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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators to determine the type of attack - Given a scenario, analyze potential indicators associated with application attacks - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks - Explain threat actor types and attributes - Explain vulnerability scanning concepts - Compare and contrast types of social engineering attacks |
| Topic 2: Implementation | 25% | - Given a scenario, implement secure host settings - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement identity and account management controls - Given a scenario, implement secure systems design - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure network architecture concepts |
| Topic 3: Operations and Incident Response | 16% | - Explain key aspects of digital forensics - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use data sources to support an investigation - Given a scenario, use appropriate tool to assess organizational security |
| Topic 4: Architecture and Design | 21% | - Explain secure application development, deployment, and automation concepts - Explain the security implications of embedded and specialized systems - Explain the importance of security concepts in an enterprise environment - Summarize authentication and authorization design concepts - Given a scenario, implement cybersecurity resilience - Explain the importance of physical security controls - Summarize basics of cryptographic concepts - Summarize virtualization and cloud security concepts |
| Topic 5: Governance, Risk, and Compliance | 14% | - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Explain risk management processes and concepts - Explain privacy and sensitive data concepts in relation to security |
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NEW QUESTION # 62
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: D
Explanation:
Checksums ensure data integrity by detecting any unauthorized or accidental changes to the dataset. This provides a reliable way to confirm that sensitive attributes such as age, ethnicity, and diabetes status remain unaltered during development.
NEW QUESTION # 63
A security team is using an AI-based tool to try to bypass organizational boundaries. The team uses AI to look at the current state and suggest different attack vectors based on the outcome of the previous ones. Which of the following techniques is the team most likely using?
Answer: A
Explanation:
The described behavior - iteratively assessing the environment and adapting attack paths based on prior outcomes - matches automated penetration testing, where AI-driven tools simulate attack chains and adjust tactics dynamically.
NEW QUESTION # 64
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: B
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 # 65
A SOC team has an AI agent that performs web searches and calls to the SOAR solution. The team is concerned about enterprise uptime and case resolution time.
Which of the following is the most appropriate use of the AI agent?
Answer: C
Explanation:
Basic Concept: AI agents in SOC environments can automate repetitive, rules-based response actions that previously required human intervention. When the primary concerns are enterprise uptime and case resolution time, the AI agent ' s ability to autonomously execute containment actions through SOAR is the most impactful application. CompTIA SecAI+ Study Guide covers AI agent use cases in security operations.
Why A is Correct: Using the AI agent to analyze incidents and execute containment actions through SOAR playbooks directly addresses both uptime and resolution time concerns. The agent can immediately analyze alert details, determine the appropriate playbook, and execute containment actions such as isolating compromised hosts or disabling compromised accounts autonomously, without waiting for human intervention. This dramatically reduces mean time to contain threats, improving both uptime and resolution speed.
Why B is Wrong: Enriching alerts with open-source intelligence improves analyst context but is a preparatory step rather than a response action. While valuable, it does not directly reduce resolution time by taking containment actions to stop ongoing threats.
Why C is Wrong: Aggregating metrics and generating leadership reports is an administrative function that consumes agent capacity for non-operational purposes. It improves visibility but does not directly improve uptime or case resolution time for active incidents.
Why D is Wrong: Creating tabletop exercises improves team preparedness over time through training scenarios. While beneficial for long-term capability development, it does not directly address the immediate concerns of enterprise uptime and active case resolution time.
NEW QUESTION # 66
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: C
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 # 67
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