Last Updated: Oct 16, 2026
No. of Questions: 336 Questions & Answers with Testing Engine
Download Limit: Unlimited
Our SurePassExams AI-900 Korean Exam Preparation materials are famous for its high pass-rate. Actual studying content will help you pass exam for sure. Also different study methods will give you different choices and different preparing experience. AI-900 Korean exam torrent files can help you prepare easily and get doubt result with half effort. Our Soft test engine and Online test engine will provide you simulation function so that you can have a good mood after studying deeply.
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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Exam Format: | Case studies, Multiple response, Multiple choice, Drag and drop |
| Available Languages: | Japanese, Korean, Chinese (Simplified), German, French, Spanish, English, Portuguese (Brazil) |
| Related Certifications: | Microsoft Azure AI Engineer Associate Microsoft Azure Fundamentals Microsoft Azure Data Fundamentals |
| Passing Score: | 700/1000 |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| Exam Duration: | 85 minutes |
| Real Exam Qty: | 40-60 |
| Exam Price: | USD 99 (varies by region) |
| Recommended Training: | Azure AI Fundamentals Practice Modules Microsoft Learn AI-900 Learning Path |
| Exam Registration: | Pearson VUE Exam Registration Official Microsoft Certification Page |
| 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/ |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Topic 2: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Topic 3: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Topic 4: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
The AI-900 Korean exam is the official test behind the Microsoft Certified: Azure AI Fundamentals certification, a Fundamentals-level credential from Microsoft. Passing it validates the skills measured by the Microsoft Azure AI Fundamentals (AI-900 Korean Version) objectives and proves to employers that you can apply those skills in real-world scenarios, not just recite theory. The credential also sits alongside related certifications such as Microsoft Azure Data Fundamentals, Microsoft Azure Fundamentals, Microsoft Azure AI Engineer Associate, making it a strong building block in a broader Microsoft certification path. For candidates planning a longer-term career move, the AI-900 Korean exam is often the step that turns study effort into a verifiable, resumé-ready qualification.
The Microsoft Azure AI Fundamentals (AI-900 Korean Version) blueprint is divided into 4 exam domains. The first three are Describe AI workloads and considerations (20-25%), Features of natural language processing (NLP) workloads on Azure (30-35%), and Features of computer vision workloads on Azure (15-20%). The weightings matter: they tell you where the bulk of your score comes from, so allocate study time in proportion rather than spreading it evenly. Scroll back to the exam topics section above for the complete domain-by-domain breakdown of the AI-900 Korean exam.
The Microsoft Azure AI Fundamentals (AI-900 Korean Version) exam gives you 85 minutes to work through 40-60 questions. Do the math before test day: divide the total time by the question count to set a steady per-question pace, and treat that number as a hard budget rather than a guideline. When a question eats more than its share, flag it and move on — a flagged question can be revisited, but lost minutes can't. Running two or three full timed mock exams in the SurePassExams test engine before your exam date is the most reliable way to make that pacing automatic.
The passing score for the Microsoft Azure AI Fundamentals (AI-900 Korean Version) exam is 700/1000, and the official registration fee is USD 99 (varies by region). Keep in mind that a failed attempt means paying that fee in full again — there are no discounted retakes — so booking the exam before you're ready is the most expensive shortcut in certification. A practical rule: take a timed self-assessment with SurePassExams practice tests first, and only schedule your seat once you're scoring comfortably above the passing mark with time to spare. That buffer absorbs exam-day nerves and the handful of questions that always seem to come from nowhere.
According to Microsoft, the Microsoft Azure AI Fundamentals (AI-900 Korean Version) exam uses the following question formats: Multiple choice, Multiple response, Drag and drop, Case studies. Different formats reward different habits. For scenario-based items, read the final sentence of the question stem first so you know what's actually being asked before you dig into the details, and never change an answer unless you can point to the exact clue you missed. The SurePassExams question bank lets you rehearse every format until each one feels routine.
Microsoft currently offers the AI-900 Korean exam in English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil). Choose the language you can reason in fastest under time pressure — reading speed matters more than most candidates expect. If you plan to test in English, working through SurePassExams's 336 English practice questions doubles as terminology training, so the phrasing on exam day feels familiar rather than foreign.
The credential you earn by passing the Microsoft Azure AI Fundamentals (AI-900 Korean Version) exam remains valid for Does not expire (Fundamentals certification). That clock is worth protecting: set a reminder well before the expiry date and review the official recertification options early, so staying certified becomes a planned task instead of a last-minute scramble. Recertification policies are set by Microsoft and can change, so confirm the current rules on the official certification page.
Every Microsoft Azure AI Fundamentals (AI-900 Korean Version) product contains the same 336 expert-prepared practice questions; only the way you study with them changes:
Many candidates combine the PDF for daily reading with a test engine for timed weekend mock exams.
Yes. SurePassExams offers a free PDF demo for the Microsoft Azure AI Fundamentals (AI-900 Korean Version) exam, so you can review the question style, difficulty level, and answer quality before you pay anything. Every purchase also includes 365 days of free updates — and if you'd like to extend updates after that period, you can renew at a 50% discount from your member zone. Trying first costs you nothing; it simply tells you whether our material matches the way you learn.
Microsoft adjusts its exams over time, so SurePassExams reviews and updates the Microsoft Azure AI Fundamentals (AI-900 Korean Version) question bank on a continuous basis to reflect the current skills being tested. Your purchase includes 365 days of free updates; after that, you can repurchase the expired product at a 50% discount from your member zone. To catch a fresh release, check the New Releases section on SurePassExams, the status on your product's download page, or the SurePassExams newsletter — and always look for an update a few days before your exam date.
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SurePassExams backs your purchase with a 100% Money Back Guarantee: if you take the corresponding exam within 60 days of your purchase and don't pass, you can apply for a full refund. To qualify, submit a scanned copy of your exam enrollment slip and the official Score Report PDF within 2 days after the exam — refunds are processed within 7 days of submission. Attempts made within 3 days of purchase, material for exams never actually taken, free items, and expired orders are not eligible, and the candidate name must match the payer name. Prefer new material instead of a refund? You can exchange your product for two free exam products of equal value and keep the update service on your original purchase. Delivery itself is instant: your Microsoft Azure AI Fundamentals (AI-900 Korean Version) practice questions are available for download and emailed to you within one minute of payment — if nothing arrives within 2 hours, check your spam folder and contact our support team. There's no limit on how many computers you can install the product on.
One purchase covers the whole journey: a free demo to start, instant delivery when you buy, and a full year of updates while you study. Your AI-900 Korean product arrives in your mailbox within one minute of payment — if it doesn't show up within 2 hours, our support team will sort it out — and you can install it on as many computers as you need. McAfee security services protect every transaction, and your personal information is never shared with third parties.
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Azure Machine Learning Studio와 자동화된 머신 러닝(자동화 ML)을 사용하여 모델을 빌드하고 학습시키려고 합니다. 먼저 무엇을 만들어야 할까요?
Correct Answer: D 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
다음 각 문장에 대해, 문장이 사실이라면 '예'를 선택하세요. 그렇지 않으면 '아니요'를 선택하세요.
참고: 정답 하나당 1점입니다.
Correct Answer:

Explanation:
The correct answers are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore fundamental principles of machine learning." In supervised machine learning, data is typically divided into three main subsets:
* Training set - used to train the model, i.e., to teach the algorithm the patterns and relationships between input features and output labels.
* Validation set - used to evaluate the model during training to tune hyperparameters and prevent overfitting.
* Test set - used after training to assess the final model's performance on unseen data.
Let's analyze each statement in light of these definitions:
* "A validation set includes the set of input examples that will be used to train a model." # NoThis is incorrect because the training set, not the validation set, contains the input examples used for model training. The validation set is separate from the training data to ensure unbiased evaluation.
* "A validation set can be used to determine how well a model predicts labels." # YesThis is correct. The validation set helps assess how effectively the model generalizes during training. It measures performance and helps tune model parameters for optimal results.
* "A validation set can be used to verify that all the training data was used to train the model." # NoThis is false. The validation set is not used to verify the completeness of training data usage. It exists independently to evaluate the model's performance during training cycles.
According to Microsoft Learn, using a validation set helps ensure that a model generalizes well and avoids overfitting to the training data. It plays a crucial role in refining and optimizing models before final testing.
스캔한 계약서 이미지에서 세부 정보를 추출하려면 어떤 도구를 사용해야 할까요?
Correct Answer: A 🗳️
문장을 완성하려면 답변란에서 적절한 옵션을 선택하세요.
Correct Answer:

Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Prepare data for machine learning", feature engineering refers to the process of transforming raw data into meaningful features that can be effectively used by machine learning algorithms. This includes steps such as scaling, normalization, encoding categorical variables, handling missing values, and creating new features derived from existing ones.
The question states:
"Ensuring that the numeric variables in training data are on a similar scale." This directly describes a data normalization or standardization step, which is a core component of feature engineering. The purpose of scaling numeric variables is to ensure that all features contribute equally to the model's learning process. Without normalization, features with large numeric ranges (such as "income in dollars") could dominate smaller-scale features (like "age in years"), leading to biased model performance.
In Azure Machine Learning, this is typically done using the Normalize Data module or transformations in the data preparation stage. Microsoft Learn explains that normalization and feature scaling are applied before model training to ensure that gradient-based algorithms (such as regression or neural networks) converge more efficiently and produce more accurate results.
The other options are not correct:
* Data ingestion refers to collecting and importing data into a system.
* Feature selection involves choosing the most relevant features, not scaling them.
* Model training is the phase where the algorithm learns patterns from the processed data, which occurs after feature engineering.
Therefore, ensuring that numeric variables are on a similar scale is a step in Feature Engineering.
비슷한 구매 습관을 가진 사람들의 그룹을 식별하려면 어떤 유형의 머신 러닝을 사용해야 합니까?
Correct Answer: A 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
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