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AWS-Certified-Machine-Learning-Specialtyオンライン試験 - Amazon AWS-Certified-Machine-Learning-Specialty資格勉強 - Royalholidayclubbed
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弊社のソフトを利用して、あなたはAmazonのAWS-Certified-Machine-Learning-Specialtyオンライン試験試験に合格するのが難しくないことを見つけられます。Royalholidayclubbedの提供する資料と解答を通して、あなたはAmazonのAWS-Certified-Machine-Learning-Specialtyオンライン試験試験に合格するコツを勉強することができます。あなたに安心でソフトを買わせるために、あなたは無料でAmazonのAWS-Certified-Machine-Learning-Specialtyオンライン試験ソフトのデモをダウンロードすることができます。 Royalholidayclubbedは実際の環境で本格的なAmazonのAWS-Certified-Machine-Learning-Specialtyオンライン試験「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、RoyalholidayclubbedのAmazonのAWS-Certified-Machine-Learning-Specialtyオンライン試験「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。 どの版でもAmazonのAWS-Certified-Machine-Learning-Specialtyオンライン試験試験の復習資料は効果的なのを保証します。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty その夢は私にとってはるか遠いです。
弊社のAWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialtyオンライン試験試験問題集によって、あなたの心と精神の満足度を向上させながら、勉強した後AWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialtyオンライン試験試験資格認定書を受け取って努力する人生はすばらしいことであると認識られます。 きっと望んでいるでしょう。では、常に自分自身をアップグレードする必要があります。
Amazon AWS-Certified-Machine-Learning-Specialtyオンライン試験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 AWS-Certified-Machine-Learning-Specialtyオンライン試験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。もしあなたはAWS-Certified-Machine-Learning-Specialtyオンライン試験試験に合格しなかったら、全額返金のことを承諾します。
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あなたは各バーションのAmazonのAWS-Certified-Machine-Learning-Specialtyオンライン試験試験の資料をダウンロードしてみることができ、あなたに一番ふさわしいバーションを見つけることができます。暇な時間だけでAmazonのAWS-Certified-Machine-Learning-Specialtyオンライン試験試験に合格したいのですか。
AWS-Certified-Machine-Learning-Specialty 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
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