- A
Vertex AI
Why wrong: Vertex AI provides access to Gemini Pro and Ultra models, not Nano.
- B
Google AI Studio
Why wrong: Google AI Studio provides access to Gemini Pro and Ultra, not Nano, which is for on-device inference.
- C
Gemini API directly
Why wrong: The Gemini API provides access to cloud models; Nano is an on-device model not accessible via the API.
- D
MediaPipe and Android AICore
Gemini Nano is available through MediaPipe and Android AICore for on-device inference on mobile devices.
Question 18 of 997
Generative AI Leader Google AI Ecosystem and Strategy Practice Question
This Generative AI Leader practice question tests your understanding of google ai ecosystem and strategy. 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 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?
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.
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.
- ✗
Vertex AI
Why it's wrong here
Vertex AI provides access to Gemini Pro and Ultra models, not Nano.
- ✗
Google AI Studio
Why it's wrong here
Google AI Studio provides access to Gemini Pro and Ultra, not Nano, which is for on-device inference.
- ✗
Gemini API directly
Why it's wrong here
The Gemini API provides access to cloud models; Nano is an on-device model not accessible via the API.
- ✓
MediaPipe and Android AICore
Why this is correct
Gemini Nano is available through MediaPipe and Android AICore for on-device inference on mobile devices.
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 cloud-based Gemini access (API, AI Studio, Vertex AI) and on-device inference via MediaPipe/AICore, trapping candidates who assume all Gemini models are accessed through the same cloud API.
Detailed technical explanation
How to think about this question
MediaPipe provides the cross-platform pipeline framework for on-device ML inference, while Android AICore manages model loading, execution, and hardware acceleration (e.g., using GPU, DSP, or NPU) on supported Android devices. Gemini Nano is quantized to 4-bit precision to fit within mobile memory constraints, and its execution is orchestrated by AICore's runtime, which handles model caching and lifecycle management for low-latency inference.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
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Google AI Ecosystem and Strategy — study guide chapter
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google AI Ecosystem and Strategy — This question tests Google AI Ecosystem and Strategy — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this Generative AI Leader 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.
About these practice questions
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Last reviewed: Jul 4, 2026
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.
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