Microsoft Azure AI Fundamentals Exam Certification Details:
| Exam Code | AI-900 |
| Exam Price | $99 (USD) |
| Books / Training | Course AI-900T00: Microsoft Azure AI Fundamentals |
| Duration | 60 mins |
| Exam Name | Microsoft Certified - Azure AI Fundamentals |
| Schedule Exam | Pearson VUE |
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Sample Questions | Microsoft Azure AI Fundamentals Sample Questions |
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft AI-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe core concepts of machine learning |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Translator |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions |
| 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 |
Microsoft Azure AI Fundamentals (AI-900日本語版) Sample Questions:
ワークロードを処理する自然言語のタイプを適切なシナリオに一致させます。
答えるには、適切なワークロードタイプを左側の列から右側のシナリオにドラッグします。各ワークロードタイプは、1回使用することも、複数回使用することも、まったく使用しないこともできます。
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:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of Natural Language Processing (NLP) workloads on Azure", Azure Cognitive Services provides several text analytics and language understanding workloads that perform different types of language processing tasks. Each workload extracts specific information or performs distinct analysis operations on text data.
* Entity Recognition # Extracts persons, locations, and organizations from the textEntity recognition is a feature of Azure Cognitive Service for Language (formerly Text Analytics). It identifies and categorizes named entities in unstructured text, such as people, organizations, locations, dates, and more. The study guide defines this workload as: "Entity recognition locates and classifies named entities in text into predefined categories." This allows applications to extract structured information from raw text data- for example, identifying "Microsoft" as an organization and "Seattle" as a location.
* Sentiment Analysis # Evaluates text along a positive-negative scaleSentiment analysis determines the emotional tone or opinion expressed in a piece of text. It classifies text as positive, negative, neutral, or mixed, which is widely used for social media monitoring, customer feedback, and product reviews.
Microsoft's official documentation describes it as: "Sentiment analysis evaluates text and returns a sentiment score indicating whether the sentiment is positive, negative, neutral, or mixed."
* Translation # Returns text translated to the specified target languageThe Translator service, part of Azure Cognitive Services, automatically translates text from one language to another. It supports multiple languages and provides near real-time translation. The AI-900 content specifies that
"translation workloads are used to automatically translate text between languages using machine translation models." In summary:
* Entity Recognition # Extracts entities like names and locations.
* Sentiment Analysis # Determines emotional tone.
* Translation # Converts text between languages.
# Final Answers:
* Extracts persons, locations, and organizations # Entity recognition
* Evaluates text along a positive-negative scale # Sentiment analysis
* Returns text translated to the specified target language # Translation
機械学習のプロセス中、いつ評価指標をレビューする必要がありますか?
- A. モデルをトレーニングする前。
- B. 検証データでモデルをテストした後。
- C. データをクリーンアップした後。
- D. モデルのタイプを選択する前。
Correct Answer: B 🗳️
Explanation: Only visible for Dumpexams members. You can sign-up / login (it's free).
文を正しく完成させる答えを選択してください。
Correct Answer:
Explanation:
This question is drawn from the Microsoft Azure AI Fundamentals (AI-900) syllabus section "Describe features of natural language processing (NLP) workloads on Azure." According to the Microsoft Learn materials, Natural Language Processing (NLP) is a branch of artificial intelligence that allows computers to analyze, understand, and generate human language. NLP enables machines to work with text or speech data in a way that extracts meaning, sentiment, and intent.
Microsoft defines NLP as enabling scenarios such as language detection, text classification, key phrase extraction, sentiment analysis, and named entity recognition. The example given-classifying emails as
"work-related" or "personal"-is a text classification task, which falls under NLP capabilities. The AI model processes the textual content of emails, identifies linguistic patterns, and categorizes them based on the detected topic or context.
Let's analyze the other options:
* Predict the number of future car rentals # This is a forecasting task, handled by machine learning regression models, not NLP.
* Predict which website visitors will make a transaction # This is a classification or prediction problem in machine learning, not NLP, since it deals with behavioral or numerical data rather than language.
* Stop a process in a factory when extremely high temperatures are registered # This is an IoT or anomaly detection scenario, focusing on sensor data, not language understanding.
Therefore, only classifying email messages as work-related or personal correctly represents an NLP use case.
It illustrates how NLP can analyze written text and make intelligent categorizations-a key capability covered in AI-900's natural language workloads section.
あなたの会社は、スマートホームデバイスでの音声認識テクノロジーの使用を検討しています。同社は、特定のユーザーグループを意図せずに除外する可能性のある障壁を特定したいと考えています。
これは、責任あるAIのためのマイクロソフトの指導原則の例ですか？
- A. 公平性
- B. 包括性
- C. 説明責任
- D. プライバシーとセキュリティ
Correct Answer: B 🗳️
Explanation: Only visible for Dumpexams members. You can sign-up / login (it's free).
Form Recognizerサービスを使用できる2つのシナリオはどれですか？それぞれの正解は完全な解決策を提示します。
注：正しい選択はそれぞれ1ポイントの価値があります。
- A. カタログで商品の画像を検索します。
- B. 領収書から小売業者を特定します。
- C. フォームをフランス語から英語に翻訳します。
- D. 請求書から請求書番号を抽出します。
Correct Answer: B,D 🗳️
Explanation: Only visible for Dumpexams members. You can sign-up / login (it's free).
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