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| Section | Objectives |
|---|---|
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Azure AI resource provisioning and configuration - Responsible AI principles and governance |
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
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NEW QUESTION # 14
You plan to configure an evaluation in Microsoft Foundry for a Retrieval Augmented Generation (RAG) chat app. You need to provide scores for groundedness, relevance, and harmful-content categories. Which two evaluation categories can you use? Each correct answer presents part of the solution.
Answer: D,E
Explanation:
Select AI quality (AI assisted) metrics for groundedness and relevance. Groundedness evaluates whether the generated answer is supported by the retrieved RAG context, while relevance evaluates whether the answer appropriately addresses the user's query. These evaluations use an AI judge model to assess meaning and contextual alignment rather than relying only on exact token matches. Microsoft Foundry categorizes groundedness and relevance as quality evaluators and requires a compatible judge-model deployment for AI- assisted evaluation.
Select risk and safety metrics to evaluate harmful-content categories. These evaluators detect and score content involving violence, sexual material, self-harm, and hate or unfairness. They provide the safety dimension required alongside the RAG quality measurements.
AI quality NLP metrics include mathematically calculated measures such as F1, BLEU, GLEU, ROUGE, and METEOR. These primarily compare token or n-gram overlap with reference answers and do not provide the requested groundedness and content-harm assessments. A fluency evaluator measures language readability, while a similarity evaluator compares a response with ground truth; neither is a complete evaluation category covering the requirements.
Study Guide alignment: evaluate generative AI solutions by using quality, groundedness, relevance, risk, and safety metrics .
NEW QUESTION # 15
You need to recommend a solution to support the planned changes and technical requirements for Agent1 to use the product information stored in storage1.
What should you include in the recommendation?
Answer: A
Explanation:
The correct recommendation is Azure AI Search. The case study states that the product detail sheets are stored as PDFs in storage1, and that Agent1 must be enabled to retrieve and use detailed product information from those sheets. It also specifies that the indexing pipeline must enable semantic and vector search, and that Agent1 must answer natural language questions about product details by using the product sheet information.
Azure AI Search is the Azure service designed to ingest content from sources such as Azure Blob Storage, create searchable indexes, and support keyword, semantic, hybrid, and vector retrieval for Retrieval Augmented Generation (RAG) solutions.
Microsoft's Azure AI Search guidance states that integrated vectorization can chunk content and generate embeddings during indexing, enabling vector search over source documents. It also states that Azure AI Search supports text and vector queries and can improve raw content for search-related scenarios through enrichment pipelines. Azure Translator is unrelated to retrieval. Document Intelligence can extract document structure, but it is not the retrieval index for Agent1. Grounding with Bing Search retrieves public web content, not Contoso's private PDFs in storage1. Reference topics: Azure AI Search, RAG, semantic search, vector search, Azure Blob Storage indexing, and agent grounding.
NEW QUESTION # 16
You build a chatbot that uses the Azure OpenAI GPT-4 model to generate song lyrics.
You need to ensure that responses do NOT contain lyrics from popular songs that might have been ingested during model training.
Which Azure AI Content Safety API should you use?
Answer: A
Explanation:
The Protected material text detection feature in Azure AI Content Safety is specifically designed to identify and block responses that may contain copyrighted or protected content, such as lyrics from popular songs. Since GPT-4 may have been trained on publicly available data, this feature helps ensure that the chatbot does not generate copyrighted lyrics by detecting and filtering out protected material.
NEW QUESTION # 17
You are building a voice agent for a pharmacy. It must accept spoken questions, reply with synthesised speech, and accurately recognise specialised medicine names that a general speech model often mishears. Which capability should you configure?
Answer: D
Explanation:
A custom speech model improves recognition of specialised vocabulary, such as medicine names, by adapting speech-to-text (STT) to your domain, and AI-103 covers integrating speech, including custom speech models, as an agent modality. This directly addresses the accuracy problem in the scenario.
NEW QUESTION # 18
You have a configurable guardrail in a Microsoft Foundry project.
You have a Python application. A text blocklist named Conf identialTerns already contains terms that must be detected in user messages. The application stores each incoming user message in a variable named inpot_text.
You plan to moderate input_text before the text is sent to a language model.
You need to analyze input_text by using the existing blocklist.
Answer:
Explanation:
Explanation:
AnalyzeTextOptions
request = AnalyzeTextOptions(
text=input_text,
blocklist_names=[blocklist_name],
halt_on_blocklist_hit=True
)
response = client.analyze_text(request)
AnalyzeTextOptions is the request model used by the Azure AI Content Safety Python SDK to submit text for moderation. Its text property contains the user message, while blocklist_names identifies one or more existing custom blocklists that must be applied during analysis. The value must be supplied as a list, so the correct assignment is [blocklist_name]. The request is then passed to ContentSafetyClient.analyze_text(), which returns an AnalyzeTextResult containing any blocklist matches and applicable harm-category analysis.
Setting halt_on_blocklist_hit=True stops additional harm-category processing when a blocklisted term is detected. This is appropriate when a blocklist match alone is sufficient to prevent the message from reaching the language model.
AddOrUpdateTextBlocklistItemsOptions manages terms within a blocklist; it does not analyze input.
AnalyzeImageOptions is intended for image moderation, and TextBlocklist represents blocklist metadata rather than a text-analysis request.
Study Guide alignment: configure safety filters, guardrails, blocklists, risk detection, and content moderation for Azure AI applications.
NEW QUESTION # 19
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