周りの多くの人は全部Amazon MLS-C01復習時間資格認定試験にパースしまして、彼らはどのようにできましたか。今には、あなたにOmgzlookを教えさせていただけませんか。我々社サイトのAmazon MLS-C01復習時間問題庫は最新かつ最完備な勉強資料を有して、あなたに高品質のサービスを提供するのはMLS-C01復習時間資格認定試験の成功にとって唯一の選択です。 Omgzlookの専門家チームがAmazonのMLS-C01復習時間認証試験に対して最新の短期有効なトレーニングプログラムを研究しました。AmazonのMLS-C01復習時間「AWS Certified Machine Learning - Specialty」認証試験に参加者に対して30時間ぐらいの短期の育成訓練でらくらくに勉強しているうちに多くの知識を身につけられます。 そうすれば、あなたは簡単にMLS-C01復習時間復習教材のデモを無料で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。
Omgzlookのガイダンスとヘルプを通して、初めにAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty復習時間「AWS Certified Machine Learning - Specialty」の認証を受けるあなたは、気楽に試験に合格すことができます。 Amazon MLS-C01 復習テキスト「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 復習テキスト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。
その中で、Omgzlookが他のサイトをずっと先んじてとても人気があるのは、OmgzlookのAmazonのMLS-C01復習時間試験トレーニング資料が本当に人々に恩恵をもたらすことができて、速く自分の夢を実現することにヘルプを差し上げられますから。MLS-C01復習時間認定試験はIT業界の新たなターニングポイントの一つです。試験に受かったら、あなたはIT業界のエリートになることができます。
Amazon MLS-C01復習時間 - あなたに絶対向いていると信じていますよ。
Omgzlookは実際の環境で本格的なAmazonのMLS-C01復習時間「AWS Certified Machine Learning - Specialty」の試験の準備過程を提供しています。もしあなたは初心者若しくは専門的な技能を高めたかったら、OmgzlookのAmazonのMLS-C01復習時間「AWS Certified Machine Learning - Specialty」の試験問題があなたが一歩一歩自分の念願に近くために助けを差し上げます。試験問題と解答に関する質問があるなら、当社は直後に解決方法を差し上げます。しかも、一年間の無料更新サービスを提供します。
IT認証はIT業種での競争な手段の一つです。認証に受かったらあなたは各方面でよく向上させます。
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