Generative AI Leader Fundamentals of Generative AI Practice Question
A media company is using a foundation model in Vertex AI to summarize long articles. They notice that summaries sometimes omit key details from the middle of very long articles. Which action should they take to improve summary completeness while staying within the model's context window?
⚠ Common exam trap
The trap here is thinking that prompt wording or temperature can force a model to retain content that falls outside its effective context window.
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
✓
Split the article into chunks, summarize each chunk, then combine the summaries in a final prompt.
Long articles can exceed a model's effective context, causing middle content to be overlooked. Chunking the article, summarizing each part, and then combining those summaries ensures all sections are processed and represented. This hierarchical approach stays within context limits and improves completeness, directly addressing the observed omission of key details.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a system instruction telling the model to always include all key details.
Why it's wrong here
Instructions alone cannot overcome the fundamental limit of the context window. If the article's middle content is truncated or not attended to, the model cannot include what it never effectively receives. While clear instructions help, they do not replace the need to fit content within the model's input capacity, so this option does not reliably fix the omission.
- ✓
Split the article into chunks, summarize each chunk, then combine the summaries in a final prompt.
Why this is correct
Chunking and hierarchical summarization keeps each request within the context window and ensures middle content is processed. By summarizing segments separately and then synthesizing, the model can capture details that would otherwise be dropped due to long-context limitations. This approach is a standard pattern for handling documents that exceed a single prompt's capacity while maintaining completeness.
- ✗
Switch to a smaller model variant to reduce latency and cost.
Why it's wrong here
A smaller model typically has a reduced context window and lower capacity to retain details, which would worsen the omission problem. The goal is to improve completeness, not reduce cost or latency. Choosing a smaller model ignores the root cause of the issue, which is input length relative to the model's context handling, and would likely degrade summary quality further.
- ✗
Increase the model's temperature setting to encourage more creative output.
Why it's wrong here
Temperature controls randomness, not context retention. Raising it would make summaries more varied and possibly less faithful, not more complete. The issue is that the article exceeds or strains the context window, causing middle content to be missed. Temperature adjustment does not address input length or positional attention limitations, so it would not solve the omission problem.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.