AmazonのMLS-C01試験問題解説集ソフトを使用するすべての人を有効にするために最も快適なレビュープロセスを得ることができ、我々は、AmazonのMLS-C01試験問題解説集の資料を提供し、PDF、オンラインバージョン、およびソフトバージョンを含んでいます。あなたの愛用する版を利用して、あなたは簡単に最短時間を使用してAmazonのMLS-C01試験問題解説集試験に合格することができ、あなたのIT機能を最も権威の国際的な認識を得ます！ あなたは我々社の提供する質高いAmazon MLS-C01試験問題解説集問題集を使用して、試験に参加します。もし無事にMLS-C01試験問題解説集試験に合格したら、あなたはもっと自信になって、更なる勇気でやりたいことをしています。 IT業界での競争がますます激しくなるうちに、あなたの能力をどのように証明しますか。
AWS Certified Specialty MLS-C01 Goldmile-Infobizの問題集は最大のお得だね！
AWS Certified Specialty MLS-C01試験問題解説集 - AWS Certified Machine Learning - Specialty Goldmile-Infobizを選択したら、成功が遠くではありません。 機会が一回だけありますよ。いまAmazonのMLS-C01 関連受験参考書試験問題のフルバージョンを取ることができます。
AmazonのMLS-C01試験問題解説集認定試験を受けることを決めたら、Goldmile-Infobizがそばにいて差し上げますよ。Goldmile-Infobizはあなたが自分の目標を達成することにヘルプを差し上げられます。あなたがAmazonのMLS-C01試験問題解説集「AWS Certified Machine Learning - Specialty」認定試験に合格する需要を我々はよく知っていますから、あなたに高品質の問題集と科学的なテストを提供して、あなたが気楽に認定試験に受かることにヘルプを提供するのは我々の約束です。
Amazon MLS-C01試験問題解説集 - あなたを成功への道に引率します。
短い時間に最も小さな努力で一番効果的にAmazonのMLS-C01試験問題解説集試験の準備をしたいのなら、Goldmile-InfobizのAmazonのMLS-C01試験問題解説集試験トレーニング資料を利用することができます。Goldmile-Infobizのトレーニング資料は実践の検証に合格すたもので、多くの受験生に証明された１００パーセントの成功率を持っている資料です。Goldmile-Infobizを利用したら、あなたは自分の目標を達成することができ、最良の結果を得ます。
我々の知名度はとても高いです。これは受験生の皆さんが資料を利用した後の結果です。
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 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