Quick Answer
The correct approach is to split the long article into smaller sections and summarize each section separately. This is necessary because Azure OpenAI models, such as GPT-3.5 or GPT-4, have a fixed context window—typically 4096 or 8192 tokens—and a 10,000-token article far exceeds that limit, causing truncation or failure. By applying a chunking strategy, you break the document into token-compliant segments, summarize each independently, and then combine those summaries into a coherent final output, effectively bypassing the context window constraint. On the AI-102 exam, this tests your understanding of token limits and document preprocessing, often appearing as a scenario where you must choose between splitting, truncating, or using a different model. A common trap is assuming you can simply increase the max_tokens parameter, but that only controls output length, not input capacity. Memory tip: think “chunk and merge”—split to fit, summarize each piece, then stitch the summaries together.
This AI-102 practice question tests your understanding of implement generative ai solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
You need to generate a summary of a long article using Azure OpenAI. The article is 10,000 tokens long. What should you do to fit the article within the model's context window?
Split the article into smaller sections and summarize each section separately.
Chunking the input fits within the context window.
Increase the temperature parameter.
Why wrong: Temperature does not affect token limits.
Use a model with a smaller context window.
Why wrong: Smaller context window makes the problem worse.
Set max_tokens to a lower value.
Why wrong: max_tokens limits output length, not input.
Answer choices
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 smaller sections and summarize each section separately.
Option A is correct because the article exceeds the model's context window (typically 4096 or 8192 tokens for GPT-3.5/4). Splitting the article into smaller sections and summarizing each separately allows you to process the entire content within the token limits, then combine the summaries for a final coherent output. This is a standard chunking strategy for long documents when using Azure OpenAI.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Split the article into smaller sections and summarize each section separately.
Why this is correct
Chunking the input fits within the context window.
Related concept
Read the scenario before looking for a memorised answer.
Increase the temperature parameter.
Why it's wrong here
Temperature does not affect token limits.
Use a model with a smaller context window.
Why it's wrong here
Smaller context window makes the problem worse.
Set max_tokens to a lower value.
Why it's wrong here
max_tokens limits output length, not input.
Common exam traps
The trap here is that candidates confuse parameters that control output behavior (temperature, max_tokens) with the fundamental input token limit, leading them to incorrectly believe adjusting these parameters can bypass the context window restriction.
Trap categories for this question
Command / output trap
max_tokens limits output length, not input.
Detailed technical explanation
Under the hood, Azure OpenAI models have a fixed context window (e.g., 4096 tokens for gpt-35-turbo) that includes both input and output tokens. When input exceeds this limit, the API either throws an error or silently truncates the input. Chunking with overlap (e.g., 10-20% overlap) ensures context continuity between sections, and using a map-reduce pattern (summarize each chunk, then summarize the summaries) is a proven technique for long-document summarization in production.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Split the article into smaller sections and summarize each section separately. — Option A is correct because the article exceeds the model's context window (typically 4096 or 8192 tokens for GPT-3.5/4). Splitting the article into smaller sections and summarizing each separately allows you to process the entire content within the token limits, then combine the summaries for a final coherent output. This is a standard chunking strategy for long documents when using Azure OpenAI.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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