- A
The region where the model is hosted
Why wrong: Region is irrelevant to model capabilities; any region can host the model.
- B
Model size (number of parameters)
Larger models often have better understanding but higher cost.
- C
Cost per inference call
Why wrong: Cost is important but secondary to technical capability for model selection.
- D
Support for the specific language of the documents
Models vary in multilingual support; must cover the document language.
- E
Token limits for input and output
Legal documents may be long, so token limits affect feasibility.
Question 809 of 1,000
AIF-C01 Fundamentals of Generative AI Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. 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.
An organization is evaluating different foundation models (FMs) on Amazon Bedrock for a legal document analysis task. Which THREE factors should they consider when selecting a model? (Choose 3.)
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
✓
Model size (number of parameters)
Model size (number of parameters) is a key factor because it directly influences the model's capacity to understand complex legal language, reasoning, and context. Larger models generally offer higher accuracy and nuanced comprehension, but they also require more computational resources and may have higher latency, which must be balanced against the specific requirements of legal document analysis.
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.
- ✗
The region where the model is hosted
Why it's wrong here
Region is irrelevant to model capabilities; any region can host the model.
- ✓
Model size (number of parameters)
Why this is correct
Larger models often have better understanding but higher cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cost per inference call
Why it's wrong here
Cost is important but secondary to technical capability for model selection.
- ✓
Support for the specific language of the documents
Why this is correct
Models vary in multilingual support; must cover the document language.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Token limits for input and output
Why this is correct
Legal documents may be long, so token limits affect feasibility.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between technical model selection criteria (like parameter count, language support, and token limits) and operational or financial considerations (like region and cost), expecting candidates to recognize that cost and region are not direct determinants of model capability for a given task.
Detailed technical explanation
How to think about this question
Under the hood, model size correlates with the number of parameters and the depth of the neural network, which affects the model's ability to capture long-range dependencies and domain-specific terminology in legal texts. For instance, a 70B parameter model like Llama 3 may outperform a 7B model on tasks requiring precise clause interpretation, but it also demands more GPU memory and may exceed the token limits of certain inference endpoints. In practice, organizations often benchmark multiple models on a representative sample of legal documents to balance accuracy, throughput, and cost.
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?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Model size (number of parameters) — Model size (number of parameters) is a key factor because it directly influences the model's capacity to understand complex legal language, reasoning, and context. Larger models generally offer higher accuracy and nuanced comprehension, but they also require more computational resources and may have higher latency, which must be balanced against the specific requirements of legal document analysis.
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: Jul 4, 2026
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