Try and practice the latest Microsoft : AI-900 Korean real questions & answers

Microsoft Teams, Microsoft Cortana, Amazon Alex에서 사용할 수 있는 대화형 AI 솔루션을 구축할 계획입니다. 어떤 서비스를 사용해야 할까요?

• A. Azure 봇 서비스
• B. Azure 인지 검색
• C. 언어 서비스
• D. 연설

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사용자가 음성 명령을 사용하여 스마트 기기를 제어할 수 있는 AI 솔루션이 있습니다.
이 솔루션은 어떤 두 가지 유형의 자연어 처리(NLP) 워크로드를 사용합니까? 각 정답은 솔루션의 일부를 나타냅니다.
참고: 정답 하나당 1점입니다.

• A. 음성-텍스트 변환
• B. 언어 모델링
• C. 텍스트 음성 변환
• D. 핵심어 추출
• E. 번역

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문장을 올바르게 완성하는 답을 선택하세요.

Explanation:
validation.
In the Microsoft Azure AI Fundamentals (AI-900) study materials, a key concept in machine learning model development is splitting data into subsets for training, validation, and testing. A randomly extracted subset of data from a dataset is most commonly used for validation - that is, for evaluating the performance of the model during or after training.
Here's how this process works:
* Training set - This portion of the dataset is used to train the machine learning model. The model learns patterns, relationships, and parameters from this data.
* Validation set - This is a randomly selected subset (separate from training data) used to fine-tune model hyperparameters and evaluate how well the model generalizes to unseen data. It helps detect overfitting
- when the model performs well on training data but poorly on new data.
* Test set - A final, untouched dataset used to measure the model's real-world performance after all training and tuning are complete.
By reserving a random subset for validation, data scientists ensure that the model's performance metrics reflect generalization, not memorization of the training data.
Let's review the incorrect options:
* Algorithms - These are the mathematical frameworks or methods used to build models (e.g., decision trees, neural networks). They are not data subsets.
* Features - These are input variables (attributes) used by the model, not randomly selected data subsets.
* Labels - These are target values or outcomes the model predicts; again, not data subsets.
Therefore, in alignment with Azure AI-900's machine learning fundamentals, the correct completion is:
# "A randomly extracted subset of data from a dataset is commonly used for validation of the model."

다음 각 문장에 대해, 문장이 사실이라면 '예'를 선택하세요. 그렇지 않으면 '아니요'를 선택하세요.
참고: 정답 하나당 1점입니다.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of the Computer Vision and Custom Vision services", the Custom Vision service is used to train, deploy, and improve custom image classification and object detection models using your own labeled data.
* Multilabel or Multiclass Selection - NOThe statement is false because the Multilabel or Multiclass choice applies only to image classification models, not object detection models. In image classification,
"Multiclass" means one label per image, while "Multilabel" means multiple labels per image. In contrast, object detection models identify and locate multiple objects in an image using bounding boxes; thus, this classification-type selection does not apply.
* Object Detection Locates Content in an Image - YESThis statement is true. The object detection functionality in Custom Vision is designed to both identify what objects appear in an image and determine their location through bounding box coordinates. For example, a model could detect and locate multiple products on a store shelf. Microsoft documentation describes object detection as
"identifying the presence and location of objects in an image."
* Predefined Domains - YESThis statement is true as well. When you create a new Custom Vision project, you must select a domain, which is a predefined optimization setting tailored to specific use cases such as retail, food, landmarks, or general images. These domains are designed to improve model accuracy by applying specialized transfer learning features based on the type of images you will analyze.
In summary:
* Classification type (Multilabel/Multiclass): No (only for classification models)
* Detect object location: Yes
* Choose predefined domain: Yes

Azure Machine Learning Studio와 자동화된 머신 러닝(자동화 ML)을 사용하여 모델을 빌드하고 학습시키려고 합니다. 먼저 무엇을 만들어야 할까요?

• A. Jupyter 노트북
• B. 등록된 데이터 세트
• C. 머신 러닝 디자이너 파이프라인
• D. 머신 러닝 작업 공간

Explanation: Only visible for Actual4Cert members. You can sign-up / login (it's free).