NewValidDumpsには専門的なエリート団体があります。認証専門家や技術者及び全面的な言語天才がずっと最新のAmazonのMLS-C01専門知識試験を研究していますから、AmazonのMLS-C01専門知識認定試験に受かりたかったら、NewValidDumpsのサイトをクッリクしてください。あなたに成功に近づいて、夢の楽園に一歩一歩進めさせられます。 もしあなたはまだAmazonのMLS-C01専門知識試験に合格するのために悩まればNewValidDumpsは今あなたを助けることができます。NewValidDumpsは高品質の学習資料をあなたを助けて優秀なAmazonのMLS-C01専門知識会員の認証を得て、もしあなたはAmazon MLS-C01専門知識の認証試験を通して自分を高めるの選択を下ろして、NewValidDumpsはとてもよい選択だと思います。 試験の目標が変わる限り、あるいは我々の勉強資料が変わる限り、すぐに更新して差し上げます。

AWS Certified Specialty MLS-C01 NewValidDumpsを選んだら、成功への扉を開きます。

MLS-C01 - AWS Certified Machine Learning - Specialty専門知識認定試験は専門知識と情報技術を検査する試験で、NewValidDumpsが一日早くAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty専門知識認定試験「AWS Certified Machine Learning - Specialty」に合格させるのサイトで試験の前に弊社が提供する訓練練習問題をテストして、短い時間であなたの収穫が大きいです。 IT技術の急速な発展につれて、IT認証試験の問題は常に変更されています。したがって、NewValidDumpsのMLS-C01 模擬問題問題集も絶えずに更新されています。

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Amazon MLS-C01専門知識 - 正しい方法は大切です。

NewValidDumpsのAmazonのMLS-C01専門知識試験トレーニング資料は受験生が模擬試験場で勉強させます。受験生は問題を選べ、テストの時間もコントロールできます。NewValidDumpsというサイトで、あなたはストレスと不安なく試験の準備をすることができますから、一般的な間違いを避けられます。そうしたら、あなたは自信を得ることができて、実際の試験で経験を活かして気楽に合格します。

試験が更新されているうちに、我々はAmazonのMLS-C01専門知識試験の資料を更新し続けています。できるだけ100％の通過率を保証使用にしています。

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