MLS-C01 練習問題 & Amazon AWS Certified Machine Learning Specialty 試験攻略 - Royalholidayclubbed

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MLS-C01練習問題問題集のカーバー率が高いので、勉強した問題は試験に出ることが多いです。だから、弊社の提供するMLS-C01練習問題問題集を暗記すれば、きっと試験に合格できます。数年以来の整理と分析によって開発されたMLS-C01練習問題問題集は権威的で全面的です。我々社のMLS-C01練習問題問題集を参考した後、ほっとしました。弊社のMLS-C01練習問題ソフト版問題集はかねてより多くのIT事業をしている人々は順調にAmazon　MLS-C01練習問題資格認定を取得させます。 Royalholidayclubbedは同業の中でそんなに良い地位を取るの原因は弊社のかなり正確な試験の練習問題と解答そえに迅速の更新で、このようにとても良い成績がとられています。

MLS-C01練習問題認定試験に合格することは難しいようですね。

このような受験生はMLS-C01 - AWS Certified Machine Learning - Specialty練習問題認定試験で高い点数を取得して、自分の構成ファイルは市場の需要と互換性があるように充分な準備をするのは必要です。一回だけでAmazonのMLS-C01 復習対策試験に合格したい？Royalholidayclubbedは君の欲求を満たすために存在するのです。Royalholidayclubbedは君にとってベストな選択になります。

AmazonのMLS-C01練習問題試験は挑戦がある認定試験です。現在、書籍の以外にインターネットは知識の宝庫として見られています。Royalholidayclubbed で、あなたにあなたの宝庫を見つけられます。

Amazon MLS-C01練習問題 - あなたもIT認証資格を取りたいですか。

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練習問題問題集を学ぶ必要があります。弊社のMLS-C01練習問題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。あなたは自分の望ましいAmazon MLS-C01練習問題問題集を選らんで、学びから更なる成長を求められます。心はもはや空しくなく、生活を美しくなります。

最新のMLS-C01練習問題試験問題を知りたい場合、試験に合格したとしてもRoyalholidayclubbedは無料で問題集を更新してあげます。RoyalholidayclubbedのMLS-C01練習問題教材を購入したら、あなたは一年間の無料アップデートサービスを取得しました。

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 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: 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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