is Wrong: Automated penetration testing proactively exploits vulnerabilities to assess security posture.
It is an offensive security assessment technique, not a real-time threat detection technique for identifying malicious links in email.
Why D is Wrong: Automated incident response executes predefined response actions when security incidents are detected, such as isolating endpoints or blocking users. It operates after threats are detected, not during the detection phase that identifies malicious websites and links.
NEW QUESTION # 162
A cybersecurity engineer implements a large language model (LLM) tool to automate an incident management workflow. However, the output contains items that do not exist in the real world. Which of the following is a technique to address this issue?
Answer: D
Explanation:
The described problem is hallucination: the LLM is generating plausible-looking information that is not grounded in real-world facts. Retrieval-augmented generation is the best option because RAG supplements the model ' s prompt with information retrieved from an authoritative external knowledge source before the response is generated. This allows incident- management responses to be grounded in actual tickets, security documentation, threat intelligence, asset information, or other trusted records instead of relying exclusively on the model ' s internal parameters. CompTIA SecAI+ explicitly includes RAG, vector storage, and embeddings within its AI data concepts and also identifies hallucinations as an issue that organizations must monitor and audit. Watermarking helps identify AI-generated content but does not improve factual grounding. Output filtering can block prohibited or unsafe output patterns, but it cannot reliably supply missing factual information. Data rebalancing addresses uneven distributions in training datasets and is unrelated to grounding an LLM ' s incident-management responses. RAG therefore most directly reduces unsupported or fabricated information by giving the model relevant factual context at inference time.
NEW QUESTION # 163
A company discovers that attackers exploited an unpatched vulnerability in a web server. Which control BEST prevents this?
Answer: C
Explanation:
Timely patching directly prevents exploitation of known vulnerabilities.
NEW QUESTION # 164
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