Question 488 of 991

Using OCI Generative AI Service →mediumMultiple ChoiceObjective-mapped

When to Fine-Tune

This 1Z0-1127 practice question tests your understanding of using oci generative ai service. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.

A healthcare startup uses OCI Generative AI to automatically generate patient summary reports from clinical notes. They use the Cohere command model (command-r-plus) with default parameters. Over the past week, the team has noticed two issues: (1) the summaries occasionally contain medical inaccuracies, such as incorrect drug dosages or misinterpreted lab results, and (2) the response time has increased from an average of 2 seconds to over 10 seconds. The application has a high volume of concurrent requests, and the startup has already increased the max tokens to 4096 and set temperature to 0.1. The model appears to perform well on general language tasks but struggles with specialized medical terminology. The team is looking for a long-term solution that balances accuracy, latency, and cost. What should they do?

A
Switch to a larger base model, such as Llama 2 70B, to improve knowledge of medical terms
Why wrong: Larger models generally have higher latency and cost, and may still not be specialized enough without fine-tuning. This is not the most efficient solution.
B
Fine-tune the Cohere command model using a curated dataset of medical notes and correct summaries
Fine-tuning adapts the model to the medical domain, improving accuracy for the specific use case. This also reduces the need for high token counts, indirectly improving latency.
C
Implement a caching layer to store and reuse responses for identical queries
Why wrong: Caching reduces latency for repeated queries but does not solve inaccuracies for new or slightly different patient notes.
D
Reduce the max tokens parameter from 4096 to 1024 to decrease response time
Why wrong: Reducing max tokens may lower latency but does not address the medical inaccuracies; it could even truncate important information.

Full breakdown with real-world context →

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

✓

Fine-tune the Cohere command model using a curated dataset of medical notes and correct summaries

Fine-tuning the Cohere command model on a curated dataset of medical notes and correct summaries directly addresses the core issue: the model's poor performance on specialized medical terminology. Unlike prompt engineering or parameter adjustments, fine-tuning adapts the model's weights to the medical domain, improving accuracy without sacrificing latency or incurring the high cost of a larger base model. This approach balances accuracy, latency, and cost by using a smaller, optimized model that can handle concurrent requests efficiently.

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.

✗
Switch to a larger base model, such as Llama 2 70B, to improve knowledge of medical terms
Why it's wrong here
Larger models generally have higher latency and cost, and may still not be specialized enough without fine-tuning. This is not the most efficient solution.
✓
Fine-tune the Cohere command model using a curated dataset of medical notes and correct summaries
Why this is correct
Fine-tuning adapts the model to the medical domain, improving accuracy for the specific use case. This also reduces the need for high token counts, indirectly improving latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Implement a caching layer to store and reuse responses for identical queries
Why it's wrong here
Caching reduces latency for repeated queries but does not solve inaccuracies for new or slightly different patient notes.
✗
Reduce the max tokens parameter from 4096 to 1024 to decrease response time
Why it's wrong here
Reducing max tokens may lower latency but does not address the medical inaccuracies; it could even truncate important information.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Oracle often tests the misconception that larger models or simple parameter tweaks (like temperature or max tokens) can solve domain-specific accuracy issues, when in fact fine-tuning is the appropriate long-term solution for specialized tasks.

Detailed technical explanation

How to think about this question

Fine-tuning uses a curated dataset to adjust the model's weights via supervised learning, which is more effective than prompt engineering for domain adaptation. The Cohere command-r-plus model supports fine-tuning through OCI Generative AI, allowing the startup to create a specialized version that retains the base model's efficiency while improving accuracy on medical terminology. Under the hood, fine-tuning modifies the attention mechanisms and embedding layers to prioritize medical context, reducing the need for larger models or expensive inference-time adjustments.

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.

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FAQ

Questions learners often ask

What does this 1Z0-1127 question test?

Using OCI Generative AI Service — This question tests Using OCI Generative AI Service — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Fine-tune the Cohere command model using a curated dataset of medical notes and correct summaries — Fine-tuning the Cohere command model on a curated dataset of medical notes and correct summaries directly addresses the core issue: the model's poor performance on specialized medical terminology. Unlike prompt engineering or parameter adjustments, fine-tuning adapts the model's weights to the medical domain, improving accuracy without sacrificing latency or incurring the high cost of a larger base model. This approach balances accuracy, latency, and cost by using a smaller, optimized model that can handle concurrent requests efficiently.

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.

About these practice questions

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Same concept, more angles

2 more ways this is tested on 1Z0-1127

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A company wants to use OCI Generative AI service to automatically generate product descriptions for an e-commerce catalog. They have 10,000 products. What is the best approach to ensure high-quality, consistent descriptions?

medium

A.Use a pre-trained summarization model.
B.Use a template-based generation with keyword insertion.
C.Use the built-in chat model with few-shot examples in the prompt.
✓ D.Fine-tune a base model on a dataset of existing product descriptions.

Why D: Fine-tuning a base model on a dataset of existing product descriptions is the best approach because it adapts the model to the specific domain, style, and vocabulary of the e-commerce catalog. This ensures high-quality, consistent outputs across 10,000 products by learning the patterns and terminology from the company's own data, rather than relying on generic or template-based methods.

Variation 2. Which THREE factors should be considered when choosing between fine-tuning a model and using a pre-trained model with prompt engineering? (Select three.)

hard

A.Required response time
✓ B.Size of available dataset
C.Internet connectivity
✓ D.Available budget for compute resources
✓ E.Need for domain-specific terminology

Why B: Option B is correct because the size of the available dataset is a critical factor: fine-tuning requires a sufficiently large, labeled dataset (typically thousands of examples) to adjust model weights effectively, while prompt engineering can work with zero or few examples. If the dataset is too small, fine-tuning risks overfitting and poor generalization, making prompt engineering the safer choice.

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