分享最新版本的AI-900題庫 -免費下載Microsoft Azure AI Fundamentals - AI-900擬真試題

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最新AI-900考古題, AI-900考試備考經驗, AI-900測試, AI-900更新, AI-900證照考試

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Microsoft AI-900 Exam Syllabus Topics:

Section Weight Objectives
Describe fundamental principles of machine learning on Azure 30-35% - Describe Azure Machine Learning capabilities
- Identify common machine learning tasks
- Describe features of no-code automated ML
- Describe core machine learning concepts
Describe features of computer vision workloads on Azure 15-20% - Identify Azure AI services for computer vision
- Describe Azure capabilities for computer vision
- Identify common computer vision tasks
Describe features of Natural Language Processing (NLP) workloads on Azure 15-20% - Identify Azure AI services for NLP
- Identify common NLP tasks
- Describe Azure capabilities for NLP
Describe features of Generative AI workloads on Azure 15-20% - Describe generative AI concepts
- Describe Azure OpenAI Service capabilities
- Identify responsible AI considerations for generative AI
Describe AI workloads and considerations 15-20% - Identify features of common AI workloads
- Identify guiding principles for responsible AI

>> 最新AI-900考古題 <<

最新AI-900考古題 |100％通過|最新問題

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最新的 Microsoft Certified: Azure AI Fundamentals AI-900 免費考試真題 (Q86-Q91):

問題 #86
Select the answer that correctly completes the sentence.

答案：

解題說明：

Explanation


問題 #87
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.

答案：

解題說明：

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.


問題 #88
You have the following apps:
* App1: Understands the public perception of a brand or topic
* App2: Applies profanity filters to speech-to-text
What does each app use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Explanation:

App1: "Understands the public perception of a brand or topic" # Sentiment analysis According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn's Natural Language Processing (NLP) documentation, Sentiment analysis is a feature of the Azure AI Language Service that determines the emotional tone or attitude expressed in text. It classifies text as positive, negative, neutral, or mixed, which makes it ideal for analyzing customer opinions, brand perception, or product feedback.
For example, an organization can use sentiment analysis to process customer reviews or social media posts to determine how people feel about a particular brand or topic. This insight helps companies assess customer satisfaction, public perception, and marketing impact.
App2: "Applies profanity filters to speech-to-text" # Language detection The task of applying profanity filters occurs during or after speech-to-text transcription, which involves identifying the language used so that the correct filter can be applied. Language detection is an NLP feature that determines which language is being spoken or written. Once the language is detected, appropriate profanity filtering rules are automatically applied to remove or mask offensive words from transcribed text.
Other options such as Captioning or Named Entity Recognition (NER) are not relevant:
* Captioning describes images or videos, not speech filtering.
* NER identifies people, locations, or organizations but does not handle profanity or language detection.
Therefore, based on Azure AI NLP features:
* App1 uses Sentiment analysis
* App2 uses Language detection


問題 #89
Which Azure OpenAI model should you use to summarize the text from a document?

  • A. Whisper
  • B. GPT
  • C. Codex
  • D. DALL-E

答案：B


問題 #90
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0


問題 #91
......

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