Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A financial analytics firm is building a generative AI application that must analyze long earnings call transcripts and produce summaries. The transcripts often exceed 200,000 tokens, and the firm wants to minimize cost while maintaining high accuracy. They plan to use Gemini models on Vertex AI. Which approach should they take?
⚠ Common exam trap
The trap here is believing that fine-tuning can overcome a model's context window limitation, when fine-tuning only adapts behavior and does not expand the maximum input length.
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 gemini-1.5-pro with its long context window and pass the entire transcript in a single request.
Gemini 1.5 Pro's extended context window allows the entire transcript to be processed in one request, preserving cross-references and reducing the need for complex chunking pipelines. Chunking loses context, gemini-1.0-pro lacks the required context length, and fine-tuning does not increase context capacity, so the long-context model is the most accurate and cost-effective choice.
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 gemini-1.0-pro and rely on its built-in automatic long-document summarization feature.
Why it's wrong here
Gemini 1.0 Pro does not have a built-in automatic long-document summarization feature, and its context window is much smaller than Gemini 1.5 Pro's. It cannot process a 200,000-token transcript in one request, and no such automatic feature exists, so this approach would fail to meet the requirement.
- ✗
Split the transcript into 1,000-token chunks and summarize each chunk separately, then concatenate the summaries.
Why it's wrong here
Chunking a 200,000-token transcript into 1,000-token pieces and summarizing each independently loses cross-chunk context, which is critical for understanding financial narratives. It also multiplies the number of model calls, increasing latency and cost. The concatenated summaries may miss key relationships, reducing accuracy compared to a single long-context request.
- ✗
Fine-tune a smaller Gemini model on the firm's past transcripts and use it for summarization.
Why it's wrong here
Fine-tuning adjusts model behavior for style or domain adaptation but does not expand the context window. A smaller model still cannot ingest a 200,000-token transcript in one request, and fine-tuning would not solve the length limitation. It also adds training cost and complexity without addressing the core need for long-context processing.
- ✓
Use gemini-1.5-pro with its long context window and pass the entire transcript in a single request.
Why this is correct
Gemini 1.5 Pro supports a context window of up to 2 million tokens, which can accommodate transcripts exceeding 200,000 tokens in a single request. This avoids the complexity and potential accuracy loss of chunking and summarization pipelines, and it often reduces overall cost by eliminating multiple inference calls and preprocessing steps.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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