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| Section | Weight | Objectives |
|---|---|---|
| Architecture and Design | 21% | - Explain the importance of physical security controls - Explain the security implications of embedded and specialized systems - Explain secure application development, deployment, and automation concepts - Summarize authentication and authorization design concepts - Given a scenario, implement cybersecurity resilience - Summarize basics of cryptographic concepts - Summarize virtualization and cloud security concepts - Explain the importance of security concepts in an enterprise environment |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators to determine the type of attack - Given a scenario, analyze potential indicators associated with network attacks - Compare and contrast types of social engineering attacks - Explain vulnerability scanning concepts - Explain threat actor types and attributes - Explain penetration testing concepts |
| Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Explain privacy and sensitive data concepts in relation to security - Given a scenario, follow organizational security policies and procedures |
| 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 - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, apply mitigation techniques or controls to secure an environment - Explain key aspects of digital forensics |
| Implementation | 25% | - Given a scenario, implement authentication and authorization solutions - 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 secure network architecture concepts - Given a scenario, implement secure mobile device policies - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement identity and account management controls |
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NEW QUESTION # 160
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: A
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 # 161
A human resources officer is using AI to evaluate resumes and help select candidates that meet minimum criteria. To improve the results, the human resources officer adjusts the query parameters and includes an example resume that matches a successful candidate. Which if the following best describes this query?
Answer: C
Explanation:
One-shot prompting provides the model with a single example (in this case, a successful resume) to guide how it should process future inputs. This technique helps the AI better align its output with the desired evaluation criteria.
NEW QUESTION # 162
A data scientist investigates reports that a production machine learning (ML) model no longer performs with accuracy.
The data scientist finds the following pipeline log entries:
Which of the following should the security team do to mitigate future occurrences?
Answer: B
Explanation:
Basic Concept: When unauthorized changes to an ML training pipeline cause model degradation, the root cause is insufficient access control and change management around the pipeline. Preventing future occurrences requires implementing governance controls that ensure all pipeline changes are reviewed and approved before execution. CompTIA SecAI+ Study Guide covers MDLC change management controls.
Why B is Correct: Enabling human review and approval workflows in the repository creates a mandatory gate requiring authorized reviewers to examine and approve any changes to training pipeline code before they can be merged and executed. This prevents unauthorized modifications from reaching the pipeline by enforcing a review process where any suspicious or unauthorized changes will be caught and rejected before they affect model training and performance.
Why A is Wrong: Static code scanning analyzes code for vulnerabilities and coding standard violations.
While it improves code quality and security, it does not prevent unauthorized individuals from submitting and merging malicious changes to the pipeline without proper review.
Why C is Wrong: Retraining with more data and epochs addresses model performance restoration after the fact but does not prevent future unauthorized pipeline modifications. If the pipeline remains unprotected, the same attack could occur again on the new model.
Why D is Wrong: Keeping multiple model copies enables rapid restoration of a previous version when a deployed model is found to be compromised. While useful for recovery, it is a reactive measure that does not prevent unauthorized pipeline changes from occurring and affecting future model training.
NEW QUESTION # 163
A company introduces a large language model (LLM) in an application in order to monitor for a potential denial-of-service attack.
Which of the following should the company use to measure the utilization of the LLM?
Answer: B
Explanation:
Basic Concept: LLMs process and generate text as discrete units called tokens, which represent words or word pieces. The number of tokens consumed is the primary metric for LLM resource utilization and billing.
Understanding token consumption is essential for both performance monitoring and cost management.
CompTIA SecAI+ Study Guide covers token-based measurement under AI system management.
Why A is Correct: Tokens are the fundamental unit of measurement for LLM utilization. Both input tokens sent in prompts and output tokens generated in responses are counted. Token consumption directly correlates with computational resource usage, response time, and API cost. Monitoring token counts enables the company to establish usage baselines and detect the abnormal token consumption spikes associated with DoS attacks or denial-of-wallet attacks against the LLM.
Why B is Wrong: A transformer is the neural network architecture that underlies modern LLMs. It is an architectural description, not a measurable utilization metric. You cannot quantify LLM resource consumption in " transformers. " Why C is Wrong: Chain of thought is a prompting technique that encourages models to reason step-by-step before providing answers. It is an inference strategy for improving response quality, not a unit of measurement for monitoring LLM utilization or resource consumption.
Why D is Wrong: A prompt is the input text submitted to the LLM. While monitoring prompt volumes provides request count information, tokens provide a more granular and meaningful utilization metric because they capture both the complexity of inputs and the length of generated outputs.
NEW QUESTION # 164
A data scientist is working with unlabeled data and wants to build a clustering model.
Which of the following techniques should a data scientist use?
Answer: D
Explanation:
Basic Concept: Different ML learning paradigms handle different data situations. The availability of labeled versus unlabeled data determines which learning approach is appropriate. Building clustering models specifically requires learning from data without predefined category labels. CompTIA SecAI+ Study Guide covers ML learning paradigms under basic AI concepts.
Why C is Correct: Unsupervised learning works with unlabeled data by discovering inherent patterns, structures, and groupings within the data without predefined categories. Clustering is the canonical unsupervised learning task, where algorithms like k-means, hierarchical clustering, or DBSCAN group similar data points together based on feature similarity. Since the data scientist has unlabeled data and wants to find natural groupings, unsupervised learning is the appropriate and correct technique.
Why A is Wrong: Supervised learning requires labeled training data where each example has a corresponding correct output label. The data scientist explicitly has unlabeled data, making supervised learning inapplicable without first completing the labor-intensive task of manually labeling all examples.
Why B is Wrong: Reinforcement learning trains agents to take actions in an environment to maximize cumulative rewards through trial and error. It is designed for sequential decision-making problems, not for finding groupings in static, unlabeled datasets.
Why D is Wrong: Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data. It requires at least some labels to guide learning. The scenario specifies working with unlabeled data only, making unsupervised learning the pure fit.
NEW QUESTION # 165
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