hardMultiple Select
AIF-C01 Practice Question: Fine-tuning an Amazon Titan Text model on custom…
A company is fine-tuning an Amazon Titan Text model on custom data using Amazon Bedrock. They want to ensure the fine-tuned model retains general language capabilities while learning domain-specific knowledge. Which THREE best practices should they follow? (Select THREE.)
⚠ Common exam trap
A common misconception in fine-tuning LLMs is that freezing layers or using only domain-specific data is effective, when in practice, low learning rates and diverse datasets are required to prevent catastrophic forgetting and overfitting.
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
✓
Monitor validation loss to detect overfitting and stop training early if needed
Option C is correct because monitoring validation loss during fine-tuning lets you detect when the model begins to overfit the domain data and apply early stopping, preserving generalization to broader language tasks. Option D is correct because using a low learning rate makes smaller weight updates, which reduces the risk of catastrophic forgetting of the model's pre-trained general language capabilities while it absorbs domain knowledge. Option E is correct because mixing general and domain-specific examples in the training dataset helps the fine-tuned model retain broad language understanding alongside the new specialized knowledge. Option A is not appropriate here because freezing the first few layers is a parameter-efficient technique aimed at reducing overfitting and compute, not a stated best practice for preserving general capabilities in Amazon Bedrock Titan fine-tuning. Option B is incorrect because training only on domain-specific data maximizes specialization at the expense of general language ability, which is exactly the outcome the company wants to avoid.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Freeze the first few layers of the model to prevent overfitting
Why it's wrong here
Bedrock fine-tuning adjusts the full model; freezing early layers is a technique for frameworks like PyTorch or SageMaker, not an exposed Bedrock option. It tempts because layer freezing genuinely preserves general capabilities in custom training pipelines, but Bedrock offers no such control.
- ✗
Train only on domain-specific data to maximize accuracy
Why it's wrong here
Training solely on domain data causes catastrophic forgetting, erasing the general language capabilities the company wants retained. It is tempting because maximising in-domain accuracy is the usual goal of fine-tuning; mixing general and domain data is what preserves both, which Bedrock supports.
- ✓
Monitor validation loss to detect overfitting and stop training early if needed
Why this is correct
Monitoring validation loss detects divergence between training and held-out data, triggering early stopping before the model memorises domain samples and degrades general language capabilities. This directly satisfies the stem's constraint of retaining general ability while acquiring domain knowledge during Amazon Bedrock fine-tuning.
- ✓
Use a low learning rate to avoid catastrophic forgetting
Why this is correct
A low learning rate limits the magnitude of weight updates during fine-tuning, so the model's pretrained representations shift gradually rather than being overwritten. This mitigates catastrophic forgetting, preserving general language capability while domain knowledge is absorbed.
- ✓
Use a diverse dataset that includes both general and domain-specific examples
Why this is correct
Mixing general and domain-specific examples during fine-tuning prevents catastrophic forgetting, the tendency of gradient updates on narrow data to overwrite the pretrained weights underpinning broad language ability. This directly satisfies the stem's constraint that the model retain general capabilities while acquiring domain knowledge, since the general examples anchor those representations.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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