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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Topic 2: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Topic 3: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Topic 4: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
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NEW QUESTION # 26
You build a QnA Maker bot by using a frequently asked questions (FAQ) page.
You need to add professional greetings and other responses to make the bot more user friendly.
What should you do?
Answer: B
Explanation:
According to the Microsoft Learn module "Build a QnA Maker knowledge base", QnA Maker allows developers to create bots that answer user queries based on documents like FAQs or manuals. To make a bot more natural and conversational, Microsoft provides a "chit-chat" feature - a prebuilt, professionally written set of responses to common conversational phrases such as greetings ("Hello"), small talk ("How are you?"), and polite phrases ("Thank you").
Adding chit-chat improves user experience by making the bot sound friendlier and more human-like. It doesn' t alter the main Q&A logic but enhances the bot's tone and responsiveness.
The other options are not correct:
* A. Increase the confidence threshold makes the bot more selective in responses but doesn't add new conversational features.
* B. Enable active learning improves knowledge base accuracy over time through user feedback.
* C. Create multi-turn questions adds conversational flow for related topics but doesn't add greetings or casual dialogue.
Thus, to make the bot more personable, the correct action is to Add chit-chat.
Reference:Microsoft Learn - Add chit-chat to a QnA Maker knowledge base
NEW QUESTION # 27
To complete the sentence, select the appropriate option in the answer area.
Computer vision capabilities can be Deployed to....................
Answer:
Explanation:
see the answer in below Explanation
Integrate a facial recognition feature into an app.
NEW QUESTION # 28
Match the Microsoft guiding principles for responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Reliability and safety
To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
Box 2: accountability
Box 3: Privacy and security
As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
NEW QUESTION # 29
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is Azure AI Custom Vision.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Explore computer vision in Azure", the Azure AI Custom Vision service is specifically designed to allow users to train, test, and deploy custom image classification and object detection models using their own images.
The Azure AI Custom Vision service extends the capabilities of Azure's general-purpose Computer Vision API by enabling organizations to upload their own labeled datasets, define custom tags (labels), and train models that are optimized for their specific use cases. This makes it ideal for object detection scenarios-such as detecting equipment in a manufacturing line, identifying products on store shelves, or recognizing medical images-where general-purpose models may not suffice.
By contrast, the Azure AI Computer Vision service provides pre-built models for tasks such as image description, tagging, face detection, and OCR (optical character recognition). It does not allow users to train their own models with custom data. Similarly, Azure AI Document Intelligence is used for extracting structured information from documents (forms, receipts, invoices), and Azure Video Analyzer for Media focuses on analyzing video content for insights and metadata extraction.
The AI-900 study guide emphasizes that the Custom Vision service supports two key model types:
* Image Classification - categorizing entire images based on predefined tags.
* Object Detection - identifying and locating multiple objects within an image by drawing bounding boxes.
Therefore, when the question specifies "train an object detection model by using your own images," the correct Azure service is Azure AI Custom Vision, as it provides the necessary tools for training, evaluating, and deploying custom computer vision models tailored to a user's dataset.
Hence, the verified correct answer is: Azure AI Custom Vision.
NEW QUESTION # 30
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:
NEW QUESTION # 31
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