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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Which three techniques are commonly used to reduce the risk of prompt injection in LLM applications? (Choose three.)

⚠ Common exam trap

Oracle often tests the distinction between security controls and model parameters, so the trap here is that candidates mistakenly think adjusting model settings like temperature can reduce injection risk, when in fact only input/output controls and system prompt design are effective.

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

✓

Output filtering.

Output filtering (B) is correct because it acts as a post-processing defense that scans the LLM's generated output for malicious content, such as leaked system prompts or injected commands, before it reaches the user. This technique helps mitigate the impact of successful prompt injections by catching and neutralizing harmful outputs that bypass input controls.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Enabling prompt validation against regex patterns.
Why it's wrong here
Regex validation can help but is not a primary technique; input/output filtering is more effective.
✓
Output filtering.
Why this is correct
Filtering outputs can block dangerous responses.
✗
Increasing temperature.
Why it's wrong here
Temperature affects randomness, not security.
✓
Input sanitization.
Why this is correct
Sanitizing user inputs can prevent malicious content from being injected.
✓
Using role-based system prompts.
Why this is correct
System prompts can restrict model behavior and reduce injection risk.

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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Why each option matters

Option-by-option breakdown

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