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AWS-Certified-Machine-Learning-Specialty合格率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。 Goldmile-Infobizは現在の実績を持っているのは受験生の皆さんによって実践を通して得られた結果です。真実かつ信頼性の高いものだからこそ、Goldmile-Infobizの試験参考書は長い時間にわたってますます人気があるようになっています。 Goldmile-Infobizはあなたにとって最も正確な選択ですから。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty でも、成功へのショートカットがを見つけました。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty合格率 - AWS Certified Machine Learning - Specialty あなたはどのような方式で試験を準備するのが好きですか。 Goldmile-InfobizはあなたがAmazonのAWS-Certified-Machine-Learning-Specialty 学習範囲認定試験に合格する確保です。Goldmile-Infobiz のトレーニング試験は問題と解答に含まれています。
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AmazonのAWS-Certified-Machine-Learning-Specialty合格率試験の認定はIT業種で欠くことができない認証です。では、どうやって、最も早い時間でAmazonのAWS-Certified-Machine-Learning-Specialty合格率認定試験に合格するのですか。Goldmile-Infobizは君にとって最高な選択になっています。Goldmile-InfobizのAmazonのAWS-Certified-Machine-Learning-Specialty合格率試験トレーニング資料はGoldmile-InfobizのIT専門家たちが研究して、実践して開発されたものです。その高い正確性は言うまでもありません。もし君はいささかな心配することがあるなら、あなたはうちの商品を購入する前に、Goldmile-Infobizは無料でサンプルを提供することができます。
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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