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A2010-578 - Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma - BrainDump Information

Vendor Name : IBM
Exam Code : A2010-578
Exam Name : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma
Questions and Answers : 120 Q & A
Updated On : March 19, 2019
PDF Download Mirror : A2010-578 Braindumps
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A2010-578 exam Dumps Source : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

Test Code : A2010-578
Test Name : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma
Vendor Name : IBM
Q&A : 120 Real Questions

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IBM Assess: Fundamentals of Applying

IBM, Fujitsu and Accenture Listed as Visionary Leaders through 360Quadrants for software Modernization | killexams.com Real Questions and Pass4sure dumps

CHICAGO, March 1, 2019 /PRNewswire/ -- 360Quadrants powered via MarketsandMarkets™, the world's simplest evaluation platform that mixes knowledgeable analysis with crowdsourced insights has launched a quadrant on software modernization functions to support companies make sooner and extra advised selections. the first release of the quadrant has IBM, Fujitsu, and Accenture sharing space as Visionary Leaders. 360Quadrants are generated post analysis of agencies (product portfolios and company approach). Quadrants may be up to date every three months, and the position of providers will mirror how patrons, trade consultants, and different providers expense them on different parameters.

Quadrant highlights

50+ businesses offering application modernization features have been analyzed of which 31 groups were shortlisted and categorised on a quadrant under Visionary Leaders, Innovators, Dynamic Differentiators, and emerging Leaders.

IBM Cloud Migration factory, Fujitsu world software Modernization, Accenture software portfolio assessment services, Capgemini Collaborative business experience, Tech Mahindra application Modernization, Dell, CGI neighborhood, HCL Legacy Modernization, Avanade, Hexaware technologies, Virtusa, Cognizant swift Mobilization tool, Oracle Modernization service, Wipro, ATOS application Transformation and management, DXC know-how, Yash technologies, Infosys, Syntel, and Micro focus business application Modernization have been recognized as visionary leaders as they've based product portfolios and a robust market presence and business strategy.

Bell Integrator Language-to-Language Conversion, Macrosoft Legacy software Modernization, Sensiple, Nous Infosystems, superior desktop software, Asysco B.V., and UST world Legacy Modernization capabilities were identified as innovators as they have got focused product portfolios, however a mediocre company strategy.

Astadia and Cyber Infrastructure (P) confined have been identified as rising corporations as they've a gap product providing however bad company strategy. Idexcel and OSSCube have been identified as dynamic differentiators.

The 360Quadrants platform gives essentially the most granular utility modernization features assessment between providers.

Methodology

The methodology used to rank utility modernization services companies concerned the use of wide secondary analysis to identify key vendors by way of regarding annual studies, press releases, investor displays, white papers, and quite a few related directories and databases. 31 key companies have been shortlisted on the groundwork of their breadth of product choices, organization dimension, and other criteria. The ratings and weights for shortlisted providers against each and every parameter had been finalized post research.  After the scores had been finalized, each dealer changed into positioned in respective quadrants based on their score in the product offering and company method parameters.

About 360Quadrants

360Quadrants just about compares organizations in emerging technologies on 6 broad maturities: product maturity, company maturity, use-case maturity, investment maturity, expertise maturity, and enterprise result maturity. every company is reviewed via four stakeholders—patrons, trade consultants, other carriers, and MarketsandMarkets™ analysts—to make it independent. 360 aims to simplify and de-possibility complex buy selections. consumers get to customise their quadrant towards their specific needs. The combined insights from friends, analysts, consultants, and providers reduce the bias and helps the purchaser find the greatest healthy answer. carriers get to position themselves to win most efficient new consumers, customize their quadrants, come to a decision key parameters, and position themselves strategically in a gap space, to be consumed by giants and start-usaalike. experts get to develop their own company and increase their thought management. The 360 platform aims the constructing of a social community that hyperlinks trade consultants with corporations worldwide.

360Quadrants has additionally launched quadrants in fields like Cognitive Analytics solutions, AI in Fintech solutions, and Predictive Analytics application.

About MarketsandMarkets™

MarketsandMarkets™ provides quantified B2B analysis on 30,000 excessive boom niche alternatives/threats so that they can influence the revenues of 70% to 80% of businesses worldwide. MarketsandMarkets™ presently capabilities 7500 shoppers international including eighty% of international Fortune 1000 corporations. practically seventy five,000 properly officers across eight industries global strategy MarketsandMarkets™ for their pain facets around revenues selections.

Contact:Mr. Manoj Singhvimanoj.singhvi@marketsandmarkets.comMarketsandMarkets™ research private Ltd.Tower B5, workplace a hundred and one, Magarpatta SEZ,Hadapsar, Pune - 411013, IndiaPhone: +1-888-600-6441

View customary content material:http://www.prnewswire.com/information-releases/ibm-fujitsu-and-accenture-listed-as-visionary-leaders-via-360quadrants-for-software-modernization-300804898.html

source MarketsandMarkets

Copyright (C) 2019 PR Newswire. All rights reserved


IBM X-drive purple will use Onapsis ERP expertise to aid organizations discover vital vulnerabilities | killexams.com Real Questions and Pass4sure dumps

Onapsis, the international leaders in ERP cybersecurity and compliance, announced IBM safety’s crew of veteran hackers, X-drive crimson, will use its ERP know-how to support companies identify exploitable vulnerabilities of their company-vital applications.

X-drive crimson will use Onapsis’ ERP expertise when performing vulnerability assessments and penetration testing against SAP and Oracle applications to help immediately find widely used and unknown vulnerabilities.

clients can access X-force purple’s services during the X-force red Portal, the team’s cloud-based mostly communications and collaboration platform. the usage of the X-drive purple Portal, clients can register for exams and assessments, investigate their popularity, view findings as they are uncovered, view remediation recommendations, and communicate at once with X-force crimson testers, doing away with time-ingesting back and forth and the manual sharing of spreadsheets.

“we are very excited to be part of X-force red’s vulnerability assessment offering. within the face of explosive growth in attacks to ERP methods, as evidenced with the aid of the USA branch of native land security releasing two essential alerts in the past three years, companies have realized they need to include ERP continual vulnerability assessment and monitoring into their safety classes. With Onapsis’ patented ERP cybersecurity know-how, combined with X-force purple’s safety advantage and attacker mind-set, businesses can now right away be aware their security posture, and get hold of actionable counsel on the way to be sure the core of their business is relaxed,” spoke of Mariano Nunez, CEO and Co-founder, Onapsis Inc.

“SAP and Oracle ERP are purposes that many corporations use for sensitive company tactics,” spoke of Charles Henderson, world accomplice and Head of X-drive purple. “on account of their magnitude and the type of records they hang, it's critical these functions are scanned and confirmed continuously so that important vulnerabilities can also be remediated before attackers locate them. Our collaboration with Onapsis will make that mission come to fruition.”

X-force pink gives you vulnerability assessment and protection checking out programs that center of attention on uncovering vulnerabilities throughout purposes, hardware, personnel, internet-connected contraptions, networks, cars, ATMs, blockchain and nearly every thing else.

The group is made out of veteran hackers who apply the identical tools, options, practices and attitude as attackers, uncovering exploitable vulnerabilities that may additionally lead criminals to the topped jewels.

This collaboration additional highlights Onapsis’ expanded effort on growing the international ERP safety associate ecosystem. Onapsis also works closely with the IBM safety features neighborhood for protecting, continuous monitoring, addressing compliance and enabling cloud migrations of one of the world’s greatest agencies.


linked Healthcare Market 2019 Highlights and Fundamentals – Accenture, IBM, SAP, GE Healthcare, Oracle, Microsoft, Airstrip know-how, Medtronic | killexams.com Real Questions and Pass4sure dumps

Harrisburg, NC -- (SBWIRE) -- 01/23/2019 -- international related HEALTHCARE MARKET measurement, reputation AND FORECAST 2019-2025

The record gives a valuable supply of insightful facts for company strategists and competitive analysis of linked Healthcare Market. It provides the linked Healthcare trade overview with boom evaluation and futuristic cost, revenue and many other features. The research analysts provide an elaborate description of the cost chain and its distributor analysis. This related Healthcare analyze gives finished facts which reinforces the knowing, scope and utility of this record.

in keeping with the file, global related healthcare market became valued at approximately USD 1,860.26 million in 2017 and is anticipated to generate profits of round USD 10,798.45 million through end of 2025, growing at a CAGR of around 27.47% between 2018 and 2025.

The document gifts the market competitive panorama and a corresponding specific evaluation of the principal dealer/key avid gamers available in the market. good groups within the world related Healthcare Market: Accenture, IBM, SAP, GE Healthcare, Oracle, Microsoft, Airstrip expertise, Medtronic, Allscripts, Boston Scientific, Athenahealth, Cerner, Philips, Agamatrix, Qualcomm, AliveCor and others.

click on the hyperlink to get a free sample copy of the report:https://www.marketinsightsreports.com/reports/01181057103/international-related-healthcare-market-measurement-reputation-and-forecast-2019-2025/inquiry?source=releasewire&Mode=34

global connected HEALTHCARE MARKET split by way of PRODUCT type AND applications:

This report segments the global connected Healthcare market on the basis of types are:TelemedicineHome MonitoringAssisted LivingClinical Monitoring

On the foundation of software, the international related Healthcare market is segmented into:prognosis and TreatmentMonitoring ApplicationsEducation and AwarenessWellness and PreventionHealthcare ManagementOthers

REGIONAL evaluation FOR connected HEALTHCARE MARKET:

For complete knowing of market dynamics, the world related Healthcare market is analyzed across key geographies particularly: united states, China, Europe, Japan, South-east Asia, India and others. each and every of these regions is analyzed on foundation of market findings across main nations in these areas for a macro-level figuring out of the market.

have an effect on OF THE linked HEALTHCARE MARKET document:-complete assessment of all alternatives and possibility within the connected Healthcare market.- linked Healthcare market recent improvements and principal pursuits.-unique study of company thoughts for increase of the related Healthcare market-leading avid gamers.-Conclusive examine in regards to the growth plot of linked Healthcare market for impending years.-In-depth understanding of linked Healthcare market-specific drivers, constraints and predominant micro markets.-beneficial influence internal vital technological and market latest developments outstanding the connected Healthcare market.

The document has one hundred fifty tables and figures browse the record description and TOC:https://www.marketinsightsreports.com/experiences/01181057103/international-related-healthcare-market-size-fame-and-forecast-2019-2025?supply=releasewire&Mode=34

WHAT ARE THE MARKET elements that are defined in the document?

-Key Strategic tendencies: The study also comprises the key strategic traits of the market, comprising R&D, new product launch, M&A, agreements, collaborations, partnerships, joint ventures, and regional boom of the leading competitors working in the market on a world and regional scale.

-Key Market points: The record evaluated key market points, including profits, expense, means, skill utilization rate, gross, production, creation fee, consumption, import/export, provide/demand, charge, market share, CAGR, and gross margin. additionally, the study presents a complete examine of the key market dynamics and their latest tendencies, together with pertinent market segments and sub-segments.

-Analytical equipment: The world connected Healthcare Market record contains the accurately studied and assessed statistics of the important thing trade avid gamers and their scope available in the market by way of means of a number of analytical tools. The analytical equipment corresponding to Porter's 5 forces analysis, SWOT evaluation, feasibility look at, and funding return evaluation were used to analyze the boom of the important thing avid gamers operating available in the market.

Customization of the report: This report can be customized as per your wants for further data up to 3 businesses or countries or 40 analyst hours.Please connect with our income crew ([email protected]).

ABOUT US:MarketInsightsReports provides syndicated market research on industry verticals together with Healthcare, suggestions and communication know-how (ICT), technology and Media, chemical compounds, substances, energy, Heavy business, and many others. MarketInsightsReports gives world and regional market intelligence insurance, a 360-diploma market view which comprises statistical forecasts, competitive landscape, special segmentation, key developments, and strategic techniques.

CONTACT US: Irfan Tamboli (Head of revenue) - Market Insights ReportsPhone: + 1704 266 3234 | +ninety one-750-707-8687[email protected] | [email protected]

For more counsel on this press unencumber talk over with: http://www.sbwire.com/press-releases/linked-healthcare-market-2019-highlights-and-fundamentals-accenture-ibm-sap-ge-healthcare-oracle-microsoft-airstrip-technology-medtronic-1129108.htm

Irfan TamboliSales HeadMarket Insights ReportsTelephone: 1-704-266-3234Email: click to electronic mail Irfan TamboliWeb: https://www.marketinsightsreports.com/


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Citrix Certification Guide: Overview and Career Paths | killexams.com real questions and Pass4sure dumps

Citrix is a cloud-computing company that offers solutions for mobility, desktop virtualization, cloud networking, cloud platforms, collaboration and data sharing that enable users to work securely from anywhere on a variety of devices. The company's products fall into five main categories: secure digital workspace, app virtualization and VDI, Enterprise Mobility Management (EMM), file sync and sharing, and networking. Popular Citrix products include XenApp, XenDesktop, XenMobile, NetScaler and ShareFile. More than 400,000 organizations and more than 100 million users run Citrix XenApp and XenDesktop products globally.

Citrix Systems started in 1989 and is headquartered in Fort Lauderdale, Florida. Today, the company offers a number of professional certifications that are recognized globally.

Citrix Certification Program Overview

Citrix certifications are solutions-based to reflect the needs of organizations and IT professionals. The three main Citrix certification topic areas are Desktop Virtualization, Networking and Enterprise Mobility Management. Citrix also offers credentials for Enterprise File Synchronization and Sharing. Last, but not least, Citrix Cloud certifications were introduced in 2018. 

Within the virtualization, networking and mobility certification topic areas, Citrix credentials are available at associate, professional and expert levels. These levels reflect the skills, knowledge and experience required of professionals who earn such Citrix certifications. Associate-level exams typically cost $200, whereas professional- and expert-level exams cost $300.

Here’s a summary of current certifications available from Citrix:

  • Citrix Certified Associate – Virtualization (CCA-V)
  • Citrix Certified Professional – Virtualization (CCP-V)
  • Citrix Certified Expert – Virtualization (CCE-V)
  • Citrix Certified Associate – Networking (CCA-N)
  • Citrix Certified Professional – Networking (CCP-N)
  • Citrix NetScaler SD-WAN Certified (CC-SDWAN)
  • Citrix Certified Professional – Mobility (CCP-M)
  • Citrix ShareFile Certified (CC-SHAREFILE)
  • XenApp and XenDesktop Service on Citrix Cloud Certified (CC-XAD-CC)
  • XenApp and XenDesktop Service Integration with Microsoft Azure Certified (CC-XAD-MA)
  • Citrix Virtualization Certifications CCA-V: Citrix Certified Associate – Virtualization

    The Citrix Certified Associate – Virtualization (CCA-V) certification is for operators and administrators proficient in installing, configuring, managing, maintaining, monitoring and troubleshooting XenApp and XenDesktop 7.15 solutions. There are no prereqs, but you must pass exam 1Y0-203 to earn this certification.

    The Citrix XenApp and XenDesktop 7.15 Administration exam (1Y0-203) covers 14 topics including an architectural overview, installation, provisioning, access, configuring, securing, monitoring, troubleshooting and more in a XenApp and XenDesktop 7.15 environment.

    CCP-V: Citrix Certified Professional – Virtualization

    The Citrix Certified Professional – Virtualization (CCP-V) certification is for engineers and consultants who manage and support complex  XenApp and XenDesktop 7.15 provisioning environments and solutions. To earn the CCP-V certification, you must have attained CCA-V certification and pass exam 1Y0-311.

    The Citrix XenApp and XenDesktop 7.15 LTSR Advanced Administrator exam (1Y0-311) covers 14 topics including advanced architecture, Virtual Desktop Agents (VDA), HDX and multimedia, provisioning (infrastructure, services, integrating and supporting), layering, vDisk streaming and more.

    CCE-V: Citrix Certified Expert – Virtualization

    The Citrix Certified Expert – Virtualization (CCE-V) certification is geared toward solution designers who may be architects, engineers or consultants, and who assess and design comprehensive XenApp and XenDesktop 7.15 or XenDesktop 7.6 solutions. To earn the CCE-V certification you must have already earned the CCA-V and CCP-V certifications and pass either of the 1Y0-402 or 1Y0-401 exams.

    The Designing Citrix XenDesktop 7.6 Solutions Exam (1Y0-401) is divided into six sections, which cover designing and documenting infrastructure and security infrastructure, designing integrated virtualization solutions, analyzing existing environments, conducting planning and risk assessments, and assessing existing environments. The Citrix XenApp and XenDesktop 7.15 Assessment, Design and Advanced Configurations exam (1Y0-402) covers 11 topics including design methodology, disaster recovery, high availability, access and security, store and resource design and more. 

    Required Exams

    Citrix Certified Associate – Virtualization (CCA-V)

    Citrix XenApp and XenDesktop 7.15 Administration (1Y0-203)

    Citrix Certified Professional – Virtualization (CCP-V)

    Citrix XenApp and XenDesktop 7.15 LTSR Advanced Administrator (1Y0-311)

    Citrix Certified Expert – Virtualization (CCE-V)

    Designing Citrix XenDesktop 7.6 Solutions (1Y0-401)

    OR

    Citrix XenApp and XenDesktop 7.15 Assessment, Design and Advanced Configurations  (1Y0-402)

    Citrix Virtualization Certification Ladder

    Citrix's Virtualization certifications build upon one another. Once you earn the Citrix Certified Associate (CCA-V) certification (exam 1Y0-203), you can take the Citrix Certified Professional (CCP-V) exam (1Y0-311). Once you've passed that exam, you can take either Citrix Certified Expert (CCE-V) exam: (1Y0-401) or (1Y0-402).

    These Virtualization certifications are valid for three years from the date you obtain them. If a new exam is released during this time, you must pass the latest exam for your level before the previous exam’s expiration date to maintain certification currency.

    Citrix Networking Certifications CCA-N: Citrix Certified Associate – Networking

    If you're a network or systems administrator who is an expert in app and desktop virtualization, and you want to expand your expertise to include NetScaler Gateway 12 for data, app and desktop solutions, the Citrix Certified Associate - Networking (CCA-N) certification is for you. Training is for administrators who have limited or no previous NetScaler experience.

    You must pass either the Citrix NetScaler 12 Essentials and Unified Gateway (1Y0-230) or Citrix NetScaler 12 Essentials and Traffic Management (1Y0-240) exam to earn the credential. Exam 1Y0-230 targets basic networking, Unified Gateway (including configuration), integration, managing client connections, load balancing, NetScaler platforms, AppExpert Expressions and policies, SSL offload, authentication and security. Exam 1Y0-240 covers basic networking, NetScaler platforms, high availability, load balancing, SSL offing, security, troubleshooting, default policies, URL transformation, switching, optimization and load balancing for global servers.   

    CCP-N: Citrix Certified Professional – Networking

    The Citrix Certified Professional - Networking (CCP-N) certification is aimed toward network engineers and people with some networking experience who implement, manage and optimize Citrix NetScaler Application Firewall in Citrix NetScalrer 12 environments, manage and optimize NetScaler application traffic, and manage NetScaler environments using NetScaler Management and Analytics System (NMAS).

    The five-day training Citrix NetScaler 12.x Advanced Topics – Security, Management and Optimization  (CNS-320) will help you do just that. It's for individuals with some understanding of NetScaler Essentials, traffic management and Unified Gateway.

    In addition to earning the CCA-N, you must pass the Citrix NetScaler Advanced Topics – Security, Management and Optimization (1Y0-304) exam to achieve the CCP-N certification. The exam targets firewalls, expressions, attacks, optimization (tuning and front-end), caching, monitoring and troubleshooting, monitoring and managing NetScaler configurations, instances and weblogging.  

    Citrix NetScaler SD-WAN Certified (CC-SDWAN)

    The newest addition to the Citrix cert portfolio is the Citrix NetScaler SD-WAN (CC-SDWAN), which is for professionals who configure the technology and improve its performance and reliability.

    To prepare for the credential, Citrix offers a four-module NetScaler SD-WAN hands-on workshop training course. Candidates must pass an exam at the end of each module with a score of 80 percent or higher. The modules are SD-WAN Overview, SD-WAN Architecture, SD-WAN Quality of Service, and SD-WAN Deployment Modes and Configuration. Additional modules that focus on SD-WAN 9.0, 9.1 and 9.2 features are included in the training. Citrix awards the certification only after passing all modules.

     

    Required Exams

    Citrix Certified Associate – Networking (CCA-N)

    Citrix NetScaler 12 Essentials and Unified Gateway (1Y0-230)

    OR

    Citrix NetScaler 12 Essentials and Traffic Management (1Y0-240)

    Citrix Certified Professional – Networking (CCP-N)

    Citrix NetScaler Advanced Topics – Security, Management and Optimization (1Y0-304)

    Citrix NetScaler SD-WAN Certified (CC-SDWAN)

    Four exams at the end of each NetScaler SD-WAN training module

    Citrix Networking Certification Ladder

    Candidates interested in a Citrix networking career should first obtain the CCA-N credential. From there, candidates will move to the CCP-N (which takes the CCA-N as a prerequisite). Candidates interested in pursuing a career path in NetScaler SD-WAN should pursue the CC-SDWAN credential. All networking credentials are valid for three years, after which you must recertify by passing the then-current exam or attending an instructor-led training course.

    Citrix Mobility Certifications CCP-M: Citrix Certified Professional – Mobility

    Mobility is a relatively new certification category for Citrix. Of the three possible credential levels, only the professional certification is currently live. (Associate and expert certifications for mobility are still in development.) The Citrix Certified Professional – Mobility (CCP-M) certification is aimed at mobile designers and developers. Such professionals must be familiar with designing, installing, configuring and managing a Citrix XenMobile Enterprise solution, including mobile device management, mobile application management and secure productivity.

    To obtain the CCP-M certification you must pass exam Designing, Deploying, and Managing Citrix XenMobile 10 Enterprise Solutions (1Y0-371) exam. The exam covers four areas: designing, installing, configuring and managing components of solutions using XenMobile 10 Enterprise.

    Enterprise File Synchronization and Sharing Citrix ShareFile Certified (CC-SHAREFILE)

    The Citrix ShareFile Certified (CC-SHAREFILE) credential targets Enterprise File Sync and Sharing (EFSS) solution professionals who manage corporate data sharing, access, synchronization and security using Citrix ShareFile solutions.  

    To earn the CC-SHAREFILE designation, candidates must complete the ShareFile Foundations training course and pass each module exam with a score of 80 percent or higher. The ShareFile Foundations course contains six modules: Getting Started with ShareFile, Management and Administration, Securing a ShareFile Deployment, Exploring the End User Experience, Supporting a ShareFile Deployment, and ShareFile Deployment Demos.

    Citrix Cloud Services Certifications                              

    Introduced in 2018, the Citrix Cloud Services certifications are the newest addition to the Citrix certification portfolio. Citrix offers two Citrix Cloud Services credentials: the XenApp and XenDesktop Service on Citrix Cloud Certified (CC-XAD-CC) and XenApp and XenDesktop Service Integration with Microsoft Azure Certified (CC-XAD-MA).

    XenApp and XenDesktop Service on Citrix Cloud Certified (CC-XAD-CC)

    The Citrix Cloud Certified (CC-XAD-CC) credential targets professionals who use XenApp and XenDesktop Service to deliver apps and desktops using the Citrix Cloud platform. To earn the CC-XAD-CC, candidates must take one of the following training courses: Moving to the XenApp and XenDesktop Service on Citrix Cloud and Microsoft Azure (CXD-252) or Moving to the XenApp and XenDesktop Service on Citrix Cloud (CXD-250).

    XenApp and XenDesktop Service Integration with Microsoft Azure Certified (CC-XAD-MA)

    The XenApp and XenDesktop Service Integration with Microsoft Azure Certified credential is focused on professionals who integrate and deliver XenApp and XenDesktop Service desktop and apps to Microsoft Azure. To earn the designation, candidates must take either the Moving to the XenApp and XenDesktop Service on Citrix Cloud and Microsoft Azure (CXD-252) or the Citrix XenApp and XenDesktop Service on Microsoft Azure (CXD-251) training course.  

    Exam release dates have not yet been announced but interested candidates may earn the credentials by taking the associated recommended training course.

      Required Exams

    XenApp and XenDesktop Service on Citrix Cloud Certified (CC-XAD-CC)

    Attend one of the following training courses:

    Moving to the XenApp and XenDesktop Service on Citrix Cloud and Microsoft Azure (CXD-252)

    OR

    Moving to the XenApp and XenDesktop Service on Citrix Cloud (CXD-250

    XenApp and XenDesktop Service Integration with Microsoft Azure Certified (CC-XAD-MA)

    Attend one of the following training courses:

    Moving to the XenApp and XenDesktop Service on Citrix Cloud and Microsoft Azure (CXD-252)

    OR

    Citrix XenApp and XenDesktop Service on Microsoft Azure (CXD-251)

    Related Jobs and Training

    Citrix careers tend to concentrate on their respective certification focus areas, namely, virtualization, networking and mobility. While Citrix professionals may be found in a variety of roles, they are most often known for their contributions as designers, architects, engineers and system administrators. You’ll also find Citrix certified professionals employed across all industry sectors.

    A simple search on SimplyHired.com (a popular job search board) found the following:

  • An Illinois-based collection company seeks a CCP-V desktop engineering professional  
  • A Kentucky computer company is searching for a Citrix systems engineer for cloud services  
  • A North Carolina IT provider seeks a senior Citrix engineer  
  • We also found a plethora of employers seeking engineering pros, including Citrix engineers for desktop services, data center, systems, solutions, managed services, virtualization, operations, converged infrastructure and VDI. Other common roles include managers, analysts, operators, infrastructure specialists, architects (cloud solutions, infrastructure and virtualization architects) and mobile designers.

    Citrix offers a variety of online resources to help you achieve your desired certification. Start by reviewing the site for each certification. You'll find helpful links to exam prep guides, as well as instructor-led training locations, virtual instructor-led courses and eLearning through Citrix Education. While not required, training is highly recommended. Also, check out Citrix's education blogs for up-to-date information on exam developments.

    The most valuable resources for the exams are Citrix exam prep guides. They contain sample questions, percentage weightings of major domains covered in each exam, objectives, extra resources and more.


    Resilience and efficiency in transportation networks | killexams.com real questions and Pass4sure dumps

    Abstract

    Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under normal conditions to improve the efficiency of urban road systems, analytic support for investments that improve resilience (defined as system recovery from additional disruptions) is still scarce. In this effort, we represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. We built road networks for 40 of the urban areas defined by the U.S. Census Bureau. We developed and calibrated a model to evaluate traffic delays using link loads. The loads may be regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the average annual delay per peak-period auto commuter, and modeled results were found to be close to observed data, with the notable exception of New York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was found to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under normal conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should be considered explicitly in roadway project selection and justify investment opportunities related to disaster and other disruptions.

    INTRODUCTION

    Existing roadway design standards emphasize the efficient movement of vehicles through a transportation network (1–4). Efficiency in this context may include identification of the shortest or fastest route (1, 5–7), or the route that minimizes congestion (8). It is the primary criterion on which road networks are modeled and design alternatives are considered (6, 7, 9, 10). The Texas A&M Transportation Institute defines and reports traffic delay in urban areas as the annual delay per auto commuter (11). Other studies define efficiency as delay for the individual driver in terms of time spent moving or stopped (7), or mean travel time between all origin-destination pairs in the network (9). However, as the experience of any motorist in large American cities can attest, conditions beyond the scope of the roadway design, including congestion, accidents, bad weather, construction, and special events (for example, a marathon race), can cause costly delays and frustrating inefficiencies that result in fuel waste, infrastructure deterioration, and increased pollution (12, 13). Evaluating road networks based only on efficiency under normal operating conditions results in little to no information about how the system performs under suboptimal or disrupted conditions.

    Infrastructure systems that exhibit adaptive response to stress are typically characterized as resilient (14–21). Given the essential role of transportation in emergency response, provision of essential services, and economic well-being, the resilience of roadway networks has received increasing policy attention. Nonetheless, scholars have yet to converge on a shared understanding of resilience suitable to guide design, operation, and reconstruction of roadway networks. Although resilience in infrastructure systems is characterized as a multidimensional concept (22, 23), in many engineering and civil infrastructure implementations, resilience is defined as the ability of a system to prepare for, absorb, recover from, and adapt to disturbances (16). Specific to transportation, resilience has been defined as “the ability of the system to maintain its demonstrated level of service or to restore itself to that level of service in a specified timeframe” (24). Others describe transportation resilience as simply the ability of a system to minimize operational loss (25) or use the term synonymously with robustness, redundancy, reliability, or vulnerability (26–28).

    Current efforts in transportation resilience research have focused on framework development and quantification methods. These efforts include the specification of resilience indicators, such as total traffic delay (24), economic loss (29), post-disaster maximum flow (30), and autonomous system components (31). Practical concerns with this type of resilience evaluation are that it relies on uncertain performance data and often omits indicators that are unquantifiable (19). Other resilience approaches apply traffic network modeling to identify locations for critical buildings (for example, hospitals and fire stations) (32), minimize trip distance for individual passengers (33), and minimize travel time across the system (12). One drawback of existing network resilience methods is that they are data-intensive, often requiring limited information about resources for unusual road system repair (26, 28) or network behavior following a disruptive event (34). Moreover, existing resilience quantification approaches lack calibration and testing across a range of transportation systems. Because many disruptive events, and their associated consequences, are difficult to predict, resilient road systems must be characterized and evaluated by the capacity to adapt to a variety of different stress scenarios. Partly because of these obstacles, joint consideration of efficiency and resilience has yet to be implemented for transportation networks.

    Here, we study the interconnections between resilience and efficiency (20) among road transportation networks in 40 major U.S. cities. We develop an urban roadway efficiency model, calibrate it on the basis of the observed data (11) of annual delay per peak-period auto commuter, and apply the model to calculate efficiency in 40 cities. Then, we model traffic response to random roadway disruptions and recalculate expected delays to determine the sensitivity of each city to loss of roadway linkages. The results may reveal important considerations for assessing proposals for improvement of roadway infrastructure that maintain efficiency under stress conditions.

    METHODS

    The Methods section appears here to help clarify the subsequent sections. To develop the urban roadway efficiency model, we defined the urban area boundaries, constructed the road networks, and evaluated the population density within cities using the Census Bureau data sets (35, 36) and OpenStreetMap (OSM) data sets (37). We relied on these data to assess commuter patterns, which we used to measure efficiency and resilience of road networks.

    Alternative approaches to transportation have been offered and include those based on percolation theory and cascading failures (38–40), human mobility pattern studies (41–43), queueing (44, 45), and the use of historical data to predict traffic. We review these approaches in the Supplementary Materials and note that the main benefit of our model is that it relies solely on readily available public data, rather than on particular data sets that may or may not be practical to obtain for any particular region. The model’s algorithmic simplicity allows us to consider spatial topologies of cities in high resolution including tens of thousands of nodes and links. We did not create a more accurate transportation model than the existing ones, but we were able to obtain measurable characteristics of transportation systems (average delays) using our model.

    Geospatial boundaries and population density

    To define geospatial boundaries for the transportation infrastructure networks, we used the U.S. Census Bureau geospatial data set (35) for urban areas—densely developed residential, commercial, and other nonresidential areas (46). We approximated the exact urban area polygon with a simplified manually drawn one (Fig. 1A) and included all roadways within 40 km (25 miles) of it in the network. For each of the links, we calculated its length on the basis of the polyline defining the link and assigned a number of lanes m and the FFSs (see the Supplementary Materials).

    Fig. 1 Definition of urban areas and assignment of nodes’ population.

    (A) Boston, MA-NH-RI urban area as defined by the U.S. Census Bureau shapefiles (gray background). To simplify the model and the algorithms calculating the distance from network nodes to the city boundary, we approximate each of the urban areas shapefiles with a coarse manually drawn polygon (pink outline). (B) Assignment of the number of people departing from each of the network nodes. Population distribution (color polygons; red corresponds to higher population density), Voronoi polygons (black outline), and network nodes (dots) in Downtown Boston.

    We next estimated population in vicinity of each intersection i using the Census Tract data (36). To this end, we split the map into Voronoi cells centered at intersections and then evaluated the population of each cell Ni asEmbedded Image

    Embedded Image

    (1)

    Above, Nt is the population of Census Tract t, and Pi and Pt are the polygons of the cell and the tract, respectively (Fig. 1B and table S2).

    Transportation model

    We built on the gravity model to generate commuting patterns. The gravity model (47) is a classical model for trip distribution assignment and is extensively adopted in most metropolitan planning and statewide travel demand models in the United States (48–51). Other trip distribution models include, for example, destination choice models (52, 53). However, these models are not as widely used in large scale, because the detailed data required by these models are frequently unavailable (48).

    We assumed that (i) the flow of commuters from origin region o to destination region d is proportional to the population at the destination Nd and that (ii) the flow of commuters depends on the distance xod between the origin and destination and is given by a distance factor, P(xod). Using these assumptions, we assessed the fraction of individuals commuting from region o to destination region d, fod, asEmbedded Image

    Embedded Image

    (2)

    Then, the commuter flow from origin region o to destination region d isEmbedded Image

    Embedded Image

    (3)

    Although individual driving habits may vary (54), we assumed that all drivers tended to optimize their commute paths such that their travel time was minimized. This assumption allowed us to calculate commute paths for every origin-destination pair using inferred FFSs. To calculate commuter flows between all pairs of intersections, we estimated distances xod as the distance of the shortest time path from o to d. Furthermore, in place of the distance factor P(xod), we used the distribution of trip lengths from the U.S. Federal Highway Administration National Household Travel Survey (55, 56), which we approximated with the exponential function (Fig. 2A and table S3).

    Fig. 2 Model details.

    (A) Distance factor P(xod) (Eq. 2) of trips given the distance between nodes (solid line) and the statistical data (bars). (B) Dependency of speed on density for V = 100 km/hour.

    Next, we defined the commuter load on each road segment asEmbedded Image

    Embedded Image

    (4)where θod(ij) is a binary variable equal to 0 when the link ij is not on the shortest path connecting nodes o and d, and 1 otherwise. Note that in Eq. 4, we only considered origins that were not farther than 30 km from the urban area boundary polygon. The nodes farther than 30 km from the boundary were only used as destinations to evaluate the fraction of commuters not going toward the urban area (Eq. 2).

    Because most commuters travel during peak periods, commuter loads Lij can be regarded as traffic-based centrality measures estimating the number of individuals using corresponding road segments. Then, the cumulative time lost by all commuters isEmbedded Image

    Embedded Image

    (5)where Vij and vij are, respectively, the FFS and the actual traffic speed along the ij road segment, lij is its length, l0 is the length correction due to traffic signals, and β is the proportionality coefficient same for all urban areas. The summation in Eq. 5 includes only links, whose origins and destinations are within the boundary polygon. A similar equation was obtained for the moving delay in the study of Jiang and Adeli (45), where the authors looked at the delay induced from road repairs.

    The actual traffic speed vij depends on many factors including the speed limit, the number of drivers on the road, and road conditions. Although there exist a number of approaches to estimate actual traffic speed (57, 58), we chose to use the Daganzo model (59) to derive the traffic speed, as shown in the Supplementary MaterialsEmbedded Image

    Embedded Image

    (6)where vmin is the minimum speed in the traffic, vveh is the correction for the finite size of the car, and α is the proportionality coefficient (Fig. 2B).

    Efficiency and resilience metrics

    We measured efficiency as the average annual delay per peak-period auto commuter. In practice, lower delay means higher efficiency. There are multiple ways to map from delays to efficiency, such as taking the inverse values of delays, taking negative values of delays, etc. To avoid ambiguity and facilitate the interpretation of results, we used the delays themselves to quantify the transportation efficiency of urban areas.

    We operationalized resilience through the change in traffic delays relative to stress, which is modeled as loss or impairment of roadway linkages. Looking at resilience from the network science perspective, we focused on topological features of cities, rather than on recovery resources available. Sterbenz et al. (60) evaluated a network’s resilience as a range of operational conditions for which it stays in the acceptable service region and highlighted that remediation mechanisms drive the operational state toward improvement. We are studying how availability of alternate routes helps remediate the consequences of the initial disruption to the network. In the traffic context, the immediate impact of a given physical disruption (and the time for it to unfold) in terms of closing lanes or reducing speed limits on affected roads will not vary much from network to network, although the number and type of these disruptions will. Likewise, the speed of restoring full functionality (through action in the physical domain) is not so much dependent on the road network as it is on the nature of the disruption (snow versus earthquake versus flood) and the resources that the city allocates to such repair. The level of functionality that these repairs achieve ought to be the full predisruption functionality, that is, eventually all roads can be fully cleared or restored. However, the immediate loss of function for a given traffic flow can very quickly be partially recovered after a disruption by action in the information domain, namely, rerouting of traffic. From the new steady state at that level of functionality, full functionality is gradually restored. Thus, our model proxies for resilience and is calibrated against the data that proxy for efficiency. At the same time, we note that to fully capture resilience characteristics of a transportation system, it is required to analyze recovery resources available and the effectiveness of coordination between the relevant authorities. Lower additional delay corresponds to higher resilience, but using the same reasoning that we had for efficiency, we quantified resilience through additional delays.

    RESULTS Efficiency

    Together, our traffic model has three parameters (proportionality coefficient α, minimum speed vmin, and finite vehicle size correction vveh) and is summarized in Eqs. 5 and 6. Given parameter values of the model, one can estimate the total delay incurred by all commuters in any given suburban area or, equivalently, the average delay per commuter. We take vveh = 9 km/hour and vmin = 5 km/hour and calibrate the model to determine the value of α to match the real data on the annual average delay per peak-period auto commuter provided by the Urban Mobility Scorecard (11).

    We divide the 40 urban areas into two equally sized groups for model calibration and validation, respectively. We have found that for the 20 urban areas used for calibration, the R-squared coefficient took values in the range (−0.01 to 0.83) (Fig. 3 and Supplementary Materials). This allows us to set model parameters α and β (see Methods) as follows: α = 4.30 × 104 hour−1 and β = 10.59. These values correspond to the Pearson coefficient of 0.91 (P = 2.17 × 10−8).

    Fig. 3 Modeled and observed delays in 40 urban areas.

    Pearson correlation coefficients and P values between observed and modeled delays are (0.91, 2.17 × 10−8) for the 20 cities used to calibrate the model and (0.63, 3.00 × 10−3) for the 20 cities used to validate the model. Observed delays were taken from the Texas A&M Transportation Institute Urban Mobility Scorecard (11).

    To validate the model, we estimate travel delays in 20 different urban areas. As seen from Fig. 3, the estimated travel delays are significantly correlated (R = 0.63, P = 3.00 × 10−3) with actual delay times (11), validating the transportation model. Figure 4 is a Google Maps representation of real and modeled results for Los Angeles and San Francisco. Road conditions under real, average traffic patterns at 8 a.m. provided by Google Maps are in Fig. 4 (A and D). Modeled conditions are given for comparison in Fig. 4 (B and E). Finally, Fig. 4 (C and F) shows the new, modeled traffic patterns that result from redistribution of travel in response to a disruption of 5% of the links.

    Fig. 4 Traffic distributions.

    Typical congestion at 8 a.m. for Los Angeles (top) and San Francisco (bottom) as given by Google Maps (A and D), modeled with no disruptions (B and E), and modeled with a 5% link disruption (C and F). Notably, in Los Angeles, the disruption results in traffic redistribution to smaller roads, whereas in San Francisco, it results in increased congestion along the major highways.

    Resilience

    Our approach to model stress is inspired by percolation theory. For every independent simulation of stress, we select a finite fraction of affected road segments r at random, with the probability of failure proportional to segment length. We collect statistics for 20 realizations of the percolation. On failed segments, free-flow speeds (FFSs) are reduced to 1 km/hour (representing near-total loss), and loads L and traffic delays are then recalculated using the updated FFSs. Low-stress scenarios (r < 0.1) might be caused by accidents or construction. Larger disruptions might occur during power failures that disrupt traffic signals or severe flooding that makes many roadways nearly impassable. Finally, widespread stress might be caused by snow, ice, or dust storms that affect nearly the entire roadway system. Figure 5 displays the analysis of delay times in six representative urban areas for the full spectrum of adverse event severities, r ⋲ [0; 1]. In addition, fig. S5 shows the results for all urban areas. Some routes within a single urban area experience longer delays than others. The inset of Fig. 5 shows the delay distribution for both Los Angeles, which is narrowly clustered, and Boston, where greater variability between roadways is evident. Traffic delay times grow rapidly as r increases and reach saturation (all routes moving at 1 km/hour) as r approaches 1. We determine the most resilient urban transportation network to be Salt Lake City, UT, whereas the least resilient among the 40 metropolitans is shown to be Washington, DC.

    Fig. 5 Dependency of the additional delay on the severity of the links disruption for six representative urban areas.

    Error bars show mean values ± SD. The inset shows distribution densities for two selected urban areas for 1000 realizations of 5% disruption. Note that San Francisco’s unique topology makes it susceptible to failures of a small number of discrete roadways, and this produces an anomalous impact at 5 to 15% disruption.

    Figure 6 shows both the efficiency (in blue) and resilience response (additional delays due to 5% link disruption, in orange) for the 40 urban areas modeled. Some cities with high efficiency under normal operating conditions (that is, low delays) nevertheless exhibit low resilience (that is, a sharp increase in traffic delays) under stress. Virginia Beach, VA; Providence, RI; and Jacksonville, FL all fall into this category of urban areas in which traffic operates well under ordinary circumstances but rapidly become snarled under mild stress. On the other hand, Los Angeles is notorious for traffic delays under all conditions—yet minor stress levels result in little degradation of efficiency. By contrast, normal traffic delays in San Francisco are comparable to Los Angeles, but mild stress in San Francisco results in large increases in additional delays. These examples indicate that resilience (that is, additional delay response to stress) is independent of normal operating efficiency.

    Fig. 6 Comparison of resilience and efficiency metrics.

    Annual impact of 5% disruption (additional delay) has a low correlation with normal annual delay per peak-period auto commuter (delay). Pearson R = 0.49, P = 1.18 × 10−3.

    DISCUSSION

    The disturbances affecting the road infrastructure are often complex, and their impact on the structure and function of roadway systems may be unknown (28, 31). These disturbances might be natural and irregular, such as distributed road closures caused by an earthquake or homogeneous vehicle slowing down because of a snowstorm. The disturbances might also be anthropogenic and intentional, such as a street fair or marathon race. Whatever the disturbance, the results of this analysis allow several meaningful inferences to be made that may have important implications for highway transportation policy. The first is that resilience and efficiency represent different aspects related to the nature of transportation systems; they are not correlated and should be considered jointly as complementary characteristics of roadway networks.

    Second, there are characteristic differences in the resilience of different urban areas, and these differences are persistent at mild, medium, or widespread levels of stress (Fig. 5). Except for San Francisco, CA, which is the most fragile of all cities represented in Fig. 5 at stress levels r < 20% but then surpassed by Boston, MA and Washington, DC, the rank ordering of urban area resilience is insensitive to stress levels. That is, cities that exhibit relatively low resilience under mild stress are the same cities that exhibit low levels of resilience (relative to peers) under widespread roadway impairment. This suggests that the characteristics that impart resilience (such as availability or alternate routes through redundancy of links) are protective against both the intermittent outages caused by occasional car crashes and those caused by snow and ice storms. For cities without resilience, a widespread hazard such as snow may lead to a cascade of conditions (for example, crashes) that rapidly deteriorate into gridlock. This was exactly the case for Washington, DC 20 January 2016 under only 2.5 × 10−2 m or 2.5 cm of snow (61), and for Atlanta, GA 2 years earlier, which experienced 5.1 × 10−2 m or 5.1 cm of snow in the middle of the day that resulted in traffic jams that took days to disentangle (62). Whereas popular explanations of these traffic catastrophes focus on the failure of roadway managers to prepare plows and emergency response equipment, Fig. 5 suggests that cities with similar climates (Memphis, TN and Richmond, VA) are less likely to be affected, regardless of the availability of plow or sand trucks.

    The third inference follows from Fig. 6, which suggests that urban areas that make capital investments to reduce traffic delays under normal operating conditions may nevertheless be vulnerable to traffic delays under mild stress conditions. Because these stressors are inevitable, whether from crashes, construction, special events, extreme weather, equipment malfunctions, or even deliberate attack, investment strategies that prioritize reduction of normal operating delays may have the unintended consequence of exacerbating tail risks—that is, the risk of worse catastrophe under unlikely but possible conditions.

    Finally, the exceptional position of New York City in Fig. 3 calls attention to the fact that substitutes for roadway transportation are available in many cities and have an important role to play in relieving traffic congestion. According to the Texas A&M Institute (63, 64), public transit reduces delays per peak-period auto commuter in the New York urban area by 63 hours, in Chicago by 23 hours, and by less than 20 hours in other urban areas. Because our model considers only roadway transit, and New York City contains a myriad of nonroad-based options to avoid roadway congestion, it is unlikely that our model can provide informative results for the New York urban area.

    Although interest has increased in policies that enhance roadway resilience, few analytic tools are available to guide new investments in achieving resilience goals. It is widely understood that roadway infrastructure is expensive, both in acquiring land for rights-of-way and in construction of improvements, and thus, decisions regarding alignment, crossing, and access made over a period of decades may have long-lasting consequences that are observable in traffic data today. Consequently, urban areas exhibit different unintentional traffic characteristics, including delays under normal and random stress conditions. Investments motivated exclusively by expected efficiencies under normal operating conditions are unreliable safeguards against loss of efficiency under stress conditions. Therefore, new analytic tools are required that allow designers to assess the adaptive capacity of roadway infrastructure and assess the potential of new investments to provide enhanced resilience. The adaptive network-based model described herein is one such approach.

    SUPPLEMENTARY MATERIALS

    Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/12/e1701079/DC1

    Alternative approaches to model transportation

    Mapping from OSM Foundation shapefiles to network nodes and links

    Population assignment algorithm

    Distance factor of the likelihood of travel between nodes

    Estimation of the traffic speed from the density of vehicles

    Model calibration procedure

    Sensitivity of the model to ramp speeds

    Additional delay as a function of the severity of link disruption

    table S1. Mapping original OSM types to network link types and assignment of the number of lanes.

    table S2. The algorithm of the node population assignment.

    table S3. Distance factor P(xod) of the likelihood of travel between nodes.

    table S4. Model sensitivity to ramp speed coefficient.

    fig. S1. Effects of the removal of nodes of degree 2.

    fig. S2. Density-flow relationship in the Daganzo traffic model.

    fig. S3. Model calibration.

    fig. S4. Modeled delays for ramp speed coefficients of 1/3 and 1/2.

    fig. S5. Dependency of the additional delay on the severity of the link disruption for all 40 urban areas.

    This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

    REFERENCES AND NOTES
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  • Acknowledgments: We would like to thank S. Buldyrev (Yeshiva University) and J. Palma-Oliveira (University of Lisbon) for their insightful comments. Funding: This study was supported by the U.S. Army Engineer Research and Development Center and by the Defense Threat Reduction Agency, Basic Research Program (P. Tandy, program manager). A.A.G. was additionally supported by the Virginia Transportation Research Council and Virginia Department of Transportation. T.S. was supported by the NSF under grant no. 1441352. Author contributions: A.A.G., M.K., and I.L. conceived the model and designed the simulations. A.A.G. developed software and performed data retrieval and simulations. A.A.G. and M.K. analyzed results. I.L. provided senior guidance. A.A.G., M.K., J.M.K., T.S., and I.L. wrote the paper and contributed to the interpretation of the results. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. Map data were copyrighted by OSM contributors and are available at www.openstreetmap.org.


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    Scribd : https://www.scribd.com/document/356764454/Pass4sure-A2010-578-Assess-Fundamentals-of-Applying-Tivoli-Service-Availability-Performance-Ma-exam-braindumps-with-real-questions-and-practice-soft
    Dropmark-Text : http://killexams.dropmark.com/367904/12023865
    Youtube : https://youtu.be/4Z3o2BW2x28
    Blogspot : http://killexams-braindumps.blogspot.com/2017/10/look-at-these-a2010-578-real-question.html
    RSS Feed : http://feeds.feedburner.com/JustStudyTheseIbmA2010-578QuestionsAndPassTheRealTest
    publitas.com : https://view.publitas.com/trutrainers-inc/where-can-i-get-help-to-pass-a2010-573-exam
    Google+ : https://plus.google.com/112153555852933435691/posts/N67MCfd19Ma?hl=en
    Calameo : http://en.calameo.com/books/004923526b6f8f3044c0a
    Box.net : https://app.box.com/s/iginewcbmes1crxhu6bed56d8l819yii
    zoho.com : https://docs.zoho.com/file/5bym214ca77d8bb30459280764ae29017cbbd
    coursehero.com : "Excle"






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