품질은 정확도 모두 보장되는 문제집입니다.Amazon인증MLS-C01최신기출자료시험은 여러분이 it지식을 한층 업할수 잇는 시험이며 우리 또한 일년무료 업데이트서비스를 제공합니다. Goldmile-Infobiz는Amazon인증MLS-C01최신기출자료시험에 대하여 가이드를 해줄 수 있는 사이트입니다. Goldmile-Infobiz는 여러분의 전업지식을 업그레이드시켜줄 수 잇고 또한 한번에Amazon인증MLS-C01최신기출자료시험을 패스하도록 도와주는 사이트입니다. Amazon MLS-C01최신기출자료 덤프를 다운받아 열공하세요. Amazon MLS-C01최신기출자료 덤프는 고객님의Amazon MLS-C01최신기출자료시험패스요망에 제일 가까운 시험대비자료입니다. 우리 Goldmile-Infobiz사이트에서 제공되는Amazon인증MLS-C01최신기출자료시험덤프의 일부분인 데모 즉 문제와 답을 다운받으셔서 체험해보면 우리Goldmile-Infobiz에 믿음이 갈 것입니다.
우리Amazon MLS-C01최신기출자료인증시험자료는 100%보장을 드립니다.
여러분이Amazon MLS-C01 - AWS Certified Machine Learning - Specialty최신기출자료인증시험으로 나 자신과 자기만의 뛰어난 지식 면을 증명하고 싶으시다면 우리 Goldmile-Infobiz의Amazon MLS-C01 - AWS Certified Machine Learning - Specialty최신기출자료덤프자료가 많은 도움이 될 것입니다. 여러분은 우선 우리 Goldmile-Infobiz사이트에서 제공하는Amazon인증MLS-C01 최신시험후기시험덤프의 일부 문제와 답을 체험해보세요. 우리 Goldmile-Infobiz를 선택해주신다면 우리는 최선을 다하여 여러분이 꼭 한번에 시험을 패스할 수 있도록 도와드리겠습니다.만약 여러분이 우리의 인증시험덤프를 보시고 시험이랑 틀려서 패스를 하지 못하였다면 우리는 무조건 덤프비용전부를 환불해드립니다.
바로 우리Goldmile-Infobiz IT인증덤프제공사이트입니다. Goldmile-Infobiz는 고품질 고적중율을 취지로 하여 여러분들인 한방에 시험에서 패스하도록 최선을 다하고 있습니다. Amazon인증MLS-C01최신기출자료시험준비중이신 분들은Goldmile-Infobiz 에서 출시한Amazon인증MLS-C01최신기출자료 덤프를 선택하세요.
Amazon인증 Amazon MLS-C01최신기출자료덤프는 기출문제와 예상문제로 되어있어 시험패스는 시간문제뿐입니다.
Amazon 인증MLS-C01최신기출자료인증시험공부자료는Goldmile-Infobiz에서 제공해드리는Amazon 인증MLS-C01최신기출자료덤프가 가장 좋은 선택입니다. Goldmile-Infobiz에서는 시험문제가 업데이트되면 덤프도 업데이트 진행하도록 최선을 다하여 업데이트서비스를 제공해드려 고객님께서소유하신 덤프가 시장에서 가장 최신버전덤프로 되도록 보장하여 시험을 맞이할수 있게 도와드립니다.
Goldmile-Infobiz의 Amazon인증 MLS-C01최신기출자료덤프의 무료샘플을 이미 체험해보셨죠? Goldmile-Infobiz의 Amazon인증 MLS-C01최신기출자료덤프에 단번에 신뢰가 생겨 남은 문제도 공부해보고 싶지 않나요? Goldmile-Infobiz는 고객님들의 시험부담을 덜어드리기 위해 가벼운 가격으로 덤프를 제공해드립니다. Goldmile-Infobiz의 Amazon인증 MLS-C01최신기출자료로 시험패스하다 더욱 넓고 좋은곳으로 고고싱 하세요.
MLS-C01 PDF DEMO:
QUESTION NO: 1
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)
A. Decrease dropout.
B. Increase regularization.
C. Increase feature combinations.
D. Decrease feature combinations.
E. Decrease regularization.
F. Increase dropout.
Answer: A,B,C
QUESTION NO: 2
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s) Which visualization will accomplish this?
A. A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor
Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.
B. A scatter plot showing (he performance of the objective metric over each training iteration
C. A histogram showing whether the most important input feature is Gaussian.
D. A scatter plot showing the correlation between maximum tree depth and the objective metric.
Answer: A
QUESTION NO: 3
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?
A. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data
Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations
B. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
C. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data
Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS
Glue to generate personalized product recommendations
Answer: C
QUESTION NO: 4
A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?
A. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database
B. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database
C. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
D. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database
Answer: D
QUESTION NO: 5
A Machine Learning Specialist built an image classification deep learning model. However the
Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and
75%r respectively.
How should the Specialist address this issue and what is the reason behind it?
A. The learning rate should be increased because the optimization process was trapped at a local minimum.
B. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
C. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
D. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
Answer: C
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Updated: May 28, 2022