What is the duration, language, and format of AI-900: Microsoft Azure AI Fundamentals Exam
- Number of Questions: 100 to 120 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice)
- Passing Score: 70%
- Type of Questions: This test format is multiple choice
- Languages in which this exam is available in: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish
- Length of Examination: 50 mins
The Microsoft AI-900 exam, usually known as the Microsoft Azure AI Fundamentals, is well suited to baseline-level IT specialists with knowledge of artificial intelligence (AI) & Machine Learning (ML), and the relevant Microsoft Azure concepts. This is the only test that you should pass to qualify for the Microsoft Certified: Azure AI Fundamentals certification.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft Azure AI Fundamentals Exam Certification Details:
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Exam Name | Microsoft Certified - Azure AI Fundamentals |
| Schedule Exam | Pearson VUE |
| Sample Questions | Microsoft Azure AI Fundamentals Sample Questions |
| Books / Training | Course AI-900T00: Microsoft Azure AI Fundamentals |
| Exam Price | $99 (USD) |
| Duration | 60 mins |
| Exam Code | AI-900 |
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- Microsoft AI MVP Book: Practical Guide to Microsoft AI Written by 17 AI and Azure MVPs from all around the world
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- Azure AI Fundamentals: Study Guide and Practice Exam for Microsoft AI-900 Exam
This guide by David Voss is based on the current test curriculum and targets trainees looking to gain foundational knowledge of the AI solutions and services on Azure. It comprises practice test questions and explanations that are consistent with Microsoft’s online learning tools. So, if you want to prepare with confidence and pass your exam easily, get your Kindle copy today from Amazon for only around $12.
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Microsoft AI-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify common NLP tasks - Identify Azure AI services for NLP - Describe Azure capabilities for NLP |
| Topic 2: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision - Identify common computer vision tasks |
| Topic 3: Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe features of no-code automated ML - Describe core machine learning concepts - Describe Azure Machine Learning capabilities |
| Topic 4: Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI - Describe generative AI concepts |
| Topic 5: Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
Microsoft Azure AI Fundamentals (AI-900日本語版) Sample Questions:
画像を生成するには、Azure OpenAI サービスをデプロイします。
サービスが有害なコンテンツに対して最高レベルの保護を提供していることを確認する必要があります。
何をすべきでしょうか?
- A. 大規模言語モデル (LLM) をカスタマイズします。
- B. コンテンツ フィルターの設定を構成します。
- C. Azure OpenAI サービスで使用されるモデルを変更します。
- D. システムプロンプトを設定する
Correct Answer: B 🗳️
Explanation: Only visible for DumpsQuestion members. You can sign-up / login (it's free).
次の各ステートメントについて、ステートメントがtrueの場合は、[はい]を選択します。それ以外の場合は、[いいえ]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:
* "The Azure OpenAI GPT-3.5 Turbo model can transcribe speech to text." - NOThis statement is false.
The GPT-3.5 Turbo model is a text-based large language model (LLM) designed for natural language understanding and generation, such as answering questions, summarizing text, or writing content. It does not process or transcribe audio input. Speech-to-text capabilities belong to Azure AI Speech Services, specifically the Speech-to-Text API, not Azure OpenAI.
* "The Azure OpenAI DALL-E model generates images based on text prompts." - YESThis statement is true. The DALL-E model, available within Azure OpenAI Service, is a generative AI model that creates original images from natural language descriptions (text prompts). For example, given a prompt like "a futuristic city at sunset," DALL-E generates a unique, high-quality image representing that concept. This aligns with generative AI workloads in the AI-900 study guide, where DALL-E is specifically mentioned as an image-generation model.
* "The Azure OpenAI embeddings model can convert text into numerical vectors based on text similarities." - YESThis statement is also true. The embeddings model in Azure OpenAI converts text into multi-dimensional numeric vectors that represent semantic meaning. These embeddings enable tasks such as semantic search, recommendations, and text clustering by comparing similarity scores between vectors. Words or phrases with similar meanings have vectors close together in the embedding space.
In summary:
* GPT-3.5 Turbo # Text generation (not speech-to-text)
* DALL-E # Image generation from text prompts
* Embeddings # Convert text into numerical semantic representations
Correct selections: No, Yes, Yes.
Al ベースのローン承認システムがあります。
テスト中に、システムに性別バイアスがあることがわかりました。
これはどの責任ある AI 原則に違反していますか?
- A. 説明責任
- B. 信頼性と安全性
- C. 透明度
- D. 公平性
Correct Answer: D 🗳️
Explanation: Only visible for DumpsQuestion members. You can sign-up / login (it's free).
Machine Learning デザイナーを使用して Azure Machine Learning モデルをデプロイする予定ですが、どの 4 つのアクションを順番に実行する必要がありますか? 回答するには、アクションのリストから適切なアクションを回答領域に移動し、正しい順序で並べます。
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", the standard workflow for creating and deploying a machine learning model - especially within Azure Machine Learning Designer - follows a structured sequence of steps to ensure that the model is trained effectively and evaluated correctly.
Here's the detailed breakdown of the correct order:
* Import and prepare a dataset:This is always the first step in the machine learning lifecycle. The dataset is imported into Azure Machine Learning and cleaned or preprocessed. Preparation might include handling missing values, normalizing data, removing outliers, and encoding categorical variables. This ensures the dataset is ready for modeling.
* Split the data randomly into training data and validation data:The dataset is then divided into two parts
- the training set and the validation (or testing) set. Typically, around 70-80% of the data is used for training and 20-30% for validation. This step ensures that the model can be evaluated on unseen data later, preventing overfitting.
* Train the model:During this stage, the machine learning algorithm learns patterns from the training data. Azure Machine Learning Designer provides multiple algorithms (classification, regression, clustering, etc.) that can be applied using "Train Model" components.
* Evaluate the model against the validation dataset:Finally, the trained model's performance is tested using the validation dataset. Evaluation metrics such as accuracy, precision, recall, or RMSE (depending on the model type) are calculated to assess how well the model generalizes to new data.
The incorrect option - "Evaluate the model against the original dataset" - is not used in proper ML workflows, because evaluating on the same data used for training would give misleadingly high accuracy due to overfitting.
文を完成させるには、回答領域で適切なオプションを選択します。
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, object detection is a type of computer vision workload that not only identifies objects within an image but also determines their location by drawing bounding boxes around them. This functionality is clearly described in the Microsoft Learn module "Identify features of computer vision workloads." In this scenario, the AI system analyzes an image to find a vehicle and then returns a bounding box showing where that vehicle is located within the image frame. That ability - to detect, classify, and localize multiple objects - perfectly defines object detection.
Microsoft's study content contrasts object detection with other computer vision workloads as follows:
* Image classification: Determines what object or scene is present in an image as a whole but does not locate it (e.g., "this is a car").
* Object detection: Identifies what objects are present and where they are, usually returning coordinates for bounding boxes (e.g., "car detected at position X, Y").
* Optical Character Recognition (OCR): Extracts text content from images or scanned documents.
* Facial detection: Specifically locates human faces within an image or video feed, often as part of face recognition systems.
In Azure, object detection capabilities are available through services such as Azure Computer Vision, Custom Vision, and Azure Cognitive Services for Vision, which can be trained to detect vehicles, products, or other objects in various image datasets.
Therefore, based on the AI-900 study guide and Microsoft Learn materials, the verified and correct answer is Object detection, as it accurately describes the process of returning a bounding box indicating an object's position in an image.
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