Amazon MLS-C01日本語関連対策 & MLS-C01試験関連情報

当社Amazonの専門家は、MLS-C01トレーニング資料を毎日更新し、最新の更新をタイムリーに提供します。当社の製品および購入手順に関する疑問または質問がある場合は、いつでも当社のオンライン顧客サービス担当者にご連絡ください。古いクライアントに割引を提供します。購入前にMLS-C01テスト問題を無料でダウンロードして試用できます。したがって、当社の製品には多くのメリットがあります。 MLS-C01試験問題を購入する前に、無料デモでMLS-C01模擬テストの特性と機能を知ることができます。

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty)試験は、AWSクラウドプラットフォーム上で機械学習モデルを構築、トレーニング、展開する能力を検証したいプロフェッショナル向けに設計されています。この認定試験では、Amazon SageMaker、AWS Deep Learning AMIs、Amazon Rekognition、およびAmazon ComprehendなどのAWSサービスを使用して機械学習ソリューションを設計および実装できる能力が候補者に求められます。この認定は、機械学習の分野でスキルを証明したいデータサイエンティスト、ソフトウェア開発者、および機械学習エンジニアに最適です。

>> Amazon MLS-C01日本語関連対策 <<

正確的-権威のあるMLS-C01日本語関連対策試験-試験の準備方法MLS-C01試験関連情報

結果として、MLS-C01の質問トレントはユーザーレベルのニーズに合わせて調整され、文化レベルは不均一であり、大学生が学校に多く、労働者に多くの仕事があり、さらには教育レベルが低い人もいます。オフなので、ユーザーのさまざまなレベルの違いに適応するために、テキスト情報の表現に特に焦点を当てた教材を作成するときにMLS-C01試験の質問が行われるため、MLS-C01学習ガイドの内容を理解できますMLS-C01試験に簡単に合格します。

AWS-Certified-Machine-Learning-Specialty試験は、AWSサービスを使用して機械学習ソリューションを設計、実装、展開したいと考える個人のスキルと知識を検証することを目的とした難しい認定試験です。この試験に合格した候補者は、機械学習の専門知識を証明し、データサイエンスや機械学習の分野でキャリアアップの可能性を高めることができます。

Amazon AWS Certified Machine Learning - Specialty 認定 MLS-C01 試験問題 (Q16-Q21):

質問 # 16
A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?

A. Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
B. Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
C. Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
D. Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.

正解：D

解説：
The best storage option for the trucking company is to use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Amazon S3 is the ideal choice for building a data lake because it offers high durability, scalability, availability, and security. You can store any type of data in Amazon S3, such as images, videos, audio, text, etc. You can also use AWS services such as Amazon Rekognition, Amazon SageMaker, and Amazon EMR to analyze and process the data in the data lake. To ensure the data is only accessible to specific IAM users, you can use bucket policies to grant or deny access to the S3 buckets based on the IAM user's identity or role. Bucket policies are JSON documents that specify the permissions for the bucket and the objects in it. You can use conditions to restrict access based on various factors, such as IP address, time, source, etc. By using bucket policies, you can control who can access the data in the data lake and what actions they can perform on it.
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Build a Data Lake Foundation with Amazon S3 AWS Machine Learning Training - Using Bucket Policies and User Policies

質問 # 17
A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?

A. Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
B. Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
C. Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
D. Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.

正解：D

解説：
The best storage option for the trucking company is to use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Amazon S3 is the ideal choice for building a data lake because it offers high durability, scalability, availability, and security. You can store any type of data in Amazon S3, such as images, videos, audio, text, etc. You can also use AWS services such as Amazon Rekognition, Amazon SageMaker, and Amazon EMR to analyze and process the data in the data lake. To ensure the data is only accessible to specific IAM users, you can use bucket policies to grant or deny access to the S3 buckets based on the IAM user's identity or role. Bucket policies are JSON documents that specify the permissions for the bucket and the objects in it. You can use conditions to restrict access based on various factors, such as IP address, time, source, etc. By using bucket policies, you can control who can access the data in the data lake and what actions they can perform on it.
References:
* AWS Machine Learning Specialty Exam Guide
* AWS Machine Learning Training - Build a Data Lake Foundation with Amazon S3
* AWS Machine Learning Training - Using Bucket Policies and User Policies

質問 # 18
A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color.
Which solution will meet these requirements with the LEAST amount of effort?

A. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
B. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
C. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.
D. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.

正解：A

解説：
The solution that will meet the requirements with the least amount of effort is to use a semantic segmentation algorithm to identify a visitor's hair in video frames, and pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color. This solution can leverage the existing Amazon SageMaker algorithms and frameworks to perform the tasks of hair segmentation and classification.
Semantic segmentation is a computer vision technique that assigns a class label to every pixel in an image, such that pixels with the same label share certain characteristics. Semantic segmentation can be used to identify and isolate different objects or regions in an image, such as a visitor's hair in a video frame. Amazon SageMaker provides a built-in semantic segmentation algorithm that can train and deploy models for semantic segmentation tasks. The algorithm supports three state-of-the-art network architectures: Fully Convolutional Network (FCN), Pyramid Scene Parsing Network (PSP), and DeepLab v3. The algorithm can also use pre- trained or randomly initialized ResNet-50 or ResNet-101 as the backbone network. The algorithm can be trained using P2/P3 type Amazon EC2 instances in single machine configurations1.
ResNet-50 is a convolutional neural network that is 50 layers deep and can classify images into 1000 object categories. ResNet-50 is trained on more than a million images from the ImageNet database and can achieve high accuracy on various image recognition tasks. ResNet-50 can be used to determine hair style and hair color from the segmented hair regions in the video frames. Amazon SageMaker provides a built-in image classification algorithm that can use ResNet-50 as the network architecture. The algorithm can also perform transfer learning by fine-tuning the pre-trained ResNet-50 model with new data. The algorithm can be trained using P2/P3 type Amazon EC2 instances in single or multiple machine configurations2.
The other options are either less effective or more complex to implement. Using an object detection algorithm to identify a visitor's hair in video frames would not segment the hair at the pixel level, but only draw bounding boxes around the hair regions. This could result in inaccurate or incomplete hair segmentation, especially if the hair is occluded or has irregular shapes. Using an XGBoost algorithm to determine hair style and hair color would require transforming the segmented hair images into numerical features, which could lose some information or introduce noise. XGBoost is also not designed for image classification tasks, and may not achieve high accuracy or performance.
1: Semantic Segmentation Algorithm - Amazon SageMaker
2: Image Classification Algorithm - Amazon SageMaker

質問 # 19
A Machine Learning Specialist is attempting to build a linear regression model.
Given the displayed residual plot only, what is the MOST likely problem with the model?

A. Linear regression is inappropriate. The underlying data has outliers.
B. Linear regression is appropriate. The residuals have a zero mean.
C. Linear regression is appropriate. The residuals have constant variance.
D. Linear regression is inappropriate. The residuals do not have constant variance.

正解：C

質問 # 20
A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: "comedy," "adventure," and "documentary." A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun," and "humor." The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB.
How should the ML specialist fix the problem?

A. Use the AMAZON.SearchQuery built-in slot types for custom searches in the database.
B. Add the unrecognized words as synonyms in the custom slot type.
C. Add the unrecognized words in the enumeration values list as new values in the slot type.
D. Create a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot.

正解：A

質問 # 21
......

MLS-C01試験関連情報: https://jp.fast2test.com/MLS-C01-premium-file.html

Lou Bell Lou Bell

Biography

Amazon MLS-C01日本語関連対策 & MLS-C01試験関連情報

正確的-権威のあるMLS-C01日本語関連対策試験-試験の準備方法MLS-C01試験関連情報

Amazon AWS Certified Machine Learning - Specialty 認定 MLS-C01 試験問題 (Q16-Q21):

Archives