MLA-C01학습자료100%유효한덤프자료

Amazon MLA-C01인증덤프는 실제 MLA-C01시험의 가장 최근 시험의 기출문제를 기준으로 하여 만들어진 최고품질을 자랑하는 최고적중율의 시험대비자료입니다. 저희 MLA-C01덤프로 MLA-C01시험에 도전해보지 않으실래요? MLA-C01시험에서 불합격 받을시 덤프비용은 환불해드리기에 부담없이 구매하셔도 됩니다.환불의 유일한 기준은 불합격 성적표이고 환불유효기간은 구매일로부터 60일까지입니다.

Amazon MLA-C01 시험을 어떻게 통과할수 있을가 고민중이신 분들은Itexamdump를 선택해 주세요. Itexamdump는 많은 분들이 IT인증시험을 응시하여 성공하도록 도와주는 사이트입니다. 최고급 품질의Amazon MLA-C01시험대비 덤프는Amazon MLA-C01시험을 간단하게 패스하도록 힘이 되어드립니다. Itexamdump 의 덤프는 모두 엘리트한 전문가들이 만들어낸 만큼 시험문제의 적중률은 아주 높습니다.

>> MLA-C01학습자료 <<

MLA-C01시험패스 가능 덤프문제 & MLA-C01시험대비 덤프 최신자료

우리Itexamdump에서는 각종IT시험에 관심있는분들을 위하여, 여러 가지 인증시험자료를 제공하는 사이트입니다. 우리Itexamdump는 많은 분들이 IT인증시험을 응시하여 성공할수록 도와주는 사이트입니다. 우리의 파워는 아주 대단하답니다. 여러분은 우리Itexamdump 사이트에서 제공하는Amazon MLA-C01관련자료의 일부분문제와답등 샘플을 무료로 다운받아 체험해봄으로 우리에 믿음이 생기게 될 것입니다.

최신 AWS Certified Associate MLA-C01 무료샘플문제 (Q68-Q73):

질문 # 68
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?

A. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.
B. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.
C. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.
D. Apply statistics from a well-known dataset to normalize the production samples.

정답：B

설명：
To ensure consistency between training and inference, themin-max normalization statistics (min and max values)calculated during training must be retained and applied to normalize production inference data. Using the same statistics ensures that the model receives data in the same scale and distribution as it did during training, avoiding discrepancies that could degrade model performance. Calculating new statistics from production data would lead to inconsistent normalization and affect predictions.

질문 # 69
An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models.
The ML engineer must receive alerts when changes in data quality occur.
Which solution will meet these requirements?

A. Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.
B. Deploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.
C. Deploy the models by using Amazon Elastic Container Service (Amazon ECS) on AWS Fargate. Use Amazon EventBridge to monitor the data quality and to send alerts.
D. Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.

정답：D

설명：
Amazon SageMaker batch transform is ideal for obtaining inferences from large datasets in an asynchronous manner, as it processes data in batches rather than requiring real-time inputs.
SageMaker Model Monitor allows scheduled monitoring of data quality, detecting shifts in input data characteristics, and generating alerts when changes in data quality occur.
This solution provides a fully managed, efficient way to handle both asynchronous inference and data quality monitoring with minimal operational overhead.

질문 # 70
A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker endpoint. The ML engineer needs to explain to company stakeholders how the model makes predictions.
Which solution will provide an explanation for the model's predictions?

A. Show the distribution of inferences from A/# testing in Amazon CloudWatch.
B. Use SageMaker Clarify on the deployed model.
C. Use SageMaker Model Monitor on the deployed model.
D. Add a shadow endpoint. Analyze prediction differences on samples.

정답：B

설명：
SageMaker Clarify is designed to provide explainability for ML models. It can analyze feature importance and explain how input features influence the model's predictions. By using Clarify with the deployed SageMaker model, the ML engineer can generate insights and present them to stakeholders to explain the sentiment analysis predictions effectively.

질문 # 71
A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.
Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)
* Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
* Store the resulting data back in Amazon S3.
* Use Amazon Athena to infer the schemas and available columns.
* Use AWS Glue crawlers to infer the schemas and available columns.
* Use AWS Glue DataBrew for data cleaning and feature engineering.

정답：

설명：

Explanation:
Step 1: Use AWS Glue crawlers to infer the schemas and available columns.Step 2: Use AWS Glue DataBrew for data cleaning and feature engineering.Step 3: Store the resulting data back in Amazon S3.
* Step 1: Use AWS Glue Crawlers to Infer Schemas and Available Columns
* Why?The data is stored in .csv files with unlabeled columns, and Glue Crawlers can scan the raw data in Amazon S3 to automatically infer the schema, including available columns, data types, and any missing or incomplete entries.
* How?Configure AWS Glue Crawlers to point to the S3 bucket containing the .csv files, and run the crawler to extract metadata. The crawler creates a schema in the AWS Glue Data Catalog, which can then be used for subsequent transformations.
* Step 2: Use AWS Glue DataBrew for Data Cleaning and Feature Engineering
* Why?Glue DataBrew is a visual data preparation tool that allows for comprehensive cleaning and transformation of data. It supports imputation of missing values, renaming columns, feature engineering, and more without requiring extensive coding.
* How?Use Glue DataBrew to connect to the inferred schema from Step 1 and perform data cleaning and feature engineering tasks like filling in missing rows/columns, renaming unlabeled columns, and creating derived features.
* Step 3: Store the Resulting Data Back in Amazon S3
* Why?After cleaning and preparing the data, it needs to be saved back to Amazon S3 so that it can be used for training machine learning models.
* How?Configure Glue DataBrew to export the cleaned data to a specific S3 bucket location. This ensures the processed data is readily accessible for ML workflows.
Order Summary:
* Use AWS Glue crawlers to infer schemas and available columns.
* Use AWS Glue DataBrew for data cleaning and feature engineering.
* Store the resulting data back in Amazon S3.
This workflow ensures that the data is prepared efficiently for ML model training while leveraging AWS services for automation and scalability.

질문 # 72
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training.
After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?

A. Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.
B. Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
C. Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.
D. Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.

정답：D

설명：
To minimize communication overhead during distributed training:
1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.
2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.
3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.
This configuration optimizes communication efficiency and minimizes overhead.

질문 # 73
......

경쟁이 치열한 IT업계에서 굳굳한 자신만의 자리를 찾으려면 국제적으로 인정받는 IT자격증 취득은 너무나도 필요합니다. Amazon인증 MLA-C01시험은 IT인사들중에서 뜨거운 인기를 누리고 있습니다. Itexamdump는 IT인증시험에 대비한 시험전 공부자료를 제공해드리는 전문적인 사이트입니다.한방에 쉽게Amazon인증 MLA-C01시험에서 고득점으로 패스하고 싶다면Itexamdump의Amazon인증 MLA-C01덤프를 선택하세요.저렴한 가격에 비해 너무나도 높은 시험적중율과 시험패스율, 언제나 여러분을 위해 최선을 다하는Itexamdump가 되겠습니다.

MLA-C01시험패스 가능 덤프문제: https://www.itexamdump.com/MLA-C01.html

Itexamdump에서 제공하는Amazon MLA-C01덤프로 시험 준비하시면 편안하게 시험을 패스하실 수 있습니다, 우리Itexamdump 사이트에서Amazon MLA-C01관련자료의 일부분 문제와 답 등 샘플을 제공함으로 여러분은 무료로 다운받아 체험해보실 수 있습니다, Itexamdump의 부지런한 IT전문가들이 자기만의 지식과 끊임없는 노력과 경험으로 최고의Amazon MLA-C01합습자료로Amazon MLA-C01인증시험을 응시하실 수 있습니다.Amazon MLA-C01인증시험은 IT업계에서의 비중은 아주 큽니다, Amazon MLA-C01인증시험은 전업적지식이 강한 인증입니다.

우리는 긍정적인 대답을 들었으면 하는데, 오른쪽 해창선 선수에 한 인영이 나타나더니 두 손을 번쩍 들고 양 주먹을 몇 차례 쥐었다 폈다, Itexamdump에서 제공하는Amazon MLA-C01덤프로 시험 준비하시면 편안하게 시험을 패스하실 수 있습니다.

시험대비 MLA-C01학습자료 덤프 최신 데모문제

우리Itexamdump 사이트에서Amazon MLA-C01관련자료의 일부분 문제와 답 등 샘플을 제공함으로 여러분은 무료로 다운받아 체험해보실 수 있습니다, Itexamdump의 부지런한 IT전문가들이 자기만의 지식과 끊임없는 노력과 경험으로 최고의Amazon MLA-C01합습자료로Amazon MLA-C01인증시험을 응시하실 수 있습니다.Amazon MLA-C01인증시험은 IT업계에서의 비중은 아주 큽니다.

Amazon MLA-C01인증시험은 전업적지식이 강한 인증입니다, 우리 Itexamdump선택함으로 여러분은 성공을 선택한 것입니다.

Mark Reed Mark Reed

Biography

MLA-C01학습자료100%유효한덤프자료

MLA-C01시험패스 가능 덤프문제 & MLA-C01시험대비 덤프 최신자료

최신 AWS Certified Associate MLA-C01 무료샘플문제 (Q68-Q73):

시험대비 MLA-C01학습자료 덤프 최신 데모문제

About DATPROF

Contact