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NEW QUESTION: 1
A company hosts a portfolio of e-commerce websites across the Oregon, N.Virginia, Ireland and Sydney AWS regions. Each site keeps log files that captures user behavior. The company has built an application that generates batches of product recommendations with collaborative filtering in Oregon. Oregon was selected because the flagship site is hosted there and provides the largest collection of data to train machine learning models against. The other regions do NOT have enough historic data to train accurate machine learning models.
Which set of data processing steps improves recommendations for each region?
A. Use the e-commerce application in Oregon to write replica log files in each other region
B. Use the CloudWatch Logs agent to consolidate logs into a single CloudWatch logs group
C. Use Amazon S3 bucket replication to consolidate log entries and builds a single model in Oregon
D. Use Kinesis as a butler for web logs and replicate logs to the Kinesis streams of a neighboring region
Answer: D

NEW QUESTION: 2
로드 균형 조정 서비스의 어떤 기능이 서비스에 대한 후속 연결을 온라인 상태 인 한 동일한 노드로 강제 전환하려고합니까?
A. 세션 유지
B. 세션 다중화
C. 노드 균형
D. 세션 지속성
Answer: D
Explanation:
설명
Session persistence is a feature of the load balancing service. It attempts to force subsequent connections to a service to be redirected to the same node as long as it is online.
References:

NEW QUESTION: 3
A community is concerned about the high volume of commuter traffic in front of its
elementary school between 8:30 A.M. and 9:00 A.M. The numbers of cars that passed without dropping off a student during that half hour on consecutive school days are:
65, 72, 83, 96, 53, 76, 54, 70, 75, 48, 56.
What is the mean (average) of these numbers of cars?
A. 0
B. 1
C. 2
D. 3
E. 4
Answer: A

NEW QUESTION: 4
If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?
A. Classifier
B. Clustering estimator
C. Unsupervised learning
D. Regressor
Answer: D
Explanation:
Regression is the supervised learning task for modeling and predicting continuous, numeric variables.
Examples include predicting real-estate prices, stock price movements, or student test scores.
Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades. Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.
Reference: https://elitedatascience.com/machine-learning-algorithms