2021年最新の100%試験高合格率AI-900問題集PDF
合格させる試験完全版AI-900問題集140解答
Microsoft AI-900 認定試験の出題範囲：
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質問 21
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 22
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. the authentication key
- B. the REST endpoint
- C. the training endpoint
- D. the model name
正解: B,D
解説:
Section: Describe fundamental principles of machine learning on Azure
Explanation:
A: The trained model is stored as a Dataset module in the module palette. You can find it under My Datasets.
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
D: You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-batch-predictions-designer
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
質問 23
You are developing a solution that uses the Text Analytics service.
You need to identify the main talking points in a collection of documents.
Which type of natural language processing should you use?
- A. language detection
- B. sentiment analysis
- C. key phrase extraction
- D. entity recognition
正解: C
解説:
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.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
質問 24
To complete the sentence, select the appropriate option in the answer area.
正解:
解説:
Explanation
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features Speech recognition means Speech to Text. In the above example as a person speaks the words are converted into text of the same language. Hence Speech to Text also called Speech recognition is the right answer.
Speech recognition - the ability to detect and interpret spoken input.
Speech synthesis - the ability to generate spoken output.
https://docs.microsoft.com/en-us/learn/modules/recognize-synthesize-speech/1-introduction
質問 25
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?
- A. Form Recognizer
- B. Text Analytics
- C. Custom Vision
- D. Ink Recognizer
正解: A
解説:
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 26
To complete the sentence, select the appropriate option in the answer area.
正解:
解説:
Explanation:
Privacy and security.
As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used. At Microsoft, we are continuing to research privacy and security breakthroughs (see next unit) and invest in robust compliance processes to ensure that data collected and used by our AI systems is handled responsibly.
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
質問 27
To complete the sentence, select the appropriate option in the answer area.
正解:
解説:
Explanation:
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Incorrect Answers:
Not features: 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://www.cloudfactory.com/data-labeling-guide
質問 28
Match the facial recognition tasks to the appropriate questions.
To answer, drag the appropriate task from the column on the left to its question 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: verification
Face verification: Check the likelihood that two faces belong to the same person and receive a confidence score.
Box 2: similarity
Box 3: Grouping
Box 4: identification
Face detection: Detect one or more human faces along with attributes such as: age, emotion, pose, smile, and facial hair, including 27 landmarks for each face in the image.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/#features
質問 29
What is a use case for classification?
- A. predicting whether someone uses a bicycle to travel to work based on the distance from home to work
- B. predicting how many minutes it will take someone to run a race based on past race times
- C. analyzing the contents of images and grouping images that have similar colors
- D. predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night.
正解: A
解説:
Section: Describe features of computer vision workloads on Azure
Explanation:
Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.
Incorrect Answers:
A: This is Regression.
B: This is Clustering.
D: This is Regression.
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
質問 30
You run a charity event that involves posting photos of people wearing sunglasses on Twitter.
You need to ensure that you only retweet photos that meet the following requirements:
* Include one or more faces.
* Contain at least one person wearing sunglasses.
What should you use to analyze the images?
- A. the Analyze Image operation in the Computer Vision service
- B. the Verify operation in the Face service
- C. the Detect operation in the Face service
- D. the Describe Image operation in the Computer Vision service
正解: C
解説:
Section: Describe Artificial Intelligence workloads and considerations
Explanation/Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
質問 31
You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments.
This is an example of which Microsoft guiding principle for responsible AI?
- A. inclusiveness
- B. reliability and safety
- C. fairness
- D. accountability
正解: A
質問 32
You use Azure Machine Learning designer to build a model pipeline. What should you create before you can run the pipeline?
- A. a Jupyter notebook
- B. a compute resource
- C. a registered model
正解: B
質問 33
You have the following dataset.
You plan to use the dataset to train a model that will predict the house price categories of houses.
What are Household Income and House Price Category? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
正解:
解説:
Explanation
Box 1: A feature
Box 2: A label
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results
質問 34
You have insurance claim reports that are stored as text.
You need to extract key terms from the reports to generate summaries.
Which type of Al workload should you use?
- A. computer vision
- B. conversational Al
- C. natural language processing
- D. anomaly detection
正解: C
解説:
Explanation
Key phrase extraction is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s).
Key phase extraction is a part of Text Analytics. The Text Analytics service is a part of the Azure Cognitive Services offerings that can perform advanced natural language processing over raw text.
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
質問 35
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 36
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:
Reference:
https://developers.google.com/machine-learning/practica/image-classification
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builder
https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/
質問 37
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 38
When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable.
This is an example of which Microsoft guiding principle for responsible AI?
- A. privacy and security
- B. transparency
- C. inclusiveness
- D. fairness
正解: B
解説:
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai
質問 39
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://www.cloudfactory.com/data-labeling-guide
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
質問 40
To complete the sentence, select the appropriate option in the answer area.
正解:
解説:
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 41
You are building a knowledge base by using QnA Maker. Which file format can you use to populate the knowledge base?
- A. ZIP
- B. XML
- C. PDF
- D. PPTX
正解: C
質問 42
You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create Question: 57
solution.
NOTE: Each correct selection is worth one point.
- A. Use automated machine learning to train a model based on a file that contains the Question:s.
- B. Import chit-chat content from a predefined data source.
- C. Manually enter the Questions and answers.
- D. Connect the bot to the Cortana channel and ask Questions by using Cortana.
- E. Generate the Questions and answers from an existing webpage.
正解: B,C,E
解説:
Extract question answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types
質問 43
You need to make the press releases of your company available in a range of languages.
Which service should you use?
- A. Text Analytics
- B. Translator Text
- C. Language Understanding (LUIS)
- D. Speech
正解: B
解説:
Section: Describe features of Natural Language Processing (NLP) workloads on Azure Explanation:
Translator is a cloud-based machine translation service you can use to translate text in near real-time through a simple REST API call. The service uses modern neural machine translation technology and offers statistical machine translation technology. Custom Translator is an extension of Translator, which allows you to build neural translation systems.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/translator/
質問 44
You are evaluating whether to use a basic workspace or an enterprise workspace in Azure Machine Learning.
What are two tasks that require an enterprise workspace? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Create a dataset from a comma-separated value (CSV) file.
- B. Use a graphical user interface (GUI) to run automated machine learning experiments.
- C. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer.
- D. Create a compute instance to use as a workstation.
正解: B,C
解説:
Note: Enterprise workspaces are no longer available as of September 2020. The basic workspace now has all the functionality of the enterprise workspace.
Reference:
https://www.azure.cn/en-us/pricing/details/machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace
質問 45
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