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Microsoft AI-900日本語 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals
Exam Number:AI-900
Exam Duration:85 minutes
Certificate Validity Period:Does not expire (Fundamentals certification)
Real Exam Qty:40-60
Exam Price:USD 99 (varies by region)
Related Certifications:Microsoft Azure AI Engineer Associate
Microsoft Azure Data Fundamentals
Microsoft Azure Fundamentals
Passing Score:700/1000
Available Languages:English, Spanish, Portuguese (Brazil), Korean, Chinese (Simplified), Japanese, German, French
Exam Format:Multiple response, Multiple choice, Case studies, Drag and drop
Recommended Training:Microsoft Learn AI-900 Learning Path
Azure AI Fundamentals Practice Modules
Exam Registration:Official Microsoft Certification Page
Pearson VUE Exam Registration
Sample Questions: DOWNLOAD DEMO
Exam Way:Online proctored exam or in-person test center
Pre Condition:No formal prerequisites required; basic understanding of cloud and AI concepts recommended
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/

Microsoft AI-900日本語 Exam Syllabus Topics:

SectionWeightObjectives
Fundamentals of machine learning on Azure25-30%- Core machine learning concepts
  • 1. Supervised vs unsupervised learning
    • 2. Training and validation concepts
      Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
      • 1. Responsible AI principles
        • 2. Common AI workloads
          Features of natural language processing (NLP) workloads on Azure30-35%- Text analytics and language understanding
          • 1. Key phrase extraction
            • 2. Language modeling and translation
              • 3. Sentiment analysis
                Features of computer vision workloads on Azure15-20%- Computer vision solutions
                • 1. Image classification
                  • 2. Object detection
                    • 3. OCR and image analysis

                      Microsoft AI-900日本語 Questions? We Have Answers.

                      The AI-900日本語 exam is Microsoft's gatekeeper for the Microsoft Certified: Azure AI Fundamentals certification, a credential at the Fundamentals level. Passing it says you can apply the Microsoft Azure AI Fundamentals (AI-900日本語版) objectives to real problems — a claim employers can act on. It also sits within a broader certification family covering Microsoft Azure Data Fundamentals, Microsoft Azure Fundamentals, Microsoft Azure AI Engineer Associate, so the work you put in here compounds. Easier said than earned — which is exactly why structured practice beats self-directed wandering.

                      The Microsoft Azure AI Fundamentals (AI-900日本語版) blueprint spans 4 domains, led by Features of computer vision workloads on Azure (15-20%), Fundamentals of machine learning on Azure (25-30%), and Describe AI workloads and considerations (20-25%). Read the weightings as marching orders: the domain with the largest share of the score gets the largest share of your week. The full topic list is in the exam topics section above — study to it, not around it.

                      The Microsoft Azure AI Fundamentals (AI-900日本語版) exam packs 40-60 questions into 85 minutes. Convert that into a personal tempo before test day: minutes divided by questions is your per-item budget, and anything burning double its budget gets flagged and revisited at the end. Rehearse the tempo against a live timer — every Pass4training engine session enforces one — until finishing with review time to spare feels normal.

                      Microsoft Azure AI Fundamentals (AI-900日本語版) requires 700/1000 to pass, and each attempt costs USD 99 (varies by region) — including retakes, at full price. The financially sane strategy is arriving over-prepared: drill with Pass4training practice questions until your timed scores clear the mark across several consecutive sessions, then register. Over-preparation is cheaper than re-registration.

                      Microsoft lists the Microsoft Azure AI Fundamentals (AI-900日本語版) question formats as: Multiple choice, Multiple response, Drag and drop, Case studies. Formats shape tactics — locate the actual question sentence inside long scenario stems before reading the rest, and re-verify every selection on multi-answer items. Pass4training's 336 practice questions drill these formats until only the content feels new on exam day.

                      The AI-900日本語 exam is offered in English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil). Choose the language where your comprehension stays fastest under the clock — slow reading is a hidden tax on every question. If you test in English, daily sessions with Pass4training's English AI-900日本語 practice questions double as vocabulary training.

                      The certification earned through the Microsoft Azure AI Fundamentals (AI-900日本語版) exam stays valid for Does not expire (Fundamentals certification). Mark the expiry date the day you pass and look into the recertification path early — a planned renewal is an errand, an expired credential is a project. Microsoft sets these rules, so confirm the current ones on the official certification page as your window approaches.

                      All three versions carry the same 336 expert-written questions — you're choosing a study environment, not a content tier:

                      • PDF version — printable, expert-prepared, instantly downloadable, readable anywhere and anytime. Includes 365 days of free updates and a free demo.
                      • Desktop Test Engine — Windows software simulating the real exam environment, with two practice modes, offline access, and unlimited installations.
                      • Online Test Engine — any browser on Windows, Mac, Android, or iOS, with test history and performance review built in.

                      Most candidates end up mixing formats — PDF for reading, an engine for timed rehearsal.

                      Yes. Pass4training's free Microsoft Azure AI Fundamentals (AI-900日本語版) PDF demo contains genuine sample questions with verified answers, so you can judge the quality yourself. Every purchase includes 365 days of free updates, and renewing after expiry costs 50% of the regular price from your member zone. Inspect first — confident material has nothing to hide.

                      Continuously — the digitized world changes daily, and the bank keeps pace. As Microsoft adjusts the Microsoft Azure AI Fundamentals (AI-900日本語版) exam, our experts renovate the material to match, and updates are free for 365 days in your member zone. Track them via the New Releases section or the Pass4training newsletter, and re-verify your version 3-4 days before your exam. Expired products repurchase at 50% off to restart the service.

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                      Delivery first: your Microsoft Azure AI Fundamentals (AI-900日本語版) practice questions are downloadable immediately and emailed within one minute of payment — if 2 hours pass, check spam and contact support. Install on unlimited computers. If the exam goes wrong, the 100% Money Back Guarantee applies under clear conditions: take the corresponding exam within 60 days of purchase, and if you don't pass, submit a scanned enrollment slip and your official Score Report PDF within 2 days after the exam — full refunds process within 7 days. Not eligible: attempts within 3 days of purchase, exams never actually taken, free items, and expired orders; candidate name must match payer name. Prefer to keep preparing? Exchange for two free products of equal value and keep your update service.

                      Microsoft Azure AI Fundamentals (AI-900日本語版) Sample Questions:

                      Question #1

                      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。

                      Reveal Solution  Discussion  0

                      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", there are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Within supervised learning, two common approaches are regression and classification, while clustering is a primary example of unsupervised learning.
                      * "You train a regression model by using unlabeled data." - No.Regression models are trained with labeled data, meaning the input data includes both features (independent variables) and target labels (dependent variables) representing continuous numerical values. Examples include predicting house prices or sales forecasts. Unlabeled data (data without target output values) cannot be used to train regression models; such data is used in unsupervised learning tasks like clustering.
                      * "The classification technique is used to predict sequential numerical data over time." - No.
                      Classification is used for categorical predictions, where outputs belong to discrete classes, such as spam
                      /not spam or disease present/absent. Predicting sequential numerical data over time refers to time series forecasting, which is typically a regression or forecasting problem, not classification. The AI-900 syllabus clearly separates classification (categorical prediction) from regression (continuous value prediction) and time series (temporal pattern analysis).
                      * "Grouping items by their common characteristics is an example of clustering." - Yes.This statement is correct. Clustering is an unsupervised learning technique used to group similar data points based on their features. The AI-900 study materials describe clustering as the process of "discovering natural groupings in data without predefined labels." Common examples include customer segmentation or document grouping.
                      Therefore, based on Microsoft's AI-900 training objectives and definitions:
                      * Regression # supervised learning using labeled continuous data (No)
                      * Classification # categorical prediction, not sequential numeric forecasting (No)
                      * Clustering # grouping by similarity (Yes)

                      Question #2

                      ユーザーのプロンプトに基づいてイメージを生成する必要があります。どの Azure OpenAI モデルを使用する必要がありますか?

                      • A. Whisper
                      • B. DALL-E
                      • C. GPT-4
                      • D. GPT-3
                      Reveal Solution  Discussion  0

                      Correct Answer: B  🗳️

                      Explanation: Only visible for Pass4training members. You can sign-up / login (it's free).

                      Question #3

                      Azure OpenAI GPT-3.5 モデルを使用するチャット ソリューションからの応答で、より多様な範囲のトークンを生成するには、どのパラメーターを構成する必要がありますか?

                      • A. 過去のメッセージが含まれています
                      • B. プレゼンスペナルティ
                      • C. シーケンスを停止
                      • D. 最大応答
                      Reveal Solution  Discussion  0

                      Correct Answer: B  🗳️

                      Explanation: Only visible for Pass4training members. You can sign-up / login (it's free).

                      Question #4

                      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。

                      Reveal Solution  Discussion  0

                      Correct Answer:


                      Explanation:

                      According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", the term features refers to the input variables or independent variables used by a machine learning model to make predictions. These are the measurable properties or attributes of the data that influence the output (target) value.
                      In a supervised learning process, data is typically divided into two parts:
                      * Features # The input variables used by the model to learn patterns (e.g., customer age, income, credit score).
                      * Label (Target) # The outcome or value the model is trying to predict (e.g., whether a loan will be approved or the amount of a house price).
                      During training, the model uses the features to understand how input data correlates with the target output.
                      Once trained, the model applies the same learned relationships to predict outcomes for new, unseen data using only the features.
                      For example:
                      * In a regression model predicting house prices, features might include square footage, number of bedrooms, and location.
                      * In a classification model predicting loan approval, features might include applicant income, credit score, and debt ratio.
                      To contrast with other options:
                      * Dependent variables (or labels) are the outcomes the model predicts.
                      * Identifiers (like customer IDs) are unique values that do not help the model learn relationships and are typically excluded from features.
                      * Labels are the target outputs, not the inputs.
                      Therefore, in Azure Machine Learning and AI-900 terminology, data values used to make a prediction are called "features."

                      Question #5

                      Azure Al Vision サービスを使用して実行できるアクションはどれですか?

                      • A. トレーニングビデオのサムネイルを作成する
                      • B. 手書きの文字からデータを抽出する
                      • C. 文書からキーフレーズを抽出する
                      • D. ライブビデオストリームで動物の品種を識別する
                      Reveal Solution  Discussion  0

                      Correct Answer: D  🗳️

                      Explanation: Only visible for Pass4training members. You can sign-up / login (it's free).

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