1Z0-1127-25 LLM Fundamentals Practice Question
A team is building a code generation assistant using OCI Generative AI. They notice that the model occasionally produces code with subtle security vulnerabilities. Which approach would most effectively reduce this risk without compromising the assistant's usefulness?
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
✓
Fine-tune the model on a dataset of secure code examples and security best practices
Fine-tuning on a curated dataset of secure code examples can teach the model to avoid common vulnerability patterns while retaining its general coding ability. RAG with security docs could also help, but fine-tuning directly addresses the model's behavior more comprehensively.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a larger context window to include all project files in every prompt
Why it's wrong here
Increasing context does not teach the model to avoid security issues; it just provides more context.
- ✗
Switch to a model with more parameters
Why it's wrong here
Larger models may still produce vulnerabilities; parameter count alone does not fix security.
- ✗
Use greedy decoding to reduce randomness in code generation
Why it's wrong here
Greedy decoding reduces randomness but does not address the underlying knowledge of security.
- ✓
Fine-tune the model on a dataset of secure code examples and security best practices
Why this is correct
Fine-tuning on secure examples helps the model learn to generate safer code by adjusting its weights.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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