AIF-C01 Applications of Foundation Models Practice Question
Which THREE of the following are factors to consider when selecting a foundation model for a text generation task?
⚠ Common exam trap
AWS often tests the distinction between model-level attributes (e.g., token pricing, training data, parameter count) and platform-level operational features (e.g., scaling, output modalities), leading candidates to incorrectly select options like automatic scaling or multimodal support for a text-only task.
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
✓
Pricing per token
Pricing per token is a critical factor because foundation model APIs (e.g., Amazon Bedrock, OpenAI) charge based on the number of input and output tokens. For text generation tasks, token costs directly impact operational budgets, especially for high-volume or long-context applications. Selecting a model with lower per-token pricing can significantly reduce inference costs without sacrificing quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Supported output modalities
Why it's wrong here
Output modalities matter for multi-modal but not for text-only generation.
- ✓
Pricing per token
Why this is correct
Cost per token affects operational expense.
- ✓
Model size (parameters)
Why this is correct
Model size influences capability and cost.
- ✓
Training data source and diversity
Why this is correct
Relevant and diverse training data improves task performance.
- ✗
Availability of automatic scaling
Why it's wrong here
Auto scaling is infrastructure, not a model characteristic.
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