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| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10-15% | - Process and index video content - Integrate vision capabilities into applications - Extract text and handwriting from images - Build and deploy custom vision models - Analyze images and detect objects/features |
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Build conversational AI and chatbots - Implement translation and summarization |
| 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 |
| Implement an agentic solution | 5-10% | - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration |
| Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Orchestrate multiple models and containers - Deploy and manage generative models - Integrate Azure OpenAI and other generative models |
| Plan and manage an Azure AI solution | 20-25% | - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Select suitable AI models - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions |
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NEW QUESTION # 15
Drag and Drop Question
You have an Azure subscription.
You are building a chatbot that will use an Azure OpenAI model.
You need to deploy the model.
Which three 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.
Answer:
Explanation:
Explanation:
Apply for access to Azure OpenAI - Before you can use Azure OpenAI models, you need to apply for access as it is a gated service.
Provision an Azure OpenAI resource - After getting access, you need to create (provision) an Azure OpenAI resource in your subscription.
Deploy the GPT model - Once the resource is set up, you can deploy the GPT model, which will power your chatbot.
NEW QUESTION # 16
You Have a chatbot that uses the Azure Al Language custom question answering service. The model used by the service was trained by using an internal support FAQ document.
You discover that the chatbot fails to provide correct answers to common questions.
You need to increase the accuracy of the responses provided by the chatbot. The solution must minimize development effort.
Which three actions should you perform in sequence from Language Studio? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
* Open the Review suggestions pane.
* Explanation: This step allows you to access suggestions generated by the Azure AI Language service based on user interactions and queries. Reviewing these suggestions helps identify potential improvements or alternative phrasings that can enhance the model's understanding and accuracy with minimal effort.
* Review and accept the alternative phrases.
* Explanation: After opening the review suggestions pane, you can evaluate and accept alternative phrases or questions that users might have asked. Accepting these updates trains the model to recognize variations in how questions are posed, thereby improving its ability to provide accurate responses without requiring extensive manual retraining.
* Enable active learning.
* Explanation: Enabling active learning allows the model to continuously improve by learning from user interactions and feedback over time. This feature automatically suggests refinements based on real-world usage, minimizing development effort while enhancing the chatbot's accuracy as it adapts to common questions.
Answer AreaRationaleThese steps are sequenced to progressively improve the chatbot's performance with minimal development effort. Starting with reviewing suggestions leverages existing data, accepting alternative phrases refines the model's understanding, and enabling active learning ensures ongoing improvement, aligning with the goal of increasing accuracy efficiently.
NEW QUESTION # 17
Hotspot Question
You are developing an application that will use the Azure AI Vision client library. The application has the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: No
The code generates description and tags. See line 3,4.
Box 2: Yes
The ComputerVision.analyzeImageInStreamAsync operation extracts a rich set of visual features based on the image content.
Box 3: Yes
File.OpenRead reads a local file.
Reference:
https://docs.microsoft.com/en-us/dotnet/api/system.io.file.openread?view=net-6.0
https://docs.microsoft.com/en-
us/java/api/com.microsoft.azure.cognitiveservices.vision.computervision.computervision.analyzei mageinstreamasync?view=azure-java-legacy
NEW QUESTION # 18
You have an Azure subscription that contains an Azure OpenA1 resource. You configure a model that has the following settings:
* Temperature: 1
* Top probabilities: 0.5
* Max response tokens: 100
You ask the model a question and receive the following response.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth point.
Answer:
Explanation:
Explanation:
The subscription will be charged 86 tokens for the execution of the session. No The text completion was truncated because the Max response tokens value was exceeded. No The prompt_tokens value will be included in the calculation of the Max response tokens value. No From the response payload:
usage.prompt_tokens = 37
usage.completion_tokens = 86
usage.total_tokens = 123
finish_reason = " stop "
Billing is for total tokens (input + output), not just completion tokens.
Azure OpenAI bills on both prompt (input) and completion (output) tokens. Here, the total is 123 tokens, so it is not just 86. Hence No.
Not truncated by max response tokens.
If a response hits the maximum allowed output tokens, finish_reason would be " length " . Here it is " stop " and the model produced only 86 completion tokens while Max response tokens was set to 100, so the limit was not reached. Hence No.
max response tokens applies only to the generated output, not the prompt.
max_tokens (Max response tokens) limits the number of tokens the model may generate in the completion; it does not include prompt_tokens . Hence No.
Microsoft References
Azure OpenAI Service - Chat Completions API (parameters, max_tokens , response usage , finish_reason ).
https://learn.microsoft.com/azure/ai-services/openai/reference#chat-completions Azure OpenAI pricing and token usage model (charges for input and output tokens).
https://learn.microsoft.com/azure/ai-services/openai/overview#pricing
Token counting and limits (context vs. max output tokens).
https://learn.microsoft.com/azure/ai-services/openai/concepts/tokenizers#token-limits
NEW QUESTION # 19
You are developing the shopping on-the-go project.
You need to build the Adaptive Card for the chatbot.
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:
Box 1: name.en
Box 2: "$when": "${stockLevel != 'OK'}"
Product displays must include images and warnings when stock levels are low or out of stock.
Box 3:image.altText.en
NEW QUESTION # 20
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