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Amazon AWS認定マシンラーニングスペシャリティ（AWS認定機械学習 - 専門）認定試験は、Amazonを使用して機械学習（ML）ソリューションを設計、実装、展開、維持する能力を検証するために設計された専門レベルの認定です。 Webサービス（AWS）。この認定試験は、AWSにMLソリューションの構築と展開に関する専門知識を実証したいデータサイエンティスト、ソフトウェア開発者、および機械学習の実践者を対象としています。

Amazon MLS-C01認定を達成することは、専門家が機械学習の専門知識を実証し、キャリアを促進する優れた方法です。また、機械学習の分野で熟練した専門家を雇おうとしている組織にとっても貴重な資格です。 Amazon MLS-C01で認定されることにより、候補者は、機械学習の急速に進化する分野の最新のトレンドとテクノロジーに最新の状態を維持することに献身的に示すことができます。

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我々社のAmazon　MLS-C01問題集を購入するかどうかと疑問があると、弊社It-PassportsのMLS-C01問題集のサンプルをしてみるのもいいことです。試用した後、我々のMLS-C01問題集はあなたを試験に順調に合格させると信じられます。なぜと言うのは、我々社の専門家は改革に応じて問題の更新と改善を続けていくのは出発点から勝つからです。

Amazon AWS Certified Machine Learning - Specialty 認定 MLS-C01 試験問題 (Q152-Q157):

質問 # 152
While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function What should the Specialist do to ensure better convergence during backpropagation?

A. Model regulanzation
B. Data augmentation for the minority class
C. Dimensionality reduction
D. Data normalization

正解：D

解説：
Data normalization is a data preprocessing technique that scales the features to a common range, such as [0, 1] or [-1, 1]. This helps reduce the impact of features with high magnitude on the cost function and improves the convergence during backpropagation. Data normalization can be done using different methods, such as min-max scaling, z-score standardization, or unit vector normalization. Data normalization is different from dimensionality reduction, which reduces the number of features; model regularization, which adds a penalty term to the cost function to prevent overfitting; and data augmentation, which increases the amount of data by creating synthetic samples. References:
Data processing options for AI/ML | AWS Machine Learning Blog
Data preprocessing - Machine Learning Lens
How to Normalize Data Using scikit-learn in Python
Normalization | Machine Learning | Google for Developers

質問 # 153
A data scientist is training a large PyTorch model by using Amazon SageMaker. It takes 10 hours on average to train the model on GPU instances. The data scientist suspects that training is not converging and that resource utilization is not optimal.
What should the data scientist do to identify and address training issues with the LEAST development effort?

A. Use the SageMaker Debugger confusion and feature_importance_overweight built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.
B. Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.
C. Use CPU utilization metrics that are captured in Amazon CloudWatch. Configure a CloudWatch alarm to stop the training job early if low CPU utilization occurs.
D. Use high-resolution custom metrics that are captured in Amazon CloudWatch. Configure an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected.

正解：B

解説：
The solution C is the best option to identify and address training issues with the least development effort. The solution C involves the following steps:
* Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues. SageMaker Debugger is a feature of Amazon SageMaker that allows data scientists to monitor, analyze, and debug machine learning models during training. SageMaker Debugger provides a set of built-in rules that can automatically detect common issues and anomalies in model training, such as vanishing or exploding gradients, overfitting, underfitting, low GPU utilization, and more1. The data scientist can use the vanishing_gradient rule to check if the gradients are becoming too small and causing the training to not converge. The data scientist can also use the LowGPUUtilization rule to check if the GPU resources are underutilized and causing the training to be inefficient2.
* Launch the StopTrainingJob action if issues are detected. SageMaker Debugger can also take actions based on the status of the rules. One of the actions is StopTrainingJob, which can terminate the training job if a rule is in an error state. This can help the data scientist to save time and money by stopping the training early if issues are detected3.
The other options are not suitable because:
* Option A: Using CPU utilization metrics that are captured in Amazon CloudWatch and configuring a CloudWatch alarm to stop the training job early if low CPU utilization occurs will not identify and address training issues effectively. CPU utilization is not a good indicator of model training performance, especially for GPU instances. Moreover, CloudWatch alarms can only trigger actions based on simple thresholds, not complex rules or conditions4.
* Option B: Using high-resolution custom metrics that are captured in Amazon CloudWatch and configuring an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected will incur more development effort than using SageMaker Debugger. The data scientist will have to write the code for capturing, sending, and analyzing the custom metrics, as well as for invoking the Lambda function and stopping the training job. Moreover, this solution may not be able to detect all the issues that SageMaker Debugger can5.
* Option D: Using the SageMaker Debugger confusion and feature_importance_overweight built-in rules and launching the StopTrainingJob action if issues are detected will not identify and address training issues effectively. The confusion rule is used to monitor the confusion matrix of a classification model, which is not relevant for a regression model that predicts prices. The feature_importance_overweight rule is used to check if some features have too much weight in the model, which may not be related to the convergence or resource utilization issues2.
1: Amazon SageMaker Debugger
2: Built-in Rules for Amazon SageMaker Debugger
3: Actions for Amazon SageMaker Debugger
4: Amazon CloudWatch Alarms
5: Amazon CloudWatch Custom Metrics

質問 # 154
A library is developing an automatic book-borrowing system that uses Amazon Rekognition. Images of library members' faces are stored in an Amazon S3 bucket. When members borrow books, the Amazon Rekognition CompareFaces API operation compares real faces against the stored faces in Amazon S3.
The library needs to improve security by making sure that images are encrypted at rest. Also, when the images are used with Amazon Rekognition. they need to be encrypted in transit. The library also must ensure that the images are not used to improve Amazon Rekognition as a service.
How should a machine learning specialist architect the solution to satisfy these requirements?

A. Enable server-side encryption on the S3 bucket. Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support.
B. Switch to using an Amazon Rekognition collection to store the images. Use the IndexFaces and SearchFacesByImage API operations instead of the CompareFaces API operation.
C. Switch to using the AWS GovCloud (US) Region for Amazon S3 to store images and for Amazon Rekognition to compare faces. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN.
D. Enable client-side encryption on the S3 bucket. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN.

正解：B

質問 # 155
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 appropriate. The residuals have a zero mean.
B. Linear regression is inappropriate. The underlying data has outliers.
C. Linear regression is appropriate. The residuals have constant variance.
D. Linear regression is inappropriate. The residuals do not have constant variance.

正解：D

解説：
Explanation
A residual plot is a type of plot that displays the values of a predictor variable in a regression model along the x-axis and the values of the residuals along the y-axis. This plot is used to assess whether or not the residuals in a regression model are normally distributed and whether or not they exhibit heteroscedasticity.
Heteroscedasticity means that the variance of the residuals is not constant across different values of the predictor variable. This violates one of the assumptions of linear regression and can lead to biased estimates and unreliable predictions. The displayed residual plot shows a clear pattern of heteroscedasticity, as the residuals spread out as the fitted values increase. This indicates that linear regression is inappropriate for this data and a different model should be used. References:
Regression - Amazon Machine Learning
How to Create a Residual Plot by Hand
How to Create a Residual Plot in Python

質問 # 156
A data scientist is designing a repository that will contain many images of vehicles. The repository must scale automatically in size to store new images every day. The repository must support versioning of the images.
The data scientist must implement a solution that maintains multiple immediately accessible copies of the data in different AWS Regions.
Which solution will meet these requirements?

A. AWS Storage Gateway Volume Gateway
B. Amazon S3 with S3 Cross-Region Replication (CRR)
C. Amazon Elastic Block Store (Amazon EBS) with snapshots that are shared in a secondary Region
D. Amazon Elastic File System (Amazon EFS) Standard storage that is configured with Regional availability

正解：B

解説：
For a repository containing a large and dynamically scaling collection of images, Amazon S3 is ideal due to its scalability and versioning capabilities. Amazon S3 natively supports automatic scaling to accommodate increasing storage needs and allows versioning, which enables tracking and managing different versions of objects.
To meet the requirement of maintaining multiple, immediately accessible copies of data across AWS Regions, S3 Cross-Region Replication (CRR) can be enabled. CRR automatically replicates new or updated objects to a specified destination bucket in another AWS Region, ensuring low-latency access and disaster recovery.
By setting up CRR with versioning enabled, the data scientist can achieve a multi-Region, scalable, and version-controlled repository in Amazon S3.

質問 # 157
......

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MLS-C01資格参考書 & MLS-C01受験内容

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Amazon AWS Certified Machine Learning - Specialty 認定 MLS-C01 試験問題 (Q152-Q157):

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