本当に助かりました。先月、AWS-Certified-Machine-Learning-Specialty模擬対策問題試験に参加しました。今日は、試験の結果をチエックし、嬉しいことに、AWS-Certified-Machine-Learning-Specialty模擬対策問題試験に合格しました。 AWS-Certified-Machine-Learning-Specialty模擬対策問題試験参考書は定期的に更新されますからです。そして、弊社は定期的にAWS-Certified-Machine-Learning-Specialty模擬対策問題試験参考書を検査し、問題の答えの正確率を確保しています。 Goldmile-Infobizが提供した問題集を利用してAmazonのAWS-Certified-Machine-Learning-Specialty模擬対策問題試験は全然問題にならなくて、高い点数で合格できます。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 明るい未来を準備してあげます。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty模擬対策問題 - AWS Certified Machine Learning - Specialty 優秀な試験参考書は話すことに依頼することでなく、受験生の皆さんに検証されることに依頼するのです。 Goldmile-Infobizというサイトは素晴らしいソースサイトで、AmazonのAWS-Certified-Machine-Learning-Specialty 難易度受験料の試験材料、研究材料、技術材料や詳しい解答に含まれています。問題集が提供したサイトは近年で急速に増加しています。
Goldmile-Infobizは全面的に受験生の利益を保証します。皆さんからいろいろな好評をもらいました。しかも、Goldmile-Infobizは当面の市場で皆さんが一番信頼できるサイトです。
Amazon AWS-Certified-Machine-Learning-Specialty模擬対策問題 - そうすると、我々の信頼性をテストできます。
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Goldmile-InfobizのAmazonのAWS-Certified-Machine-Learning-Specialty模擬対策問題試験トレーニング資料はIT認証試験を受ける人々の必需品です。このトレーニング資料を持っていたら、試験のために充分の準備をすることができます。
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 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: 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 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