Related Certification(s):
Amazon Specialty, Amazon AWS Certified Machine Learning Certifications
Amazon MLS-C01 Exam Topics - You’ll Be Tested in Actual Exam
The Amazon MLS-C01 exam is a comprehensive assessment designed to evaluate your knowledge and skills in working with Amazon Web Services (AWS). It covers a wide range of topics, ensuring that you possess a deep understanding of AWS services and best practices. The exam topics include an overview of AWS, where you'll learn about its fundamental concepts, services, and key features. You'll also delve into designing and implementing AWS architectures, focusing on scalability, high availability, and fault tolerance. Security is a critical aspect, and the exam covers measures to protect data and systems, including encryption, access control, and identity management. Cost optimization is another key area, teaching you strategies to minimize AWS costs without compromising performance. Additionally, you'll explore networking and content delivery, learning about VPCs, route tables, and CDNs. The exam also covers database storage options, such as Amazon RDS and DynamoDB, and teaches you how to choose the right storage solution for your needs. Furthermore, you'll learn about deployment and management, including strategies for automated deployment, infrastructure as code, and managing AWS resources efficiently. Lastly, the exam touches on analytics, machine learning, and artificial intelligence, introducing you to AWS services like Amazon SageMaker and Amazon EMR.
Amazon MLS-C01 Exam Short Quiz
Attempt this Amazon MLS-C01 exam quiz to self-assess your preparation for the actual Amazon AWS Certified Machine Learning - Specialty exam. CertBoosters also provides premium Amazon MLS-C01 exam questions to pass the Amazon AWS Certified Machine Learning - Specialty exam in the shortest possible time. Be sure to try our free practice exam software for the Amazon MLS-C01 exam.
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Amazon MLS-C01 Exam Quiz
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AmazonMLS-C01
Q1:
A machine learning (ML) engineer is preparing a dataset for a classification model. The ML engineer notices that some continuous numeric features have a significantly greater value than most other features. A business expert explains that the features are independently informative and that the dataset is representative of the target distribution.
After training, the model's inferences accuracy is lower than expected.
Which preprocessing technique will result in the GREATEST increase of the model's inference accuracy?
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ANormalize the problematic features.
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BBootstrap the problematic features.
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CRemove the problematic features.
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DExtrapolate synthetic features.
AmazonMLS-C01
Q2:
A bank has collected customer data for 10 years in CSV format. The bank stores the data in an on-premises server. A data science team wants to use Amazon SageMaker to build and train a machine learning (ML) model to predict churn probability. The team will use the historical dat
a. The data scientists want to perform data transformations quickly and to generate data insights before the team builds a model for production.
Which solution will meet these requirements with the LEAST development effort?
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AUpload the data into the SageMaker Data Wrangler console directly. Perform data transformations and generate insights within Data Wrangler.
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BUpload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the S3 bucket into SageMaker Data Wrangler. Perform data transformations and generate insights within Data Wrangler.
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CUpload the data into the SageMaker Data Wrangler console directly. Allow SageMaker and Amazon QuickSight to access the data that is in an Amazon S3 bucket. Perform data transformations in Data Wrangler and save the transformed data into a second S3 bucket. Use QuickSight to generate data insights.
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DUpload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the bucket into SageMaker Data Wrangler. Perform data transformations in Data Wrangler. Save the data into a second S3 bucket. Use a SageMaker Studio notebook to generate data insights.
AmazonMLS-C01
Q3:
A company plans to build a custom natural language processing (NLP) model to classify and prioritize user feedback. The company hosts the data and all machine learning (ML) infrastructure in the AWS Cloud. The ML team works from the company's office, which has an IPsec VPN connection to one VPC in the AWS Cloud.
The company has set both the enableDnsHostnames attribute and the enableDnsSupport attribute of the VPC to true. The company's DNS resolvers point to the VPC DNS. The company does not allow the ML team to access Amazon SageMaker notebooks through connections that use the public internet. The connection must stay within a private network and within the AWS internal network.
Which solution will meet these requirements with the LEAST development effort?
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ACreate a VPC interface endpoint for the SageMaker notebook in the VPC. Access the notebook through a VPN connection and the VPC endpoint.
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BCreate a bastion host by using Amazon EC2 in a public subnet within the VPC. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.
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CCreate a bastion host by using Amazon EC2 in a private subnet within the VPC with a NAT gateway. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.
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DCreate a NAT gateway in the VPC. Access the SageMaker notebook HTTPS endpoint through a VPN connection and the NAT gateway.
AmazonMLS-C01
Q4:
A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data.
The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.
Which action will MOST likely improve the performance for the forecasting model?
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AAggregate sales from stores in the same geographic area.
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BApply smoothing to correct for seasonal variation.
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CChange the forecast frequency from daily to weekly.
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DReplace missing values in the dataset by using linear interpolation.
AmazonMLS-C01
Q5:
A machine learning (ML) specialist is training a linear regression model. The specialist notices that the model is overfitting. The specialist applies an L1 regularization parameter and runs the model again. This change results in all features having zero weights.
What should the ML specialist do to improve the model results?
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AIncrease the L1 regularization parameter. Do not change any other training parameters.
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BDecrease the L1 regularization parameter. Do not change any other training parameters.
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CIntroduce a large L2 regularization parameter. Do not change the current L1 regularization value.
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DIntroduce a small L2 regularization parameter. Do not change the current L1 regularization value.
