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当社Amazonには多くの専門家や教授がいます。 当社のすべてのAWS-Certified-Machine-Learning-Specialty研究トレントは、ShikenPASSさまざまな分野のこれらの優秀な専門家および教授によって設計されています。 AWS-Certified-Machine-Learning-Specialtyテストトレントが他の学習教材よりも高い品質を持っていることを確認できます。 私たちのデザインの目的は、学習を改善し、最短時間でAWS-Certified-Machine-Learning-Specialty認定を取得できるようにすることです。 認定資格を取得したい場合は、AWS Certified Machine Learning - Specialtyガイド急流が最適です。

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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty)試験は、Amazon Web Services（AWS）プラットフォーム上の機械学習分野での候補者のスキルと知識を検証するために設計されています。この認定は、機械学習の概念を熟知し、機械学習モデルを実装および展開するためのAWSサービスの使い方に堪能な個人を対象としています。

Amazon MLS-C01認定試験に合格するには、理論的知識と実践的なスキルの組み合わせが必要です。候補者は、機械学習の概念とアルゴリズムを強く理解しているだけでなく、AWSサービスでの実践的な経験を持っている必要があります。認定試験は、候補者の知識を実際のシナリオに適用し、複雑な問題を解決する能力をテストするように設計されています。この認定は3年間有効であり、最新のテクノロジーと業界のトレンドを最新の状態に保つために再認定が必要です。

Amazon AWS Certified Machine Learning - Specialty 認定 AWS-Certified-Machine-Learning-Specialty 試験問題 (Q84-Q89):

質問 # 84
A manufacturing company uses machine learning (ML) models to detect quality issues. The models use images that are taken of the company's product at the end of each production step. The company has thousands of machines at the production site that generate one image per second on average.
The company ran a successful pilot with a single manufacturing machine. For the pilot, ML specialists used an industrial PC that ran AWS IoT Greengrass with a long-running AWS Lambda function that uploaded the images to Amazon S3. The uploaded images invoked a Lambda function that was written in Python to perform inference by using an Amazon SageMaker endpoint that ran a custom model. The inference results were forwarded back to a web service that was hosted at the production site to prevent faulty products from being shipped.
The company scaled the solution out to all manufacturing machines by installing similarly configured industrial PCs on each production machine. However, latency for predictions increased beyond acceptable limits. Analysis shows that the internet connection is at its capacity limit.
How can the company resolve this issue MOST cost-effectively?

• A. Extend the long-running Lambda function that runs on AWS IoT Greengrass to compress the images and upload the compressed files to Amazon S3. Decompress the files by using a separate Lambda function that invokes the existing Lambda function to run the inference pipeline.
• B. Use auto scaling for SageMaker. Set up an AWS Direct Connect connection between the production site and the nearest AWS Region. Use the Direct Connect connection to upload the images.
• C. Set up a 10 Gbps AWS Direct Connect connection between the production site and the nearest AWS Region. Use the Direct Connect connection to upload the images. Increase the size of the instances and the number of instances that are used by the SageMaker endpoint.
• D. Deploy the Lambda function and the ML models onto the AWS IoT Greengrass core that is running on the industrial PCs that are installed on each machine. Extend the long-running Lambda function that runs on AWS IoT Greengrass to invoke the Lambda function with the captured images and run the inference on the edge component that forwards the results directly to the web service.

正解：D

解説：
Explanation
The best option is to deploy the Lambda function and the ML models onto the AWS IoT Greengrass core that is running on the industrial PCs that are installed on each machine. This way, the inference can be performed locally on the edge devices, without the need to upload the images to Amazon S3 and invoke the SageMaker endpoint. This will reduce the latency and the network bandwidth consumption. The long-running Lambda function can be extended to invoke the Lambda function with the captured images and run the inference on the edge component that forwards the results directly to the web service. This will also simplify the architecture and eliminate the dependency on the internet connection.
Option A is not cost-effective, as it requires setting up a 10 Gbps AWS Direct Connect connection and increasing the size and number of instances for the SageMaker endpoint. This will increase the operational costs and complexity.
Option B is not optimal, as it still requires uploading the images to Amazon S3 and invoking the SageMaker endpoint. Compressing and decompressing the images will add additional processing overhead and latency.
Option C is not sufficient, as it still requires uploading the images to Amazon S3 and invoking the SageMaker endpoint. Auto scaling for SageMaker will help to handle the increased workload, but it will not reduce the latency or the network bandwidth consumption. Setting up an AWS Direct Connect connection will improve the network performance, but it will also increase the operational costs and complexity. References:
AWS IoT Greengrass
Deploying Machine Learning Models to Edge Devices
AWS Certified Machine Learning - Specialty Exam Guide

 

質問 # 85
A company is building a predictive maintenance model based on machine learning (ML). The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in an Amazon SageMaker notebook. The job should read data from Amazon S3, process it, and upload it back to the same S3 bucket. The preprocessing code is stored in a container image in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow.
Which set of actions should the ML specialist take to meet these requirements?

• A. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs and to access Amazon ECR. Attach the role to the SageMaker notebook instance. Set up both an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMaker Processing jobs from the notebook.
• B. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook.
• C. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Set up an S3 endpoint in the default VPC. Create Amazon SageMaker Processing jobs with the access key and secret key of the IAM user with appropriate KMS and ECR permissions.
• D. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions.

正解：D

解説：
The correct solution for granting permissions for data preprocessing is to use the following steps:
* Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. This role allows the ML specialist to run Processing jobs from the notebook code1
* Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions. This role allows the Processing job to access the data in the encrypted S3 bucket, decrypt it with the KMS CMK, and pull the container image from ECR23 The other options are incorrect because they either miss some permissions or use unnecessary steps. For example:
* Option A uses a single IAM role for both the notebook instance and the Processing job. This role may have more permissions than necessary for the notebook instance, which violates the principle of least privilege4
* Option C sets up both an S3 endpoint and a KMS endpoint in the default VPC. These endpoints are not required for the Processing job to access the data in the encrypted S3 bucket. They are only needed if the Processing job runs in network isolation mode, which is not specified in the question.
* Option D uses the access key and secret key of the IAM user with appropriate KMS and ECR permissions. This is not a secure way to pass credentials to the Processing job. It also requires the ML specialist to manage the IAM user and the keys.
1: Create an Amazon SageMaker Notebook Instance - Amazon SageMaker
2: Create a Processing Job - Amazon SageMaker
3: Use AWS KMS-Managed Encryption Keys - Amazon Simple Storage Service
4: IAM Best Practices - AWS Identity and Access Management
Network Isolation - Amazon SageMaker
Understanding and Getting Your Security Credentials - AWS General Reference

 

質問 # 86
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?

• A. The precision of the model is 86%, which is greater than the accuracy of the model.
• B. The precision of the model is 86%, which is less than the accuracy of the model.
• C. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
• D. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.

正解：D

解説：
Based on the model evaluation results, this is a viable model for production because the model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives. The accuracy of the model is the proportion of correct predictions out of the total predictions, which can be calculated by adding the true positives and true negatives and dividing by the total number of observations. In this case, the accuracy of the model is (10 + 76) / 100 = 0.86, which means that the model correctly predicted
86% of the customers' churn status. The cost incurred by the company as a result of false positives and false negatives is the loss or damage that the company suffers when the model makes incorrect predictions. A false positive is when the model predicts that a customer will churn, but the customer actually does not churn. A false negative is when the model predicts that a customer will not churn, but the customer actually churns. In this case, the cost of a false positive is the incentive that the company offers to the customer who is predicted to churn, which is a relatively low cost. The cost of a false negative is the revenue that the company loses when the customer churns, which is a relatively high cost. Therefore, the cost of a false positive is less than the cost of a false negative, and the company would prefer to have more false positives than false negatives.
The model has 10 false positives and 4 false negatives, which means that the company's cost is lower than if the model had more false negatives and fewer false positives.

 

質問 # 87
A retail company uses a machine learning (ML) model for daily sales forecasting. The company's brand manager reports that the model has provided inaccurate results for the past 3 weeks.
At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company's ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.
What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?

• A. Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.
• B. Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.
• C. Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.
• D. Create a line chart with the weekly mean absolute error (MAE) of the model.

正解：A

解説：
Explanation
The best way to visualize the model's degradation is to create a histogram of the model errors over the last 3 weeks and compare it with a histogram of the model errors from before that period. A histogram is a graphical representation of the distribution of numerical data. It shows how often each value or range of values occurs in the data. A model error is the difference between the actual value and the predicted value. A high model error indicates a poor fit of the model to the data. By comparing the histograms of the model errors, the ML team can see if there is a significant change in the shape, spread, or center of the distribution. This can indicate if the model is underfitting, overfitting, or drifting from the data. A line chart or a scatter plot would not be as effective as a histogram for this purpose, because they do not show the distribution of the errors. A line chart would only show the trend of the errors over time, which may not capture the variability or outliers. A scatter plot would only show the relationship between the errors and another variable, such as daily sales, which may not be relevant or informative for the model's performance. References:
Histogram - Wikipedia
Model error - Wikipedia
SageMaker Model Monitor - visualizing monitoring results

 

質問 # 88
An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the data. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining.
How should a machine learning specialist meet these requirements?

• A. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site- to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.
• B. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.
• C. Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.
• D. Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.

正解：D

解説：
The best option is to use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.
This option meets the following requirements:
* It ensures that only nonsensitive data is transferred to the cloud by using table mapping to filter out the tables that contain sensitive data1.
* It uses IPsec to secure the data transfer by enabling SSL encryption for the AWS DMS endpoint2.
* It uploads the data to Amazon S3 each day for model retraining by using the ongoing replication feature of AWS DMS3.
The other options are not as effective or feasible as the option above. Creating an AWS Glue job to connect to the PostgreSQL DB instance and ingest data through an AWS Site-to-Site VPN connection directly into Amazon S3 is possible, but it requires more steps and resources than using AWS DMS. Also, it does not specify how to filter out the sensitive data from the tables. Creating an AWS Glue job to connect to the PostgreSQL DB instance and ingest all data through an AWS Site-to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job is also possible, but it is more complex and error-prone than using AWS DMS. Also, it does not use IPsec as required. Using PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection, and then using AWS Glue to move data from Amazon EC2 to Amazon S3 is not feasible, because PostgreSQL logical replication does not support replicating only a subset of data4. Also, it involves unnecessary data movement and additional costs.
Table mapping - AWS Database Migration Service
Using SSL to encrypt a connection to a DB instance - AWS Database Migration Service Ongoing replication - AWS Database Migration Service Logical replication - PostgreSQL

 

質問 # 89
......

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