The answer is to configure the Azure OpenAI deployment to use 'Add your data' with the search index. This is correct because Azure Cognitive Search acts as the retrieval engine that indexes the extracted text from PDFs, enabling the chatbot to perform vector or keyword searches over proprietary content. By grounding the model on this indexed data, the chatbot can retrieve relevant passages to answer questions without retraining the underlying model. On the AI-102 exam, this scenario tests your understanding of the Retrieval Augmented Generation (RAG) pattern, which is the standard approach for grounding Azure OpenAI on private documents. A common trap is assuming you need to fine-tune the model on the PDFs, but the exam emphasizes that RAG with Azure Cognitive Search is the correct, scalable solution. Memory tip: think "RAG on the index, not fine-tune on the text."
AI-102 Implement generative AI solutions Practice Question
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.
Which TWO actions are required to enable a custom chatbot built with Azure OpenAI to answer questions based on a company's internal PDF documents?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Ingest the PDFs into an Azure Cognitive Search index
Option D is correct because Azure Cognitive Search provides the indexing and retrieval capabilities needed to make PDF content searchable. By ingesting PDFs into an Azure Cognitive Search index, the chatbot can perform vector or keyword searches over the extracted text, enabling it to retrieve relevant passages to answer user questions. This is the standard approach for grounding a custom chatbot on proprietary documents without modifying the underlying 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.
✗
Use Azure AI Document Intelligence to extract text from PDFs before indexing
Why it's wrong here
If PDFs are already text-based, extraction is not required; Cognitive Search can handle PDFs directly.
✗
Deploy Azure AI Content Safety to filter responses
Why it's wrong here
Content Safety is not a required step for answering from PDFs.
✗
Fine-tune the GPT model on the PDF content
Why it's wrong here
Fine-tuning is not required for retrieval-augmented generation; it is an alternative approach.
✓
Ingest the PDFs into an Azure Cognitive Search index
Why this is correct
Indexing enables retrieval of relevant content from PDFs.
Related concept
Read the scenario before looking for a memorised answer.
✓
Configure the Azure OpenAI deployment to use 'Add your data' with the search index
Why this is correct
This integration allows the model to use indexed documents as context.
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 confuse fine-tuning (option C) with the RAG pattern, mistakenly believing they must retrain the model on proprietary data, when in fact the 'Add your data' feature with a search index is the correct and simpler approach for question-answering over internal documents.
Detailed technical explanation
How to think about this question
Under the hood, the 'Add your data' feature in Azure OpenAI automatically orchestrates the retrieval step: it sends the user query to the Azure Cognitive Search index, retrieves the top-k relevant chunks (using semantic or hybrid search), and injects them into the GPT model's context window as grounding data. This approach avoids the cost and complexity of fine-tuning while ensuring the model can answer questions based on the latest PDF content without retraining. A subtle behavior is that the index must include chunked text with metadata (e.g., document title, page number) to enable citation and accurate retrieval.
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: Ingest the PDFs into an Azure Cognitive Search index — Option D is correct because Azure Cognitive Search provides the indexing and retrieval capabilities needed to make PDF content searchable. By ingesting PDFs into an Azure Cognitive Search index, the chatbot can perform vector or keyword searches over the extracted text, enabling it to retrieve relevant passages to answer user questions. This is the standard approach for grounding a custom chatbot on proprietary documents without modifying the underlying 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.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You need to create a chatbot that uses Azure OpenAI to answer questions about your company's internal policies. The responses must be based only on the provided policy documents. Which approach should you use?
easy
A.Use the model's pre-existing knowledge about common policies.
B.Fine-tune a GPT model on the policy documents.
C.Use prompt engineering to instruct the model to only use policy knowledge.
✓ D.Use Retrieval-Augmented Generation (RAG) with an Azure AI Search index of the documents.
Why D: Option B is correct because RAG uses provided documents as source. Option A is wrong because fine-tuning on policies may still generate ungrounded responses. Option C is wrong because prompt engineering alone does not enforce grounding. Option D is wrong because the model's training data is generic.
Last reviewed: Jun 11, 2026
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