The correct answer is to increase the `max_tokens` parameter, typically to 1000, because this parameter defines the maximum number of tokens the model can generate in a single response. When a response stops mid-sentence, it indicates the token limit was exhausted before the model could complete its output, so raising the limit provides the necessary room for full generation. On the Microsoft Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding of API configuration and response handling, often appearing in troubleshooting questions where truncation is the symptom. A common trap is confusing `max_tokens` with `temperature` or `top_p`, which control creativity, not response length. Remember the memory tip: “Truncation means tokens are done—raise the ceiling to get the full run.”
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Exhibit
{
"model": "gpt-4",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is the capital of France?"}
],
"max_tokens": 500,
"temperature": 0.7,
"top_p": 0.95,
"frequency_penalty": 0,
"presence_penalty": 0,
"stop": null
}
Refer to the exhibit. You are troubleshooting an Azure OpenAI API call that is returning incomplete responses. The response stops mid-sentence. Which parameter should you adjust?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase max_tokens to 1000.
The `max_tokens` parameter controls the maximum number of tokens the model can generate in a single response. When a response stops mid-sentence, it typically means the token limit was reached before the model could complete its output. Increasing `max_tokens` to 1000 provides more room for the model to finish its generation, resolving the truncation issue.
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.
✓
Increase max_tokens to 1000.
Why this is correct
Increases token budget for response.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove the stop parameter.
Why it's wrong here
Stop is null, so not causing issue.
✗
Increase temperature to 1.0.
Why it's wrong here
Temperature does not limit length.
✗
Increase top_p to 1.0.
Why it's wrong here
Top_p does not limit length.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse parameters that control output length (`max_tokens`) with those that control output diversity (`temperature`, `top_p`) or early stopping (`stop`), leading them to pick options that change style rather than capacity.
Detailed technical explanation
How to think about this question
Under the hood, `max_tokens` defines the total token budget shared between the prompt and the completion. If the prompt is long, the available tokens for the response shrink, so increasing `max_tokens` to 1000 ensures the model has sufficient capacity to finish. In real-world scenarios, this parameter is critical for tasks like code generation or long-form text, where the default value (often 16 or 256) is too low for complete outputs.
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 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
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.
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase max_tokens to 1000. — The `max_tokens` parameter controls the maximum number of tokens the model can generate in a single response. When a response stops mid-sentence, it typically means the token limit was reached before the model could complete its output. Increasing `max_tokens` to 1000 provides more room for the model to finish its generation, resolving the truncation issue.
What should I do if I get this AI-102 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.
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Question Discussion
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