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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts - Describe use cases for generative AI |
| Topic 2: Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe automated machine learning - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning |
| Topic 3: Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Computer Vision |
| Topic 4: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
| Topic 5: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Translator |
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NEW QUESTION # 129
You plan to deploy an Azure Machine Learning model as a service that will be used by client applications.
Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
The correct order of processes before deploying a model as a service is:
(1) Data preparation # (2) Model training # (3) Model evaluation.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore the machine learning process", machine learning follows a structured lifecycle that involves several sequential stages. Before a model can be deployed, the data must be properly prepared, the model must be trained, and then its performance must be evaluated to ensure accuracy and reliability.
* Data Preparation:The first stage involves collecting, cleaning, and transforming raw data into a usable format. Azure Machine Learning provides tools like Data Wrangler, Data Labeling, and Data Transformation pipelines to ensure the dataset is accurate and consistent. As per Microsoft Learn, "data preparation is essential to remove noise, handle missing values, and split the dataset into training and testing sets." This step ensures the model learns from quality input.
* Model Training:In this step, algorithms are applied to the prepared training data to create a predictive model. The system learns patterns and relationships from the data. Azure Machine Learning allows model training using AutoML, custom code, or designer pipelines. The training process produces a model that can make predictions, but it still needs to be tested before deployment.
* Model Evaluation:Once trained, the model's performance is tested against unseen (test) data.
Evaluation metrics like accuracy, precision, recall, and F1-score are analyzed to verify if the model meets business and performance requirements. Microsoft Learn defines this stage as "assessing the model's performance to determine its readiness for deployment." After these three processes, the model can then be deployed as a web service using Azure Machine Learning endpoints. Model retraining happens later when new data becomes available, and data encryption is a security process, not part of model development steps.
NEW QUESTION # 130
You need to provide customers with the ability to query the status of orders by using phones, social media, or digital assistants.
What should you use?
Answer: C
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify Azure services for conversational AI," the Azure AI Bot Service is specifically designed to create intelligent conversational agents (chatbots) that can interact with users across multiple communication channels, such as web chat, social media, phone calls, Microsoft Teams, and digital assistants.
In this scenario, customers need the ability to query the status of their orders through various interfaces - including voice and text platforms. Azure AI Bot Service enables this by integrating with Azure AI Language (for understanding natural language), Azure Speech (for speech-to-text and text-to-speech capabilities), and Azure Communication Services (for telephony or chat integration).
The bot can interpret user input like "Where is my order?" or "Check my delivery status," call backend systems (such as an order database or API), and then respond appropriately to the user through the same communication channel.
Let's analyze the incorrect options:
* B. Azure AI Translator Service: Used for real-time text translation between languages; it doesn't handle conversation logic or database queries.
* C. Azure AI Document Intelligence model: Extracts data from structured and semi-structured documents (e.g., invoices, receipts), not user queries.
* D. Azure Machine Learning model: Builds and deploys predictive models, but doesn't provide conversational or multi-channel interaction capabilities.
Thus, for enabling multi-channel conversational experiences where customers can inquire about order statuses using voice, chat, or digital assistants, the most appropriate solution is Azure AI Bot Service, as outlined in Azure's AI conversational workload documentation.
NEW QUESTION # 131
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:
NEW QUESTION # 132
Match the Azure Cognitive Services to the appropriate Al workloads.
To answer, drag the appropriate service from the column on the left to its workload on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
NEW QUESTION # 133
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 134
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