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What You Will Learn from Microsoft AI-900 Exam
Outline the business value of the exam. The importance of having this certification. Identify the technologies covered on Microsoft AI-900 exam. Tasks you will perform every day. Skill sets that are not available anywhere else. Potential for career advancement throughout the organization. People who take this exam should have experience in the IT industry and should understand the architecture and business requirements of Azure. Jump start your career in Azure. Stuck when you are in the process of testing. Challenging questions and high level of difficulty. Mistakes while taking the exam or during the preparing for it. Microsoft AI-900 exam dumps help to make it easier for students. Beginners can also use it as a refresher course. Reviews of the best service providers. Track the progress of the exam.
Details on Azure services and differences among them. Documents that are critical to the success of the exam. Environment to take the exam in. Sufficient understanding of Azure and its services. High level of difficulty and many challenging questions. Sit for the exam to prove your skills. Affiliate marketing and earn money for yourself. Explanations that demonstrate your understanding. Receive a valuable document to help you pass the exam. Reliability of the exam. Desktop, laptop, and mobile devices. Subscribe to receive the latest study materials. Ensure you are comfortable with the exam environment. Formats suited for different learning styles.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Explain Conversational Artificial Intelligence Workloads Features on Azure (15-20%)
In the last section, you will face with the following subtopics:
- Establish Azure services for Conversational Artificial Intelligence – The potential candidates for the Microsoft AI-900 exam should have the capacity to identify the capabilities of Azure Bot Service and QnA Maker service.
- Identify the basic use cases for Conversational Artificial Intelligence (AI) – The students should be able to identify the features and usage for a range of elements. These include personal digital assistants, webchat bots, and telephone voice menus. It also covers the skills in identifying the basic features of conversational Artificial Intelligence solutions.
Microsoft AI-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Language |
| Topic 2: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision |
| Topic 3: 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 4: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe core concepts of machine learning |
| Topic 5: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
Microsoft Azure AI Fundamentals (AI-900日本語版) Sample Questions:
あなたは音楽祭をサポートするための言語理解（LUIS）アプリケーションを作成しています。
「メインステージでどのアクトが再生されているか」など、スケジュールされたショーについてユーザーが質問できるようにする必要があります。 「メインステージで演じているのはどのアクト？」という質問。どのタイプの要素の例ですか？
- A. ドメイン
- B. 発話
- C. 意図
- D. エンティティ
Correct Answer: B 🗳️
Explanation: Only visible for ActualPDF members. You can sign-up / login (it's free).
Azure OpenAI の REST API エンドポイントにリクエストを送信するにはどの形式を使用する必要がありますか?
- A. YAML
- B. JSON
- C. XML
- D. CSV
Correct Answer: B 🗳️
Explanation: Only visible for ActualPDF members. You can sign-up / login (it's free).
あなたは、小売店からの画像を処理し、競合他社の製品を識別するツールを構築しています。
ソリューションは、会社から提供された画像を使用してトレーニングする必要があります。
どの Azure Al サービスを使用する必要がありますか?
- A. 顔
- B. Azure Al コンピューター ビジョン
- C. Azure Al Document Intelligence
- D. アズール アル カスタム ビジョン
Correct Answer: D 🗳️
Explanation: Only visible for ActualPDF members. You can sign-up / login (it's free).
Webサイト用のチャットボットを開発する必要があります。チャットボットは、次のドキュメントの情報に基づいてユーザーの質問に答える必要があります
* MicrosoftWordドキュメントの製品トラブルシューティングガイド
* Webページのよくある質問（FAQ）リスト
ドキュメントを処理するためにどのサービスを使用する必要がありますか？
- A. 言語を要求しない
- B. テキスト分析
- C. QnAメーカー
- D. Azureボットサービス
Correct Answer: C 🗳️
Explanation: Only visible for ActualPDF members. You can sign-up / login (it's free).
次のデータセットがあります。
データセットを使用して、住宅の住宅価格カテゴリを予測するモデルをトレーニングすることを計画しています。
世帯収入と住宅価格のカテゴリとは何ですか？回答するには、回答領域で適切なオプションを選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。
Correct Answer:
Explanation:
In machine learning, especially within the Microsoft Azure AI Fundamentals (AI-900) framework, datasets used for supervised learning are composed of features (inputs) and labels (outputs). According to the Microsoft Learn module "Explore the machine learning process", a feature is any measurable property or attribute used by the model to make predictions, whereas a label is the actual value or category the model is trying to predict.
* Household Income # FeatureA feature (also known as an independent variable) represents the input data that the machine learning algorithm uses to detect patterns or correlations. In this dataset, Household Income is a numeric value that influences the prediction of house price categories. During training, the model learns how variations in household income correlate with changes in the house price category.
Microsoft Learn defines features as "the attributes or measurable inputs that are used to train the model." Thus, Household Income serves as a predictive input or feature.
* House Price Category # LabelThe label (or dependent variable) represents the output the model aims to predict. It is the known result during training that helps the algorithm learn correct mappings between features and outcomes. In this scenario, House Price Category-which can take values such as "Low,"
"Middle," or "High"-is the classification outcome that the model will predict based on household income (and possibly other variables). According to Microsoft Learn, "the label is the variable that contains the known values that the model is trained to predict." In summary, the dataset defines a supervised learning classification problem, where Household Income is the feature (input) and House Price Category is the label (output) that the model will learn to predict.
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