AIP-C01 practice question: Coordinating several models (specialists, ensembles, routing)
Implementation and Integration A free question from our original AIP-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A media company runs a content platform where users upload posts that mix photos, code snippets and long articles. Each post goes through one processing pipeline that must produce three outputs: tags for each photo, a review of each code snippet, and a summary of each article.
The quality target differs by task. Image tagging, code review and summarization each need the strongest model that is available for that task, and the team has evaluated several candidates for each one. Summaries must appear in under three seconds, and the monthly model spend must stay within budget.
The team also wants to replace the model for one task without redeploying the other two tasks.
Which architecture meets these requirements?
Choose one answer.
AA. Send every post to one large general-purpose multimodal model with a long system prompt that covers tagging, code review and summarization, and parse the three outputs from one response.
BB. Use a routing step that detects each part's content type and calls the specialist model that scored best for that task, each with its own prompt and configuration.
CC. Send each post to all three candidate models for each task, and use a fourth model to choose the best output, so that the strongest answer is always selected.
DD. Fine-tune one small text model on examples of all three task types, and convert each photo to a text caption first so that the same model handles every part of a post.
Show the answer and explanation
Answer: B
A. Wrong: One model call per post is simple and cheap, but the single prompt cannot be tuned per task. Swapping that model would also change all three outputs at once, which breaks the independent replacement requirement.
B. Right: A router sends each part to the model that scored best in evaluation, and each specialist keeps its own prompt and configuration. Only one specialist changes when a better model appears, so the other tasks are not redeployed.
C. Wrong: Calling every candidate multiplies model spend and adds a second model call on the critical path, which makes the three-second summary target harder to meet. Choosing among outputs needs its own evaluation as well.
D. Wrong: A captioned photo loses detail that tagging depends on, so the tagging quality target is missed. One shared model also couples all three tasks, so replacing one of them means retraining and redeploying the rest.
Explanation
Specialist models chosen per task reach the quality target for each part of a post, and a router sends each part only to the model that handles it. Because each specialist has its own configuration, one can be replaced without touching the others. A single general model, an ensemble of every candidate and a single fine-tuned text model each fail at least one of the requirements.
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