AIF-C01 Licensing and Usage Terms Practice Question
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?
⚠ Common exam trap
Candidates may focus on technical aspects like model architecture or training algorithm, overlooking the practical constraints of licensing, cost, and context window, which are equally critical for deployment.
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
✓
Licensing and usage terms
Option A (Licensing and usage terms) is correct because foundation models on SageMaker come with varying licenses (e.g., Apache 2.0, Llama Community License, or proprietary commercial terms), and the team must verify the license permits their intended commercial code-generation use case before deployment. Option B (Cost per token for inference) is correct because SageMaker endpoints bill based on instance hours and/or token throughput, so understanding per-token inference cost is essential for budgeting a production code-generation workload at scale. Option C (Context window length) is correct because code generation often requires ingesting large files or repositories, and a model's maximum context window (e.g., 4K, 8K, 32K, or 128K tokens) directly determines how much source code can be passed in a single prompt. Option D is not a primary selection criterion because the training algorithm (e.g., RLHF, SFT) is an internal implementation detail that does not directly affect deployment fit or task suitability. Option E is not a primary criterion because model size and architecture are secondary considerations already reflected in measurable factors like cost, latency, and context window, rather than being a distinct decision driver for this scenario.
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
Licensing and usage terms determine whether a code generation model can legally be deployed commercially on Amazon SageMaker, covering permitted use, redistribution and derivative works. This satisfies the stem's deployment constraint, since unsuitable terms block production use regardless of model quality or code fine-tuning.
- ✓
Cost per token for inference
Why this is correct
Cost per token for inference governs the running expense of deploying the code generation model on Amazon SageMaker at scale. This satisfies the stem's deployment requirement, since token pricing directly affects feasibility for high-volume code generation workloads and budget planning.
- ✓
Context window length
Why this is correct
Context window length determines how much code and prompt text the model can process at once, directly affecting code generation quality on large files. This satisfies the stem's code generation requirement, since insufficient context truncates inputs and degrades output for repository-scale tasks.
- ✗
The training algorithm used
Why it's wrong here
The training algorithm is an implementation detail of how a model was produced, not a selection criterion for a pre-trained foundation model. It is tempting because algorithm choice matters when training custom models from scratch, but here the team selects an existing code-tuned model for SageMaker deployment, so inference fit and licensing govern.
- ✗
Model size and architecture
Why it's wrong here
Model size and architecture are valid selection factors, but the question asks which criteria matter for a code-tuned model deployable on SageMaker; this option is listed among the incorrect set here. It is tempting because size drives latency, cost and context limits, and architecture determines capability, so it would be correct for general model evaluation.
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