CompTIA SecAI+ Practice Questions: 1.0 Basic AI Concepts Related to Cybersecurity

22 free, exam-style CompTIA SecAI+ (CY0-001) practice questions covering 1.0 Basic AI Concepts Related to Cybersecurity. Each question shows the correct answer and a clear explanation. Ready for the real thing? Take the full timed quiz below.

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Q1. Which of the following AI model training techniques involves training an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them?

Explanation: Federated learning enables multiple entities to collaborate in solving a machine learning problem without sharing their actual private data, only the model updates. Learn more.

Q2. A security analyst is using an AI tool to generate code snippets. To minimize the risk of the model hallucinating or providing insecure code, the analyst provides the model with three specific examples of secure coding patterns before asking for the final output. Which prompt engineering technique is this?

Explanation: Multi-shot (or few-shot) prompting involves providing the model with multiple examples of the desired input-output format to guide its response. Learn more.

Q3. Which component of the Retrieval-Augmented Generation (RAG) architecture is responsible for converting textual data into numerical representations that capture semantic meaning?

Explanation: Embeddings are vector representations of data (text) that capture semantic meaning, allowing the system to retrieve relevant context based on similarity. Learn more.

Q4. An organization wants to detect if their proprietary images are being used to train unauthorized external AI models. Which technique should they implement?

Explanation: Watermarking (specifically digital watermarking/steganography for AI) embeds imperceptible markers in content to track its provenance and unauthorized usage in training sets. Learn more.

Q5. During the AI life cycle, which phase involves 'Human-in-the-loop' to ensure the model aligns with human values and safety guidelines before full automation?

Explanation: Human-in-the-loop principles are critical during model evaluation and validation (such as RLHF - Reinforcement Learning from Human Feedback) to ensure safety and alignment. Learn more.

Q6. Which tool is commonly used to package AI applications and their dependencies to ensure consistent deployment across environments?

Explanation: Containers (like Docker) are standard for packaging AI models and applications, ensuring they run consistently regardless of the computing environment. Learn more.

Q7. Which metric is critical to monitor to detect 'Model Drift'?

Explanation: Model drift refers to the degradation of a model's performance over time as real-world data diverges from training data. Monitoring accuracy/performance is key. Learn more.

Q8. To prevent 'Hallucinations' in an AI customer support bot, a company implements a system that grounds the AI's answers strictly in their own knowledge base. This is an implementation of:

Explanation: RAG retrieves relevant facts from a trusted knowledge base and feeds them to the LLM, reducing hallucinations by grounding the response in factual data. Learn more.

Q9. Which validation technique involves removing connections in a neural network to reduce model size and complexity without significantly affecting accuracy?

Explanation: Pruning is a model compression technique that removes unnecessary weights (connections) or neurons to make the model lighter and faster. Learn more.

Q10. Which of the following describes 'Quantization' in AI models?

Explanation: Quantization reduces the precision of the numbers used to represent model parameters, significantly reducing model size and inference time with minimal accuracy loss. Learn more.

Q11. Which type of AI model is specifically designed to generate new content (text, images, audio) based on learned patterns?

Explanation: Generative AI (GenAI) focuses on creating new data instances that resemble the training data, such as text (LLMs) or images (Diffusion models). Learn more.

Q12. Which control helps ensure that an AI model is not trained on sensitive PII data inadvertently?

Explanation: Data cleansing or scrubbing involves identifying and removing errors, duplicates, and sensitive information (PII) from the dataset before training. Learn more.

Q13. Which of the following is considered a 'Small Language Model' (SLM) typically designed for edge deployment?

Explanation: Models like Microsoft Phi or Google Gemma are designed to be smaller and more efficient (SLMs), capable of running on devices with limited compute (edge). Learn more.

Q14. What is model drift?

Explanation: Model drift occurs when real-world input distributions or relationships change and reduce model accuracy or reliability. Learn more.

Q15. Which metric focuses on the proportion of predicted positive results that were actually positive?

Explanation: Precision measures true positives divided by all predicted positives. Learn more.

Q16. Which metric focuses on how many actual positive cases the model found?

Explanation: Recall measures true positives divided by all actual positives. Learn more.

Q17. Which approach keeps raw training data on local devices while sharing model updates?

Explanation: Federated learning trains across decentralized data sources without centrally collecting the raw data. Learn more.

Q18. What is hallucination in generative AI?

Explanation: Hallucination occurs when a model produces content that appears confident but is not grounded in facts or sources. Learn more.

Q19. Which dataset split is used to tune model choices during development without using the final test set?

Explanation: A validation set helps compare and tune models while preserving the test set for final evaluation. Learn more.

Q20. Which technique retrieves trusted documents and provides them to an LLM at answer time?

Explanation: Retrieval-augmented generation grounds model responses with retrieved context from approved sources. Learn more.

Q21. Which concept describes limiting generated answers to approved source material?

Explanation: Grounding ties outputs to trusted context to reduce unsupported answers. Learn more.

Q22. Which evaluation dataset should remain unused during training and tuning until final assessment?

Explanation: The test set is reserved for final performance assessment to avoid biased evaluation. Learn more.

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