AWS-Certified-Machine-Learning-Specialtyテスト問題集 - AWS-Certified-Machine-Learning-Specialty関連資格知識 & AWS-Certified-Machine-Learning-Specialty - Omgzlook

あなたが安心で試験のために準備すればいいです。たぶん、あなたは苦しく準備してAmazonのAWS-Certified-Machine-Learning-Specialtyテスト問題集試験に合格できないのを心配しています。おそらくあなたはお金がかかって買ったソフトが役に立たないのを心配しています。 早くOmgzlookの問題集を君の手に入れましょう。あなたはインターネットでAmazonのAWS-Certified-Machine-Learning-Specialtyテスト問題集認証試験の練習問題と解答の試用版を無料でダウンロードしてください。 我々Omgzlookが自分のソフトに自信を持つのは我々のAmazonのAWS-Certified-Machine-Learning-Specialtyテスト問題集ソフトでAmazonのAWS-Certified-Machine-Learning-Specialtyテスト問題集試験に参加する皆様は良い成績を取りましたから。

AWS-Certified-Machine-Learning-Specialtyテスト問題集問題集を利用して試験に合格できます。

したがって、OmgzlookのAWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialtyテスト問題集問題集も絶えずに更新されています。 そのため、AWS-Certified-Machine-Learning-Specialty 入門知識試験参考書に対して、お客様の新たな要求に迅速に対応できます。それは受験者の中で、AWS-Certified-Machine-Learning-Specialty 入門知識試験参考書が人気がある原因です。

あるいは、無料で試験AWS-Certified-Machine-Learning-Specialtyテスト問題集問題集を更新してあげるのを選択することもできます。こんな保障がありますから、心配する必要は全然ないですよ。OmgzlookのAWS-Certified-Machine-Learning-Specialtyテスト問題集問題集は多くの受験生に検証されたものですから、高い成功率を保証できます。

Amazon AWS-Certified-Machine-Learning-Specialtyテスト問題集 - 常々、時間とお金ばかり効果がないです。

Omgzlookの経験豊富な専門家チームはAmazonのAWS-Certified-Machine-Learning-Specialtyテスト問題集認定試験に向かって専門性の問題集を作って、とても受験生に合っています。Omgzlookの商品はIT業界中で高品質で低価格で君の試験のために専門に研究したものでございます。

Omgzlookは多くの受験生を助けて彼らにAmazonのAWS-Certified-Machine-Learning-Specialtyテスト問題集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テスト問題集試験を研究して解答を詳しく分析しますから。試験が更新されているうちに、我々はAmazonのAWS-Certified-Machine-Learning-Specialtyテスト問題集試験の資料を更新し続けています。

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