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Exam AI-900: Microsoft Azure AI Fundamentals
Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.
This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.
Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.
Part of the requirements for: Microsoft Certified: Azure AI Fundamentals
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For people working in technology, having an Azure certification becomes important. The high demand for Azure certified experts, the high salary and benefits. Microsoft Azure AI Fundamentals Exam Certification give you skills and knowledge about a particular area of technology.
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Subjects of the test can be found on Microsoft Learning website. Clustering of data is one of the most important task on AI. It is used for improving accuracy. Labels are used to achieve this. Journey map is used to achieve this. Data grouping is used to achieve this. Validation is a process of finding what the probabilities are for the data to contain a given property. Remember to take the exam as early as possible.
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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High level of difficulty and many challenging questions. Identify the different types of AI models. AI design choosing tools. Who should take the exam? Artificial intelligence. Describe the strengths and weaknesses for artificial intelligence.
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Microsoft AI-900 Korean Exam Syllabus Topics:
| 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
|
Microsoft Azure AI Fundamentals (AI-900 Korean Version) Sample Questions:
모델을 학습할 때 행을 무작위로 여러 개의 하위 집합으로 나누어야 하는 이유는 무엇입니까?
- A. 더 나은 정확도를 얻기 위해 모델을 두 번 훈련합니다.
- B. 더 나은 성능을 얻기 위해 여러 모델을 동시에 훈련합니다.
- C. 모델을 학습하는 데 사용되지 않은 데이터를 사용하여 모델을 테스트합니다.
Correct Answer: C 🗳️
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특정 지역의 동물 개체수를 예측해야 합니다.
어떤 Azure Machine Learning 유형을 사용해야 합니까?
- A. 클러스터링
- B. 분류
- C. 회귀
Correct Answer: C 🗳️
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문장을 올바르게 완성하는 답을 선택하세요.
Correct Answer:
Explanation:
In Azure Machine Learning Designer, the Dataset output visualization feature is specifically used to explore and understand the distribution of values in potential feature columns before model training begins. This capability is critical for data exploration and preprocessing, two essential stages of the machine learning pipeline described in the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning learning paths.
When a dataset is imported into Azure Machine Learning Designer, users can right-click on the dataset output port and select "Visualize". This launches the dataset visualization pane, which provides detailed statistical summaries for each column, including:
* Data type (numeric, categorical, string, Boolean)
* Minimum, maximum, mean, and standard deviation values for numeric columns
* Frequency counts and distinct values for categorical columns
* Missing value counts
This visual inspection helps determine which columns should be used as features, which might need normalization or encoding, and which contain missing or irrelevant data. It is a vital step in ensuring the dataset is clean and ready for model training.
Let's examine why other options are incorrect:
* Normalize Data module is used to scale numeric data, not to visualize distributions.
* Select Columns in Dataset module is used to include or exclude columns, not to analyze them.
* Evaluation results visualization feature is used after model training to interpret performance metrics like accuracy or recall, not data distributions.
Therefore, based on official Microsoft documentation and AI-900 study materials, to explore the distribution of values in potential feature columns, you use the Dataset output visualization feature in Azure Machine Learning Designer.
자동차 판매 데이터가 포함된 대규모 데이터 세트가 있습니다.
차량 유형에 따라 차량 판매 가치를 예측하려면 자동화된 머신 러닝(자동화 ML) 모델을 학습시켜야 합니다.
어떤 작업을 선택해야 할까요? 답변하려면 답변 영역에서 적절한 작업을 선택하세요.
참고: 정답 하나당 1점입니다.
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning documentation, regression is the appropriate machine learning task when the goal is to predict continuous numeric values- such as prices, sales amounts, or other measurable quantities.
In this scenario, the dataset contains motor vehicle sales data, and the objective is to predict vehicle sale values. A vehicle's sale value (price) is a continuous numeric variable, meaning it can take on a wide range of possible numbers (for example, $15,000, $28,500, $42,300). Regression models are designed to analyze relationships between input features (like make, model, mileage, age, fuel type) and a continuous output variable (price).
Automated Machine Learning (AutoML) in Azure simplifies this process by automatically testing multiple regression algorithms (e.g., Linear Regression, Random Forest, Gradient Boosted Trees) and hyperparameters to find the best-performing model.
Let's evaluate why the other options are incorrect:
* Classification - Used for predicting discrete categories (e.g., car type: sedan, SUV, or truck). Sale value prediction is not categorical.
* Time series forecasting - Used when predicting future values based on time-dependent data (e.g., sales over months or years). The question focuses on predicting price based on vehicle characteristics, not over time.
* Natural Language Processing (NLP) - Deals with text-based data, not numeric vehicle data.
* Computer Vision - Applies to image-based tasks (e.g., detecting car types from photos).
Therefore, per Microsoft Learn's Automated ML task selection guidance, when predicting a numeric output like vehicle sale value, the correct machine learning task type is Regression.
# Final answer: Regression
GitHub Copilot은 클라이언트 측 JavaScript에 대한 제안을 하기 위해 어떤 OpenAI 모델을 사용합니까?
- A. 코덱스
- B. FROM-E
- C. GPT-3
- D. GPT-4
Correct Answer: A 🗳️
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