MLS-C01 教育資料 - MLS-C01 最新関連参考書 & AWS Certified Machine Learning Specialty - Royalholidayclubbed

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Royalholidayclubbedは多くのIT職員の夢を達成することであるウェブサイトです。IT夢を持っていたら、速くRoyalholidayclubbedに来ましょう。Royalholidayclubbedにはすごいトレーニング即ち AmazonのMLS-C01教育資料試験トレーニング資料があります。もちろん、我々はあなたに一番安心させるのは我々の開発する多くの受験生に合格させるAmazonのMLS-C01教育資料試験のソフトウェアです。我々はあなたに提供するのは最新で一番全面的なAmazonのMLS-C01教育資料問題集で、最も安全な購入保障で、最もタイムリーなAmazonのMLS-C01教育資料試験のソフトウェアの更新です。そうしたら、この資料があなたに適用するかどうかを確かめてから購入することができます。

安心にMLS-C01教育資料試験を申し込みましょう。

自分の能力を証明するために、MLS-C01 - AWS Certified Machine Learning - Specialty教育資料試験に合格するのは不可欠なことです。 PDF版のMLS-C01 参考書問題集は読みやすくて、忠実に試験の問題を再現することができます。テストエンジンとして、ソフトウェア版のMLS-C01 参考書問題集はあなたの試験の準備についての進捗状況をテストするために利用することができます。

我々Royalholidayclubbedは一番行き届いたアフタサービスを提供します。Amazon　MLS-C01教育資料試験問題集を購買してから、一年間の無料更新を楽しみにしています。あなたにAmazon　MLS-C01教育資料試験に関する最新かつ最完備の資料を勉強させ、試験に合格させることだと信じます。

Amazon MLS-C01教育資料 - あなたに一年間の無料更新サービスを提供します。

Amazon MLS-C01教育資料「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教育資料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。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。

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MLS-C01 PDF DEMO:

QUESTION NO: 1
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: 2
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: 3
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: 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 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

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Updated: May 28, 2022

by RHC.

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