Amazon AWS-Certified-Machine-Learning-Specialty시험내용시험은 Goldmile-Infobiz 에서 출시한Amazon AWS-Certified-Machine-Learning-Specialty시험내용덤프로 도전하시면 됩니다. Amazon AWS-Certified-Machine-Learning-Specialty시험내용 덤프를 페펙트하게 공부하시면 시험을 한번에 패스할수 있습니다. 구매후 일년무료 업데이트 서비스를 제공해드리기에Amazon AWS-Certified-Machine-Learning-Specialty시험내용시험문제가 변경되어도 업데이트된 덤프를 받으면 가장 최신시험에 대비할수 있습니다. Goldmile-Infobiz는 또 여러분이 원하도 필요로 하는 최신 최고버전의AWS-Certified-Machine-Learning-Specialty시험내용문제와 답을 제공합니다. Goldmile-Infobiz는 전문적인 IT인증시험덤프를 제공하는 사이트입니다.AWS-Certified-Machine-Learning-Specialty시험내용인증시험을 패스하려면 아주 현병한 선택입니다. Amazon AWS-Certified-Machine-Learning-Specialty시험내용 덤프를 구매하여 1년무료 업데이트서비스를 제공해드립니다.
시중에서 가장 최신버전인Amazon AWS-Certified-Machine-Learning-Specialty시험내용덤프로 시험패스 예약하세요.
Goldmile-Infobiz는 여러분이 빠른 시일 내에Amazon AWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty시험내용인증시험을 효과적으로 터득할 수 있는 사이트입니다.Amazon AWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty시험내용덤프는 보장하는 덤프입니다. 여러분은 아직도Amazon AWS-Certified-Machine-Learning-Specialty 응시자료인증시험의 난이도에 대하여 고민 중입니까? 아직도Amazon AWS-Certified-Machine-Learning-Specialty 응시자료시험 때문에 밤잠도 제대로 이루지 못하면서 시험공부를 하고 있습니까? 빨리빨리Goldmile-Infobiz를 선택하여 주세요. 그럼 빠른 시일내에 많은 공을 들이지 않고 여러분으 꿈을 이룰수 있습니다.
IT인증시험이 다가오는데 어느 부분부터 공부해야 할지 망설이고 있다구요? 가장 간편하고 시간을 절약하며 한방에 자격증을 취득할수 있는 최고의 방법을 추천해드립니다. 바로 우리Goldmile-Infobiz IT인증덤프제공사이트입니다. Goldmile-Infobiz는 고품질 고적중율을 취지로 하여 여러분들인 한방에 시험에서 패스하도록 최선을 다하고 있습니다.
Amazon AWS-Certified-Machine-Learning-Specialty시험내용 - Goldmile-Infobiz의 자료만의 제일 전면적이고 또 최신 업데이트일 것입니다.
Goldmile-Infobiz의 Amazon인증 AWS-Certified-Machine-Learning-Specialty시험내용덤프는 최근 유행인 PDF버전과 소프트웨어버전 두가지 버전으로 제공됩니다.PDF버전을 먼저 공부하고 소프트웨어번으로 PDF버전의 내용을 얼마나 기억하였는지 테스트할수 있습니다. 두 버전을 모두 구입하시면 시험에서 고득점으로 패스가능합니다.
IT업계에서 일자리를 찾고 계시다면 많은 회사에서는Amazon AWS-Certified-Machine-Learning-Specialty시험내용있는지 없는지에 알고 싶어합니다. 만약Amazon AWS-Certified-Machine-Learning-Specialty시험내용자격증이 있으시다면 여러분은 당연히 경쟁력향상입니다.
AWS-Certified-Machine-Learning-Specialty 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