MLS-C01덤프문제집최신덤프데모다운

Pass4Test에서는 시장에서 가장 최신버전이자 적중율이 가장 높은 Amazon인증 MLS-C01덤프를 제공해드립니다. Amazon인증 MLS-C01덤프는 IT업종에 몇십년간 종사한 IT전문가가 실제 시험문제를 연구하여 제작한 고품질 공부자료로서 시험패스율이 장난 아닙니다. 덤프를 구매하여 시험에서 불합격성적표를 받으시면 덤프비용 전액을 환불해드립니다.

Pass4Test는 응시자에게 있어서 시간이 정말 소중하다는 것을 잘 알고 있으므로 Amazon MLS-C01덤프를 자주 업데이트 하고, 오래 되고 더 이상 사용 하지 않는 문제들은 바로 삭제해버리며 새로운 최신 문제들을 추가 합니다. 이는 응시자가 확실하고도 빠르게Amazon MLS-C01덤프를 마스터하고Amazon MLS-C01시험을 패스할수 있도록 하는 또 하나의 보장입니다.

>> MLS-C01덤프문제집 <<

시험대비에 가장 적합한 MLS-C01덤프문제집 인증덤프

불과 1,2년전만 해도 Amazon MLS-C01덤프를 결제하시면 수동으로 메일로 보내드리기에 공휴일에 결제하시면 덤프를 보내드릴수 없어 고객님께 페를 끼쳐드렸습니다. 하지만 지금은 시스템이 업그레이드되어Amazon MLS-C01덤프를 결제하시면 바로 사이트에서 다운받을수 있습니다. Pass4Test는 가면갈수록 고객님께 편리를 드릴수 있도록 나날이 완벽해질것입니다.

AWS Certified Machine Learning -Specialty Certification Exam은 AWS (Amazon Web Services) 플랫폼에서 기계 학습 솔루션을 구현하고 배포하는 데있어 전문 지식을 시연하려는 전문가를위한 고도로 인기있는 자격 증명입니다. 이 인증은 데이터 과학, 소프트웨어 엔지니어링 및 AWS를 활용하여 기계 학습 모델을 구축하고 배포하는 데 관심이있는 기타 관련 분야의 배경 지식을 가진 개인을 위해 설계되었습니다.

최신 AWS Certified Specialty MLS-C01 무료샘플문제 (Q198-Q203):

질문 # 198
A company processes millions of orders every day. The company uses Amazon DynamoDB tables to store order information. When customers submit new orders, the new orders are immediately added to the DynamoDB tables. New orders arrive in the DynamoDB tables continuously.
A data scientist must build a peak-time prediction solution. The data scientist must also create an Amazon OuickSight dashboard to display near real-lime order insights. The data scientist needs to build a solution that will give QuickSight access to the data as soon as new order information arrives.
Which solution will meet these requirements with the LEAST delay between when a new order is processed and when QuickSight can access the new order information?

A. Use Amazon Kinesis Data Firehose to export the data from Amazon DynamoDB to Amazon S3.Configure OuickSight to access the data in Amazon S3.
B. Use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3.
Configure OuickSight to access the data in Amazon S3.
C. Use an API call from OuickSight to access the data that is in Amazon DynamoDB directly
D. Use AWS Glue to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.

정답：B

설명：
The best solution for this scenario is to use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3, and then configure QuickSight to access the data in Amazon S3. This solution has the following advantages:
* It allows near real-time data ingestion from DynamoDB to S3 using Kinesis Data Streams, which can capture and process data continuously and at scale1.
* It enables QuickSight to access the data in S3 using the Athena connector, which supports federated queries to multiple data sources, including Kinesis Data Streams2.
* It avoids the need to create and manage a Lambda function or a Glue crawler, which are required for the other solutions.
The other solutions have the following drawbacks:
* Using AWS Glue to export the data from DynamoDB to S3 introduces additional latency and complexity, as Glue is a batch-oriented service that requires scheduling and configuration3.
* Using an API call from QuickSight to access the data in DynamoDB directly is not possible, as QuickSight does not support direct querying of DynamoDB4.
* Using Kinesis Data Firehose to export the data from DynamoDB to S3 is less efficient and flexible than using Kinesis Data Streams, as Firehose does not support custom data processing or transformation, and has a minimum buffer interval of 60 seconds5.
1: Amazon Kinesis Data Streams - Amazon Web Services
2: Visualize Amazon DynamoDB insights in Amazon QuickSight using the Amazon Athena DynamoDB connector and AWS Glue | AWS Big Data Blog
3: AWS Glue - Amazon Web Services
4: Visualising your Amazon DynamoDB data with Amazon QuickSight - DEV Community
5: Amazon Kinesis Data Firehose - Amazon Web Services

질문 # 199
A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following:
68% accuracy on the training set
67% accuracy on the validation set
What can the machine learning specialist do to improve the system's accuracy?

A. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
B. Add more data to the training set and retrain the model using transfer learning to reduce the bias.
C. Train a new model using the current neural network architecture.
D. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.

정답：B

설명：
The problem described in the question is a case of underfitting, where the neural network model performs poorly on both the training and validation sets. This means that the model has not learned the features of the data well enough and has high bias. To solve this issue, the machine learning specialist should consider the following change:
* Add more data to the training set and retrain the model using transfer learning to reduce the bias:
Adding more data to the training set can help the model learn more patterns and variations in the data and improve its performance. Transfer learning can also help the model leverage the knowledge from the pre-trained network and adapt it to the new data. This can reduce the bias and increase the accuracy of the model.
Transfer learning for TensorFlow image classification models in Amazon SageMaker Transfer learning for custom labels using a TensorFlow container and "bring your own algorithm" in Amazon SageMaker Machine Learning Concepts - AWS Training and Certification

질문 # 200
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines.
* Provide a quick and easy way to understand metadata.
Which approach meets trfese requirements?

A. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
D. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.

정답：B

설명：
To build a robust serverless data lake on Amazon S3 that meets the requirements, the financial services company should use the following AWS services:
* AWS Glue crawler: This is a service that connects to a data store, progresses through a prioritized list of classifiers to determine the schema for the data, and then creates metadata tables in the AWS Glue Data Catalog1. The company can use an AWS Glue crawler to crawl the S3 data and infer the schema, format, and partition structure of the data. The crawler can also detect schema changes and update the metadata tables accordingly. This enables the company to support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum, which are serverless interactive query services that use the AWS Glue Data Catalog as a central location for storing and retrieving table metadata23.
* AWS Lambda function: This is a service that lets you run code without provisioning or managing servers. You pay only for the compute time you consume - there is no charge when your code is not running. You can also use AWS Lambda to create event-driven ETL pipelines, by triggering other AWS services based on events such as object creation or deletion in S3 buckets4. The company can use an AWS Lambda function to trigger an AWS Glue ETL job, which is a serverless way to extract, transform, and load data for analytics. The AWS Glue ETL job can perform various data processing tasks, such as converting data formats, filtering, aggregating, joining, and more.
* AWS Glue Data Catalog: This is a managed service that acts as a central metadata repository for data assets across AWS and on-premises data sources. The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos, and use that metadata to query and transform the data. The company can use the AWS Glue Data Catalog to search and discover metadata, such as table definitions, schemas, and partitions. The AWS Glue Data Catalog also integrates with Amazon Athena, Amazon Redshift Spectrum, Amazon EMR, and AWS Glue ETL jobs, providing a consistent view of the data across different query and analysis services.
1: What Is a Crawler? - AWS Glue
2: What Is Amazon Athena? - Amazon Athena
3: Amazon Redshift Spectrum - Amazon Redshift
4: What is AWS Lambda? - AWS Lambda
AWS Glue ETL Jobs - AWS Glue
What Is the AWS Glue Data Catalog? - AWS Glue

질문 # 201
A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs The workflow consists of the following processes
* Start the workflow as soon as data is uploaded to Amazon S3
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3
* Store the results of joining datasets in Amazon S3
* If one of the jobs fails, send a notification to the Administrator
Which configuration will meet these requirements?

A. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
B. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
C. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3 Use AWS Glue to join the datasets in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
D. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

정답：B

설명：
To develop a daily ETL workflow containing multiple ETL jobs that can start as soon as data is uploaded to Amazon S3, the best configuration is to use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that respond to events such as data uploads to Amazon S3. You can also use Lambda to invoke other AWS services such as AWS Step Functions and AWS Glue.
AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. You can use Step Functions to create a state machine that defines the sequence and logic of your ETL workflow. You can also use Step Functions to handle errors and retries, and to monitor the execution status of your workflow.
AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics. You can use Glue to create and run ETL jobs that can join data from multiple sources in Amazon S3. You can also use Glue to catalog your data and make it searchable and queryable.
Amazon CloudWatch is a service that monitors your AWS resources and applications. You can use CloudWatch to create alarms that trigger actions when a metric or a log event meets a specified threshold. You can also use CloudWatch to send notifications to Amazon Simple Notification Service (SNS) topics, which can then deliver the notifications to subscribers such as email addresses or phone numbers.
Therefore, by using these services together, you can achieve the following benefits:
You can start the ETL workflow as soon as data is uploaded to Amazon S3 by using Lambda functions to trigger Step Functions workflows.
You can wait for all the datasets to be available in Amazon S3 by using Step Functions to poll the S3 buckets and check the data completeness.
You can join the datasets with terabyte-sized datasets in Amazon S3 by using Glue ETL jobs that can scale and parallelize the data processing.
You can store the results of joining datasets in Amazon S3 by using Glue ETL jobs to write the output to S3 buckets.
You can send a notification to the Administrator if one of the jobs fails by using CloudWatch alarms to monitor the Step Functions or Glue metrics and send SNS notifications in case of a failure.

질문 # 202
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.
How should the Data Scientist correct this issue?

A. Replace the age field value for records with a value of 0 with the mean or median value from the dataset.
B. Use k-means clustering to handle missing features.
C. Drop the age feature from the dataset and train the model using the rest of the features.
D. Drop all records from the dataset where age has been set to 0.

정답：D

질문 # 203
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Pass4Test의 Amazon인증 MLS-C01덤프를 선택하여Amazon인증 MLS-C01시험공부를 하는건 제일 현명한 선택입니다. 시험에서 떨어지면 덤프비용 전액을 환불처리해드리고Amazon인증 MLS-C01시험이 바뀌면 덤프도 업데이트하여 고객님께 최신버전을 발송해드립니다. Amazon인증 MLS-C01덤프뿐만아니라 IT인증시험에 관한 모든 덤프를 제공해드립니다.

MLS-C01시험패스 인증덤프자료: https://www.pass4test.net/MLS-C01.html

Pass4Test에서 출시한 Amazon MLS-C01덤프만 있으면 학원다닐 필요없이 시험패스 가능합니다, Pass4Test에서 Amazon인증 MLS-C01덤프를 구입하시면 퍼펙트한 구매후 서비스를 제공해드립니다, Amazon MLS-C01 덤프는Amazon MLS-C01시험문제변경에 따라 주기적으로 업데이트를 진행하여 저희 덤프가 항상 가장 최신버전이도록 보장해드립니다, Pass4Test의 Amazon MLS-C01덤프가 고객님의 시험패스테 조금이나마 도움이 되신다면 행복으로 느끼겠습니다, MLS-C01시험은 it인증 인기자격증을 취득하는 필수과목입니다.

가능하면 다른 사람들 몰래, 그리고 검을 빼들고 사진여와 맞붙었다, Pass4Test에서 출시한 Amazon MLS-C01덤프만 있으면 학원다닐 필요없이 시험패스 가능합니다, Pass4Test에서 Amazon인증 MLS-C01덤프를 구입하시면 퍼펙트한 구매후 서비스를 제공해드립니다.

MLS-C01덤프문제집 시험은 저희 덤프로 패스가능

Amazon MLS-C01 덤프는Amazon MLS-C01시험문제변경에 따라 주기적으로 업데이트를 진행하여 저희 덤프가 항상 가장 최신버전이도록 보장해드립니다, Pass4Test의 Amazon MLS-C01덤프가 고객님의 시험패스테 조금이나마 도움이 되신다면 행복으로 느끼겠습니다.

MLS-C01시험은 it인증 인기자격증을 취득하는 필수과목입니다.