我々はあなたに提供するのは最新で一番全面的なAmazonのAWS-Certified-Machine-Learning-Specialty模擬解説集問題集で、最も安全な購入保障で、最もタイムリーなAmazonのAWS-Certified-Machine-Learning-Specialty模擬解説集試験のソフトウェアの更新です。無料デモはあなたに安心で購入して、購入した後1年間の無料AmazonのAWS-Certified-Machine-Learning-Specialty模擬解説集試験の更新はあなたに安心で試験を準備することができます、あなたは確実に購入を休ませることができます私たちのソフトウェアを試してみてください。もちろん、我々はあなたに一番安心させるのは我々の開発する多くの受験生に合格させるAmazonのAWS-Certified-Machine-Learning-Specialty模擬解説集試験のソフトウェアです。 その権威性が高いと言えます。Omgzlookを選ぶなら、絶対に後悔させません。 AmazonのAWS-Certified-Machine-Learning-Specialty模擬解説集試験に失敗しても、我々はあなたの経済損失を減少するために全額で返金します。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty もし合格しないと、われは全額で返金いたします。
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一年間で更新するなる、第一時間であなたのメールボックスに送ります。かねてIT認定試験資料を開発する会社として、高品質のAmazon AWS-Certified-Machine-Learning-Specialty模擬解説集試験資料を提供したり、ビフォワ.アフタサービスに関心を寄せたりしています。我々社の職員は全日であなたのお問い合わせを待っております。
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OmgzlookのAmazonのAWS-Certified-Machine-Learning-Specialty模擬解説集試験問題資料は質が良くて値段が安い製品です。我々は低い価格と高品質の模擬問題で受験生の皆様に捧げています。我々は心からあなたが首尾よく試験に合格することを願っています。あなたに便利なオンラインサービスを提供して、Amazon AWS-Certified-Machine-Learning-Specialty模擬解説集試験問題についての全ての質問を解決して差し上げます。
AWS-Certified-Machine-Learning-Specialty模擬解説集はAmazonのひとつの認証で、AWS-Certified-Machine-Learning-Specialty模擬解説集がAmazonに入るの第一歩として、AWS-Certified-Machine-Learning-Specialty模擬解説集「AWS Certified Machine Learning - Specialty」試験がますます人気があがって、AWS-Certified-Machine-Learning-Specialty模擬解説集に参加するかたもだんだん多くなって、しかしAWS-Certified-Machine-Learning-Specialty模擬解説集認証試験に合格することが非常に難しいで、君はAWS-Certified-Machine-Learning-Specialty模擬解説集に関する試験科目の問題集を購入したいですか？
AWS-Certified-Machine-Learning-Specialty 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