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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, apply mitigation techniques or controls to secure an environment - Explain key aspects of digital forensics |
| Topic 2: Implementation | 25% | - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure host settings - Given a scenario, implement secure mobile device policies - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure systems design - Given a scenario, implement identity and account management controls |
| Topic 3: Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Given a scenario, follow organizational security policies and procedures - 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 |
| Topic 4: Attacks, Threats, and Vulnerabilities | 24% | - Compare and contrast types of social engineering attacks - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with application attacks - Explain penetration testing concepts - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with network attacks |
| Topic 5: Architecture and Design | 21% | - Explain the security implications of embedded and specialized systems - Explain the importance of security concepts in an enterprise environment - Explain secure application development, deployment, and automation concepts - Summarize virtualization and cloud security concepts - Summarize authentication and authorization design concepts - Given a scenario, implement cybersecurity resilience - Explain the importance of physical security controls - Summarize basics of cryptographic concepts |
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NEW QUESTION # 10
A security analyst finds that the AI system is under a denial-of-wallet attack.
Which of the following should the analyst enforce to protect the company? (Choose two.)
Answer: B,E
Explanation:
Basic Concept: A denial-of-wallet (DoW) attack deliberately generates excessive API calls or token consumption to exhaust an organization ' s AI budget. Since LLM providers charge based on tokens processed, attackers can cause significant financial damage by driving massive usage. CompTIA SecAI+ Study Guide addresses financial abuse vectors in AI systems.
Why E is Correct: API rate controls limit the number of requests a user or application can make within a defined time period. By capping request frequency, rate controls directly prevent attackers from generating the massive API call volume needed to execute a denial-of-wallet attack.
Why F is Correct: Output token controls cap the maximum number of tokens the model can generate per response. Since billing is based on tokens consumed including outputs, limiting output tokens directly caps the cost per request, preventing attackers from triggering extremely long, expensive responses.
Why A is Wrong: Endpoint access controls manage device or network access. They do not directly limit token consumption or API call volume that drives denial-of-wallet costs.
Why B is Wrong: A CDN distributes content geographically to improve performance and absorb traffic. It does not control LLM API billing or token consumption.
Why C is Wrong: Model fine-tuning adjusts model parameters for improved performance on specific tasks. It is a training process that does not address active cost-exhaustion attacks.
Why D is Wrong: Modality controls restrict which input types such as text, images, or audio a model accepts.
While useful for reducing attack surface, they do not directly address the rate or volume of API calls in a DoW attack.
NEW QUESTION # 11
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: B
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 # 12
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:
A data engineer is responsible for preparing, cleaning, and formatting data pipelines. When information is incorrectly formatted or missing values, the data engineer ensures data integrity and quality before it is used by AI models.
NEW QUESTION # 13
A company discovers that attackers exploited an unpatched vulnerability in a web server. Which control BEST prevents this?
Answer: A
Explanation:
Timely patching directly prevents exploitation of known vulnerabilities.
NEW QUESTION # 14
A cybersecurity administrator needs a security mechanism that can validate input.
Which of the following controls should the administrator use?
Answer: A
Explanation:
Basic Concept: Input validation is a fundamental security principle that checks incoming data against expected criteria before processing it. For AI systems, this requires a mechanism capable of inspecting the semantic content and structure of inputs - not just their volume or format. CompTIA SecAI+ Study Guide identifies prompt firewalls as the primary input validation control for AI systems.
Why A is Correct: A prompt firewall validates incoming inputs by inspecting their content against security policies, detecting malicious patterns such as injection strings or jailbreaking attempts, enforcing structural rules, and blocking non-compliant inputs before they reach the AI model. Unlike network firewalls that operate on packet headers, a prompt firewall understands the semantic content of AI prompts, making it the appropriate input validation mechanism for AI systems.
Why B is Wrong: Rate limits control how frequently inputs are submitted, not what those inputs contain. A malicious prompt submitted within rate limits will not be detected or blocked - rate limiting does not validate the content or intent of individual inputs.
Why C is Wrong: Token limits cap the maximum length of inputs and outputs in terms of tokens. While this can prevent excessively long inputs from being processed, it does not inspect input content for malicious patterns or validate that inputs conform to policy requirements.
Why D is Wrong: Input quantity is a generic term that might refer to limiting the number or size of inputs.
Like token limits and rate limits, quantity controls do not validate the content of inputs for security compliance or detect malicious prompt patterns.
NEW QUESTION # 15
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