The answer is the model's license and terms of use, along with its size and number of parameters. The license and terms of use are critical because they govern your legal rights for commercial deployment, redistribution, and fine-tuning—violating these can lead to serious compliance issues, especially when comparing open-source models like Llama 2 versus proprietary GPT-based models. The model’s size and number of parameters directly affect computational cost, training time, and the model’s capacity to learn from your dataset, making it a practical constraint for fine-tuning on OCI. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this question tests your ability to distinguish between technical feasibility and legal readiness—a common trap is focusing only on performance metrics like accuracy while ignoring licensing restrictions. Remember the mnemonic “License and Size” to recall that both legal rights and computational scale are the two key factors for selecting a base model for fine-tuning.
1Z0-1127 Deploying and Managing Generative AI on OCI Practice Question
This 1Z0-1127 practice question tests your understanding of deploying and managing generative ai on oci. 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 should be considered when selecting a base model for fine-tuning on OCI Generative AI service?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model's size and number of parameters
When selecting a base model for fine-tuning on OCI Generative AI service, the model's size and number of parameters (B) directly impact computational cost, training time, and the model's capacity to learn from your dataset. The model's license and terms of use (C) are critical because commercial use, redistribution, and fine-tuning rights vary per model (e.g., Llama 2 vs. GPT-based models), and violating these can lead to legal or compliance issues.
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.
✗
The model's training dataset size
Why it's wrong here
Base model is pre-trained; you add your own dataset for fine-tuning.
✓
The model's size and number of parameters
Why this is correct
Larger models consume more resources and cost more to serve.
Related concept
Read the scenario before looking for a memorised answer.
✓
The model's license and terms of use
Why this is correct
Must comply with licensing for commercial use.
Related concept
Read the scenario before looking for a memorised answer.
✗
The model's training framework (PyTorch vs TensorFlow)
Why it's wrong here
Fine-tuning uses OCI managed infrastructure; framework is abstracted.
✗
The model's built-in features like content filtering
Why it's wrong here
Content filtering can be added post-deployment; not a base model selection factor.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle often tests the misconception that technical details like training framework or dataset size are relevant, when in fact the exam focuses on operational and legal factors (size/license) that directly affect deployment and compliance in OCI's managed service.
Detailed technical explanation
How to think about this question
Model size (parameters) determines the memory footprint and inference latency; for example, a 7B parameter model requires ~14 GB in FP16, while a 70B model needs ~140 GB, affecting GPU selection and cost. License terms vary widely: Llama 2's community license allows fine-tuning for commercial use, but some models (e.g., GPT-4 via API) prohibit fine-tuning entirely. OCI Generative AI service supports models like Cohere Command and Llama, each with distinct parameter counts and licensing, so you must verify compatibility with your use case.
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.
Deploying and Managing Generative AI on OCI — This question tests Deploying and Managing Generative AI on OCI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model's size and number of parameters — When selecting a base model for fine-tuning on OCI Generative AI service, the model's size and number of parameters (B) directly impact computational cost, training time, and the model's capacity to learn from your dataset. The model's license and terms of use (C) are critical because commercial use, redistribution, and fine-tuning rights vary per model (e.g., Llama 2 vs. GPT-based models), and violating these can lead to legal or compliance issues.
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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