最新の2024年10月 Microsoft AI-900問題集で更新された272問あります [Q50-Q68]

最新の2024年10月 Microsoft AI-900問題集で更新された272問あります

PDF無料ダウンロードにはAI-900有効な練習テスト問題

質問 # 50
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：

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 # 51
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

質問 # 52
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:

Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/text-analytics/overview
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-services/ You can use the Speech service to transcribe a call to text - Yes we can use Speech to Text API to achieve this
https://docs.microsoft.com/en-us/learn/modules/recognize-synthesize-speech/1-introduction You can use a speech service to translate the audio of a call to a different language - Yes we can use Speech translation service to achieve this The Speech service includes the following application programming interfaces (APIs):
Speech-to-text - used to transcribe speech from an audio source to text format.
Text-to-speech - used to generate spoken audio from a text source.
Speech Translation - used to translate speech in one language to text or speech in another.
https://docs.microsoft.com/en-us/learn/modules/translate-text-with-translation-service/2-get-started-azure You can use text analytics service to extract key entities from a call transcript -Yes Text Analytics API helps to achieve this
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure

質問 # 53
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Explanation:

質問 # 54
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

質問 # 55
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 # 56
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: No
Box 2: Yes
Box 3: Yes
Anomaly detection encompasses many important tasks in machine learning:
Identifying transactions that are potentially fraudulent.
Learning patterns that indicate that a network intrusion has occurred.
Finding abnormal clusters of patients.
Checking values entered into a system.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection

質問 # 57
You plan to use Azure Cognitive Services to develop a voice controlled personal assistant app.
Match the Azure Cognitive Services to the appropriate tasks.
To answer, drag the appropriate service from the column on the left to its description on the right Each service may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

正解：

解説：

質問 # 58
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：

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score

質問 # 59
You have a dataset that contains information about taxi journeys that occurred during a given period.
You need to train a model to predict the fare of a taxi journey.
What should you use as a feature?

A. the trip ID of individual taxi journeys
B. the trip distance of individual taxi journeys
C. the fare of individual taxi journeys
D. the number of taxi journeys in the dataset

正解：B

解説：
Explanation
The label is the column you want to predict. The identified Features are the inputs you give the model to predict the Label.
Example:
The provided data set contains the following columns:
vendor_id: The ID of the taxi vendor is a feature.
rate_code: The rate type of the taxi trip is a feature.
passenger_count: The number of passengers on the trip is a feature.
trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model.
trip_distance: The distance of the trip is a feature.
payment_type: The payment method (cash or credit card) is a feature.
fare_amount: The total taxi fare paid is the label.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices

質問 # 60
You are developing a natural language processing solution in Azure. The solution will analyze customer reviews and determine how positive or negative each review is.
This is an example of which type of natural language processing workload?

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

正解：D

解説：
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language- processing

質問 # 61
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 # 62
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 # 63
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Explanation

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 # 64
To complete the sentence, select the appropriate option in the answer area.

正解：

解説：

Explanation:
Reliability & Safety
https://en.wikipedia.org/wiki/Tay_(bot)
"To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. It's also important to be able to verify that these systems are behaving as intended under actual operating conditions. How they behave and the variety of conditions they can handle reliably and safely largely reflects the range of situations and circumstances that developers anticipate during design and testing. We believe that rigorous testing is essential during system development and deployment to ensure AI systems can respond safely in unanticipated situations and edge cases, don't have unexpected performance failures, and don't evolve in ways that are inconsistent with original expectations"

質問 # 65
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 # 66
You need to predict the income range of a given customer by using the following dataset.

Which two fields should you use as features? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Age
B. Last Name
C. First Name
D. Education Level
E. Income Range

正解：A、D

解説：
Explanation
First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.

質問 # 67
To complete the sentence, select the appropriate option in the answer area.