The answer is the number of input tokens and the number of output tokens. This is because OCI Generative AI pricing operates on a per-token basis, where every word or subword unit in both your prompt and the model’s response is counted and billed. Input tokens represent the text you provide to guide the generation, while output tokens are the generated text itself, making both essential for calculating total cost. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this concept tests your understanding of the pay-per-use model, often appearing as a straightforward two-factor selection. A common trap is confusing token count with character count or request duration, but remember that OCI charges strictly for tokens processed, not time. For a quick memory tip, think “In and Out” — input and output tokens are the two keys to cost calculation.
1Z0-1127 Fundamentals of Large Language Models Practice Question
This 1Z0-1127 practice question tests your understanding of fundamentals of large language models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which two factors are essential for calculating the cost of using OCI Generative AI for text generation? (Choose two.)
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 tokens.
The cost of using OCI Generative AI for text generation is primarily determined by the number of input tokens (the prompt you send) and the number of output tokens (the generated response). OCI charges per token processed, making these two factors essential for cost calculation.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Model architecture (encoder-only vs decoder-only).
Why it's wrong here
Model architecture does not directly impact billing; pricing is per token regardless.
✗
Number of API calls per minute.
Why it's wrong here
API calls are not directly billed; cost is based on tokens processed.
✗
Temperature setting.
Why it's wrong here
Temperature affects generation but not cost.
✓
Number of input tokens.
Why this is correct
Input tokens are a direct factor in cost calculation.
Related concept
Read the scenario before looking for a memorised answer.
✓
Number of output tokens.
Why this is correct
Output tokens are charged per token.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that API call frequency or model architecture parameters directly influence cost, when in reality only token counts (input and output) are the billing units.
Detailed technical explanation
How to think about this question
OCI Generative AI services use a token-based pricing model where each token represents a piece of text (roughly 0.75 words for English). Input tokens include the system prompt, user query, and any context, while output tokens are the generated response. The total cost is calculated as (input_tokens * price_per_input_token) + (output_tokens * price_per_output_token), with prices varying by model tier (e.g., Cohere Command vs. Llama).
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Number of input tokens. — The cost of using OCI Generative AI for text generation is primarily determined by the number of input tokens (the prompt you send) and the number of output tokens (the generated response). OCI charges per token processed, making these two factors essential for cost calculation.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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