- A
Supported output modalities
Why wrong: Output modalities matter for multi-modal but not for text-only generation.
- B
Pricing per token
Cost per token affects operational expense.
- C
Model size (parameters)
Model size influences capability and cost.
- D
Training data source and diversity
Relevant and diverse training data improves task performance.
- E
Availability of automatic scaling
Why wrong: Auto scaling is infrastructure, not a model characteristic.
Question 894 of 1,000
AIF-C01 Applications of Foundation Models Practice Question
This AIF-C01 practice question tests your understanding of applications of foundation models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Which THREE of the following are factors to consider when selecting a foundation model for a text generation 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.
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.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Model size (parameters)
Why this is correct
Model size influences capability and cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Training data source and diversity
Why this is correct
Relevant and diverse training data improves task performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Availability of automatic scaling
Why it's wrong here
Auto scaling is infrastructure, not a model characteristic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Trap categories for this question
Command / output trap
Output modalities matter for multi-modal but not for text-only generation.
Detailed technical explanation
How to think about this question
Token-based pricing varies by model architecture; for example, a 7B parameter model may cost $0.004 per 1K tokens while a 70B model costs $0.07 per 1K tokens. Training data source and diversity affect model bias, domain knowledge, and factual accuracy—models trained on diverse, curated datasets (e.g., The Pile, Common Crawl) generalize better for text generation. Model size (parameters) correlates with capacity for complex reasoning but also increases latency and cost, requiring trade-offs based on task requirements.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AIF-C01 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.
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Last reviewed: Jun 30, 2026
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