每天半小时搞定 AoAWS (二十)

Architecting on AWS 学习笔记系列文章导航页面


65.A company has an infrastructure that consists of machines which keep sending log information every 5 minutes.
The number of these machines can run into thousands and it is required to ensure that the data can be analyzed at a later stage. Which of the following would help in fulfilling this requirement?
A. Use Kinesis Firehose with S3 to take the logs and store them in S3 for furtherprocessing.
B. Launch an Elastic Beanstalk application to take the processing job of the logs.
C. Launch an EC2 instance with enough EBS volumes to consume the logs which can be usedof r further processing.
D. Use CloudTrail to store all the logs which can be analyzed at a later stage.

Note:
Amazon Kinesis Data Firehose is the easiest way to load streaming data into data stores and analytics tools. It can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, Amazon Elasticsearch Service,and Splunk, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today. For more information on Amazon Kinesis firehose, please visit the following URL: (https://aws.amazon.com/kinesis/data-firehose/)

https://aws.amazon.com/cn/elasticbeanstalk/

https://aws.amazon.com/cn/cloudtrail/


66.An application hosted in AWS allows users to upload videos to an S3 bucket.
A user is required to be given access to upload some videos for a week based on the profile. How can be this be accomplished in the best way possible?
A. Create an IAM bucket policy to provide access for a week’s duration.
B. Create a pre-signed URL for each profile which will last for a week’s duration.
C. Create an S3 bucket policy to provide access for a week’s duration.
D. Create an IAM role to provide access for a week’s duration.


67.A company is planning to use Docker containers and necessary container orchestration tools for their batch processing requirements.
There is a requirement for batch processing for both critical and non-critical data. Which of the following is the best implementation step for this requirement, to ensure that cost is effectively managed?
A. Use Kubernetes for container orchestration and Reserved instances for all underlying instances.
B. Use ECS orchestration and Reserved Instances for all underlying instances.
C. Use Docker for container orchestration and a combination of Spot and Reserved Instances for the underlying instances.
D. Use ECS for container orchestration and a combination of Spot and Reserved Instances for the underlying instances.


引言 非线性函数极值寻优是工程优化和科学计算中的核心问题,传统方法在处理高维、多峰或不可导函数时往往效果不佳。神经网络与遗传算法的结合为解决这类复杂优化问题提供了新思路。本文将从计算机专业角度,详细分析神经网络遗传算法在非线性函数极值寻优中的原理、实现方法及优化策略。 混合算法原理与架构 遗传算法(GA)与神经网络(NN)的混合架构充分发挥了两者的优势:神经网络提供强大的非线性拟合能力,遗传算法则提供全局搜索能力。该混合系统的工作流程可分为三个关键阶段: 神经网络建模阶段:构建BP神经网络结构(如2-5-1),通过训练数据学习目标函数的输入输出关系。激活函数通常选择Sigmoid或ReLU,损失函数采用均方误差(MSE)。 遗传算法优化阶段:将神经网络参数编码为染色体(实数编码),以网络预测精度作为适应度函数fitness = 1/(1+MSE)。通过选择、交叉(概率0.4-0.9)和变异(概率0.01-0.2)操作进化种群。 协同优化阶段:遗传算法优化后的参数初始化神经网络,再进行BP微调,形成"全局搜索+局部优化"的双重机制。 关键技术实现 神经网络建模 采用MATLAB的Neural Network Toolbox实现,关键步骤包括: net = feedforwardnet([5]); % 单隐藏层5神经元 net.trainFcn = 'trainlm'; % Levenberg-Marquardt算法 net = train(net, input, target); % 网络训练 遗传算法优化 适应度函数设计与参数编码是核心: function fitness = ga_fitness(x) = sim(net, x'); % 神经网络预测 fitness = 1/(1+mse(y-target)); end 种群规模建议50-
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