1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A company uses OCI GenAI to build a content moderation system that filters toxic language in user-generated comments. They have a small labeled dataset of 1,000 comments (500 toxic, 500 non-toxic) and need an efficient solution that balances accuracy, cost, and latency. They are considering different model options: fine-tuning a large LLM (e.g., Cohere Command), using a pre-trained LLM with prompting, fine-tuning a smaller BERT-based classifier, or building a rule-based system. The team has moderate ML experience and wants to deploy using OCI Data Science. Which approach is most efficient for this binary classification task?
⚠ Common exam trap
Oracle often tests the misconception that larger LLMs (like Cohere Command) are always superior for classification tasks, ignoring the practical constraints of small datasets, cost, and latency that make fine-tuned BERT models the optimal choice for binary classification.
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 a BERT-based classifier (e.g., 'bert-base-uncased') on the dataset.
Fine-tuning a BERT-based classifier (e.g., 'bert-base-uncased') is the most efficient approach because BERT is specifically designed for text classification tasks, requiring far fewer computational resources and lower latency than large LLMs. With only 1,000 labeled samples, BERT can achieve high accuracy through transfer learning, while keeping inference costs minimal—ideal for a production content moderation system on OCI Data Science.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fine-tune a BERT-based classifier (e.g., 'bert-base-uncased') on the dataset.
Why this is correct
BERT is efficient for classification, fine-tunes quickly on small data, and has low inference cost.
- ✗
Develop a rule-based system using regular expressions and keyword lists.
Why it's wrong here
Rules are brittle and cannot handle diverse or adversarial toxic language.
- ✗
Use a pre-trained LLM with a toxic/non-toxic prompt.
Why it's wrong here
Prompting is less reliable and more expensive per request than a dedicated classifier.
- ✗
Fine-tune the Cohere Command model on the labeled dataset.
Why it's wrong here
Large LLMs are costly and slow for simple classification tasks with small data.
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