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
Extracting text and running direct vector similarity search bypasses Azure OpenAI entirely, so the model never receives retrieved passages and cannot ground its answers. It is tempting because embeddings and vector databases underpin retrieval, but the required configuration is Azure OpenAI's 'On Your Data' feature wired to Azure Cognitive Search.
- ✗
Fine-tune the GPT model on the product documentation dataset.
Why it's wrong here
Fine-tuning alters the model's weights to learn style and format, not to cite retrievable source documents, so fabricated answers persist and citations are impossible. It is tempting because fine-tuning uses your own data, but it suits tone or task adaptation; grounding requires retrieval-augmented generation against Azure Cognitive Search.
- ✗
Enable the semantic ranker in Azure Cognitive Search to improve the relevance of search results.
Why it's wrong here
The semantic ranker only reorders an existing result set using language models; it neither retrieves documents nor injects them into the prompt, so the model remains ungrounded. It is tempting because it improves search relevance, but it is the right choice for ranking quality once retrieval is already configured.
- ✓
Configure Azure OpenAI on your data with Azure Cognitive Search as the data source.
Why this is correct
Azure OpenAI on your data retrieves relevant chunks from Azure Cognitive Search and injects them into the prompt, grounding responses in your product documentation and reducing fabrication. This retrieval-augmented approach directly satisfies the requirement to constrain answers to indexed, authoritative content rather than the model's parametric memory.
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Senior Network & Security Engineer · founder of Courseiva
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