AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 building a generative AI solution using Azure OpenAI Service. The application must retrieve information from a large private knowledge base. You need to ensure the model uses only relevant documents from the knowledge base to generate answers. Which feature should you configure?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Azure OpenAI On Your Data with vector search
B is correct because Azure OpenAI On Your Data with vector search enables the model to retrieve only the most semantically relevant documents from a private knowledge base by converting both the user query and the documents into high-dimensional vectors and performing similarity search. This ensures the model's responses are grounded in the specific, relevant information without exposing the entire knowledge base to the model.
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.
✗
Implement a custom prompt flow
Why it's wrong here
Prompt flow is a tool to build LLM-based applications, but not the specific retrieval feature.
✓
Use Azure OpenAI On Your Data with vector search
Why this is correct
This feature enables retrieval-augmented generation (RAG) using vector search.
Related concept
Read the scenario before looking for a memorised answer.
✗
Configure a content filter
Why it's wrong here
Content filters block harmful content, not retrieve documents.
✗
Fine-tune the model with the knowledge base
Why it's wrong here
Fine-tuning does not enable dynamic retrieval of documents.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse fine-tuning (D) with retrieval-augmented generation (RAG), assuming that training the model on the knowledge base is the best way to ground answers, when in fact RAG with vector search is the correct pattern for dynamic, relevant document retrieval without modifying the base model.
Detailed technical explanation
How to think about this question
Under the hood, Azure OpenAI On Your Data with vector search uses an embedding model (e.g., text-embedding-ada-002) to generate vector representations of both the user query and the document chunks, then performs cosine similarity or Euclidean distance search in a vector index (often built on Azure Cognitive Search). A subtle behavior is that the chunk size and overlap strategy directly impact retrieval accuracy—too large chunks may include irrelevant context, while too small chunks may miss key information. In a real-world scenario, this approach is critical for a customer support chatbot that must pull the latest troubleshooting steps from a frequently updated knowledge base without retraining the model.
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: Use Azure OpenAI On Your Data with vector search — B is correct because Azure OpenAI On Your Data with vector search enables the model to retrieve only the most semantically relevant documents from a private knowledge base by converting both the user query and the documents into high-dimensional vectors and performing similarity search. This ensures the model's responses are grounded in the specific, relevant information without exposing the entire knowledge base to the model.
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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