無料AI-900サンプル問題で100%カバー率のリアル試験問題（更新された272問あります) [Q64-Q81]

無料AI-900サンプル問題で100%カバー率のリアル試験問題（更新された272問あります)

今すぐダウンロード！リアルMicrosoft AI-900試験問題集テストエンジン試験問題

この試験は、AIやAzureの経験がほとんどない、またはまったくない個人に対応するために作成されましたが、概念の基本的な理解を開発することに興味があります。この試験は、ヘルスケア、財務、小売など、さまざまな業界でのAIの基本とそのアプリケーションの基本を完全に理解することに焦点を当てています。

AI-900試験は、背景や経験レベルに関係なく、AIと機械学習のスキルと知識を開発しようとしている個人に適しています。この試験は、AIでのキャリアを追求することに興味があり、テクノロジーの根本的な理解を得たい学生にも理想的です。専門家にとって、AI-900試験は、AIの知識と専門知識を実証するのに役立ちます。これにより、より良い雇用機会とより高い給与につながる可能性があります。

質問 # 64
Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. compute
B. module
C. pipeline
D. dataset

正解：B、D

解説：
Explanation
You can drag-and-drop datasets and modules onto the canvas.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

質問 # 65
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：

Explanation:

質問 # 66
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:
Graphical user interface, text, application, email Description automatically generated

Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.

質問 # 67
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 # 68
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

質問 # 69
You have a bot that identifies the brand names of products in images of supermarket shelves.
Which service does the bot use?

A. Al enrichment for Azure Search capabilities
B. Language understanding capabilities
C. Computer Vision Image Analysis capabilities
D. Custom Vision Image Classification capabilities

正解：B

質問 # 70
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:
Box 1: Regression
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.
Box 2: Classification
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
Box 3: Clustering
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/studio-module-reference/linear-regression

質問 # 71
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:

Reference:
https://docs.microsoft.com/en-us/learn/paths/get-started-with-artificial-intelligence-on-azure/

質問 # 72
A company employs a team of customer service agents to provide telephone and email support to customers.
The company develops a webchat bot to provide automated answers to common customer queries. Which business benefit should the company expect as a result of creating the webchat bot solution?

A. improved product reliability
B. increased sales
C. a reduced workload for the customer service agents

正解：C

質問 # 73
Select the answer that correctly completes the sentence.

正解：

解説：

質問 # 74
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

質問 # 75
What are three stages in a transformer model? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

A. embedding calculation
B. next token prediction
C. anonymization
D. tokenization
E. object detection

正解：A、B、D

質問 # 76
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

質問 # 77
Match the machine learning tasks to the appropriate scenarios.
To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task 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/machine-learning/studio/evaluate-model-performance
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

質問 # 78
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：

Explanation:

Box 1: 11

TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/eva

質問 # 79
To complete the sentence, select the appropriate option in the answer area.
Using Recency, Frequency, and Monetary (RFM) values to identify segments of a customer base is an example of___________

正解：

解説：

質問 # 80
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