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ずっと自分自身を向上させたいあなたは、AI-900認定試験を受験する予定があるのですか。もし受験したいなら、試験の準備をどのようにするつもりですか。もしかして、自分に相応しい試験参考書を見つけたのでしょうか。では、どんな参考書は選べる価値を持っていますか。あなたが選んだのは、It-PassportsのAI-900問題集ですか。もしそうだったら、もう試験に合格できないなどのことを心配する必要がないのです。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
| Topic 2: Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe machine learning pipelines - Describe core concepts of machine learning |
| Topic 3: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Describe capabilities of Azure Language - Identify types of NLP solutions |
| Topic 4: Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service - Describe responsible AI practices for generative AI |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Face |
Microsoft AI-900認定試験の難しさで近年にほとんどの受験生は資格認定試験に合格しなっかたと良く知られます。だから、我々社の有効な試験問題集は長年にわたりMicrosoft AI-900認定資格試験問題集作成に取り組んだIT専門家によって書いてます。実際の試験に表示される質問と正確な解答はあなたのMicrosoft AI-900認定資格試験合格を手伝ってあげます。
質問 # 329
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
A screenshot of a computer Description automatically generated with medium confidence
Box 1: Yes
Custom Vision functionality can be divided into two features. Image classification applies one or more labels to an image. Object detection is similar, but it also returns the coordinates in the image where the applied label(s) can be found.
Box 2: Yes
The Custom Vision service uses a machine learning algorithm to analyze images. You, the developer, submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then, the algorithm trains to this data and calculates its own accuracy by testing itself on those same images.
Box 3: No
Custom Vision service can be used only on graphic files.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/overview
質問 # 330
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of natural language processing (NLP) workloads on Azure," Natural Language Processing refers to the branch of AI that enables computers to interpret, understand, and generate human language. One of the main NLP workloads identified by Microsoft is speech-to-text conversion, which transforms spoken words into written text.
Creating a text transcript of a voice recording perfectly fits this definition because it involves converting audio language data into text form - a process handled by speech recognition models. These models analyze the acoustic features of human speech, segment phonemes, identify words, and produce a text transcript. On Azure, this function is implemented using the Azure Cognitive Services Speech-to-Text API, part of the Language and Speech services.
Let's examine the other options to clarify why they are incorrect:
* Computer vision workload: Involves interpreting and analyzing visual data such as images and videos (e.g., object detection, facial recognition). It does not deal with speech or audio.
* Knowledge mining workload: Refers to extracting useful information from large amounts of structured and unstructured data using services like Azure Cognitive Search, not transcribing audio.
* Anomaly detection workload: Involves identifying unusual patterns in data (e.g., fraud detection or sensor anomalies), unrelated to language or speech.
In summary, when a system creates a text transcript from spoken audio, it is performing a speech recognition task-classified under Natural Language Processing (NLP). This workload helps make spoken content searchable, analyzable, and accessible, aligning with Microsoft's Responsible AI goal of enhancing accessibility through language understanding.
質問 # 331
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: 11
TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/eva
質問 # 332
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: 11
TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/eva
質問 # 333
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?
正解：D
解説：
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 # 334
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