Which service returns identical answers to HR policy questions from PDFs every time?

AI-103 By islever Editorial Last updated

Build retrieval and grounding pipelines Choose the appropriate Foundry services for generative AI and agents

Short answer

B Use Azure Language in Foundry Tools question answering so each HR policy PDF question maps to a fixed, repeatable extractive answer.

Question

You are designing a solution that will answer questions about human resources (HR) policies stored in the PDF format.
You need to ensure that the identical answer to a specific question is returned every time. The solution must minimize development effort.
Which service should you include in the solution?
  1. Azure OpenAI
  2. Azure Language in Foundry Tools Correct Answer
  3. Azure Machine Learning
  4. Azure Document Intelligence in Foundry Tools

Key Takeaways

The phrase "identical answer every time" signals extractive, knowledge-base question answering, and the trap is reaching for Azure OpenAI, whose generative responses are non-deterministic by design.

This question tests whether you can pick the Foundry Tools service that gives deterministic, repeatable answers over HR policy PDFs with minimal build effort. The answer is Azure Language in Foundry Tools, because its custom question answering knowledge base returns fixed extractive answers rather than freshly generated wording.

Common mistake: Most candidates pick Azure OpenAI because it feels like the modern way to answer questions over documents, but generative completions reword the response on every call and therefore cannot guarantee the identical answer each time.

Expert Analysis

Why the Answer Is Correct

Azure Language in Foundry Tools includes the question answering capability (formerly QnA Maker), which ingests HR policy PDFs into a curated knowledge base and returns the matched answer or source passage as-is. Because the service performs retrieval and matching against stored answer pairs rather than generating new text, the same question yields the same answer on every call. Importing PDFs and DOCX files as sources means very little custom code, satisfying the minimize-development-effort requirement. You can further harden determinism with alternate questions, metadata filters, and confidence thresholds.

Why the Other Options Are Wrong

Azure OpenAI generates responses token by token, so temperature, sampling, and prompt context can change the phrasing of otherwise correct HR policy answers. Azure Machine Learning gives you full control but requires designing, training, and hosting your own retrieval and answering pipeline, which is the opposite of minimizing effort. Azure Document Intelligence in Foundry Tools is a strong extractor of text, tables, and layout from PDFs, yet it has no knowledge base or question-to-answer matching layer, so it can feed a QA pipeline but cannot be the answering service itself. Only option B combines deterministic answers with a managed knowledge base over PDFs.

Community Comment Notes

Both commenters land on B and their reasoning matches the exam doctrine. Srikantha frames question answering as "matching user queries to static, predefined answers," which is exactly why repeated questions return identical output. Az301301X isolates the decisive requirement and concludes it "points to Exact Question Answering, not generative AI." The agreement here is useful corroboration, but the verdict rests on the deterministic-answer wording of the scenario rather than on vote counts.

Official Reference

https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/overview

This link opens the official vendor documentation in a new tab.

Exam Strategy

When a stem demands the same answer every time, treat it as a non-generative requirement and eliminate any model that composes fresh text. Look for the service that stores answers in a knowledge base and returns passages verbatim, and remember that Document Intelligence only extracts, it does not answer.

Frequently Asked Questions

Why is Azure OpenAI wrong when it can also read the HR policy PDFs?

Generative deployments sample new wording on every call, so the same HR question can produce different phrasing, which breaks the identical-answer requirement.

What role could Document Intelligence in Foundry Tools play alongside question answering?

It can preprocess scanned or complex HR PDFs into clean text, but it has no knowledge base or answer-matching layer, so it cannot guarantee repeatable answers alone.

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Discussion 5 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 5

  1. Srikantha 👍 2 Answered B
    The correct choice is B. Azure Language in Foundry Tools. Key Reasons Deterministic Responses: Azure Language includes the Question Answering feature (formerly QnA Maker). This service allows you to ingest documents like HR policy PDFs to create a knowledge base. Because it relies on matching user queries to static, predefined answers (or specific extracted passages) in the knowledge base, it guarantees that the exact identical answer is returned every time for a specific matched question. Minimal Development Effort: The Question Answering feature has built-in capabilities to automatically parse and extract question-answer pairs and hierarchical structures directly from PDFs, requiring almost no custom code compared to building a custom RAG (Retrieval-Augmented Generation) pipeline.
  2. 8ac3742 👍 1 Answered B
    Azure AI Language features Question Answering (formerly QnA Maker), which directly extracts question-and-answer pairs or exact text passages from uploaded source documents (PDFs, FAQs, manuals). When a matching question is recognized, it maps directly to the defined answer, guaranteeing the exact, identical answer every single time. Azure AI Search includes an AI Enrichment Pipeline (skillsets) where you can embed Azure AI Language as an enrichment step. During indexing, Azure AI Search extracts raw text, calls Azure AI Language to extract entities, key phrases, or translated text, and stores those extracted metadata fields directly in the search index for richer querying.
  3. marikifua 👍 1 Answered B
    The correct answer is B because Azure Language in Foundry Tools allows you to download PDF documents, create a knowledge base and link specific answers to questions. For the same question, the system returns the same answer because it takes it from the knowledge base, rather than generating it anew each time.
  4. mmpurnel 👍 1 Answered D
    A generative AI solution using Azure OpenAI could produce slightly different responses to the same question because LLMs are probabilistic by nature. Even with low temperature settings, deterministic behavior is not guaranteed.
  5. Az301301X 👍 1 Answered B
    Correct answer: B. Azure Language in Foundry Tools The key phrase is “the identical answer to a specific question is returned every time.” That points to Exact Question Answering, not generative AI.

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