1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A company is deploying a chatbot powered by OCI Generative AI. They want to inject the conversation history into the model prompt to maintain context. However, they notice that after a long conversation, the model starts to ignore earlier messages. What is the most likely cause?
⚠ Common exam trap
Oracle often tests the distinction between input-side limits (context window) and output-side limits (max_tokens), so candidates mistakenly attribute context loss to max_tokens when the real issue is the fixed context window size.
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
✓
The model has a limited context window size.
The model's context window size limits the total number of tokens (input + output) it can process at once. When the conversation history grows beyond this limit, older messages are truncated or dropped, causing the model to lose context from earlier parts of the conversation. This is a fundamental constraint of transformer-based models like those used in OCI Generative AI.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model's max_tokens limit is too low, truncating the prompt.
Why it's wrong here
max_tokens limits the output, not the input prompt.
- ✓
The model has a limited context window size.
Why this is correct
The context window determines how many input tokens the model can consider; exceeding it causes truncation.
- ✗
The top_p parameter is set to 1, causing deterministic output.
Why it's wrong here
top_p=1 is typical and does not cause context loss.
- ✗
The temperature setting is too high, causing randomness.
Why it's wrong here
Temperature affects output diversity, not the ability to retain context.
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