AI-102 Implement generative AI solutions Practice Question
You are using Azure AI Studio to deploy a fine-tuned model for code generation. After deployment, you notice that the model returns nonsensical code snippets. You need to diagnose the issue. What should you check first?
⚠ Common exam trap
In the AI-102 exam, a common trap is to immediately suspect deployment configuration issues (e.g., quotas, rate limits, or data format) when a model produces poor output. However, the first step should always be to evaluate the model's performance on a held-out test dataset to check for overfitting or data quality issues.
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
✓
Evaluate the model performance on a held-out test dataset.
Evaluating the model on a held-out test dataset is the standard first step to diagnose whether the fine-tuned model has generalized properly or is overfitting. If the model produces nonsensical code, the most likely cause is poor training data quality or overfitting, and a held-out evaluation provides a quantitative measure (e.g., perplexity, BLEU score, or exact match) to confirm this before investigating other factors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Verify that the training data is in JSONL format.
Why it's wrong here
Format checking is a pre-deployment step.
- ✗
Test the base model without fine-tuning to compare outputs.
Why it's wrong here
Base model may not produce code; comparison might not isolate issue.
- ✓
Evaluate the model performance on a held-out test dataset.
Why this is correct
Evaluation helps identify if the model learned properly.
- ✗
Check the deployment's rate limits and quotas.
Why it's wrong here
Rate limits don't cause nonsensical outputs.
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Senior Network & Security Engineer · founder of Courseiva
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