更新された2025年04月テストエンジン練習AI-900問題集と練習試験合格させます [Q20-Q36]

更新された2025年04月テストエンジン練習AI-900問題集と練習試験合格させます

問題集お試しセットAI-900テストエンジンで問題集トレーニングには283問あります

質問 # 20
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 # 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
Match the principles of responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle 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 # 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
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 # 25
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：
Explanation
Box 1: Entity recognition
Classify a broad range of entities in text, such as people, places, organisations, date/time and percentages, using named entity recognition. Whereas:- Get a list of relevant phrases that best describe the subject of each record using key phrase extraction.
Box 2: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 3: Translation
Using Microsoft's Translator text API
This versatile API from Microsoft can be used for the following:
Translate text from one language to another.
Transliterate text from one script to another.
Detecting language of the input text.
Find alternate translations to specific text.
Determine the sentence length.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics

質問 # 26
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit.

Which type of computer vision was used?

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

正解：C

解説：
Explanation
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like
"indoor", which can't be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

質問 # 27
You plan to deploy an Azure Machine Learning model as a service that will be used by client applications.
Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order.

正解：

解説：

1 - data prparation
2 - model training
3 - model evaluation
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines

質問 # 28
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.
Example: Regression Model: A Boosted Decision Tree algorithm was used to create and train the model for predicting the repayment rate.
Reference:
https://gallery.azure.ai/Experiment/Student-Loan-Repayment-Rate-Prediction

質問 # 29
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/get-started-build-detector

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：

Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0

質問 # 31
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Explanation:

質問 # 32
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 # 33
Match the Azure OpenAI large language model (LLM) process to the appropriate task.
To answer, drag the appropriate process from the column on the left to its task on the right. Each process may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.

正解：

解説：

Explanation:

質問 # 34
Match the machine learning models to the appropriate deceptions.
To answer, drag the appropriate model from the column on the left to its description on the right Each model may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.