Goldmile-InfobizはAmazonのAWS-Certified-Machine-Learning-Specialtyテスト内容「AWS Certified Machine Learning - Specialty」試験に関する完全な資料を唯一のサービスを提供するサイトでございます。Goldmile-Infobizが提供した問題集を利用してAmazonのAWS-Certified-Machine-Learning-Specialtyテスト内容試験は全然問題にならなくて、高い点数で合格できます。Amazon AWS-Certified-Machine-Learning-Specialtyテスト内容試験の合格のために、Goldmile-Infobizを選択してください。 そうだったら、Goldmile-Infobizを利用したください。AmazonのAWS-Certified-Machine-Learning-Specialtyテスト内容認定試験に受かることを悩んでいたら、Goldmile-Infobizを選びましょう。 弊社の商品は試験の範囲を広くカバーすることが他のサイトがなかなか及ばならないです。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 成功の楽園にどうやって行きますか。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialtyテスト内容 - AWS Certified Machine Learning - Specialty 弊社のGoldmile-Infobizは最大なるIT試験のための資料庫ですので、ほかの試験に興味があるなら、Goldmile-Infobizで探したり、弊社の係員に問い合わせたりすることができます。 優秀な試験参考書は話すことに依頼することでなく、受験生の皆さんに検証されることに依頼するのです。Goldmile-Infobizの参考資料は時間の試練に耐えることができます。
豊富な資料、便利なページ構成と購入した一年間の無料更新はあなたにAmazonのAWS-Certified-Machine-Learning-Specialtyテスト内容試験に合格させる最高の支持です。あなたのIT能力が権威的に認められるのがほしいですか。AmazonのAWS-Certified-Machine-Learning-Specialtyテスト内容試験に合格するのは最良の方法の一です。
Amazon AWS-Certified-Machine-Learning-Specialtyテスト内容 - ふさわしい方式を選ぶのは一番重要なのです。
花に欺く言語紹介より自分で体験したほうがいいです。Amazon AWS-Certified-Machine-Learning-Specialtyテスト内容問題集は我々Goldmile-Infobizでは直接に無料のダウンロードを楽しみにしています。弊社の経験豊かなチームはあなたに最も信頼性の高いAmazon AWS-Certified-Machine-Learning-Specialtyテスト内容問題集備考資料を作成して提供します。Amazon AWS-Certified-Machine-Learning-Specialtyテスト内容問題集の購買に何か質問があれば、我々の職員は皆様のお問い合わせを待っています。
我々Goldmile-InfobizはAmazonのAWS-Certified-Machine-Learning-Specialtyテスト内容試験の変化を注目しています。数年以来の試験問題集を研究しています。
AWS-Certified-Machine-Learning-Specialty PDF DEMO:
QUESTION NO: 1
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: 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 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: 4
A Machine Learning Specialist built an image classification deep learning model. However the
Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and
75%r respectively.
How should the Specialist address this issue and what is the reason behind it?
A. The learning rate should be increased because the optimization process was trapped at a local minimum.
B. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
C. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
D. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
Answer: C
QUESTION NO: 5
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
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Updated: May 28, 2022