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| Section | Objectives |
|---|---|
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
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145. Frage
You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.
Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.
Sometimes, mixed-language segments result in incomplete or incorrect translations.
You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.
What should you do before sending the segments to Translator?
Antwort: D
Begründung:
To fix incomplete or incorrect translations from mixed-language transcripts, you must identify and isolate the languages at the sentence or phrase level before sending the text to Azure Translator.
Azure Translator performs best when a single request contains only one source language. When it receives a mixed-language segment under a single source language code, it often fails to parse the secondary language correctly.
Here is the step-by-step pipeline you should implement inside your application before hitting the Translator API.
1. Split Segments into Sentences
Live call transcript segments can contain multiple sentences. Do not send the raw, multi-sentence segment directly to the translator if it contains mixed languages.Break the segment into individual sentences.Use regex punctuation rules or a lightweight sentence-splitting library.
2. Run Language Detection Per Sentence
Azure Translator has a built-in Detect API, but for mixed-language live calls, executing a dedicated detection step per sentence provides better control.
Reference:
https://medium.com/neural-engineer/azure-ai-speech-to-text-real-time-transcription-58bfd5fd1a28
146. Frage
You are building a chatbot.
You need to ensure that the chatbot can classify user input into separate categories. The categories must be dynamic and defined at the time of inference.
Which service should you use to classify the input?
Antwort: A
Begründung:
To classify user input into separate categories that are dynamic and defined at the time of inference, Azure OpenAI text classification is the best option. OpenAI models such as GPT can dynamically classify text into categories based on context and user-defined instructions. Since the categories need to be flexible and determined at inference time, OpenAI's natural language understanding capabilities are well-suited for this task.
147. Frage
You have a Microsoft Foundry project that uses Azure Al Search to ground an agent in internal documentation.
After a recent content update, users report that the agent ' s answers have become less accurate.
You need to identify whether the retrieved content is negatively influencing the model ' s generated responses.
Which observability signal should you review?
Antwort: D
Begründung:
The correct observability signal is B. groundedness evaluation metrics . In a RAG solution, the key diagnostic question is whether the generated answer is supported by the retrieved context. Microsoft Foundry' s built-in evaluator reference defines Groundedness as the metric that measures how grounded the response is in the retrieved context, with scoring that indicates whether the model's claims are supported by the provided source material.
This matches the issue after a content update. If retrieved chunks are stale, misleading, incomplete, or poorly aligned with the user query, groundedness results can show that generated responses are not reliably supported by the retrieved documentation. The RAG evaluator guidance explains that groundedness focuses on whether the response avoids content outside the grounding context, while other process metrics such as retrieval evaluate how relevant the retrieved chunks are. Latency traces are useful for performance troubleshooting, not response accuracy. Indexer status can reveal ingestion failures, but it does not show whether retrieved content is influencing generated answers negatively. Prediction drift is a model monitoring concept and is not the primary signal for RAG grounding quality. Reference topics: Microsoft Foundry observability, RAG evaluators, groundedness, retrieved context, and response quality evaluation.
148. Frage
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?
Antwort: C
Begründung:
You can create a Custom FAQs PDF Solution Powered by Azure OpenAI with Citations from Grounded Data.
Business Problem
Imagine an organization that frequently deals with FAQs, legal documents, or reports. They want an automated system that can:
Generate answers from questions using the information found in existing PDF documents.
Ensure citations from those documents are included in the responses.
Output a structured PDF with the Q&A and citations formatted in a professional and readable way.
Solution Overview
By leveraging Azure OpenAI on your data and Azure Cognitive Search, we can ground the OpenAI GPT model with custom documents like PDFs. When queried, the model retrieves the most relevant responses from the documents and includes citations such as document titles or page numbers and create a question-and-answer format JSON file. The system then generates a structured, professional-looking PDF with bold questions, clear answers, and citations.
Reference:
https://www.linkedin.com/pulse/creating-custom-faqs-pdf-solution-powered-azure-openai-saqlain-tahir-x9gse
149. Frage
Hotspot Question
You have a Microsoft Foundry project that contains a workflow for a customer support triage process.
You have an Ask a question node that stores user responses in a local variable named Var01.
You need to create the following Power Fx expressions:
- An if/else condition expression that ensures that Var01 contains a
value
- A Send message expression that returns the stored user response in
uppercase
How should you configure the expressions? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
150. Frage
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