MLS-C01 LEARNING MATERIALS & MLS-C01 TEST SIMULATE & MLS-C01 BEST QUESTIONS

MLS-C01 Learning Materials & MLS-C01 Test Simulate & MLS-C01 Best Questions

MLS-C01 Learning Materials & MLS-C01 Test Simulate & MLS-C01 Best Questions

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The AWS Certified Machine Learning - Specialty exam is a challenging and comprehensive certification program that requires a good amount of preparation and study. It consists of 65 multiple-choice and multiple-response questions, and the duration of the exam is 180 minutes. MLS-C01 Exam Fee is $300, and it is available in multiple languages, including English, Japanese, Korean, and Simplified Chinese.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q295-Q300):

NEW QUESTION # 295
A data engineer is preparing a dataset that a retail company will use to predict the number of visitors to stores.
The data engineer created an Amazon S3 bucket. The engineer subscribed the S3 bucket to an AWS Data Exchange data product for general economic indicators. The data engineer wants to join the economic indicator data to an existing table in Amazon Athena to merge with the business data. All these transformations must finish running in 30-60 minutes.
Which solution will meet these requirements MOST cost-effectively?

  • A. Provision an Amazon Redshift cluster. Subscribe to the AWS Data Exchange product and use the product to create an Amazon Redshift Table Merge the data in Amazon Redshift. Write the results back to Amazon S3.
  • B. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to use Amazon SageMaker Data Wrangler to merge the existing business data with the Athena table. Write the result set back to Amazon S3.
  • C. Configure the AWS Data Exchange product as a producer for an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to transfer the data to Amazon S3 Run an AWS Glue job that will merge the existing business data with the Athena table. Write the result set back to Amazon S3.
  • D. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda Function Program the Lambda function to run an AWS Glue job that will merge the existing business data with the Athena table Write the results back to Amazon S3.

Answer: B

Explanation:
Explanation
The most cost-effective solution is to use an S3 event to trigger a Lambda function that uses SageMaker Data Wrangler to merge the data. This solution avoids the need to provision and manage any additional resources, such as Kinesis streams, Firehose delivery streams, Glue jobs, or Redshift clusters. SageMaker Data Wrangler provides a visual interface to import, prepare, transform, and analyze data from various sources, including AWS Data Exchange products. It can also export the data preparation workflow to a Python script that can be executed by a Lambda function. This solution can meet the time requirement of 30-60 minutes, depending on the size and complexity of the data.
References:
Using Amazon S3 Event Notifications
Prepare ML Data with Amazon SageMaker Data Wrangler
AWS Lambda Function


NEW QUESTION # 296
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker.
The historical training data is stored in Amazon RDS.
Which approach should the Specialist use for training a model using that data?

  • A. Write a direct connection to the SQL database within the notebook and pull data in
  • B. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
  • C. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
  • D. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.

Answer: C


NEW QUESTION # 297
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?

  • A. Linear regression
  • B. Reinforcement learning
  • C. Clustering
  • D. Classification

Answer: A


NEW QUESTION # 298
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 less than the accuracy of the model.
  • B. The precision of the model is 86%, which is greater 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.

Answer: A


NEW QUESTION # 299
A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.
How can the data scientist meet these requirements?

  • A. Call the UpdateNotebookInstanceLifecycleConfig API operation
  • B. Stop and then restart the SageMaker notebook instance
  • C. Call the CreateNotebookInstanceLifecycleConfig API operation
  • D. Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance

Answer: B

Explanation:
The correct solution for updating the software on a SageMaker notebook instance is to stop and then restart the notebook instance. This will automatically apply the latest security and software updates provided by SageMaker1 The other options are incorrect because they either do not update the software or require unnecessary steps.
For example:
* Option A calls the CreateNotebookInstanceLifecycleConfig API operation. This operation creates a lifecycle configuration, which is a set of shell scripts that run when a notebook instance is created or started. A lifecycle configuration can be used to customize the notebook instance, such as installing additional libraries or packages. However, it does not update the software on the notebook instance2
* Option B creates a new SageMaker notebook instance and mounts the Amazon Elastic Block Store (Amazon EBS) volume from the original instance. This option will create a new notebook instance with the latest software, but it will also incur additional costs and require manual steps to transfer the data and settings from the original instance3
* Option D calls the UpdateNotebookInstanceLifecycleConfig API operation. This operation updates an existing lifecycle configuration. As explained in option A, a lifecycle configuration does not update the software on the notebook instance4 References:
* 1: Amazon SageMaker Notebook Instances - Amazon SageMaker
* 2: CreateNotebookInstanceLifecycleConfig - Amazon SageMaker
* 3: Create a Notebook Instance - Amazon SageMaker
* 4: UpdateNotebookInstanceLifecycleConfig - Amazon SageMaker


NEW QUESTION # 300
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