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CompTIA SecAI+ Certification Sample Questions:

1. 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?

A) Data augmentation
B) Data verification
C) Data provenance
D) Data lineage

2. Users report that the output of a generative AI application seems unrelated to the prompts and contains offensive content. A security team investigates and determines that there was an on-path attack.
Which of the following is the most likely attack method?

A) Session hijacking
B) Model hijacking
C) Domain hijacking
D) Application server hijacking

3. A group of security engineers is developing a SIEM system that will be able to ingest data from multiple structured and unstructured sources, have a chatbot integrated with an LLM that the security analyst can interact with, and provide insights from the SIEM alert data.
Which of the following techniques should the security engineers consider before collecting the data from the respective sources?

A) Vector storage
B) Cleansing
C) Verification
D) Balancing

4. A user interface engineer adds new graphics to the latest release of an AI-integrated application. During the update, the engineer accidentally causes the model to retrain on unverified data. After the update, the model begins to return many errors.
Which of the following is the best way to mitigate future errors?

A) Role-based access control
B) Web application firewall
C) Generative adversarial network
D) Model development life cycle

5. Which of the following strengthens the performance of a large language model (LLM) for malicious reconnaissance?

A) Creating a web scraper script using AI to capture the company website
B) Instructing an AI assistant to query as an administrator
C) Prompting a chatbot to describe server naming patterns and Internet Protocol (IP) ranges
D) Enhancing a foundational model with the inclusion of retrieval-augmented generation (RAG)

Solutions: