Amazon AWS Certified Solutions Architect

Total Page：16

File Type：pdf, Size：1020Kb

Amazon AWS-Certified-Cloud-Practitioner-KR Amazon AWS Certified Solutions Architect - Cloud Practitioner (AWS-Certified-Cloud- Practitioner Korean Version) Amazon AWS-Certified-Cloud-Practitioner-KR Dumps Available Here at: https://www.certification-questions.com/amazon-exam/aws-certified-cloud- practitioner-kr-dumps.html Enrolling now you will get access to 465 questions in a unique set of AWS-Certified-Cloud-Practitioner-KR dumps Question 1 AWS AWS AWS ? Options: A. AWS B. AWS C. AWS Trusted Advisor D. AWS Answer: A Explanation: AWS Personal Health Dashboard provides alerts and remediation guidance when AWS is experiencing events that may impact you. While the Service Health Dashboard displays the general status of AWS services, Personal Health Dashboard gives you a personalized view into the performance and availability of the AWS services underlying your AWS resources. Question 2 Amazon Relational Database Service (Amazon RDS) ? Options: https://www.certification-questions.com Amazon AWS-Certified-Cloud-Practitioner-KR A. AWS Amazon RDS . B. AWS . C. AWS . D. AWS . Answer: C Explanation: RDS lowers administrative burden through automatic software patching and maintenance of the underlying operating system and secondly, you still have to manually upgrade the underlying instance type of your database cluster in order to scale it up. Question 3 Amazon EBS AWS ? Options: A. AWS B. AWS C. AWS KMS D. AWS Answer: C Question 4 , Amazon EC2 ? Options: A. B. C. D. Answer: A Question 5 Amazon Rekognition ? https://www.certification-questions.com Amazon AWS-Certified-Cloud-Practitioner-KR Options: A. Amazon Rekognition B. Amazon Rekognition . C. Amazon Recognition D. Amazon Rekognition Amazon Mechanical Turk . Answer: B Question 6 AWS AWS ? Options: A. B. C. D. Answer: B Question 7 AWS . ? Options: A. AWS Management Console . B. AWS CloudFormation . C. PowerShell . D. AWS OpsWorks . Answer: D Question 8 . https://www.certification-questions.com Amazon AWS-Certified-Cloud-Practitioner-KR . ? Options: A. AWS Storage Gateway Amazon Route 53 B. Amazon RDS C. Application Load Balancer AWS Elastic Beanstalk . D. Network Load Balancer Amazon CloudFront Answer: B Question 9 AWS . Options: A. TCO ( ) . B. (capex). C. (opex). D. E. Answer: A, D Question 10 , ? Options: A. Amazon RDS B. Amazon EC2 C. Amazon ElastiCache D. AWS Fargate https://www.certification-questions.com Amazon AWS-Certified-Cloud-Practitioner-KR Answer: B Explanation: The customer is responsible for managing, support, patching and control of the guest operating system and AWS services provided like EC2. Would you like to see more? Don't miss our AWS- Certified-Cloud-Practitioner-KR PDF file at: https://www.certification-questions.com/amazon-pdf/aws-certified-cloud-practitioner- kr-pdf.html https://www.certification-questions.com.

Copy Link

Recommended publications

A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk
A Data-Driven Analysis of Workers’ Earnings on Amazon Mechanical Turk Kotaro Hara1,2, Abi Adams3, Kristy Milland4,5, Saiph Savage6, Chris Callison-Burch7, Jeffrey P. Bigham1 1Carnegie Mellon University, 2Singapore Management University, 3University of Oxford 4McMaster University, 5TurkerNation.com, 6West Virginia University, 7University of Pennsylvania [email protected] ABSTRACT temporarily out-of-work engineers to work [1,4,39,46,65]. A growing number of people are working as part of on-line crowd work. Crowd work is often thought to be low wage Yet, despite the potential for crowdsourcing platforms to work. However, we know little about the wage distribution extend the scope of the labor market, many are concerned in practice and what causes low/high earnings in this that workers on crowdsourcing markets are treated unfairly setting. We recorded 2,676 workers performing 3.8 million [19,38,39,42,47,59]. Concerns about low earnings on crowd tasks on Amazon Mechanical Turk. Our task-level analysis work platforms have been voiced repeatedly. Past research revealed that workers earned a median hourly wage of only has found evidence that workers typically earn a fraction of ~$2/h, and only 4% earned more than $7.25/h. While the the U.S. minimum wage [34,35,37–39,49] and many average requester pays more than $11/h, lower-paying workers report not being paid for adequately completed requesters post much more work. Our wage calculations are tasks [38,51]. This is problematic as income generation is influenced by how unpaid work is accounted for, e.g., time the primary motivation of workers [4,13,46,49].
[Show full text]
Amazon Mechanical Turk Developer Guide API Version 2017-01-17 Amazon Mechanical Turk Developer Guide
Amazon Mechanical Turk Developer Guide API Version 2017-01-17 Amazon Mechanical Turk Developer Guide Amazon Mechanical Turk: Developer Guide Copyright © Amazon Web Services, Inc. and/or its aﬃliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be aﬃliated with, connected to, or sponsored by Amazon. Amazon Mechanical Turk Developer Guide Table of Contents What is Amazon Mechanical Turk? ........................................................................................................ 1 Mechanical Turk marketplace ....................................................................................................... 1 Marketplace rules ............................................................................................................... 2 The sandbox marketplace .................................................................................................... 2 Tasks that work well on Mechanical Turk ...................................................................................... 3 Tasks can be completed within a web browser ....................................................................... 3 Work can be broken into distinct, bite-sized tasks .................................................................
[Show full text]
Dspace Cover Page
Human-powered sorts and joins The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Adam Marcus, Eugene Wu, David Karger, Samuel Madden, and Robert Miller. 2011. Human-powered sorts and joins. Proc. VLDB Endow. 5, 1 (September 2011), 13-24. As Published http://www.vldb.org/pvldb/vol5.html Publisher VLDB Endowment Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/73192 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike 3.0 Detailed Terms http://creativecommons.org/licenses/by-nc-sa/3.0/ Human-powered Sorts and Joins Adam Marcus Eugene Wu David Karger Samuel Madden Robert Miller {marcua,sirrice,karger,madden,rcm}@csail.mit.edu ABSTRACT There are several reasons that systems like MTurk are of interest Crowdsourcing marketplaces like Amazon’s Mechanical Turk (MTurk) database researchers. First, MTurk workflow developers often imple- make it possible to task people with small jobs, such as labeling im- ment tasks that involve familiar database operations such as filtering, ages or looking up phone numbers, via a programmatic interface. sorting, and joining datasets. For example, it is common for MTurk MTurk tasks for processing datasets with humans are currently de- workflows to filter datasets to find images or audio on a specific sub- signed with significant reimplementation of common workflows and ject, or rank such data based on workers’ subjective opinion. Pro- ad-hoc selection of parameters such as price to pay per task. We de- grammers currently waste considerable effort re-implementing these scribe how we have integrated crowds into a declarative workflow operations because reusable implementations do not exist.
[Show full text]
Amazon Mechanical Turk Requester UI Guide Amazon Mechanical Turk Requester UI Guide
Amazon Mechanical Turk Requester UI Guide Amazon Mechanical Turk Requester UI Guide Amazon Mechanical Turk: Requester UI Guide Copyright © 2014 Amazon Web Services, Inc. and/or its affiliates. All rights reserved. The following are trademarks of Amazon Web Services, Inc.: Amazon, Amazon Web Services Design, AWS, Amazon CloudFront, Cloudfront, Amazon DevPay, DynamoDB, ElastiCache, Amazon EC2, Amazon Elastic Compute Cloud, Amazon Glacier, Kindle, Kindle Fire, AWS Marketplace Design, Mechanical Turk, Amazon Redshift, Amazon Route 53, Amazon S3, Amazon VPC. In addition, Amazon.com graphics, logos, page headers, button icons, scripts, and service names are trademarks, or trade dress of Amazon in the U.S. and/or other countries. Amazon©s trademarks and trade dress may not be used in connection with any product or service that is not Amazon©s, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Amazon Mechanical Turk Requester UI Guide Table of Contents Welcome ..................................................................................................................................... 1 How Do I...? ......................................................................................................................... 1 Introduction to Mechanical Turk .......................................................................................................
[Show full text]
Privacy Experiences on Amazon Mechanical Turk
113 “Our Privacy Needs to be Protected at All Costs”: Crowd Workers’ Privacy Experiences on Amazon Mechanical Turk HUICHUAN XIA, Syracuse University YANG WANG, Syracuse University YUN HUANG, Syracuse University ANUJ SHAH, Carnegie Mellon University Crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) are widely used by organizations, researchers, and individuals to outsource a broad range of tasks to crowd workers. Prior research has shown that crowdsourcing can pose privacy risks (e.g., de-anonymization) to crowd workers. However, little is known about the specific privacy issues crowd workers have experienced and how they perceive the state ofprivacy in crowdsourcing. In this paper, we present results from an online survey of 435 MTurk crowd workers from the US, India, and other countries and areas. Our respondents reported different types of privacy concerns (e.g., data aggregation, profiling, scams), experiences of privacy losses (e.g., phishing, malware, stalking, targeted ads), and privacy expectations on MTurk (e.g., screening tasks). Respondents from multiple countries and areas reported experiences with the same privacy issues, suggesting that these problems may be endemic to the whole MTurk platform. We discuss challenges, high-level principles and concrete suggestions in protecting crowd workers’ privacy on MTurk and in crowdsourcing more broadly. CCS Concepts: • Information systems → Crowdsourcing; • Security and privacy → Human and societal aspects of security and privacy; • Human-centered computing → Computer supported cooperative work; Additional Key Words and Phrases: Crowdsourcing; Privacy; Amazon Mechanical Turk (MTurk) ACM Reference format: Huichuan Xia, Yang Wang, Yun Huang, and Anuj Shah. 2017. “Our Privacy Needs to be Protected at All Costs”: Crowd Workers’ Privacy Experiences on Amazon Mechanical Turk.
[Show full text]
Crowdsourcing for Speech: Economic, Legal and Ethical Analysis Gilles Adda, Joseph Mariani, Laurent Besacier, Hadrien Gelas
Crowdsourcing for Speech: Economic, Legal and Ethical analysis Gilles Adda, Joseph Mariani, Laurent Besacier, Hadrien Gelas To cite this version: Gilles Adda, Joseph Mariani, Laurent Besacier, Hadrien Gelas. Crowdsourcing for Speech: Economic, Legal and Ethical analysis. [Research Report] LIG lab. 2014. hal-01067110 HAL Id: hal-01067110 https://hal.archives-ouvertes.fr/hal-01067110 Submitted on 23 Sep 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Crowdsourcing for Speech: Economic, Legal and Ethical analysis Gilles Adda1, Joseph J. Mariani1,2, Laurent Besacier3, Hadrien Gelas3,4 (1) LIMSI-CNRS, (2) IMMI-CNRS, (3) LIG-CNRS, (4) DDL-CNRS September 19, 2014 1 Introduction With respect to spoken language resource production, Crowdsourcing – the process of distributing tasks to an open, unspecified population via the inter- net – offers a wide range of opportunities: populations with specific skills are potentially instantaneously accessible somewhere on the globe for any spoken language. As is the case for most newly introduced high-tech services, crowd- sourcing raises both hopes and doubts, certainties and questions. A general analysis of Crowdsourcing for Speech processing could be found in (Eskenazi et al., 2013). This article will focus on ethical, legal and economic issues of crowdsourcing in general (Zittrain, 2008a) and of crowdsourcing services such as Amazon Mechanical Turk (Fort et al., 2011; Adda et al., 2011), a major plat- form for multilingual language resources (LR) production.
[Show full text]
Using Amazon Mechanical Turk and Other Compensated Crowd Sourcing Sites Gordon B
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Opus: Research and Creativity at IPFW Indiana University - Purdue University Fort Wayne Opus: Research & Creativity at IPFW Organizational Leadership and Supervision Faculty Division of Organizational Leadership and Publications Supervision 7-2016 Using Amazon Mechanical Turk and Other Compensated Crowd Sourcing Sites Gordon B. Schmidt Indiana University - Purdue University Fort Wayne, [email protected] William Jettinghoff Indiana University - Bloomington Follow this and additional works at: http://opus.ipfw.edu/ols_facpubs Part of the Industrial and Organizational Psychology Commons, and the Organizational Behavior and Theory Commons Opus Citation Gordon B. Schmidt and William Jettinghoff (2016). sinU g Amazon Mechanical Turk and Other Compensated Crowd Sourcing Sites. Business Horizons.59 (4), 391-400. Elsevier. http://opus.ipfw.edu/ols_facpubs/91 This Article is brought to you for free and open access by the Division of Organizational Leadership and Supervision at Opus: Research & Creativity at IPFW. It has been accepted for inclusion in Organizational Leadership and Supervision Faculty Publications by an authorized administrator of Opus: Research & Creativity at IPFW. For more information, please contact [email protected]. Business Horizons (2016) 59, 391—400 Available online at www.sciencedirect.com ScienceDirect www.elsevier.com/locate/bushor Using Amazon Mechanical Turk and other compensated crowdsourcing sites a, b Gordon B. Schmidt *, William M. Jettinghoff a Division of Organizational Leadership and Supervision, Indiana University-Purdue University Fort Wayne, Neff 288D, 2101 East Coliseum Blvd., Fort Wayne, IN 46805, U.S.A. b Indiana University Bloomington, 8121 Deer Brook Place, Fort Wayne, IN 46825, U.S.A.
[Show full text]
Employment and Labor Law in the Crowdsourcing Industry
Working the Crowd: Employment and Labor Law in the Crowdsourcing Industry Alek Felstinert This Article confronts the thorny questions that arise in attempting to apply traditional employment and labor law to "crowdsourcing," an emerging online labor model unlike any that has existed to this point. Crowdsourcing refers to the process of taking tasks that would normally be delegated to an employee and distributingthem to a large pool of online workers, the "crowd, " in the form of an open call. The Article describes how crowdsourcing works, its advantages and risks, and why workers in particularsubsections of the paid crowdsourcing industry may be denied the protection of employment laws without much recourse to vindicate their rights. Taking Amazon's Mechanical Turk platform as a case study, the Article explores the nature of this employment relationship in order to determine the legal status of the "crowd." The Article also details the complications that might arise in applying existing work laws to crowd labor. Finally, the Article presents a series of brief recommendations. It encourages legislatures to clarify and expand legal protections for crowdsourced employees, and suggests ways for courts and administrative agencies to pursue the same objective within our existing legalframework. It also offers voluntary "best practices" for firms and venues involved in crowdsourcing, along with examples of how crowd workers might begin to effectively organize and advocate on their own behalf I. INTRODUCTION ....................................... 144 II. CROWDSOURCING AND COGNITIVE PIECEWORK ...... ........ 146 A. The Crowdsourcing Industry.... .................... 149 B. Why Crowdsourcing? And Why Not?......... ................ 151 1. What Firms Get Out of Crowdsourcing ........ ........ 151 tJ.D.
[Show full text]
Five Processes in the Platformisation of Cultural Production: Amazon and Its Publishing Ecosystem
Five Processes in the Platformisation of Cultural Production: Amazon and its Publishing Ecosystem Mark Davis N A RECENT ESSAY THE LITERARY SCHOLAR MARK MCGURL ASKED, I Should Amazon.com now be considered the driving force of American literary history? Is it occasioning a convergence of the state of the art of fiction writing with the state of the art of capitalism? If so, what does this say about the form and function of narrative fiction—about its role in symbolically managing, resisting, or perhaps simply ‘escaping’ the dominant sociopolitical and economic realities of our time? (447) McGurl’s essay, with its focus on the sheer scope of Amazon’s operations and their impact on literary institutions, from their online bookstore, to their ebooks division, to their Audible audiobooks division, to their Goodreads reader reviews community, provides a salutary insight into current conditions of cultural production. The fate of literary culture, as McGurl says, now rests in the hands of digital technology, with its close connections to the neoliberal commodification of work, leisure and culture. But McGurl’s essay if anything underestimates the extent to which the fate of western cultural production is increasingly tied to digital media corporations. Focused on Amazon’s impact on the literary field, it opens up possibilities for understanding the processes of platformisation that underpin the cultural activities of companies such as Amazon. © Australian Humanities Review 66 (May 2020). ISSN: 1325 8338 84 Mark Davis / Five Processes in the Platformisation of Cultural Production In this article I argue that of the five ‘GAFAM’ corporations (Google, Amazon, Facebook, Apple and Microsoft) that dominate platform capitalism in the west, Amazon and its various platforms are an exemplar of what has been called the ‘platformization of cultural production’ (Nieborg and Poell), that is, the reorganisation of cultural industries around digital platform logics.
[Show full text]
Requester Best Practices Guide
REQUESTER BEST PRACTICES GUIDE Requester Best Practices Guide Amazon Mechanical Turk is a marketplace for work where businesses (aka Requesters) publish tasks (aka HITS), and human providers (aka Workers) complete them. Amazon Mechanical Turk gives businesses immediate access to a diverse, global, on-demand, scalable workforce and gives Workers a selection of thousands of tasks to complete whenever and wherever it's convenient. There are many ways to structure your work in Mechanical Turk. This guide helps you optimize your approach to using Mechanical Turk to get the most accurate results at the best price with the turnaround time your business needs. Use this guide as you plan, design, and test your Amazon Mechanical Turk HITs. Contents Planning, Designing and Publishing Your Project (p. 1) What is an Amazon Mechanical Turk HIT? (p. 2) Divide Your Project Into Steps (p. 2) Keep HITs simple (p. 2) Make Instructions Clear and Concise (p. 2) Set Your Price (p. 4) Test Your HITs (p. 5) Designate an Owner (p. 5) Build your reputation with Workers (p. 6) Make Workers More Efficient (p. 6b) Managing Worker Accuracy (p. 7) Mechanical Turk Masters (p. 8) A Strategy for Accuracy (p. 9) Planning, Designing and Publishing Your Project What is an Amazon Mechanical Turk HIT? An Amazon Mechanical Turk HIT or Human Intelligence Task is the task you ask a Worker to complete. It may be a task that is inherently difficult for a computer to do. Divide Your Project Into Steps In the planning stage, define the goal of your project and break it down into specific steps.
[Show full text]
PRINCE: Provider-Side Interpretability with Counterfactual Explanations in Recommender Systems Azin Ghazimatin, Oana Balalau, Rishiraj Saha, Gerhard Weikum
PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems Azin Ghazimatin, Oana Balalau, Rishiraj Saha, Gerhard Weikum To cite this version: Azin Ghazimatin, Oana Balalau, Rishiraj Saha, Gerhard Weikum. PRINCE: Provider-side Inter- pretability with Counterfactual Explanations in Recommender Systems. WSDM 2020 - 13th ACM International Conference on Web Search and Data Mining, Feb 2020, Houston, Texas, United States. hal-02433443 HAL Id: hal-02433443 https://hal.inria.fr/hal-02433443 Submitted on 9 Jan 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Copyright PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems Azin Ghazimatin Oana Balalau∗ Max Planck Institute for Informatics, Germany Inria and École Polytechnique, France [email protected] [email protected] Rishiraj Saha Roy Gerhard Weikum Max Planck Institute for Informatics, Germany Max Planck Institute for Informatics, Germany [email protected] [email protected] ABSTRACT Interpretable explanations for recommender systems and other ma- chine learning models are crucial to gain user trust. Prior works that have focused on paths connecting users and items in a het- erogeneous network have several limitations, such as discovering relationships rather than true explanations, or disregarding other users’ privacy.
[Show full text]
Amazon Enters the Cloud Computing Business
S T A N F O R D U NIVERSITY 2 0 0 8 - 3 5 3 - 1 S C H O O L O F E NGINEERING WWW.CASEP UBLISHER . COM Rev. May 20, 2008 AMAZON ENTERS THE CLOUD COMPUTING BUSINESS T A B L E O F C ONTENTS 1. Introduction 2. Company Overview 2.1. The Founding of Amazon 2.2. Amazon’s Culture 2.3. Amazon’s Retail Business 2.4. Amazon’s Other Services & Products 2.5. Financial Performance of Amazon 2.6. Competition and Competitive Trends 3. Cloud Computing Overview 4. Amazon Enters the Market for Cloud Computing and Storage Services 4.1. Amazon’s Elastic Compute Cloud (EC2) 4.2. Amazon’s Simple Storage Service (S3) 4.3. Customers of Amazon’s Cloud Computing 4.4. Partners of Amazon’s Cloud Computing 5. Industry and Analyst Responses 5.1. Microsoft 5.2. Google 5.3. Sun Microsystem 5.4. IBM 5.5. Market Analysts 6. Exhibits 7. References Professors Micah Siegel (Stanford University) and Fred Gibbons (Stanford University) guided the de- velopment of this case using the CasePublisher service, available online at www.casepublisher.com as the basis for class discussion rather than to i)ustrate either e*ective or ine*ective handling of a business situation. 2008-353-1 Amazon Enters the Cloud Computing Business I NTRODUCTION Amazon CEO Jeﬀ Bezos looked at the clock on the instrument panel of his Segway Human Transporter; it was 7:52AM and he knew he would need a little luck to get to his 8:00AM meeting at Amazon's Beacon Hill headquarters.
[Show full text]