MLS-C01日本語版試験解答はAmazonのひとつの認証で、MLS-C01日本語版試験解答がAmazonに入るの第一歩として、MLS-C01日本語版試験解答「AWS Certified Machine Learning - Specialty」試験がますます人気があがって、MLS-C01日本語版試験解答に参加するかたもだんだん多くなって、しかしMLS-C01日本語版試験解答認証試験に合格することが非常に難しいで、君はMLS-C01日本語版試験解答に関する試験科目の問題集を購入したいですか? 優れたキャリアを持ったら、社会と国のために色々な利益を作ることができて、国の経済が継続的に発展していることを進められるようになります。全てのIT人員がそんなにられるとしたら、国はぜひ強くなります。 Royalholidayclubbedは提供した商品は君の成功を全力で助けさしたげます。
AWS Certified Specialty MLS-C01 「信仰は偉大な感情で、創造の力になれます。AWS Certified Specialty MLS-C01日本語版試験解答 - AWS Certified Machine Learning - Specialty しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。 最近、AmazonのMLS-C01 認証資格試験は非常に人気のある認定試験です。あなたもこの試験の認定資格を取得したいのですか。
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MLS-C01 PDF DEMO:QUESTION NO: 1 A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora * Profiles for all past and existing customers * Profiles for all past and existing insured pets * Policy-level information * Premiums received * Claims paid What steps should be taken to implement a machine learning model to identify potential new customers on social media? A. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media B. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media C. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media. D. Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media. Answer: B
QUESTION NO: 2 A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold. What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance? A. Receiver operating characteristic (ROC) curve B. Misclassification rate C. Root Mean Square Error (RM&) D. L1 norm Answer: A
QUESTION NO: 3 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
QUESTION NO: 4 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: 5 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
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Updated: May 28, 2022
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