時間が経つとともに、我々はインタネット時代に生活します。この時代にはIT資格認証を取得するは重要になります。それでは、AWS-Certified-Machine-Learning-Specialty試験解説試験に参加しよう人々は弊社Goldmile-InfobizのAWS-Certified-Machine-Learning-Specialty試験解説問題集を選らんで勉強して、一発合格して、AmazonIT資格証明書を受け取れます。 最新のAWS-Certified-Machine-Learning-Specialty試験解説試験問題を知りたい場合、試験に合格したとしてもGoldmile-Infobizは無料で問題集を更新してあげます。Goldmile-InfobizのAWS-Certified-Machine-Learning-Specialty試験解説教材を購入したら、あなたは一年間の無料アップデートサービスを取得しました。 」と感謝します。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 本当に助かりました。
Goldmile-InfobizのAmazonのAWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty試験解説試験トレーニング資料は君の成功に導く鍵で、君のIT業種での発展にも助けられます。 Goldmile-Infobizが提供した問題集を利用してAmazonのAWS-Certified-Machine-Learning-Specialty 日本語学習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 日本語学習内容試験の合格のために、Goldmile-Infobizを選択してください。
AmazonのAWS-Certified-Machine-Learning-Specialty試験解説認定試験に合格することとか、より良い仕事を見つけることとか。Goldmile-Infobizは君のAmazonのAWS-Certified-Machine-Learning-Specialty試験解説認定試験に合格するという夢を叶えるための存在です。あなたはGoldmile-Infobizの学習教材を購入した後、私たちは一年間で無料更新サービスを提供することができます。
Amazon AWS-Certified-Machine-Learning-Specialty試験解説 - また、独自の研究チームと専門家を持っています。
Goldmile-Infobizは長年にわたってずっとIT認定試験に関連するAWS-Certified-Machine-Learning-Specialty試験解説参考書を提供しています。これは受験生の皆さんに検証されたウェブサイトで、一番優秀な試験AWS-Certified-Machine-Learning-Specialty試験解説問題集を提供することができます。Goldmile-Infobizは全面的に受験生の利益を保証します。皆さんからいろいろな好評をもらいました。しかも、Goldmile-Infobizは当面の市場で皆さんが一番信頼できるサイトです。
AmazonのAWS-Certified-Machine-Learning-Specialty試験解説の認定試験証明書を取りたいなら、Goldmile-Infobizが貴方達を提供した資料をかったら、お得です。Goldmile-Infobizはもっぱら認定試験に参加するIT業界の専門の人士になりたい方のために模擬試験の練習問題と解答を提供した評判の高いサイトでございます。
AWS-Certified-Machine-Learning-Specialty PDF DEMO:
QUESTION NO: 1
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: 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 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: 4
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: 5
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
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Updated: May 28, 2022