我々Goldmile-Infobizのあなたに開発するAmazonのMLS-C01専門知識ソフトはあなたの問題を解決することができます。最初の保障はあなたに安心させる高い通過率で、第二の保護手段は、あなたは弊社のソフトを利用してAmazonのMLS-C01専門知識試験に合格しないなら、我々はあなたのすべての支払を払い戻します。あなたが安心で試験のために準備すればいいです。 弊社が提供した問題集がほかのインターネットに比べて問題のカーバ範囲がもっと広くて対応性が強い長所があります。Goldmile-Infobizが持つべきなIT問題集を提供するサイトでございます。 あなたに安心させるために、我々のソフトを利用してあなたが試験に失敗したら、我々は全額で返金するのを承諾してよりよいAmazonのMLS-C01専門知識ソフトを開発し続けます。
AWS Certified Specialty MLS-C01 Goldmile-Infobizは全面的に受験生の利益を保証します。
AWS Certified Specialty MLS-C01専門知識 - AWS Certified Machine Learning - Specialty Goldmile-Infobizはもっぱら認定試験に参加するIT業界の専門の人士になりたい方のために模擬試験の練習問題と解答を提供した評判の高いサイトでございます。 うちのAmazonのMLS-C01 更新版問題集を購入したら、私たちは一年間で無料更新サービスを提供することができます。もし学習教材は問題があれば、或いは試験に不合格になる場合は、全額返金することを保証いたします。
今の競争の激しいIT業界ではAmazonのMLS-C01専門知識試験にパスした方はメリットがおおくなります。給料もほかの人と比べて高くて仕事の内容も豊富です。でも、この試験はそれほど簡単ではありません。
Amazon MLS-C01専門知識 - しかも値段が手頃です。
Goldmile-Infobizは認定で優秀なIT資料のウエブサイトで、ここでAmazon MLS-C01専門知識認定試験の先輩の経験と暦年の試験の材料を見つけることができるとともに部分の最新の試験の題目と詳しい回答を無料にダウンロードこともできますよ。弊社のIT技術専門家たち は質が高い問題集と答えを提供し、お客様が合格できるように努めています。
私の夢はAmazonのMLS-C01専門知識認定試験に受かることです。この認証の証明書を持っていたら、全ての難問は解決できるようになりました。
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 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 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: 4
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: 5
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
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Updated: May 28, 2022