1Z0-1127-25 LLM Fundamentals Practice Question
A developer is using the OCI Generative AI service and notices that the cost per API call is higher than expected. Which factor contributes MOST to the cost of an LLM inference call?
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
✓
Number of input and output tokens
Most LLM APIs charge based on the number of input and output tokens. The token count directly affects cost. Model size, context window, and temperature are related but the direct billing metric is token count.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Number of input and output tokens
Why this is correct
Pricing is usually based on token usage; more tokens mean higher cost.
- ✗
Model size (number of parameters)
Why it's wrong here
While larger models may have higher inference costs, the direct billing is typically per token, not per parameter.
- ✗
Context window size
Why it's wrong here
Context window size may limit tokens but the cost is determined by actual tokens used, not the maximum window.
- ✗
Temperature setting
Why it's wrong here
Temperature does not affect token count or cost.
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