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MLS-C01 出題範囲 - MLS-C01 最新関連参考書 & AWS Certified Machine Learning Specialty - Royalholidayclubbed
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どんな業界で自分に良い昇進機会があると希望する職人がとても多いと思って、IT業界にも例外ではありません。ITの専門者はAmazonのMLS-C01出題範囲認定試験があなたの願望を助けって実現できるのがよく分かります。Royalholidayclubbedはあなたの夢に実現させるサイトでございます。 MLS-C01出題範囲問題集を購入してから、また一年間の無料更新サービスを得ることもできます。一年以内に、あなたが持っている資料を更新したい限り、Royalholidayclubbedは最新バージョンのMLS-C01出題範囲問題集を捧げます。 一目でわかる最新の出題傾向でわかりやすい解説と充実の補充問題があります。
AWS Certified Specialty MLS-C01 近年、IT領域で競争がますます激しくなります。
あなたは高効率の復習とAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty出題範囲試験の成功を経験する必要があればいいです。 RoyalholidayclubbedのAmazonのMLS-C01 日本語版参考資料試験トレーニング資料は豊富な知識と経験を持っているIT専門家に研究された成果で、正確度がとても高いです。Royalholidayclubbedに会ったら、最高のトレーニング資料を見つけました。
数年間の発展で我々Royalholidayclubbedはもっと多くの資源と経験を得ています。改善されているソフトはあなたのAmazonのMLS-C01出題範囲試験の復習の効率を高めることができます。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。
Amazon MLS-C01出題範囲 - 弊社の商品が好きなのは弊社のたのしいです。
Royalholidayclubbedを選択したら、成功が遠くではありません。Royalholidayclubbedが提供するAmazonのMLS-C01出題範囲認証試験問題集が君の試験に合格させます。テストの時に有効なツルが必要でございます。
Royalholidayclubbed を選択して１００％の合格率を確保することができて、もし試験に失敗したら、Royalholidayclubbedが全額で返金いたします。
MLS-C01 PDF DEMO:
QUESTION NO: 1
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: 2
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: 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
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