1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A data scientist wants to improve the accuracy of a summarization model on medical texts. Which OCI service feature is most suitable?
⚠ Common exam trap
It's easy for candidates to confuse the OCI Language service's pre-built summarization capabilities with the ability to customize a model for a specialized domain, overlooking that fine-tuning is required for significant accuracy improvements on niche text like medical records.
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
✓
OCI Generative AI fine-tuning
C is correct because OCI Generative AI fine-tuning allows a data scientist to adapt a pre-trained large language model (LLM) specifically for medical text summarization by training it on domain-specific data. This improves accuracy by aligning the model's outputs with the terminology, context, and nuances of medical literature, which generic models may not capture well.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
OCI Data Flow
Why it's wrong here
Data Flow is for large-scale data processing, not model customization.
- ✗
OCI Language service
Why it's wrong here
Language service offers pre-built NLP but does not support fine-tuning.
- ✓
OCI Generative AI fine-tuning
Why this is correct
Fine-tuning adapts a model to domain-specific data, improving accuracy.
- ✗
OCI Anomaly Detection
Why it's wrong here
Anomaly Detection is for identifying unusual patterns, not summarization.
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