The answer is model size and architecture, along with licensing and usage terms and the model’s fine-tuning for code. Model size and architecture directly affect inference latency, memory footprint, and cost on SageMaker, while licensing terms—such as those for Code Llama or StarCoder—determine legal compliance for commercial use. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your ability to balance technical constraints with governance, often appearing as a multi-select scenario where a tempting distractor is “training data size” (which is irrelevant for pre-trained models). A common trap is overlooking license restrictions when deploying on SageMaker, so remember the mnemonic “LAM” for Licensing, Architecture, and Model specialization—three pillars that prevent both performance bottlenecks and legal headaches.
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.
A data science team is evaluating foundation models for a code generation task. They need a model that is fine-tuned for code and can be deployed on Amazon SageMaker. Which THREE criteria are important to consider when selecting a model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Licensing and usage terms
Option A is correct because licensing and usage terms directly impact whether a foundation model can be legally used for commercial code generation. Models like Code Llama or StarCoder have specific licenses (e.g., Llama 2 Community License, OpenRAIL-M) that may restrict fine-tuning, redistribution, or use in proprietary products. Ignoring these terms could lead to compliance violations when deploying on Amazon SageMaker.
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.
✓
Licensing and usage terms
Why this is correct
Must be compatible with the deployment and business use.
Related concept
Read the scenario before looking for a memorised answer.
✗
Cost per token for inference
Why it's wrong here
Cost is important but secondary to technical and legal fit.
✓
Context window length
Why this is correct
A larger context window allows processing longer code sequences.
Related concept
Read the scenario before looking for a memorised answer.
✗
The training algorithm used
Why it's wrong here
The algorithm is not a primary selection criterion for pre-trained models.
✓
Model size and architecture
Why this is correct
Model size affects inference speed and memory requirements.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between model selection criteria (licensing, context window, architecture) and operational concerns (cost, training internals), tempting candidates to pick cost per token or training algorithm as relevant when they are not primary factors for selecting a fine-tuned model for deployment.
Detailed technical explanation
How to think about this question
Context window length (Option C) is critical for code generation because it determines how many lines of code or surrounding context the model can process at once; for example, a 4K token window may truncate large functions or files, while 32K+ windows (as in Code Llama 34B) allow handling entire repositories. Model size and architecture (Option E) affect inference latency, memory footprint, and fine-tuning feasibility on SageMaker; larger models (e.g., 70B parameters) require multi-GPU instances like ml.p4d.24xlarge, while smaller models (7B) can run on a single GPU, impacting cost and deployment complexity.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Licensing and usage terms — Option A is correct because licensing and usage terms directly impact whether a foundation model can be legally used for commercial code generation. Models like Code Llama or StarCoder have specific licenses (e.g., Llama 2 Community License, OpenRAIL-M) that may restrict fine-tuning, redistribution, or use in proprietary products. Ignoring these terms could lead to compliance violations when deploying on Amazon SageMaker.
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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Question Discussion
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