皆様が知っているように、Goldmile-InfobizはAmazonのAWS-Certified-Machine-Learning-Specialty学習教材試験問題と解答を提供している専門的なサイトです。AmazonのAWS-Certified-Machine-Learning-Specialty学習教材認証試験を選んだ人々が一層多くなります。AWS-Certified-Machine-Learning-Specialty学習教材試験がユニバーサルになりましたから、あなたはGoldmile-Infobiz のAmazonのAWS-Certified-Machine-Learning-Specialty学習教材試験問題と解答￥を利用したらきっと試験に合格するができます。 なぜ我々はあなたが利用してからAmazonのAWS-Certified-Machine-Learning-Specialty学習教材試験に失敗したら、全額で返金するのを承諾しますか。我々は弊社の商品があなたに試験に合格させるのを信じでいます。 それにGoldmile-Infobizは１００パーセント合格率を保証します。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty もし失敗だったら、我々は全額で返金します。
我々のAWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty学習教材習題さえ利用すれば試験の成功まで近くなると考えられます。 Goldmile-InfobizのAmazonのAWS-Certified-Machine-Learning-Specialty 専門トレーリング「AWS Certified Machine Learning - Specialty」試験トレーニング資料はIT職員としてのあなたがIT試験に受かる不可欠なトレーニング資料です。Goldmile-InfobizのAmazonのAWS-Certified-Machine-Learning-Specialty 専門トレーリング試験トレーニング資料はカバー率が高くて、更新のスピードも速くて、完全なトレーニング資料ですから、Goldmile-Infobiz を手に入れたら、全てのIT認証が恐くなくなります。
弊社Goldmile-InfobizのAWS-Certified-Machine-Learning-Specialty学習教材問題集は必ずあなたの成功へ道の秘訣です。AmazonのAWS-Certified-Machine-Learning-Specialty学習教材試験に趣味があると、躊躇わなく、我々Goldmile-Infobizで問題集のデーモをダウンロードして試すことができます。デーモ版によって、このAWS-Certified-Machine-Learning-Specialty学習教材問題集はあなたに適合するかと判断します。
Amazon AWS-Certified-Machine-Learning-Specialty学習教材認定試験はたいへん難しい試験ですね。
明日ではなく、今日が大事と良く知られるから、そんなにぐずぐずしないで早く我々社のAmazon AWS-Certified-Machine-Learning-Specialty学習教材日本語対策問題集を勉強し、自身を充実させます。我々社の練習問題は長年でAWS-Certified-Machine-Learning-Specialty学習教材全真模擬試験トレーニング資料に研究している専業化チームによって編集されます。Amazon AWS-Certified-Machine-Learning-Specialty学習教材資格問題集はPDF版、ソフト版、オンライン版を含まれ、この三つバージョンから自分の愛用することを選んでいます。他の人に先立ってAmazon AWS-Certified-Machine-Learning-Specialty学習教材認定資格を得るために、今から勉強しましょう。
無論Goldmile-InfobizのAmazonのAWS-Certified-Machine-Learning-Specialty学習教材問題集が一番頼りになります。Goldmile-Infobizには専門的なエリート団体があります。
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 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: 3
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: 4
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
QUESTION NO: 5
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
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Updated: May 28, 2022