無料更新されたMicrosoft AI-900テストエンジン問題には198問あります
ベストな問題集を使おうMicrosoft Certified: Azure AI Fundamentals AI-900専門試験問題
質問 # 102
When training a model, why should you randomly split the rows into separate subsets?
- A. to train multiple models simultaneously to attain better performance
- B. to train the model twice to attain better accuracy
- C. to test the model by using data that was not used to train the model
正解：C
解説：
Explanation
The goal is to produce a trained (fitted) model that generalizes well to new, unknown data. The fitted model is evaluated using "new" examples from the held-out datasets (validation and datasets) to estimate the model's accuracy in classifying new
https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets#:~:text=Training%20dataset,- A%20training%20dataset&text=The%20goal%20is%20to%20produce,accuracy%20in%20classifying%20new%
質問 # 103
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 # 104
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
質問 # 105
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. translation
- B. sentiment analysis
- C. entity recognition
- D. key phrase extraction
正解：D
解説：
Section: Describe features of Natural Language Processing (NLP) workloads on Azure Explanation:
Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
質問 # 106
You have a bot that identifies the brand names of products in images of supermarket shelves.
Which service does the bot use?
- A. Custom Vision Image Classification capabilities
- B. Language understanding capabilities
- C. Computer Vision Image Analysis capabilities
- D. Al enrichment for Azure Search capabilities
正解：C
質問 # 107
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-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 # 108
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 # 109
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 # 110
When training a model, why should you randomly split the rows into separate subsets?
- A. to train multiple models simultaneously to attain better performance
- B. to train the model twice to attain better accuracy
- C. to test the model by using data that was not used to train the model
正解：C
解説：
The goal is to produce a trained (fitted) model that generalizes well to new, unknown data. The fitted model is evaluated using "new" examples from the held-out datasets (validation and test datasets) to estimate the model's accuracy in classifying new data.
https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets#:~:text=Training%20dataset,- A%20training%20dataset&text=The%20goal%20is%20to%20produce,accuracy%20in%20classifying%20new%20data.
質問 # 111
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：
Explanation
Graphical user interface, text, application, chat or text message Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines
質問 # 112
Which AI service should you use to create a bot from a frequently asked questions (FAQ) document?
- A. Speech
- B. Text Analytics
- C. QnA Maker
- D. Language Understanding (LUIS)
正解：C
解説：
Section: Describe features of conversational AI workloads on Azure
Explanation/Reference:
質問 # 113
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://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features
質問 # 114
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/text-analytics/overview
質問 # 115
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.
正解：
解説：
質問 # 116
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/
質問 # 117
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Explanation
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features
質問 # 118
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
質問 # 119
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 # 120
Select the answer that correctly completes the sentence.
正解：
解説：
質問 # 121
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-us/azure/bot-service/bot-service-manage-channels?view=azure-bot-service-4.0
質問 # 122
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：
Explanation
Box 1: Model evaluation
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.
Box 2: Feature engineering
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
Box 3: Feature selection
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
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 # 123
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AI-900試験に合格するには、候補者はAIの基礎に関する知識と、AIの概念を実際のシナリオに適用する能力を実証する必要があります。この試験は、40〜60の複数選択と複数の応答の質問で構成されており、候補者は試験を完了するのに60分です。 Microsoftは、候補者が試験を受ける前にAzure Servicesの経験とプログラミングの概念の基本的な理解を持っていることを推奨しています。
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