AI-102 Practice Question: Implement natural language processing solutions
A company is building a chatbot using Azure OpenAI Service to handle customer inquiries. The bot sometimes responds with incorrect or fabricated information. The team wants to ground the model responses using their own product documentation stored in Azure Cognitive Search. Which configuration should they implement?
⚠ Common exam trap
Candidates often confuse improving search relevance (semantic ranker) with the end-to-end grounding process, forgetting that the model must actually receive and be constrained by the retrieved data to prevent fabrication.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure Azure OpenAI on your data with Azure Cognitive Search as the data source.
It directly integrates Azure Cognitive Search as a data source for Azure OpenAI, enabling the model to retrieve and ground its responses in the indexed product documentation. This approach uses a 'retrieve-then-read' pattern where the search results are injected into the prompt, reducing hallucinations by constraining the model's output to verified content.
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 and generate embeddings, then store them in a vector database for direct similarity search.
Why it's wrong here
While this can retrieve relevant documents, it does not integrate with the Azure OpenAI model's response generation; the bot would not use the retrieved content contextually.
- ✗
Fine-tune the GPT model on the product documentation dataset.
Why it's wrong here
Fine-tuning adapts the model to the domain but does not guarantee factual grounding; the model may still hallucinate. Also requires significant labeled data.
- ✗
Enable the semantic ranker in Azure Cognitive Search to improve the relevance of search results.
Why it's wrong here
Semantic ranker improves search relevance but does not affect how the Azure OpenAI model generates responses; the grounded context must be provided to the model.
- ✓
Configure Azure OpenAI on your data with Azure Cognitive Search as the data source.
Why this is correct
This enables RAG, where the model retrieves relevant chunks from the search index and uses them as context to generate responses, reducing hallucinations.
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Senior Network & Security Engineer · founder of Courseiva
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