easyMultiple Select
AIF-C01 Practice Question: A developer is selecting a foundation model on…
A developer is selecting a foundation model on Amazon Bedrock for a real-time text summarization application. Which THREE factors should they consider when choosing the model? (Choose three.)
⚠ Common exam trap
AWS often tests the ability to distinguish between essential technical requirements (like latency, cost, and output modality) and superficial or irrelevant features (like logo color or unrelated capabilities) to see if candidates focus on functional criteria for model selection.
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
✓
Latency and throughput requirements
Option B is correct because a real-time text summarization application demands low latency and high throughput, so the developer must evaluate each Bedrock foundation model's inference speed and tokens-per-second capacity to meet interactive response times. Option D is correct because Bedrock charges based on input and output tokens, so the cost per token directly affects the operating budget of a high-volume summarization workload. Option E is correct because the model must support the required output modality—text (and possibly code)—for summarization; models limited to other modalities such as embeddings or image generation would not satisfy the use case. Option A is incorrect because a model's logo color is purely cosmetic branding and has no bearing on technical or business fit. Option C is incorrect because image generation is irrelevant to text summarization, and selecting a model for that capability would waste cost and latency on unneeded functionality.
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 color of the model's logo
Why it's wrong here
Logo colour is cosmetic branding with no bearing on inference latency, context window, or output quality. It is tempting because vendor marketing pages do display model logos prominently, yet the correct factors are latency, supported languages, and context length.
- ✓
Latency and throughput requirements
Why this is correct
Latency and throughput determine how quickly requests return and how many concurrent summarisations the model sustains. This satisfies the real-time constraint: a model with slow token generation or low throughput cannot meet interactive summarisation demands, regardless of output quality.
- ✗
Model's ability to generate images
Why it's wrong here
Image generation is irrelevant to real-time text summarisation, which needs text-in, text-out models. It tempts because multimodal Bedrock models such as Titan Image Generator or Stable Diffusion do produce images, but that capability suits creative asset generation, not summarising documents.
- ✓
Cost per token for inference
Why this is correct
Inference cost per token directly determines the running expense of a real-time summarisation service, where every request consumes input and output tokens. Since summarisation runs continuously, this factor governs whether the workload stays within budget, making it a primary selection criterion alongside latency and quality.
- ✓
Supported output modalities (text, code, etc.)
Why this is correct
Supported output modalities determine whether the model can return the format the summarisation application requires, such as plain text versus code or structured output. A model lacking the needed modality cannot serve the use case regardless of other strengths, so this capability must be verified before selection.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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