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1Z0-1127-25 LLM Fundamentals Practice Question

A practitioner is choosing a model for a code generation assistant that must run on OCI with low latency. Which THREE considerations are most important?

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

✓

Model size (number of parameters)

Smaller models typically have lower latency, model families (Llama, Code Llama) are designed for code, and deployment choices affect latency. Context window size and fine-tuning cost are secondary or not directly relevant to latency.

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 size (number of parameters)
Why this is correct
Smaller models generally have lower inference latency, which is critical for real-time code generation.
✓
Whether the model is available as a managed endpoint or requires self-hosting on OCI GPU shapes
Why this is correct
Managed endpoints may provide optimized inference, while self-hosting allows custom hardware choices affecting latency.
✗
The cost of fine-tuning the model on internal codebases
Why it's wrong here
Fine-tuning cost is not directly related to inference latency; it's a one-time expense.
✓
Availability of a model specifically fine-tuned for code (e.g., Code Llama, StarCoder)
Why this is correct
Code-specific models produce better quality outputs and may require fewer tokens, indirectly improving latency.
✗
The model's context window length
Why it's wrong here
Context window length affects capability but not directly latency; longer context may even increase latency.

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1Z0-1127-25 LLM Fundamentals Practice Question

Why each option matters

Option-by-option breakdown

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