Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A developer wants to use Gemini 1.5 Pro to analyze hour-long video content and generate a summary. Which feature of Gemini 1.5 Pro is most suitable for this task?
⚠ Common exam trap
Many exam-takers confuse 'multimodal generation' (Option B) with the ability to process video, but Gemini 1.5 Pro's long context window is the specific feature designed for handling hour-long video content, not just the ability to handle multiple data types.
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
✓
Long context window (up to 1 million tokens)
Gemini 1.5 Pro's long context window of up to 1 million tokens allows it to process and analyze hour-long video content in a single pass, including both audio and visual frames. This capability is essential for generating a coherent summary of long-form video, as it can retain and reason over the entire video's context without needing to chunk or downsample the content.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Long context window (up to 1 million tokens)
Why this is correct
Gemini 1.5 Pro's context window accepts up to one million tokens, allowing an entire hour-long video plus its prompt to be processed in a single request. This satisfies the stem's requirement to analyse full-length video and generate a summary without chunking.
- ✗
Multimodal generation from text and images
Why it's wrong here
Multimodal generation from text and images covers text-and-image inputs and outputs, not hour-long video analysis, so it fails the video-summarisation requirement. It is tempting because it handles mixed media, and would be correct for generating images from text prompts or describing still images.
- ✗
Code generation and debugging
Why it's wrong here
Code generation and debugging produces and fixes source code; it cannot process hour-long video content or summarise it. It is tempting because Gemini's coding ability is widely used, and would be correct for writing functions, refactoring, or diagnosing errors in a codebase.
- ✗
Function calling to retrieve external data
Why it's wrong here
Function calling retrieves external data or triggers APIs; it does not ingest or reason over hour-long video, so it cannot produce the summary. It is tempting because function calling suits tasks needing live or external data, and would be correct for querying a database or API during generation.
Go deeper
Related to this question
About these practice questions
This Generative AI Leader question is part of Courseiva's 1,008-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.