Goldmile-Infobizが提供した問題集をショッピングカートに入れて100分の自信で試験に参加して、成功を楽しんで、一回だけAmazonのAWS-Certified-Machine-Learning-Specialty試験概要試験に合格するのが君は絶対後悔はしません。 私たちの会社は、コンテンツだけでなくディスプレイ上でも、AWS-Certified-Machine-Learning-Specialty試験概要試験材料の設計に最新の技術を採用しています。激しく変化する世界に対応し、私たちのAWS-Certified-Machine-Learning-Specialty試験概要試験資料のガイドで、あなたの長所を発揮することができます。 Goldmile-Infobizにその問題が心配でなく、わずか20時間と少ないお金をを使って楽に試験に合格することができます。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty ここにはあなたが最も欲しいものがありますから。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty試験概要 - AWS Certified Machine Learning - Specialty Goldmile-Infobizで、あなたは一番良い準備資料を見つけられます。 Goldmile-InfobizのAWS-Certified-Machine-Learning-Specialty 資格取得問題集はあなたを楽に試験の準備をやらせます。それに、もし最初で試験を受ける場合、試験のソフトウェアのバージョンを使用することができます。
もしあなたが初心者だったら、または自分の知識や専門的なスキルを高めたいのなら、Goldmile-InfobizのAmazonのAWS-Certified-Machine-Learning-Specialty試験概要問題集があなたを助けることができ、一歩一歩でその念願を実現することにヘルプを差し上げます。Goldmile-InfobizのAmazonのAWS-Certified-Machine-Learning-Specialty試験概要は試験に関する全ての質問が解決して差し上げられます。それに一年間の無料更新サービスを提供しますから、Goldmile-Infobizのウェブサイトをご覧ください。
Amazon AWS-Certified-Machine-Learning-Specialty試験概要 - 試験に合格する自信を持たなくても大丈夫です。
ご客様は弊社のAWS-Certified-Machine-Learning-Specialty試験概要問題集を購入するかどうかと判断する前に、我が社は無料に提供するサンプルをダウンロードして試すことができます。それで、不必要な損失を避けできます。ご客様はAWS-Certified-Machine-Learning-Specialty試験概要問題集を購入してから、勉強中で何の質問があると、行き届いたサービスを得られています。ご客様はAWS-Certified-Machine-Learning-Specialty試験概要資格認証試験に失敗したら、弊社は全額返金できます。その他、AWS-Certified-Machine-Learning-Specialty試験概要問題集の更新版を無料に提供します。
これはあなたが一回で試験に合格することを保証できる問題集です。成功することが大変難しいと思っていますか。
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 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: 4
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: 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