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AWS-Certified-Machine-Learning-Specialtyテストサンプル問題、AWS-Certified-Machine-Learning-Specialty独学書籍 - Amazon AWS-Certified-Machine-Learning-Specialtyサンプル問題集 - Royalholidayclubbed
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そうでない場合、今回使用してからあなたがRoyalholidayclubbedを必要な選択肢として使用できるようになります。私たちが提供するAmazonのAWS-Certified-Machine-Learning-Specialtyテストサンプル問題試験のソフトウェアはITエリートによって数年以来AmazonのAWS-Certified-Machine-Learning-Specialtyテストサンプル問題試験の内容から分析して開発されます、オンライン、PDF、およびソフトウェアが3つのバージョンあります。あなたの気に入る版を選ぶことができます。 ほんとんどお客様は我々RoyalholidayclubbedのAmazon AWS-Certified-Machine-Learning-Specialtyテストサンプル問題問題集を使用してから試験にうまく合格しましたのは弊社の試験資料の有効性と信頼性を説明できます。競争力が激しい社会に当たり、我々Royalholidayclubbedは多くの受験生の中で大人気があるのは受験生の立場からAmazon AWS-Certified-Machine-Learning-Specialtyテストサンプル問題試験資料をリリースすることです。 Royalholidayclubbedは多くの受験生を助けて彼らにAmazonのAWS-Certified-Machine-Learning-Specialtyテストサンプル問題試験に合格させることができるのは我々専門的なチームがAmazonのAWS-Certified-Machine-Learning-Specialtyテストサンプル問題試験を研究して解答を詳しく分析しますから。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 弊社の量豊かの備考資料はあなたを驚かさせます。
ITの専門者はAmazonのAWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialtyテストサンプル問題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。 我々社のAmazon AWS-Certified-Machine-Learning-Specialty 試験対応問題集とサーブすが多くの人々に認められます。最近、Amazon AWS-Certified-Machine-Learning-Specialty 試験対応問題集は通過率が高いなので大人気になります。
Royalholidayclubbedの専門家チームが君の需要を満たすために自分の経験と知識を利用してAmazonのAWS-Certified-Machine-Learning-Specialtyテストサンプル問題認定試験対策模擬テスト問題集が研究しました。模擬テスト問題集と真実の試験問題がよく似ています。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。
それはRoyalholidayclubbedのAmazon AWS-Certified-Machine-Learning-Specialtyテストサンプル問題問題集です。
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Royalholidayclubbedは君にとってベストな選択になります。ここには、私たちは君の需要に応じます。
AWS-Certified-Machine-Learning-Specialty 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
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