easyMultiple Choice
Generative AI Leader Practice Question: A developer wants to experiment with Gemini Nano…
A developer wants to experiment with Gemini Nano for on-device inference in a mobile app. Which Gemini API tier or environment provides access to Gemini Nano?
⚠ Common exam trap
A common pitfall in this exam is assuming all Gemini models are accessed through the same cloud API (Vertex AI, AI Studio, or Gemini API). Gemini Nano is specifically designed for on-device inference and is accessed via MediaPipe and Android AICore on mobile devices.
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
✓
MediaPipe and Android AICore
Gemini Nano is the smallest Gemini model designed specifically for on-device inference on mobile devices. Access to Gemini Nano is provided through MediaPipe and Android AICore, which are the frameworks that enable running the model locally on Android devices without requiring a network connection to Google's cloud servers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vertex AI
Why it's wrong here
Vertex AI hosts and tunes Gemini models in Google Cloud, requiring network calls to a remote endpoint; it offers no mechanism to package Gemini Nano into a mobile binary. It is tempting as the enterprise platform for model deployment, and would be correct when serving models from cloud infrastructure rather than running inference on the device itself.
- ✗
Google AI Studio
Why it's wrong here
Google AI Studio is a browser-based prototyping environment that calls cloud Gemini models through the API; it cannot compile Gemini Nano into an Android or iOS app. It is tempting because it provides free experimentation with Gemini, and would be correct for prompt prototyping against hosted models, not for on-device mobile inference.
- ✗
Gemini API directly
Why it's wrong here
The Gemini API serves cloud-hosted models such as Gemini Pro and Flash over Google's servers; it exposes no on-device runtime, so it cannot load Gemini Nano onto a handset. It is tempting because the API is the standard entry point for Gemini development, and it would be right for cloud inference rather than local mobile execution.
- ✓
MediaPipe and Android AICore
Why this is correct
Gemini Nano runs on-device rather than through a cloud endpoint, so it is accessed via MediaPipe and Android AICore on supported Android hardware. This satisfies the stem's constraint of on-device inference within a mobile app, which the cloud Gemini API tiers cannot provide.
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