- A
Vision AI
Vision AI can analyze images to extract features and labels.
- B
Text-to-Speech AI
Why wrong: Text-to-Speech converts text to speech, not image analysis.
- C
Natural Language AI
Why wrong: Natural Language AI processes text, not images.
- D
Translation AI
Why wrong: Translation AI translates text only.
- E
Gemini API
Gemini API can accept images and generate text captions.
Question 212 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 company wants to build a multimodal application that can analyze images and generate captions. They are considering using Google Cloud AI. Which TWO services can be directly used for this purpose?
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
✓
Vision AI
Vision AI (option A) is correct because it provides pre-trained models for image analysis, including object detection and image labeling, which are essential for generating captions. The Gemini API (option E) is correct because it is a multimodal model that can directly process images and text, enabling it to analyze images and produce descriptive captions without needing separate services.
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.
- ✓
Vision AI
Why this is correct
Vision AI can analyze images to extract features and labels.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Text-to-Speech AI
Why it's wrong here
Text-to-Speech converts text to speech, not image analysis.
- ✗
Natural Language AI
Why it's wrong here
Natural Language AI processes text, not images.
- ✗
Translation AI
Why it's wrong here
Translation AI translates text only.
- ✓
Gemini API
Why this is correct
Gemini API can accept images and generate text captions.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may mistakenly think Natural Language AI (option C) can generate captions because it handles text, but it cannot process images, while the Gemini API's multimodal capability is often overlooked in favor of more familiar single-purpose services.
Detailed technical explanation
How to think about this question
Vision AI uses deep learning models like EfficientNet and Vision Transformers to extract features from images, which can be fed into a sequence-to-sequence model (e.g., Show and Tell) for caption generation. The Gemini API, built on a multimodal transformer architecture, processes images and text jointly in a single model, allowing it to generate captions by attending to visual tokens and language tokens simultaneously. In practice, a company could use Vision AI's label detection to get keywords and then pass them to a language model, but Gemini API handles the entire pipeline end-to-end.
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: Vision AI — Vision AI (option A) is correct because it provides pre-trained models for image analysis, including object detection and image labeling, which are essential for generating captions. The Gemini API (option E) is correct because it is a multimodal model that can directly process images and text, enabling it to analyze images and produce descriptive captions without needing separate services.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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