MLS-C01 入門知識、 Amazon MLS-C01 試験解答 - AWS Certified Machine Learning Specialty - Royalholidayclubbed

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RoyalholidayclubbedはIT試験問題集を提供するウエブダイトで、ここによく分かります。最もよくて最新で資料を提供いたします。こうして、君は安心で試験の準備を行ってください。激しく変化する世界に対応し、私たちのMLS-C01入門知識試験資料のガイドで、あなたの長所を発揮することができます。また、あなたも私たちのMLS-C01入門知識試験資料を使って、個人的に重要な知識を集約し、自分の需要によって、MLS-C01入門知識試験のために様々な勉強方法を選ぶことができます。 Amazon MLS-C01入門知識「AWS Certified Machine Learning - Specialty」認証試験に合格することが簡単ではなくて、Amazon MLS-C01入門知識証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。

AWS Certified Specialty MLS-C01 私の夢は最高のIT専門家になることです。

AWS Certified Specialty MLS-C01入門知識 - AWS Certified Machine Learning - Specialty いまの市場にとてもよい問題集が探すことは難しいです。きっと望んでいるでしょう。では、常に自分自身をアップグレードする必要があります。

その資料は練習問題と解答に含まれています。弊社の資料があなたに練習を実践に移すチャンスを差し上げ、あなたはぜひAmazonのMLS-C01入門知識試験に合格して自分の目標を達成できます。Royalholidayclubbed のAmazonのMLS-C01入門知識問題集は１００パーセント検証とテストを通過したもので、認定試験に合格する専門的な指導者です。

では、はやくAmazonのAmazon MLS-C01入門知識認定試験を受験しましょう。

暇な時間だけでAmazonのMLS-C01入門知識試験に合格したいのですか。我々の提供するPDF版のAmazonのMLS-C01入門知識試験の資料はあなたにいつでもどこでも読めさせます。我々もオンライン版とソフト版を提供します。すべては豊富な内容があって各自のメリットを持っています。あなたは各バーションのAmazonのMLS-C01入門知識試験の資料をダウンロードしてみることができ、あなたに一番ふさわしいバーションを見つけることができます。

RoyalholidayclubbedのMLS-C01入門知識問題集が最高のツールです。この問題集には試験の優秀な過去問が集められ、しかも最新のシラバスに従って出題される可能性がある新しい問題も追加しました。

MLS-C01 PDF DEMO:

QUESTION NO: 1
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: 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 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: 4
A Machine Learning Specialist is using Amazon SageMaker to host a model for a highly available customer-facing application .
The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?
A. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.
B. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.
C. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.
D. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.
Answer: D

QUESTION NO: 5
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

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Updated: May 28, 2022

by RHC.

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