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快速下載MLS-C01软件版 & Amazon MLS-C01熱門考古題：AWS Certified Machine Learning - Specialty終於通過了

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Amazon AWS-Certified-Machine-Learning-Specialty（AWS 認證機器學習 - 專業級）考試是一個認證計劃，旨在為那些有興趣驗證自己在 Amazon Web Services（AWS）平臺上開發、部署和維護機器學習解決方案的專業人士提供認證。此認證計劃針對的是那些對 AWS 生態系統有深刻理解的人士，並希望擴展自己在機器學習領域的知識和技能。

Amazon AWS認證的手機學習專業（AWS認證的機器學習 - 專業）考試是Amazon Web Services（AWS）提供的認證考試（AWS），為希望在AWS平台上展示其機器學習專業知識的個人。該考試旨在測試候選人對AWS雲上機器學習概念，算法，數據工程和數據科學實踐的理解。

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想要參加 Amazon MLS-C01 考試的候選人，需具備在 AWS 平台上開發和部署機器學習模型的至少一年經驗。他們應該對機器學習算法、數據準備和模型優化技術有較強的理解。此外，候選人應該精通 Python 編程語言，並具備 Amazon S3、AWS Lambda 和 AWS CloudFormation 等 AWS 服務的使用經驗。

最新的 AWS Certified Specialty MLS-C01 免費考試真題 (Q140-Q145):

問題 #140
A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents.
The company's data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model's testing accuracy.
Which process will improve the testing accuracy the MOST?

A. Use a one-hot encoder for the categorical fields in the dataset. Perform standardization on the financial fields in the dataset. Apply L1 regularization to the data.
B. Use a label encoder for the categorical fields in the dataset. Perform L1 regularization on the financial fields in the dataset. Apply L2 regularization to the data.
C. Use a logarithm transformation on the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Use imputation to populate missing values in the dataset.
D. Use tokenization of the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Remove the outliers in the data by using the z-score.

答案：D

問題 #141
A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.
How can the ML team solve this issue?

A. Increase the cooldown period for the scale-out activity.
B. Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.
C. Replace the current endpoint with a multi-model endpoint using SageMaker.
D. Decrease the cooldown period for the scale-in activity. Increase the configured maximum capacity of instances.

答案：A

解題說明：
The correct solution for changing the scaling behavior of the SageMaker instances is to increase the cooldown period for the scale-out activity. The cooldown period is the amount of time, in seconds, after a scaling activity completes before another scaling activity can start. By increasing the cooldown period for the scale- out activity, the ML team can ensure that the new instances are ready before launching additional instances. This will prevent over-scaling and reduce costs1 The other options are incorrect because they either do not solve the issue or require unnecessary steps. For example:
* Option A decreases the cooldown period for the scale-in activity and increases the configured maximum capacity of instances. This option does not address the issue of launching additional instances before the new instances are ready. It may also cause under-scaling and performance degradation.
* Option B replaces the current endpoint with a multi-model endpoint using SageMaker. A multi-model endpoint is an endpoint that can host multiple models using a single endpoint. It does not affect the scaling behavior of the SageMaker instances. It also requires creating a new endpoint and updating the application code to use it2
* Option C sets up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint. Amazon API Gateway is a service that allows users to create, publish, maintain, monitor, and secure APIs. AWS Lambda is a service that lets users run code without provisioning or managing servers. These services do not affect the scaling behavior of the SageMaker instances. They also require creating and configuring additional resources and services34 References:
* 1: Automatic Scaling - Amazon SageMaker
* 2: Create a Multi-Model Endpoint - Amazon SageMaker
* 3: Amazon API Gateway - Amazon Web Services
* 4: AWS Lambda - Amazon Web Services

問題 #142
A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and
999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.
Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

A. Add more deep trees to the random forest to enable the model to learn more features.
B. indicate a copy of the samples in the test database in the training dataset
C. Change the cost function so that false positives have a higher impact on the cost value than false negatives
D. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
E. Change the cost function so that false negatives have a higher impact on the cost value than false positives

答案：D,E

解題說明：
The Data Science team is facing a problem of imbalanced data, where the positive class (paid users) is much less frequent than the negative class (non-paid users). This can cause the random forest model to be biased towards the majority class and have poor performance on the minority class. To mitigate this issue, the Data Science team can try the following approaches:
* C. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data. This is a technique called data augmentation, which can help increase the size and diversity of the training data for the minority class. This can help the random forest model learn more features and patterns from the positive class and reduce the imbalance ratio.
* D. Change the cost function so that false negatives have a higher impact on the cost value than false positives. This is a technique called cost-sensitive learning, which can assign different weights or costs to different classes or errors. By assigning a higher cost to false negatives (predicting non-paid when the user is actually paid), the random forest model can be more sensitive to the minority class and try to minimize the misclassification of the positive class.
References:
* Bagging and Random Forest for Imbalanced Classification
* Surviving in a Random Forest with Imbalanced Datasets
* machine learning - random forest for imbalanced data? - Cross Validated
* Biased Random Forest For Dealing With the Class Imbalance Problem

問題 #143
A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.
To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities.
Which deployment architecture for the model will address these business requirements?

A. Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.
B. Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table. Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.
C. Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.
D. Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.

答案：C

解題說明：
AWS IoT Greengrass is a service that extends AWS to edge devices, such as sensors and machines, so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. AWS IoT Greengrass enables local device messaging, secure data transfer, and local computing using AWS Lambda functions and machine learning models. AWS IoT Greengrass can run machine learning inference locally on devices using models that are created and trained in the cloud. This allows devices to respond quickly to local events, even when they are offline or have intermittent connectivity. Therefore, option B is the best deployment architecture for the model to address the business requirements of the manufacturer.
Option A is incorrect because deploying the model in Amazon SageMaker would require sending the sensor data to the cloud for inference, which would not work well for factory locations that do not have reliable or high-speed internet connectivity. Moreover, this option would not provide near-real-time inference capabilities, as there would be latency and bandwidth issues involved in transferring the data to and from the cloud. Option C is incorrect because deploying the model to an Amazon SageMaker batch transformation job would not provide near-real-time inference capabilities, as batch transformation is an asynchronous process that operates on large datasets. Batch transformation is not suitable for streaming data that requires low-latency responses. Option D is incorrect because deploying the model in Amazon SageMaker and using an IoT rule to write data to an Amazon DynamoDB table would also require sending the sensor data to the cloud for inference, which would have the same drawbacks as option A. Moreover, this option would introduce additional complexity and cost by involving multiple services, such as IoT Core, DynamoDB, and Lambda.
References:
AWS Greengrass Machine Learning Inference - Amazon Web Services
Machine learning components - AWS IoT Greengrass
What is AWS Greengrass? | AWS IoT Core | Onica
GitHub - aws-samples/aws-greengrass-ml-deployment-sample
AWS IoT Greengrass Architecture and Its Benefits | Quick Guide - XenonStack

問題 #144
A company is building a new version of a recommendation engine. Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations. The ML specialists gather data from the users' interactions on the platform and from sources such as external websites and social media.
The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3. A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance. The whole process takes more than 20 hours to finish, with each script taking at least an hour. The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.
Which approach will address all of these requirements with the LEAST development effort?

A. Create an AWS Glue job. Convert the scripts to PySpark. Execute the pipeline. Store the results in Amazon S3.
B. Load the data into Amazon DynamoDB. Convert the scripts to an AWS Lambda function. Execute the pipeline by triggering Lambda executions. Store the results in Amazon S3.
C. Create a set of individual AWS Lambda functions to execute each of the scripts. Build a step function by using the AWS Step Functions Data Science SDK. Store the results in Amazon S3.
D. Load the data into an Amazon Redshift cluster. Execute the pipeline by using SQL. Store the results in Amazon S3.

答案：B

問題 #145
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P.S. NewDumps在Google Drive上分享了免費的2026 Amazon MLS-C01考試題庫：https://drive.google.com/open?id=1gUrktlsR0V9CdYCQhkmdtDeFnBzL3rMu