他の人に先立ってAmazon　MLS-C01テスト対策書認定資格を得るために、今から勉強しましょう。明日ではなく、今日が大事と良く知られるから、そんなにぐずぐずしないで早く我々社のAmazon　MLS-C01テスト対策書日本語対策問題集を勉強し、自身を充実させます。我々社の練習問題は長年でMLS-C01テスト対策書全真模擬試験トレーニング資料に研究している専業化チームによって編集されます。 ここには、私たちは君の需要に応じます。ShobhadoshiのAmazonのMLS-C01テスト対策書問題集を購入したら、私たちは君のために、一年間無料で更新サービスを提供することができます。 Amazon　MLS-C01テスト対策書試験参考書に疑問を持たれば、Amazon会社のウエブサイトから無料でMLS-C01テスト対策書試験のためのデモをダウンロードできます。

AWS Certified Specialty MLS-C01 最もよくて最新で資料を提供いたします。

AmazonのMLS-C01 - AWS Certified Machine Learning - Specialtyテスト対策書試験を準備しているあなたに試験に合格させるために、我々Shobhadoshiは模擬試験ソフトを更新し続けています。 多くのAmazonのMLS-C01 試験勉強攻略認定試験を準備している受験生がいろいろなMLS-C01 試験勉強攻略「AWS Certified Machine Learning - Specialty」認証試験についてサービスを提供するサイトオンラインがみつけたがShobhadoshiはIT業界トップの専門家が研究した参考材料で権威性が高く、品質の高い教育資料で、一回に参加する受験者も合格するのを確保いたします。

世の中に去年の自分より今年の自分が優れていないのは立派な恥です。それで、IT人材として毎日自分を充実して、MLS-C01テスト対策書問題集を学ぶ必要があります。弊社のMLS-C01テスト対策書問題集はあなたにこのチャンスを全面的に与えられます。

Amazon MLS-C01テスト対策書 - しかも、昇進と高給も実現できます。

Shobhadoshi のAmazonのMLS-C01テスト対策書問題集はシラバスに従って、それにMLS-C01テスト対策書認定試験の実際に従って、あなたがもっとも短い時間で最高かつ最新の情報をもらえるように、弊社はトレーニング資料を常にアップグレードしています。弊社のMLS-C01テスト対策書のトレーニング資料を買ったら、一年間の無料更新サービスを差し上げます。もっと長い時間をもらって試験を準備したいのなら、あなたがいつでもサブスクリプションの期間を伸びることができます。

それはあなたを試験に準備するときにより多くの時間を節約させます。しかも、ShobhadoshiのMLS-C01テスト対策書問題集はあなたが一回で試験に合格することを保証します。

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 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: 4
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: 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