[2024年08月18日] 究極のAI-900準備ガイド！無料最新のMicrosoft練習テスト問題集
今すぐゲットせよ！高評価Microsoft AI-900試験問題集
Microsoft AI-900 認定試験は、機械学習、自然言語処理、コンピュータビジョン、認知サービスなど、AI に関連する様々なトピックをカバーしています。この試験は、候補者が AI のコアコンセプトを理解し、実世界のシナリオに適用する能力をテストするために設計されています。試験に合格した候補者は、Microsoft Azure AI サービスを使用してインテリジェントなソリューションを構築する能力を証明します。
AI-900試験は、AIおよび機械学習に関連する幅広いトピックをカバーしています。これには、AIの基礎、異なるタイプのAI、Azure AIサービス、コンピュータビジョン、自然言語処理（NLP）、および会話型AIが含まれます。試験はまた、倫理的かつ責任あるAIの実践についてもカバーしており、AIを使用する際の倫理的考慮事項、プライバシー、およびセキュリティ上の懸念も含まれています。
質問 # 26
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#deploy
質問 # 27
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features Speech recognition means Speech to Text. In the above example as a person speaks the words are converted into text of the same language. Hence Speech to Text also called Speech recognition is the right answer.
Speech recognition - the ability to detect and interpret spoken input.
Speech synthesis - the ability to generate spoken output.
https://docs.microsoft.com/en-us/learn/modules/recognize-synthesize-speech/1-introduction
質問 # 28
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/machine-learning/studio-module-reference/anomaly-detection
質問 # 29
Select the answer that correctly completes the sentence.
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：
Explanation
Box 1: Yes
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Box 2: No
Box 3: No
Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier.
Reference:
https://www.cloudfactory.com/data-labeling-guide
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
質問 # 31
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. Provide documentation to help developers debug code.
- C. Enable autoscaling to ensure that a service scales based on demand.
- D. Ensure that all visuals have an associated text that can be read by a screen reader.
正解：B
解説：
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
質問 # 32
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Explanation
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features Speech recognition means Speech to Text. In the above example as a person speaks the words are converted into text of the same language. Hence Speech to Text also called Speech recognition is the right answer.
Speech recognition - the ability to detect and interpret spoken input.
Speech synthesis - the ability to generate spoken output.
https://docs.microsoft.com/en-us/learn/modules/recognize-synthesize-speech/1-introduction
質問 # 33
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.
正解：
解説：
質問 # 34
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 # 35
What are two tasks that can be performed by using computer vision? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Translate text between languages.
- B. Detect brands in an image.
- C. Extract key phrases.
- D. Predict stock prices.
- E. Detect the color scheme in an image
正解：B、C
解説：
Section: Describe features of computer vision workloads on Azure
Explanation:
B: Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
E: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents. It uses the latest models and works with text on a variety of surfaces and backgrounds. These include receipts, posters, business cards, letters, and whiteboards. The two OCR APIs support extracting printed text in several languages.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview
質問 # 36
Match the principles of responsible AI to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation
Graphical user interface, text, application, email Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
質問 # 37
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 # 38
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 39
Match the types of AI workloads 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://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
質問 # 40
You use natural language processing to process text from a Microsoft news story.
You receive the output shown in the following exhibit.
Which type of natural languages processing was performed?
- A. entity recognition
- B. key phrase extraction
- C. sentiment analysis
- D. translation
正解：A
解説：
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
質問 # 41
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation:
質問 # 42
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 # 43
Match the types of AI workloads 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：
Explanation
Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
質問 # 44
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Explanation:
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud.
Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/
質問 # 45
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：
Explanation:
Box 1: Yes
Azure bot service can be integrated with the powerful AI capabilities with Azure Cognitive Services.
Box 2: Yes
Azure bot service engages with customers in a conversational manner.
Box 3: No
The QnA Maker service creates knowledge base, not question and answers sets.
Note: You can use the QnA Maker service and a knowledge base to add question-and-answer support to your bot. When you create your knowledge base, you seed it with questions and answers.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-add-qna
質問 # 46
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-science-process/create-features
質問 # 47
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation
質問 # 48
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature 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://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
質問 # 49
A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types.
You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?
- A. regression
- B. clustering
- C. classification
正解：C
解説：
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/int
質問 # 50
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AI-900試験問題集で合格できるには更新されたテスト問題集：https://drive.google.com/open?id=1OngZ26AEZIbzXdatPnXW5sPyqOtmAifS