- A
Use a pipeline to split videos into short clips, extract key frames, and process with Gemini 1.5 Pro (with context caching) to generate summaries.
B is correct because it handles variable-length content efficiently within model limits.
- B
Use Video Intelligence API to generate video captions, then feed captions to a text model.
Why wrong: D is wrong because it's more for analysis than generation, and adds complexity.
- C
Convert all inputs to text descriptions and use a text-only model.
Why wrong: C is wrong because it loses visual information and is not truly multi-modal.
- D
Deploy a single Vertex AI endpoint with a model that can ingest multi-modal data directly.
Why wrong: A is wrong because long videos may exceed model context windows and cause memory issues.
Quick Answer
The most scalable and cost-effective architecture is a pipeline that splits videos into short clips, extracts key frames, and processes them with Gemini 1.5 Pro using context caching. This approach is correct because it directly addresses the core challenge of a scalable multi-modal generative AI architecture: handling variable-length video inputs without incurring prohibitive token costs or latency. By breaking down long videos into manageable clips and isolating key frames, you dramatically reduce the computational load, while Gemini 1.5 Pro’s context caching reuses processed context across requests, making it ideal for videos up to 10 minutes. On the Google Cloud Generative AI Leader exam, this scenario tests your understanding of cost-optimized multi-modal pipelines, often appearing as a trap where candidates choose to process entire videos end-to-end, which wastes tokens and storage. Remember the memory tip: “Clip, cache, and key-frame” — the three pillars of efficient multi-modal summarization.
Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
This Generative AI Leader practice question tests your understanding of business strategies for generative ai solutions. 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 media company wants to build a multi-modal generative app that accepts text, image, and video inputs and produces summaries. The app must handle variable-length videos up to 10 minutes. Which architecture is most scalable and cost-effective?
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
Use a pipeline to split videos into short clips, extract key frames, and process with Gemini 1.5 Pro (with context caching) to generate summaries.
Option A is correct because splitting videos into short clips and extracting key frames reduces the computational load and token usage, while Gemini 1.5 Pro's context caching efficiently handles variable-length videos up to 10 minutes by reusing processed context across requests. This approach balances scalability (by avoiding processing entire videos at once) and cost-effectiveness (by minimizing API calls and storage), making it ideal for a multi-modal summarization app.
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.
- ✓
Use a pipeline to split videos into short clips, extract key frames, and process with Gemini 1.5 Pro (with context caching) to generate summaries.
Why this is correct
B is correct because it handles variable-length content efficiently within model limits.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Video Intelligence API to generate video captions, then feed captions to a text model.
Why it's wrong here
D is wrong because it's more for analysis than generation, and adds complexity.
- ✗
Convert all inputs to text descriptions and use a text-only model.
Why it's wrong here
C is wrong because it loses visual information and is not truly multi-modal.
- ✗
Deploy a single Vertex AI endpoint with a model that can ingest multi-modal data directly.
Why it's wrong here
A is wrong because long videos may exceed model context windows and cause memory issues.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that a single multi-modal endpoint is inherently scalable, but the trap here is that direct ingestion of raw video without preprocessing (like key frame extraction) leads to prohibitive token costs and latency, making pipeline-based approaches with caching more practical for variable-length inputs.
Detailed technical explanation
How to think about this question
Gemini 1.5 Pro's context caching works by storing processed tokens from previous requests in a cache, reducing latency and cost for repeated or overlapping content—critical for video summarization where key frames may be reused across clips. Under the hood, the model uses a transformer architecture with a 1M token context window, allowing it to handle up to ~1 hour of video when key frames are extracted at 1 fps, but caching avoids reprocessing the entire video for each summary request. In practice, this architecture is used by media companies like those on Google Cloud to summarize live streams or archived footage, where splitting into 10-second clips and caching scene-level embeddings cuts costs by up to 60%.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Business Strategies for Generative AI Solutions — This question tests Business Strategies for Generative AI Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a pipeline to split videos into short clips, extract key frames, and process with Gemini 1.5 Pro (with context caching) to generate summaries. — Option A is correct because splitting videos into short clips and extracting key frames reduces the computational load and token usage, while Gemini 1.5 Pro's context caching efficiently handles variable-length videos up to 10 minutes by reusing processed context across requests. This approach balances scalability (by avoiding processing entire videos at once) and cost-effectiveness (by minimizing API calls and storage), making it ideal for a multi-modal summarization app.
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: Jun 30, 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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