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| Section | Weight | Objectives |
|---|---|---|
| Implementation | 25% | - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure network architecture concepts - Given a scenario, implement identity and account management controls - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure host settings - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure systems design |
| Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Explain privacy and sensitive data concepts in relation to security - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures |
| 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 |
| Attacks, Threats, and Vulnerabilities | 24% | - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks - Given a scenario, analyze potential indicators associated with application attacks - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators to determine the type of attack - Compare and contrast types of social engineering attacks - Explain threat actor types and attributes |
| Architecture and Design | 21% | - Summarize authentication and authorization design concepts - Explain the security implications of embedded and specialized systems - Summarize basics of cryptographic concepts - Explain the importance of physical security controls - Given a scenario, implement cybersecurity resilience - Explain secure application development, deployment, and automation concepts - Summarize virtualization and cloud security concepts - Explain the importance of security concepts in an enterprise environment |
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NEW QUESTION # 99
A malware sample alters itself slightly each time it runs to evade signature detection. What technique is this?
Answer: A
Explanation:
Polymorphic malware changes its code structure while maintaining functionality.
NEW QUESTION # 100
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: B
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 # 101
An engineer is analyzing findings from a penetration test that indicate insufficient data encryption. The report also indicates that additional controls must be placed on the data. To protect against loss of intellectual property, the engineer must implement data security.
Part 1: Use drop-down menu to select the most appropriate protocol or cipher for each system component.
Part 2: Use the drop-down menu to select the most appropriate technique to apply to the modified data.
Answer:
Explanation:
Explanation:
Part 1 - Select the protocol or cipher
System component
Select
API gateway
TLS 1.2
Database storage
CAMELLIA
AI model storage
HMAC-SHA512
Part 2 - Select the data-protection technique
Enter the techniques from the first row to the fifth row in this exact order:
Row Data modification Select
1 The name field is removed, while patient_id and dob remain De-identification
2 ip_addr and name are removed, leaving cookie and uid Anonymization
3 The card number is replaced by 0x0193828829 Tokenization
4 sensitivity: " SECRET " is added Classification
5 Part of the PIN is replaced by XXXX Masking
See Explanation
Why these are the intended answers
API gateway # TLS 1.2
The API gateway handles data moving between the client and backend systems. TLS is the only listed transport-security protocol and therefore protects the data in transit . OWASP recommends encrypting sensitive data in transit by using TLS with secure parameters.
Database storage # CAMELLIA
CAMELLIA is a symmetric block cipher suitable for encrypting substantial quantities of stored data. It is therefore the valid storage-encryption choice among the listed options.
Do not select AES-512 . AES-512 is not a standardized AES variant. NIST defines only AES-128, AES-192, and AES-256 .
AI model storage # HMAC-SHA512
The scenario separately emphasizes protection against loss or modification of intellectual property. HMAC- SHA512 supplies message authentication and integrity verification, allowing unauthorized modification of the AI model to be detected. HMAC is not confidentiality encryption, but it is the most appropriate listed additional control for protecting model integrity. OWASP specifically identifies HMACs as cryptographic controls for stored-data integrity.
Why the other options are not selected
* SHA-2 512: Hashing only; no secret key and no encryption.
* Elliptical curve: A family of cryptographic techniques, not a specific storage or transport protocol.
* ECDH: Used for key agreement, not direct bulk-data encryption.
* AES-512: Invalid AES key size.
* RSA-2048: Primarily used for asymmetric encryption, signatures, or key transport; inefficient for bulk database or model encryption.
* CAMELLIA: Appropriate symmetric encryption for stored data.
* HMAC-SHA512: Integrity and authenticity, not confidentiality.
Final Part 2 order
* De-identification
* Anonymization
* Tokenization
* Classification
* Masking
Explanation:
De-identification removes an explicitly identifying field-in this case, the person's name-while retaining other fields needed for processing. The remaining patient ID and date of birth could potentially permit re- identification when combined with other information, so this is de-identification rather than complete anonymization. OWASP describes de-identification as deleting, scrambling, or pseudonymizing direct and indirect identifiers.
Anonymization removes identifying attributes so that the displayed record is not directly tied to the named individual. Here, both the name and IP address are removed.
Tokenization substitutes a sensitive value with a non-sensitive representative token. The original payment- card number is replaced with the hexadecimal-style value 0x0193828829. OWASP recognizes tokenization as a method for reducing exposure of stored sensitive data.
Classification labels information according to sensitivity. Adding sensitivity: " SECRET " does not conceal or transform the PIN; it categorizes the record so appropriate controls can be applied. OWASP recommends classifying processed, stored, and transmitted data and applying controls based on that classification.
Masking obscures only part of a sensitive value while preserving its general format. Changing 999-99-9999 to 999-99-XXXX is partial masking.
NEW QUESTION # 102
A security administrator needs to improve an AI model. During an initial investigation, the administrator notices that two successive login features are recorded every day, and then a successful login occurs after a specific time interval. All the successful login attempts have been during office hours.
Which of the following techniques should the administrator use to improve the AI model's security?
Answer: A
Explanation:
The administrator is analyzing repeated login behaviors and time-based patterns that precede successful access. Pattern recognition allows the AI model to detect these behavioral trends, improving its ability to identify anomalies or potential attacks while aligning with normal office-hour login behavior.
NEW QUESTION # 103
An airline corporation wants to implement a chatbot application using a large language model (LLM) so its customers can ask questions and receive answers about flight details and have the option to upload files.
Which of the following security controls should the airline use to protect against malicious input and unauthorized use beyond the service-level agreement? (Choose two.)
Answer: B,C
Explanation:
Basic Concept: LLM-based chatbots accepting user-uploaded files face two critical risk categories: malicious input injection and resource or cost abuse. CompTIA SecAI+ Study Guide highlights prompt security controls and resource management as key defensive layers for public-facing LLM applications.
Why A is Correct: Prompt guardrails intercept and filter user inputs and model outputs, blocking malicious prompts, prompt injection attempts, and harmful file content before affecting model behavior. Since users can upload files, guardrails are essential for sanitizing and validating that content before processing.
Why D is Correct: Model token quotas directly limit how much of the LLM ' s processing capacity a user can consume. This prevents abuse beyond the SLA, including denial-of-wallet attacks or resource exhaustion through excessively large inputs or repeated requests.
Why B is Wrong: Role-based access controls manage who can access what resources. While useful for internal systems, they do not address malicious input content or enforce LLM resource consumption limits for a public-facing chatbot.
Why C is Wrong: Firewall rules operate at the network layer and can block unauthorized IPs or ports but cannot inspect or filter the semantic content of prompts or control token-level LLM usage.
NEW QUESTION # 104
......
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