最新のMicrosoft AI-900のPDFと問題集で（2024）無料試験問題解答
あなたを合格させるMicrosoft Certified: Azure AI Fundamentals AI-900試験問題集で2024年07月07日には244問あります
AI-900試験では、機械学習、自然言語処理、コンピュータービジョン、会話AIなど、人工知能に関連するさまざまなトピックをカバーしています。また、AIの倫理的な考慮事項と責任ある使用に焦点を当てています。この試験は、データサイエンス、ソフトウェアエンジニアリング、またはクラウドコンピューティングのキャリアを追求することに興味がある個人に最適です。
質問 # 35
Match the types of natural languages processing 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://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
質問 # 36
You need to use Azure Machine Learning designer to build a model that will predict automobile prices.
Which type of modules should you use to complete the model? To answer, drag the appropriate modules to the correct locations. Each module 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:
Diagram Description automatically generated
Box 1: Select Columns in Dataset
For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns.
Example:
Box 2: Split data
Splitting data is a common task in machine learning. You will split your data into two separate datasets. One dataset will train the model and the other will test how well the model performed.
Box 3: Linear regression
Because you want to predict price, which is a number, you can use a regression algorithm. For this example, you use a linear regression model.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score
質問 # 37
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 # 38
Extracting relationships between data from large volumes of unstructured data is an example of which type of Al workload?
- A. natural language processing (NLP)
- B. knowledge mining
- C. computer vision
- D. anomaly detection
正解：B
質問 # 39
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation
質問 # 40
Match the tool to the Azure Machine Learning task.
To answer, drag the appropriate tool from the column on the left to its tasks on the right. Each tool may be used once, more than once, or not at all NOTE: Each correct match is worth one point.
正解：
解説：
質問 # 41
You need to develop a web-based AI solution for a customer support system. Users must be able to interact with a web app that will guide them to the best resource or answer.
Which service should you use?
- A. QnA Maker
- B. Custom Vision
- C. Translator Text
- D. Face
正解：A
解説：
Explanation
QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semistructured content, including FAQs, manuals, and documents. Answer users' questions with the best answers from the QnAs in your knowledge base-automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/
質問 # 42
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 # 43
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right.
Each machine learning 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:
1- Regression
2- Clustering
3- Classification
質問 # 44
Select the answer that correctly completes the sentence.
正解：
解説：
質問 # 45
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 # 46
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Reference:
https://azure.microsoft.com/en-in/blog/microsoft-conversational-ai-tools-enable-developers-to-build-connect-and-manage-intelligent-bots
質問 # 47
You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?
- A. classification
- B. regression
- C. clustering
正解：B
解説：
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.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression 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 # 48
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/architecture/reference-architectures/ai/conversational-bot
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-webchat-overview?view=azure-bot-service-4.0
質問 # 49
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/concept-designer
質問 # 50
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 # 51
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://machinelearningmastery.com/difference-test-validation-datasets/
質問 # 52
Which three actions improve the quality of responses returned by a generative Al solution that uses GPT-3.5?
Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
- A. Modify tokenization.
- B. Modify system messages.
- C. Provide additional examples to prompts.
- D. Add grounding data to prompts.
- E. Add training data to prompts.
正解：C、D、E
質問 # 53
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://machinelearningmastery.com/difference-test-validation-datasets/
質問 # 54
......
AI-900問題集はMicrosoft Certified: Azure AI Fundamentals認証済み試験問題と解答：https://www.goshiken.com/Microsoft/AI-900-mondaishu.html
AI-900無料試験学習ガイド！（更新された244問あります)：https://drive.google.com/open?id=1rlgw4-W0luBEJYY6sEMo-XHHDhl18NTf