1Z0-1127-25 LLM Fundamentals Practice Question
A data scientist is using OCI Generative AI to process a large batch of legal documents. The total cost is higher than expected. Which factor is most likely the primary driver of cost?
⚠ Common exam trap
The 1Z0-1127 exam often tests the misconception that API request count is the primary cost driver, when in reality token-based pricing means a single large request can cost more than hundreds of tiny requests.
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 total number of tokens processed (input + output)
In OCI Generative AI, pricing is primarily based on the total number of tokens processed, which includes both input (prompt) and output (generated) tokens. Processing large batches of legal documents generates high token counts due to lengthy text inputs and verbose outputs, directly increasing cost. The number of API requests alone does not determine cost—a single request with many tokens costs more than many requests with few tokens.
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 number of API requests made
Why it's wrong here
While request count matters, token count per request is usually the dominant cost factor for long documents.
- ✓
The total number of tokens processed (input + output)
Why this is correct
Pricing is typically per token; longer documents mean more tokens and higher cost.
- ✗
The choice of sampling strategy (e.g., top-k vs greedy)
Why it's wrong here
Sampling strategy affects output quality but not token pricing.
- ✗
The latency of the inference endpoint
Why it's wrong here
Latency does not directly affect cost; token count does.
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