最適なAI-900試験準備問題集でMicrosoft AI-900問題集PDFを試そう！[2024]
Microsoft AI-900試験受験生を確実にパスさせるAI-900学習問題集
質問 # 132
You build a machine learning model by using the automated machine learning user interface (UI).
You need to ensure that the model meets the Microsoft transparency principle for responsible AI. What should you do?
- A. Enable Explain best model.
- B. Set Max concurrent iterations to 0.
- C. Set Primary metric to accuracy.
- D. Set Validation type to Auto.
正解：A
解説：
Model Explain Ability.
Most businesses run on trust and being able to open the ML "black box" helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.
Reference:
https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine- learning-service/
質問 # 133
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：
Explanation
Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Box 3: Entity Recognition
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
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 # 134
Select the answer that correctly completes the sentence
正解：
解説：
質問 # 135
You need to predict the animal population of an area.
Which Azure Machine Learning type should you use?
- A. regression
- B. classification
- C. clustering
正解：A
質問 # 136
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
質問 # 137
Which AI service can you use to interpret the meaning of a user input such as "Call me back later?"
- A. Language Understanding (LUIS)
- B. Text Analytics
- C. Translator Text
- D. Speech
正解：A
解説：
Explanation
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis
質問 # 138
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：
Explanation:
質問 # 139
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 # 140
For which two workloads can you use computer vision? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
- A. creating photorealistic images by using three-dimensional models
- B. detecting inconsistencies and anomalies in a stream of data
- C. creating visual representations of numerical data
- D. describing the contents of an image
- E. assigning the color pixels in an image to object names
正解：D、E
質問 # 141
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 # 142
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 # 143
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
- A. classification
- B. regression
- C. clustering
正解：C
解説：
Section: Describe fundamental principles of machine learning on Azure
Explanation:
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/machine-learning- initialize-model-clustering
質問 # 144
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 # 145
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
質問 # 146
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/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
質問 # 147
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/concept-designer
質問 # 148
What is an advantage of using a custom model in Form Recognizer?
- A. A custom model can be trained to recognize a variety of form types.
- B. A custom model is less expensive than a prebuilt model.
- C. Only a custom model can be deployed on-premises.
- D. A custom model always provides higher accuracy.
正解：A
質問 # 149
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 # 150
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：
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
質問 # 151
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 # 152
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?
- A. Form Recognizer
- B. Ink Recognizer
- C. Text Analytics
- D. Custom Vision
正解：A
解説：
Section: Describe fundamental principles of machine learning on Azure
Explanation:
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud.
Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/
質問 # 153
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: Yes
Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.
The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.
Box 2: No
Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Box 3: Yes
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
質問 # 154
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完全版AI-900練習テスト問題集で244の別格な問題と解釈、今すぐゲットせよ：https://drive.google.com/open?id=1dSznHc5YflsEkp7lsqPevN6dz4t3DrTt
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