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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 2: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 4: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 5: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
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NEW QUESTION # 100
You are creating an image-editing workflow in a Microsoft Foundry project.
The workflow must meet the following requirements:
- Ensure that background objects can be removed by applying a mask-
based inpainting edit.
- Preserve the original lighting and style of the edited images.
- Use the built-in image editing controls, NOT a custom model.
You need to ensure that image edits apply exclusively inside the masked area.
How should you configure the workflow?
Answer: C
NEW QUESTION # 101
You are building a text-to-speech solution that uses Azure Speech in Foundry Tools to read instructions from the script in a text file.
You discover that the solution often pronounces technical terms incorrectly.
You need to prevent the incorrect pronunciations. The solution must minimize development effort.
What should you do?
Answer: C
Explanation:
Using Speech Synthesis Markup Language (SSML) with the <phoneme> element is the ideal way to fix mispronunciations for technical terms.
The <phoneme> tag lets you override the default text-to-speech model by explicitly defining the sounds using the International Phonetic Alphabet (IPA).
Reference:
https://learn.microsoft.com/en-us/answers/questions/5729867/pronunciation-issue-when-generating-audio-from-ssm
NEW QUESTION # 102
You have a Microsoft Foundry project that serves a high-volume chat app.
Most requests are simple FAQs, but some require advanced reasoning.
You need to reduce costs and latency for common queries, without degrading the quality of the responses to complex questions.
What should you do?
Answer: B
Explanation:
One should absolutely use a model cascade to route requests to different models based on complexity. This architectural pattern is highly effective for high-volume chat applications because it directly addresses the trade-off between operational cost, API latency, and response quality.
Using a model cascade router ensures that your high-volume Microsoft Foundry application scales efficiently by reserving expensive computational power exclusively for queries that actually require advanced cognitive processing.
Note:
1. Analyze Request Complexity
Implement a lightweight intent classifier or routing layer at the entry point of your Microsoft Foundry project. This router quickly inspects incoming user prompts using basic heuristic keyword matching, semantic embeddings, or a highly optimized, fast model (like Phi-3 or GPT-
4o-mini) to categorize the query as either a "Simple FAQ" or a "Complex Reasoning" request.
2. Route to the Optimal TierTier 1 (Fast & Cheap):
Route standard, predictable FAQ requests to a smaller, cost-effective model or a local cache/vector database lookup. This keeps latency in milliseconds and drastically lowers token costs.
Tier 2 (Advanced Reasoning): Route multi-step logic, coding, or highly contextual queries to a frontier model (like GPT-4o).
3. Implement Fallback LogicDesign the cascade to be dynamic. If the smaller Tier 1 model generates a response with low confidence, or if the user asks a follow-up question that invalidates the simple FAQ status, seamlessly upgrade the conversation loop to the Tier 2 model.
Reference:
https://medium.com/@sujathamudadla1213/what-is-the-primary-purpose-of-a-model-cascade-in-machine-learning-0b145a7bc6e2
NEW QUESTION # 103
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: C
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 # 104
You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?
Answer: D
Explanation:
The correct answer is A. an Azure Content Understanding in Foundry Tools analyzer . In Azure Content Understanding, an analyzer is the reusable configuration that defines what content type is processed, which elements are extracted, and how the output is structured. Microsoft's analyzer guidance states that analyzers define extraction for text, layout, tables, fields, and output formats such as Markdown and JSON. This makes the analyzer the first required configuration step before downstream reasoning or agent orchestration can use the extracted content.
This matches the requirement because the documents contain scanned text, tables, and multicolumn layouts.
Content Understanding document analysis is designed to transform unstructured documents into structured, machine-readable output while preserving document structures and relationships. Its Markdown representation converts unstructured documents into GitHub Flavored Markdown while maintaining content and layout for downstream use.
A generative chat completion request or Azure OpenAI Responses API call could reason over extracted content, but it is not the correct first step for OCR, layout preservation, and Markdown conversion. Azure Language focuses on text analysis after content has already been extracted. Reference topics: Content Understanding analyzers, document extraction, OCR, layout analysis, Markdown output, and downstream reasoning.
NEW QUESTION # 105
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