Free PDF Quiz 2026 Newest CY0-001: Exam CompTIA SecAI+ Certification Exam Materials

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CompTIA SecAI+ Certification Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
Which of the following are considered threat intelligence sources? (Choose two.)

Answer: B,E

Explanation:
ISACs and OSINT provide structured and open-source threat intelligence feeds.


NEW QUESTION # 41
An administrator, who works for a financial institution, is required to implement data security controls for data at rest within AI systems that involve data disclosure.
Which of the following is the most suitable control?

Answer: C

Explanation:
Basic Concept: Data at rest refers to inactive data stored in databases or storage media. Protecting it from unauthorized disclosure is a fundamental data security principle covered in the CompTIA SecAI+ Study Guide under securing AI data pipelines.
Why C is Correct: Encryption protects data at rest by rendering it unreadable to unauthorized parties without the appropriate decryption key. In a financial institution with sensitive data, encryption at rest (e.g., AES-256) is the primary control against data disclosure. Even if storage media is physically compromised, encrypted data remains unintelligible. CompTIA SecAI+ Exam Objectives highlight encryption as the primary confidentiality control for stored AI data.
Why A is Wrong: Data lineage tracks the origin and movement of data throughout its lifecycle. It improves traceability and auditability but does not prevent unauthorized disclosure of data at rest.
Why B is Wrong: Rate limits control the number of API requests within a time period. They protect against abuse and denial-of-service scenarios, not data-at-rest confidentiality.
Why D is Wrong: Data masking replaces sensitive values with fictitious substitutes, useful during development or testing. For actual production data at rest in AI systems handling real financial records, encryption provides stronger and more comprehensive confidentiality.


NEW QUESTION # 42
A team of engineers builds an application using a large language model (LLM). The application is built on Linux and is hosted on a virtual server. Users must create an account in order to access and use the platform.
Which of the following should the team do to protect the account credentials?

Answer: C

Explanation:
Basic Concept: User account credentials stored in a database must be protected against unauthorized disclosure. The security of credentials at rest requires cryptographic controls that prevent even database administrators or attackers with database access from reading plaintext passwords. CompTIA SecAI+ Study Guide covers credential security controls as part of AI application security.
Why C is Correct: Implementing hashing and encryption for credential protection is the industry-standard approach. Passwords should be hashed using strong, slow algorithms such as bcrypt, Argon2, or scrypt with unique salts, making them computationally infeasible to reverse even if the database is compromised.
Additional sensitive credential data can be encrypted. Together, hashing and encryption ensure that account credentials remain protected even if the underlying storage is accessed by unauthorized parties.
Why A is Wrong: Patching the model with new datasets updates the AI model ' s training data and knowledge. It does not address the security of user account credentials stored in the application ' s authentication database.
Why B is Wrong: Updating Linux and virtual server software patches system vulnerabilities and is important for overall security hygiene. However, it does not implement specific protections for the account credentials themselves stored in the application database.
Why D is Wrong: Deploying an authenticated API requires users to authenticate to use the API, improving access control. While this complements credential security, it does not protect the storage of credentials at rest and does not replace hashing and encryption of the credential values themselves.


NEW QUESTION # 43
Which of the following is used to train an AI model with unstructured data?

Answer: B

Explanation:
Basic Concept: Unstructured data such as free-form text, images, and audio does not have predefined labels or rigid schema. Training an AI model effectively on unstructured data requires techniques that can leverage patterns within the data itself or adapt a pre-trained model to new data types. CompTIA SecAI+ covers AI training methodologies under basic AI concepts.
Why B is Correct: Fine-tuning takes a pre-trained foundation model that has already learned rich representations from massive unstructured datasets and further trains it on a specific, potentially smaller unstructured dataset. This adapts the model to a new domain, task, or data type without requiring labeled data for every training example. Fine-tuning is the most practical and effective approach for working with unstructured data in modern AI development.
Why A is Wrong: Statistical learning typically refers to classical machine learning approaches that often assume structured, numerical data with defined features. These methods generally struggle with high- dimensional unstructured data without significant preprocessing.
Why C is Wrong: Supervised learning requires labeled training data where each example has an associated correct output label. Applying supervised learning to unstructured data requires extensive manual labeling, which is the opposite of working with raw unstructured data.
Why D is Wrong: Reinforcement learning trains models through reward signals based on actions taken in an environment. It is designed for sequential decision-making tasks and is not the standard approach for learning representations from unstructured data at scale.


NEW QUESTION # 44
A recently deployed AI system becomes persistently unavailable. A restart temporarily fixes the issue, but the issue happens again. Upon examination of application programming interface (API) logs, an analyst finds that external calls continued to use system resources after the action completed.
Which of the following is the best way to improve availability of the system?

Answer: D

Explanation:
The persistent unavailability is caused by external calls consuming resources even after completion, indicating sessions are not being closed. Enforcing session expiration ensures unused sessions are terminated, freeing resources and improving system availability.


NEW QUESTION # 45
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