AI0-001 Implementing AI Solutions Practice Question
A company is deploying a large language model (LLM) for internal knowledge management. The model will answer employee questions based on a corpus of confidential documents. The security team requires that the model not leak sensitive information and that responses be accurate. Which TWO techniques should be implemented to meet these requirements? (Choose two.)
⚠ Common exam trap
The trap here is relying on prompt engineering or differential privacy as primary security measures when they do not guarantee prevention of data leakage.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement output filtering to detect and redact sensitive information in the model's responses.
RAG with an authorized document vector database restricts the model's knowledge to permissible content, enhancing both security and accuracy. Output filtering provides a safety net to redact any sensitive information that might slip through. Together, they address the requirements robustly. Other techniques like differential privacy or prompt engineering are either insufficient or not directly aimed at preventing leakage in responses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply differential privacy during fine-tuning of the LLM on the confidential documents.
Why it's wrong here
Differential privacy adds noise to protect individual data points during training, but it does not prevent the model from memorizing and later regurgitating sensitive information from the documents. It also can degrade accuracy. Since the requirement is to prevent leakage and ensure accuracy, differential privacy alone is insufficient and may not be appropriate here.
- ✓
Implement output filtering to detect and redact sensitive information in the model's responses.
Why this is correct
Output filtering scans generated text for patterns of sensitive data (e.g., PII, confidential terms) and redacts them before delivery. This adds a layer of security by catching leaks that might occur despite other measures. Combined with RAG, it ensures that even if the model inadvertently generates sensitive content, it is not exposed to the user, thus meeting the security requirement.
- ✗
Use prompt engineering to instruct the model to refuse answering questions that might reveal sensitive information.
Why it's wrong here
Prompt engineering can help but is not a robust security measure. A malicious user could bypass instructions with adversarial prompts. It does not guarantee that the model won't leak information from its training data or retrieved context. Therefore, it is not a reliable technique to meet the strict security requirement.
- ✓
Implement retrieval-augmented generation (RAG) with a vector database containing only authorized documents.
Why this is correct
RAG grounds the LLM's responses in a specific, controlled document set. By restricting the vector database to authorized documents, the model retrieves only permissible information, reducing the risk of leaking sensitive data from its training corpus. This also improves accuracy by providing relevant context, directly addressing both security and accuracy requirements.
- ✗
Deploy the LLM in a sandboxed environment with no internet access and restrict API calls.
Why it's wrong here
Sandboxing prevents external exfiltration but does not stop the model from revealing sensitive information to authorized internal users. It also does not improve accuracy. The requirement is about the model not leaking sensitive data in its responses, not about network security. Thus, this technique does not directly address the core issue.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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