Quick Answer
The answer is training data source and diversity, along with pricing per token and model architecture. Training data source and diversity are fundamental because a foundation model’s knowledge, bias, and domain suitability are directly shaped by the breadth and quality of its training corpus—models trained on narrow or outdated data will struggle with nuanced text generation tasks. On the AWS Certified AI Practitioner AIF-C01 exam, this factor tests your understanding that model performance begins upstream with data provenance, not just fine-tuning. A common trap is focusing solely on model size or benchmark scores while ignoring that a model’s training data may lack the specific domain or language diversity your task requires. Pricing per token is equally critical, as AWS Bedrock and similar services charge per input and output token, making cost a decisive operational factor for high-volume generation. Remember the mnemonic “DCP” for Data, Cost, and Performance—three pillars that together determine the right foundation model for any text generation workload.
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?
Supported output modalities
Why wrong: Output modalities matter for multi-modal but not for text-only generation.
Pricing per token
Cost per token affects operational expense.
Model size (parameters)
Model size influences capability and cost.
Training data source and diversity
Relevant and diverse training data improves task performance.
Availability of automatic scaling
Why wrong: Auto scaling is infrastructure, not a model characteristic.
Answer choices
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
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
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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