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.
You are developing a solution that uses Azure Document Intelligence to extract data from invoices and then uses Azure OpenAI to summarize the extracted data. The solution occasionally produces summaries that omit key fields like the invoice total. What should you do to improve accuracy?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Define a structured prompt that explicitly requests each field and provide examples
Option D is correct because the issue is that the summarization prompt lacks explicit instructions for which fields to include. By defining a structured prompt that explicitly requests each key field (e.g., invoice total, date, vendor) and providing examples, you guide the Azure OpenAI model to consistently extract and include those fields in the summary, reducing omission errors. This approach leverages prompt engineering to improve output reliability without changing model parameters or size.
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.
✗
Set temperature to 0 to make the output more deterministic
Why it's wrong here
Temperature 0 reduces randomness but doesn't enforce completeness.
✗
Use a larger model like GPT-4 instead of GPT-3.5
Why it's wrong here
A larger model may still omit fields if not instructed properly.
✗
Increase the max_tokens parameter
Why it's wrong here
More tokens allow longer output but don't guarantee inclusion of specific fields.
✓
Define a structured prompt that explicitly requests each field and provide examples
Why this is correct
Structured prompt with explicit requests improves adherence to required fields.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume that model size or parameter tuning (temperature, max_tokens) is the primary fix for content omission, when in fact prompt engineering—specifically structured prompts with explicit field requests—is the correct solution for ensuring specific data is included in the output.
Trap categories for this question
Command / output trap
More tokens allow longer output but don't guarantee inclusion of specific fields.
Detailed technical explanation
How to think about this question
Under the hood, Azure OpenAI models generate responses based on the prompt's instruction and context; without explicit field-level guidance, the model may prioritize summarizing overall meaning over listing specific data points. Prompt engineering techniques like few-shot examples and structured output formats (e.g., JSON schema) directly influence the model's attention mechanism and token prediction probabilities, making them more effective than parameter tuning for content inclusion. In real-world scenarios, a prompt like 'Extract and list the invoice total, date, vendor name, and line items in bullet points' with a few examples can reduce omission rates from ~30% to near zero.
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: Define a structured prompt that explicitly requests each field and provide examples — Option D is correct because the issue is that the summarization prompt lacks explicit instructions for which fields to include. By defining a structured prompt that explicitly requests each key field (e.g., invoice total, date, vendor) and providing examples, you guide the Azure OpenAI model to consistently extract and include those fields in the summary, reducing omission errors. This approach leverages prompt engineering to improve output reliability without changing model parameters or size.
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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