Goldmile-Infobizは異なるトレーニングツールと資源を提供してあなたのAmazonのMLS-C01模試エンジンの認証試験の準備にヘルプを差し上げます。編成チュートリアルは授業コース、実践検定、試験エンジンと一部の無料なPDFダウンロードを含めています。 このインターネット時代において、社会の発展とともに、コストがより低くて内容が完全な情報が不可欠です。弊社のMLS-C01模試エンジン問題集は他のサイトに比べて、試験の範囲をカバーすることはより広くて、合理的な価格があります。 Goldmile-Infobizの AmazonのMLS-C01模試エンジン試験資料を利用したら、時間を節約することができるようになります。
AmazonのMLS-C01模試エンジン試験は挑戦がある認定試験です。
我々社のMLS-C01 - AWS Certified Machine Learning - Specialty模試エンジン練習問題は試験に参加する圧力を減らすだけでなく、お金を無駄にする煩悩を解消できます。 あなたは勇敢な人ですか。もしIT認証の準備をしなかったら、あなたはのんびりできますか。
あなたのAmazon MLS-C01模試エンジン試験に合格するのは我々が与えるサプライズです。Amazon MLS-C01模試エンジン試験の困難度なので、試験の準備をやめます。実には、正確の方法と資料を探すなら、すべては問題ではりません。
彼らにAmazonのAmazon MLS-C01模試エンジン試験に合格させました。
Goldmile-Infobizはたくさんの方がIT者になる夢を実現させるサイトでございます。Goldmile-InfobizはAmazonのMLS-C01模試エンジン認証試験について最新の対応性教育テストツールを研究し続けて、AmazonのMLS-C01模試エンジン認定試験の問題集を開発いたしました。Goldmile-Infobizが提供したAmazonのMLS-C01模試エンジン試験問題と解答が真実の試験の練習問題と解答は最高の相似性があり、一年の無料オンラインの更新のサービスがあり、100%のパス率を保証して、もし試験に合格しないと、弊社は全額で返金いたします。
Goldmile-Infobizの提供するAmazonのMLS-C01模試エンジン試験の資料とソフトは経験が豊富なITエリートに開発されて、何回も更新されています。何十ユーロだけでこのような頼もしいAmazonのMLS-C01模試エンジン試験の資料を得ることができます。
MLS-C01 PDF DEMO:
QUESTION NO: 1
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: 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 Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?
A. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
B. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
C. AWS Glue with a custom ETL script to transform the data.
D. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
Answer: B
QUESTION NO: 4
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: 5
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
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Updated: May 28, 2022