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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Explain the importance of physical security controls - Summarize authentication and authorization design concepts - Summarize virtualization and cloud security concepts - Explain the importance of security concepts in an enterprise environment - Explain the security implications of embedded and specialized systems - Explain secure application development, deployment, and automation concepts - Summarize basics of cryptographic concepts |
| Topic 2: Governance, Risk, and Compliance | 14% | - Compare and contrast various types of security controls - Explain privacy and sensitive data concepts in relation to security - Given a scenario, follow organizational security policies and procedures - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations |
| Topic 3: Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Explain key aspects of digital forensics - 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 4: Attacks, Threats, and Vulnerabilities | 24% | - Explain penetration testing concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - 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 5: Implementation | 25% | - Given a scenario, implement secure systems design - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure host settings - Given a scenario, implement identity and account management controls - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure network architecture concepts |
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NEW QUESTION # 113
A recent release of an AI software update exposes confidential customer information due to storage misconfiguration.
Which of the following data security controls will help maintain confidentiality despite the data leak?
Answer: D
Explanation:
Basic Concept: When a storage misconfiguration leads to data exposure, the question is which encryption type would have protected the confidentiality of data stored in that misconfigured storage. The three states of data
- at rest, in transit, and in use - each require different encryption mechanisms. CompTIA SecAI+ Study Guide covers encryption states and their applicability to AI data protection.
Why D is Correct: Encryption at rest protects data stored in databases, file systems, and storage media by encrypting it so that even if unauthorized parties gain access to the storage through a misconfiguration, the data remains unreadable without the decryption key. Since the exposure resulted from a storage misconfiguration that allowed access to stored data, encryption at rest would have maintained confidentiality of the customer information despite the misconfiguration granting storage access.
Why A is Wrong: Model encryption specifically protects AI model weights and parameters from unauthorized access. It does not protect customer data stored in databases or data stores associated with the AI system.
Why B is Wrong: Encryption in transit protects data moving between components over networks. It does not protect data stored at rest in misconfigured storage that is accessed directly rather than over a network connection.
Why C is Wrong: Encryption in use (homomorphic encryption or confidential computing) protects data while it is being actively processed in memory. It addresses runtime processing security, not the confidentiality of data stored in misconfigured storage that is not currently being processed.
NEW QUESTION # 114
An automobile manufacturer implements a chatbot to assist with configuration options for customer automobiles. Given a customer ' s prompt, the chatbot gives offensive responses.
Which of the following describes this behavior?
Answer: D
Explanation:
Basic Concept: AI chatbots are designed with safety guidelines and content policies that prevent them from generating harmful, offensive, or inappropriate content. When users find ways to bypass these restrictions through crafted prompts, they have " jailbroken " the model. CompTIA SecAI+ Study Guide covers jailbreaking as a key AI vulnerability category.
Why C is Correct: Jailbreaking is the process of using cleverly crafted prompts to bypass an AI model ' s built- in safety restrictions, content policies, and behavioral guardrails, causing it to produce outputs it was designed to refuse. The scenario describes a chatbot that was designed for automobile configuration assistance but is producing offensive responses following customer prompts, indicating that customers have successfully prompted the model to bypass its safety constraints and generate prohibited content.
Why A is Wrong: Model skewing refers to attacks or biases that cause a model to favor certain outputs or perspectives systematically over time, often through data manipulation. It describes a gradual distortion of model behavior, not a direct user-prompted bypass of safety restrictions in a single interaction.
Why B is Wrong: Model theft involves extracting or replicating a proprietary model ' s functionality or architecture through repeated queries. It is an intellectual property attack aimed at stealing the model ' s knowledge, not an attack that causes the model to produce offensive content.
Why D is Wrong: Insecure output handling occurs when an application fails to properly validate or sanitize AI-generated outputs before using them in ways that could cause harm such as passing AI output directly to a system command or database query. It describes a developer implementation vulnerability, not the act of a user prompting a model to bypass its safety constraints.
NEW QUESTION # 115
Faculty members at a university are concerned about potential inherent bias and inconsistency in one department's AI plagiarism detection service.
Which of the following principles will most likely to address their concerns?
Answer: B
Explanation:
Consistency ensures that an AI system applies rules and produces results in a uniform manner across all cases. This principle directly addresses concerns about bias and irregular outcomes in the plagiarism detection service.
NEW QUESTION # 116
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: A
Explanation:
Basic Concept: Class imbalance in training data - where some categories have significantly more examples than others - causes ML models to be biased toward the majority class, producing poor detection of minority class threats. Addressing this imbalance before training is critical for threat classification accuracy. CompTIA SecAI+ covers data preparation techniques under basic AI concepts.
Why B is Correct: Data augmentation addresses class imbalance by artificially increasing the number of training samples in under-represented classes. Techniques include oversampling minority classes by creating synthetic examples using methods like SMOTE (Synthetic Minority Over-sampling Technique), or undersampling majority classes. This balances label distribution and enables the model to learn decision boundaries that accurately classify all threat categories, not just the dominant ones.
Why A is Wrong: Data lineage documents the origin, movement, and transformation of data throughout its lifecycle. It provides traceability and auditability but does not address class imbalance in training data distribution.
Why C is Wrong: Data provenance records the history and context of data origins. Like lineage, it is a governance and tracking concept that does not alter data distribution for model training balance.
Why D is Wrong: Data verification confirms that data is correct and consistent with expected formats and values. It checks data quality and integrity but does not address the statistical distribution imbalance between threat classes in training datasets.
NEW QUESTION # 117
User experience is declining since the launch of a large language model (LLM) in internal networks.
Which of the following should be the highest priority for the prompt engineers?
Answer: C
Explanation:
Basic Concept: Prompt engineers are responsible for designing and refining the prompts and instructions that guide an LLM ' s behavior. When user experience is declining after an LLM launch, this signals that the model ' s outputs are not meeting quality standards. CompTIA SecAI+ addresses prompt engineering quality management under securing and optimizing AI systems.
Why C is Correct: Quality control should be the highest priority when user experience is declining. Prompt engineers must systematically evaluate model responses against quality benchmarks, identify failure patterns causing poor user experience, and iteratively refine prompts to produce accurate, relevant, and appropriately formatted responses. Quality control encompasses testing, evaluation, and continuous improvement of prompt performance.
Why A is Wrong: Customer success management is a business function focused on customer relationship management and retention. While related to user experience outcomes, it is not a technical priority that prompt engineers can directly address through their core competency of prompt design and refinement.
Why B is Wrong: Sales life cycle management is a business process for managing customer acquisition and revenue. It is entirely outside the scope of prompt engineering activities and does not address declining LLM user experience.
Why D is Wrong: Business objectives define what the organization aims to achieve with the LLM deployment. These are set at the strategic level and inform the direction for prompt engineering. They are inputs to the quality control process rather than the priority action prompt engineers should take when experience is declining.
NEW QUESTION # 118
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