P.S. JpexamがGoogle Driveで共有している無料かつ新しいAI-900ダンプ：https://drive.google.com/open?id=1llSg2NNfm9-mtgfSsgbTSmu2RyijfnxD
我々の目標はAI-900試験を準備するあなたにヘルプを提供してあなたに試験に合格させることです。この目標を達成するために、我々は時間とともに迅速に発展しています。だからこそ、我々の専門家たちの研究と分析によって開発されたAI-900問題集は高質量で的中率が高いですから、我々はあなたのAI-900試験に一発合格できるのを保証しています。
| Section | Weight | Objectives |
|---|---|---|
| Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
何よりもまず、国際市場のさまざまな国の人々のさまざまなニーズに応えるために、このWebサイトでAI-900学習質問の3種類のバージョンを用意しました。第二に、AI-900実践教材の支払い後、年間を通じて当社から最新のトレーニング教材を無料で入手できることを保証できます。最後になりましたが、私たちは週7日、1日24時間でお客様に最も思いやりのあるアフターサービスを提供します。
質問 # 264
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation
質問 # 265
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 # 266
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation
質問 # 267
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Explanation:
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Incorrect Answers:
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
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/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize- model-clustering
質問 # 268
To complete the sentence, select the appropriate option in the answer area.
正解：
解説：
Explanation:
Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must 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. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home custom vision - This is a type of computer vision service which helps in building/training models using user provided data Creating an object detection solution with Custom Vision consists of three main tasks. First you must use upload and tag images, then you can train the model, and finally you must publish the model so that client applications can use it to generate predictions.
https://docs.microsoft.com/en-us/learn/modules/detect-objects-images-custom-vision/2-object-detection-azure
質問 # 269
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AI-900スタディガイドの優れた利点の1つは、高い合格率です。これは99％に達し、同業他社の平均合格率よりもはるかに高くなっています。当社の高い合格率は、当社が業界トップのAI-900準備ガイドである理由を説明しています。自信の源は、素晴らしいAI-900試験問題です。 AI-900学習教材の練習を約20〜30時間続ける限り、試験に合格しても問題はありません。私たちの専門家は、実際の試験問題に合わせてAI-900の質問と回答を設計しました。これは、高い能力で試験に合格するのに役立ちます。
AI-900日本語版問題解説: https://www.jpexam.com/AI-900_exam.html
ちなみに、Jpexam AI-900の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1llSg2NNfm9-mtgfSsgbTSmu2RyijfnxD