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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions - Describe capabilities of Azure Face |
| Topic 2: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service |
| Topic 3: Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Describe considerations for developing AI solutions - Identify types of AI workloads |
| Topic 4: Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning - Describe automated machine learning |
| Topic 5: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator - Describe capabilities of Azure Language |
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NEW QUESTION # 180
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 181
You use drones to identify where weeds grow between rows of crops to send an Instruction for the removal of the weeds. This is an example of which type of computer vision?
Answer: C
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied.
For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image.
Reference:
https://docs.microsoft.com/en-us/ai-builder/object-detection-overview
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-overv
NEW QUESTION # 182
correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 183
Match the Al solution to the appropriate task.
To answer, drag the appropriate solution from the column on the left to its task on the right. Each solution may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
This question evaluates your understanding of how different Azure AI workloads correspond to specific tasks in image, text, and content generation scenarios, as explained in the Microsoft Azure AI Fundamentals (AI-
900) study guide and Microsoft Learn modules covering common AI workloads and Azure services.
* Generate a caption from a given image # Computer VisionThis is a computer vision task because it involves analyzing the visual elements of an image and producing descriptive text (a caption). Azure AI Vision provides image analysis and captioning capabilities through its Describe Image API, which uses deep learning models to recognize objects, scenes, and actions in an image and automatically generate natural-language descriptions (e.g., "A cat sitting on a sofa").
* Generate an image from a given caption # Generative AIThis task belongs to Generative AI, which focuses on creating new content such as text, code, or images based on prompts. Tools like Azure OpenAI Service with DALL-E can interpret text descriptions and generate realistic images that match the given caption. Generative AI is capable of creative synthesis, not just analysis, making it the appropriate category.
* Generate a 200-word summary from a 2,000-word article # Text AnalyticsText analytics (a subset of natural language processing) allows summarization, sentiment analysis, and entity recognition from large text corpora. Azure AI Language includes text summarization capabilities that condense long documents into concise summaries while preserving meaning and key information.
NEW QUESTION # 184
You have a database that contains a list of employees and their photos.
You are tagging new photos of the employees.
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:
These answers are derived from the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure." The Azure Face service, part of Azure Cognitive Services, provides advanced facial recognition capabilities including detection, verification, identification, grouping, and similarity analysis.
Let's analyze each statement:
* "The Face service can be used to group all the employees who have similar facial characteristics." # YesThe Face service supports a grouping function that automatically organizes a collection of unknown faces into groups based on visual similarity. It doesn't require labeled data; instead, it identifies clusters of similar-looking faces. This is particularly useful when building or validating datasets of people.
* "The Face service will be more accurate if you provide more sample photos of each employee from different angles." # YesAccording to Microsoft documentation, model accuracy improves when you provide multiple high-quality images of each person under different conditions-such as varying lighting, poses, and angles. This diversity allows the service to better learn unique facial characteristics and improves recognition reliability, especially for identification and verification tasks.
* "If an employee is wearing sunglasses, the Face service will always fail to recognize the employee." # NoThis is incorrect. While occlusions (like sunglasses or hats) can reduce accuracy, the service may still recognize the person depending on how much of the face remains visible. Microsoft Learn explicitly notes that partial occlusion affects recognition confidence but does not guarantee failure.
In conclusion, the Face service can group similar faces (Yes), become more accurate with diverse samples (Yes), and still recognize partially covered faces though with lower confidence (No). These principles align directly with the Face API's core functions and AI-900 learning objectives regarding computer vision and responsible AI-based facial recognition.
NEW QUESTION # 185
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