検証済み！AI-900問題集と解答でAI-900テストエンジン正確解答付き [Q19-Q41]

検証済み！AI-900問題集と解答でAI-900テストエンジン正確解答付き

あなたを必ず合格させるAI-900問題集PDF2024年最新のに更新された235問あります

AI-900試験では、AI、機械学習、自然言語処理、コンピュータービジョン、会話AIの基本的な概念など、さまざまなトピックをカバーしています。この試験では、Azure Cognitive Services、Azure Machine Learning、Azure Bot ServicesなどのAzure AIサービスに関する候補者の理解も評価しています。この試験に合格すると、候補者はAIの概念を確実に理解しており、Azure AIサービスを使用してインテリジェントなアプリケーションを構築できることが示されています。さらに、AI-900試験は、AI-100がAzure AIソリューションの設計と実装など、より高度なAzure AI認定の前提条件として機能します。

AI-900認定試験は、主に、Microsoft Azure ServicesにおけるAIテクノロジーとその潜在的なアプリケーションの基本的な理解を獲得したいビジネス利害関係者と技術専門家向けに設計されています。認定は、認知サービス、機械学習、ボットサービスなど、AIの概念とAzure AIサービスに関する候補者の知識を検証するエントリーレベルの試験です。この試験では、プライバシー、セキュリティ、コンプライアンスの考慮事項など、AIの倫理的かつ責任ある使用についてもカバーしています。

質問 # 19
You are processing photos of runners in a race.
You need to read the numbers on the runners' shirts to identity the runners in the photos.
Which type of computer vision should you use?

A. optical character recognition (OCR)
B. semantic segmentation
C. facial recognition
D. object detection

正解：A

解説：
Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr

質問 # 20
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering Regression is a form of machine learning that is used to predict a numeric label based on an item's features.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introduction

質問 # 21
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features

質問 # 22
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/

質問 # 23
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Explanation

With Microsoft's Conversational AI tools developers can build, connect, deploy, and manage intelligent bots that naturally interact with their users on a website, app, Cortana, Microsoft Teams, Skype, Facebook Messenger, Slack, and more.
Reference:
https://azure.microsoft.com/en-in/blog/microsoft-conversational-ai-tools-enable-developers-to-build-connect-and

質問 # 24
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features

質問 # 25
You have a chatbot that answers technical questions by using the Azure OpenAI GPT-3.5 large language model (LLM). Which two statements accurately describe the chatbot? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

A. Grounding data can be used to constrain the output of the chatbot.
B. The chatbot will always provide accurate data.
C. The chatbot might respond with inaccurate data.
D. The chatbot is suitable for performing medical diagnosis.

正解：A、C

質問 # 26
Match the types of computer vision to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

正解：

解説：

Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

質問 # 27
In which scenario should you use key phrase extraction?

A. identifying which documents provide information about the same topics
B. translating a set of documents from English to German
C. identifying whether reviews of a restaurant are positive or negative
D. generating captions for a video based on the audio track

正解：A

質問 # 28
You are developing a solution that uses the Text Analytics service.
You need to identify the main talking points in a collection of documents.
Which type of natural language processing should you use?

A. sentiment analysis
B. entity recognition
C. language detection
D. key phrase extraction

正解：D

解説：
Section: Describe features of Natural Language Processing (NLP) workloads on Azure Explanation:
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language- processing

質問 # 29
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

質問 # 30
For each of the following statements, select Yes If the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

正解：

解説：

質問 # 31
You have the following dataset.

You plan to use the dataset to train a model that will predict the house price categories of houses.
What are Household Income and House Price Category? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation

Box 1: A feature
Box 2: A label
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results

質問 # 32
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Explanation
Text Description automatically generated

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-label-data

質問 # 33
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

正解：

解説：

Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/overview

質問 # 34
You are building an AI system.
Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?

A. Ensure that a training dataset is representative of the population.
B. Ensure that all visuals have an associated text that can be read by a screen reader.
C. Provide documentation to help developers debug code.
D. Enable autoscaling to ensure that a service scales based on demand.

正解：C

解説：
Section: Describe Artificial Intelligence workloads and considerations
Explanation/Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

質問 # 35
You have a webchat bot that provides responses from a QnA Maker knowledge base.
You need to ensure that the bot uses user feedback to improve the relevance of the responses over time.
What should you use?

A. sentiment analysis
B. business logic
C. active learning
D. key phrase extraction

正解：C

解説：
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/improve-knowledge-base

質問 # 36
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Explanation:
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Incorrect Answers:
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize- model-clustering

質問 # 37
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Explanation

Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home custom vision - This is a type of computer vision service which helps in building/training models using user provided data Creating an object detection solution with Custom Vision consists of three main tasks. First you must use upload and tag images, then you can train the model, and finally you must publish the model so that client applications can use it to generate predictions.
https://docs.microsoft.com/en-us/learn/modules/detect-objects-images-custom-vision/2-object-detection-azure

質問 # 38
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

質問 # 39
Select the answer that correctly completes the sentence.

正解：

解説：
Explanation

質問 # 40
You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.

For each of the following Statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.