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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operations and Incident Response | 16% | - Explain key aspects of digital forensics - 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 - Given a scenario, use appropriate tool to assess organizational security |
| Topic 2: Governance, Risk, and Compliance | 14% | - Summarize regulations, standards, and frameworks that impact organizations - Explain privacy and sensitive data concepts in relation to security - Compare and contrast various types of security controls - Explain risk management processes and concepts - Given a scenario, follow organizational security policies and procedures |
| Topic 3: Attacks, Threats, and Vulnerabilities | 24% | - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators to determine the type of attack - Explain penetration testing concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with network attacks |
| Topic 4: Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Summarize authentication and authorization design concepts - Summarize virtualization and cloud security concepts - Explain secure application development, deployment, and automation concepts - Explain the importance of physical security controls - Summarize basics of cryptographic concepts - Explain the importance of security concepts in an enterprise environment - Explain the security implications of embedded and specialized systems |
| Topic 5: Implementation | 25% | - Given a scenario, implement secure network architecture concepts - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure systems design - Given a scenario, implement secure host settings - Given a scenario, implement authentication and authorization solutions - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement identity and account management controls - Given a scenario, implement secure mobile device policies |
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NEW QUESTION # 54
An employee wants a consulting company to procure a data set that contains age, ethnicity, and diabetes status. During development, the employer wants to ensure the integrity of the data.
Which of the following is the best strategy to accomplish this task?
Answer: C
Explanation:
Basic Concept: Data integrity ensures that data has not been tampered with, corrupted, or modified during storage or transmission. For AI training data that is procured from external sources, cryptographic integrity verification is essential to confirm the data arrived unmodified. CompTIA SecAI+ Study Guide covers data integrity controls for AI data pipelines.
Why A is Correct: Implementing checksums provides cryptographic verification of data integrity. A checksum or hash value such as SHA-256 is computed from the dataset at the source. The receiver computes the same hash and compares it to the provided value. Any modification to the data during transit or storage will produce a different hash, immediately detecting tampering or corruption. This is the most reliable, automated, and scalable strategy for ensuring the integrity of procured training data.
Why B is Wrong: Human evaluation can verify data quality and relevance but is impractical for verifying integrity across large datasets of medical records. Human reviewers cannot detect subtle bit-level corruption or intentional small modifications, and the process is not scalable.
Why C is Wrong: Querying the model tests model performance rather than verifying the integrity of the underlying training data. The model cannot tell you whether its training data was modified after collection or during procurement.
Why D is Wrong: Log monitoring tracks system activities and events over time. While useful for auditing access to data, it cannot retroactively confirm that data content has not been modified and does not provide cryptographic integrity guarantees.
NEW QUESTION # 55
An organization is developing and implementing AI features into a customer service application.
Which of the following practices should the organization put in place before releasing the application for customer trials?
Answer: A
Explanation:
Basic Concept: Before deploying AI applications that handle customer data in trials, protecting sensitive information through data masking and sanitization is essential. CompTIA SecAI+ Study Guide emphasizes pre-deployment data security controls as a critical step in the AI development lifecycle.
Why A is Correct: Data masking replaces sensitive real customer data with realistic but fictitious equivalents, while sanitization removes harmful or unwanted data elements. Before customer trials, these techniques prevent exposure of real PII or sensitive information, ensure the trial environment cannot leak production data, and protect the organization from privacy regulation violations. This is the most immediately actionable pre-trial security control.
Why B is Wrong: External compliance audits are formal processes typically conducted post-deployment or at planned intervals to verify regulatory compliance. They are not pre-trial security implementations and cannot prevent data exposure in a trial environment.
Why C is Wrong: Approved AI vendor lists are governance artifacts that manage vendor selection risk at the procurement stage. They do not directly protect customer data within an application being prepared for trials.
Why D is Wrong: Third-party risk management addresses risks from external vendors and partners at a strategic level. While important for overall governance, it does not constitute a direct data security control for a pre-trial release.
NEW QUESTION # 56
Which of the following is required first in order to send a prompt query and response in a language model (LLM) system when authentication is enabled?
Answer: A
Explanation:
When authentication is enabled, the first requirement for sending a prompt query and receiving a response is endpoint access control. It ensures only authenticated and authorized users or systems can interact with the LLM endpoint.
NEW QUESTION # 57
Customer feedback for an AI chatbot has a high-rate of non-answers, which is causing higher central processing unit (CPU) utilization. Which of the following should be implemented?
Answer: A
Explanation:
Implementing a response confidence level ensures the chatbot only provides answers when the model is sufficiently confident. This reduces irrelevant or empty responses, improving user experience and lowering unnecessary CPU utilization.
NEW QUESTION # 58
A malware sample alters itself slightly each time it runs to evade signature detection. What technique is this?
Answer: C
Explanation:
Polymorphic malware changes its code structure while maintaining functionality.
NEW QUESTION # 59
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