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| Section | Weight | Objectives |
|---|---|---|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
時間が経つとともに、我々はインタネット時代に生活します。この時代にはIT資格認証を取得するは重要になります。それでは、AI-900試験に参加しよう人々は弊社GoShikenのAI-900問題集を選らんで勉強して、一発合格して、MicrosoftIT資格証明書を受け取れます。
質問 # 186
You need to create a clustering model and evaluate the model by using Azure Machine Learning designer.
What should you do?
正解：A
質問 # 187
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
質問 # 188
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
This question tests understanding of AI workload types, a fundamental topic in the Microsoft Azure AI Fundamentals (AI-900) curriculum. Each workload type-Computer Vision, Natural Language Processing, Machine Learning (Regression), and Anomaly Detection-serves a specific function within the AI landscape, as explained in Microsoft Learn's module "Describe features of common AI workloads."
* Computer Vision enables computers to "see" and interpret visual information such as images or videos.
Identifying handwritten letters requires analyzing image patterns, shapes, and strokes, which is a classic image recognition task. Azure's Computer Vision API and Custom Vision services are specifically designed for such tasks.
* Natural Language Processing (NLP) involves interpreting human language, both written and spoken.
Determining the sentiment of a social media post (positive, negative, or neutral) is a typical text analytics use case within NLP, often implemented using Azure's Text Analytics for Sentiment Analysis.
* Anomaly Detection focuses on identifying data points that deviate from normal patterns. Detecting fraudulent credit card payments requires finding transactions that are unusual compared to historical spending behavior. Azure's Anomaly Detector API applies machine learning to identify such irregularities.
* Machine Learning (Regression) is used for predicting continuous numerical outcomes based on historical data. Estimating next month's toy sales is a regression problem-an example of supervised learning where the model predicts future sales values from past sales data.
Thus, based on Microsoft's official AI-900 learning objectives, the correct mapping of workloads to scenarios is:
* Computer Vision # Identify handwritten letters
* NLP # Predict sentiment
* Anomaly Detection # Fraud detection
* Machine Learning (Regression) # Predict toy sales
質問 # 189
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
These answers align with the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore conversational AI in Microsoft Azure."
1. A webchat bot can interact with users visiting a website # Yes
This statement is true. The Azure Bot Service allows developers to create intelligent chatbots that can be integrated into a webchat interface. This enables visitors to interact with the bot directly from a website, asking questions and receiving automated responses. This is a typical use case of conversational AI, where natural language processing (NLP) is used to interpret and respond to user input conversationally.
2. Automatically generating captions for pre-recorded videos is an example of conversational AI # No This statement is false. Automatically generating captions from video content is an example of speech-to-text (speech recognition) technology, not conversational AI. While it uses AI to convert spoken words into text, it lacks the two-way interactive communication characteristic of conversational AI. This task is typically handled by the Azure AI Speech service, which transcribes spoken content.
3. A smart device in the home that responds to questions such as "What will the weather be like today?" is an example of conversational AI # Yes This statement is true. Smart home assistants that engage in dialogue with users are powered by conversational AI. These devices use speech recognition to understand spoken input, natural language understanding (NLU) to determine intent, and speech synthesis (text-to-speech) to respond audibly. This represents the full conversational AI loop, where machines communicate naturally with humans.
質問 # 190
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.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
https://docs.microsoft.com/en-us/azure/cognitive-services/face/concepts/face-detection
質問 # 191
......
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