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| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Process and index video content - Analyze images and detect objects/features - Integrate vision capabilities into applications - Build and deploy custom vision models |
| Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Implement model monitoring and feedback - Deploy and manage generative models |
| Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Implement translation and summarization - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection |
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Ingest and process structured/unstructured data |
| Plan and manage an Azure AI solution | 20-25% | - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Select suitable AI models - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services |
| Implement an agentic solution | 5-10% | - Understand agent use cases and types - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration |
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NEW QUESTION # 260
Drag and Drop Question
You have an Azure subscription that contains an Azure AI Document Intelligence resource named DI1 and a storage account named sa1. The sa1 account contains a blob container named blob1 and an Azure Files share named share1.
You plan to build a custom model named Model1 in DI1.
You create sample forms and JSON files for Model1.
You need to train Model1 and retrieve the ID of the model.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
Explanation:
Upload the forms and JSON files to blob1: Ensure that your sample forms and JSON files are uploaded to the blob container named blob1 in the storage account sa1.
Create a shared access signature (SAS) URL for blob1: Generate a SAS URL for the blob container to provide secure access to the files.
Call the Build model REST API function: Use the SAS URL to call the Build model REST API function in DI1 to start the training process.
Call the Get model REST API function: After the training is complete, call the Get model REST API function to retrieve the ID of Model1.
https://learn.microsoft.com/en-us/rest/api/aiservices/document-models/build-model?view=rest- aiservices-v4.0%20(2024-07-31-preview)&tabs=HTTP#tabpanel_1_HTTP
NEW QUESTION # 261
You are building an app that will perform translations by using the Azure Al Translator service.
You need to ensure that the app will translate user-inputted text.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
In the Azure AI Translator .NET SDK ( Azure.AI.Translation.Text ), the TextTranslationClient exposes TranslateAsync overloads that accept a target language (e.g., " fr " ) and an input string to translate. The call returns a Response < IReadOnlyList < TranslatedTextItem > > . Each TranslatedTextItem includes the automatically detected source language ( DetectedLanguage.Language ) and a collection of translations in Translations . To print the translated text for the first target language, read translation.Translations[0].Text .
Therefore:
* Use TranslateAsync(targetLanguage, input) to perform the translation.
* Output translation.Translations[0].Text to display the translated result.
Microsoft References
* TextTranslationClient.TranslateAsync method (overloads, usage). Microsoft Learn
* TextTranslationClient class overview. Microsoft Learn
* TranslatedTextItem model and Translations property (array of translation results).
* Quickstart: Translator text client libraries (end-to-end usage pattern). Microsoft Learn
NEW QUESTION # 262
You are building an app by using the Semantic Kernel.
You need to include complex objects in the prompt templates of the app. The solution must support objects that contain sub-properties.
Which two prompt templates can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
Semantic Kernel supports multiple prompt template languages. Handlebars and Liquid both allow you to bind complex objects (with nested/sub-properties), iterate over collections, and use conditionals-ideal when your prompt needs structured data.
JSONL is a dataset/record format, not a prompt templating language.
YAML is a configuration/serialization format; while SK can store configs in YAML, YAML itself isn't a prompt templating engine for rendering nested objects in prompts.
"Semantic Kernel" as an option is not a template language; SK provides the runtime and also a simple legacy template syntax, but for complex/nested objects Microsoft recommends Handlebars (cross-language) or Liquid (.NET).
References (Microsoft Docs):
Handlebars prompt templates in Semantic Kernel (supports SK prompts, expressions, iteration, object binding). Microsoft Learn Liquid prompt templates in Semantic Kernel (example shows passing objects and iterating over nested data).
Microsoft Learn
SK prompt template syntax overview (context on SK templating options). Microsoft Learn
NEW QUESTION # 263
You are building a chatbot.
You need to use the Content Moderator API to identify aggressive and sexually explicit language.
Which three settings should you configure? To answer, select the appropriate settings in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 264
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Cognitive Search service.
During the past 12 months, query volume steadily increased.
You discover that some search query requests to the Cognitive Search service are being throttled.
You need to reduce the likelihood that search query requests are throttled.
Solution: You migrate to a Cognitive Search service that uses a higher tier.
Does this meet the goal?
Answer: A
Explanation:
A simple fix to most throttling issues is to throw more resources at the search service (typically replicas for query-based throttling, or partitions for indexing-based throttling). However, increasing replicas or partitions adds cost, which is why it is important to know the reason why throttling is occurring at all.
Reference:
https://docs.microsoft.com/en-us/azure/search/search-performance-analysis
NEW QUESTION # 265
......
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