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000-111 - IBM Distributed Systems Storage Solutions Version 7 - Dump Information

Vendor : IBM
Exam Code : 000-111
Exam Name : IBM Distributed Systems Storage Solutions Version 7
Questions and Answers : 269 Q & A
Updated On : October 12, 2018
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000-111 IBM Distributed Systems Storage Solutions Version 7

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000-111 exam Dumps Source : IBM Distributed Systems Storage Solutions Version 7

Test Code : 000-111
Test Name : IBM Distributed Systems Storage Solutions Version 7
Vendor Name : IBM
Q&A : 269 Real Questions

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IBM IBM Distributed Systems Storage

right here's Who Made Gartner's 2016 Magic Quadrant For dispensed File techniques And Object Storage | killexams.com Real Questions and Pass4sure dumps

back ...     4       next

leader: IBM

IBM's acquisition of Cleversafe in the fourth quarter of 2015 gave IBM, a different "chief" – a aggressive, mature, proven and scalable product within the speedy-transforming into object storage market. Gartner checked out two IBM products: IBM Spectrum Scale and IBM Cloud Object Storage. IBM Spectrum Scale is based on widely wide-spread Parallel File system (GPFS), a disbursed file device constructed by means of IBM researchers within the late Nineties. IBM Spectrum Scale is enormously scalable and gives you aggressive concurrent throughput performance because of its decentralized architecture and productive information placement across the nodes. GPFS has been sold as an integrated hardware platform, the fresh focal point on application-described deployment fashions — both on-premises and in the IBM Cloud — has the skills to boost the beauty of the product to prospective consumers, Gartner noted.

 

 

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GPU-powered SQream DB Boosts question efficiency as much as 150% for IBM POWER9 | killexams.com Real Questions and Pass4sure dumps

nowadays SQream introduced that its GPU-powered SQream DB application now includes optimized assist for the IBM POWER9 multi-core structure. The powerful trinity of SQream DB, IBM POWER9 and NVIDIA GPUs results in superior performance, with SQL question efficiency advancements of up to one hundred fifty% versus GPU-equipped x86 servers, with reduced charge and methods complexity.

GPU-accelerated analytics are an increasingly crucial part of our business,” pointed out Sumit Gupta, VP of HPC and AI for IBM Cognitive methods. “The announcement of SQream on the IBM POWER9 platform takes this thought to another level of efficiency, as the POWER9 CPU with embedded NVIDIA NVLink interface to NVIDIA’s GPUs makes it possible for SQream to permit even quicker processing of facts on POWER9 servers.”

the integration of SQream DB with IBM vigor techniques is the latest in a series of collaborations between SQream and IBM, which begun with SQream DB on IBM’s Bluemix carrier. The newest providing on IBM POWER9 takes advantage of POWER9’s business-unique CPU-to-GPU high velocity NVIDIA NVLink interface to present elevated throughput efficiency each for records loading and SQL question execution.

Our purchasers are asking us for options that can address massive facts boom and true-time analytics to aid records-driven choice making,” observed Charlie Cox, President of Datatrend, an IBM Platinum and Elite vigour associate. “GPU acceleration is quite probably one of the crucial critical advances in helping agencies make sooner and better choices with the aid of analyzing greater records faster. IBM’s POWER9 architecture combined with SQream’s answer yields cost/performance at scales we now have never considered earlier than.”

SQream DB on IBM POWER9, combined with IBM Flash Storage solutions and built-in into the technology stack, provides a reference structure that allows businesses to operate SQL analytics for enterprise intelligence and machine discovering workloads at big scale and velocity.

SQream DB is designed for multi-core processors such as POWER9 with its mighty and robust multi-core structure,” noted Ami Gal, CEO of SQream. “As statistics outlets raise exponentially, so does the should method more records whereas decreasing complexity and footprint. This joint effort with IBM provides clients with an excellent-charged mixture of IBM’s potent processor and SQream’s DB data warehouse for unparalleled speed, performance and scale, while enabling vastly more desirable analytics.”

Many enormous firms and Fortune one hundred organizations already use IBM vigor techniques, together with right retailers, insurance organizations, telecoms and banks. through combining NVIDIA’s market main GPUs, at the side of IBM POWER9 and SQream DB, organizations can reach new ranges of analytics performance while drastically reducing their environmental footprint, fees and complexity.

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SQream DB includes GPU-accelerated information warehouse optimized aid for IBM POWER9 architecture | killexams.com Real Questions and Pass4sure dumps

SQream, developer of SQream DB, announced on the OpenPOWER groundwork Summit in Europe that SQream DB now contains optimized help for the IBM POWER9 multi-core architecture.

The aggregate of SQream DB, IBM POWER9 and NVIDIA GPUs consequences in superior performance, with SQL question performance advancements of up to one hundred fifty percent in comparison to GPU-equipped x86 servers, with reduced charge and programs complexity.

with the aid of combining NVIDIA’s GPUs, along side IBM POWER9 and SQream DB, businesses can reach new degrees of analytics performance whereas decreasing their environmental footprint, charges and complexity.

the integration of SQream DB with IBM vigor methods is the latest in a collection of collaborations between SQream and IBM, which begun with SQream DB on IBM’s Bluemix carrier. The newest providing on IBM POWER9 takes expertise of POWER9’s CPU-to-GPU excessive speed NVIDIA NVLink interface to present improved throughput efficiency both for data loading and SQL query execution.

SQream DB on IBM POWER9, mixed with IBM Flash Storage options and integrated into the know-how stack, offers a reference structure that permits agencies to perform SQL analytics for company intelligence and desktop getting to know workloads at big scale and velocity.

remaining month, SQream announced typical availability of SQream DB v3.0, which allows for businesses to load big volumes of information within the range of terabytes to petabytes for analysis, whereas generating better best company intelligence sooner than every other statistics shop at these volumes.

“GPU-accelerated analytics are an more and more critical a part of our trade,” referred to Sumit Gupta, VP of HPC and AI for IBM Cognitive methods. “The announcement of SQream on the IBM POWER9 platform takes this concept to one other degree of performance, as the POWER9 CPU with embedded NVIDIA NVLink interface to NVIDIA’s GPUs permits SQream to enable even sooner processing of records on POWER9 servers.”

“Our customers are asking us for solutions that can handle huge information growth and actual-time analytics to assist data-driven resolution making,” said Charlie Cox, president of Datatrend, an IBM Platinum and Elite energy associate. “GPU acceleration is fairly maybe one of the crucial important advances in assisting corporations make sooner and more desirable selections by analyzing more statistics sooner. IBM’s POWER9 architecture combined with SQream’s solution yields cost/efficiency at scales we now have certainly not viewed earlier than.”

“SQream DB is designed for multi-core processors reminiscent of POWER9 with its potent and amazing multi-core structure,” noted Ami Gal, CEO of SQream. “As records stores increase exponentially, so does the should manner extra statistics whereas decreasing complexity and footprint. This joint effort with IBM gives users with an excellent-charged mixture of IBM’s effective processor and SQream’s DB data warehouse for unparalleled velocity, efficiency and scale, while enabling enormously more advantageous analytics.”


000-111 IBM Distributed Systems Storage Solutions Version 7

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Killexams.com 000-111 Dumps and Real Questions

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000-111 exam Dumps Source : IBM Distributed Systems Storage Solutions Version 7

Test Code : 000-111
Test Name : IBM Distributed Systems Storage Solutions Version 7
Vendor Name : IBM
Q&A : 269 Real Questions

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NVIDIA Introduces RAPIDS Open-Source GPU-Acceleration Platform for Large-Scale Data Analytics and Machine Learning | killexams.com real questions and Pass4sure dumps

HPE, IBM, Oracle, Open-Source Community, Startups Integrate RAPIDS, Giving Giant Performance Boost to End-to-End Predictive Data Analytics

MUNICH, Germany, Oct. 10, 2018 (GLOBE NEWSWIRE) --  GTC Europe—NVIDIA today announced a GPU-acceleration platform for data science and machine learning, with broad adoption from industry leaders, that enables even the largest companies to analyze massive amounts of data and make accurate business predictions at unprecedented speed.

RAPIDS™ open-source software gives data scientists a giant performance boost as they address highly complex business challenges, such as predicting credit card fraud, forecasting retail inventory and understanding customer buying behavior. Reflecting the growing consensus about the GPU’s importance in data analytics, an array of companies is supporting RAPIDS — from pioneers in the open-source community, such as Databricks and Anaconda, to tech leaders like Hewlett Packard Enterprise, IBM and Oracle.

Analysts estimate the server market for data science and machine learning at $20 billion annually, which — together with scientific analysis and deep learning — pushes up the value of the high performance computing market to approximately $36 billion.

“Data analytics and machine learning are the largest segments of the high performance computing market that have not been accelerated — until now,” said Jensen Huang, founder and CEO of NVIDIA, who revealed RAPIDS in his keynote address at the GPU Technology Conference. “The world’s largest industries run algorithms written by machine learning on a sea of servers to sense complex patterns in their market and environment, and make fast, accurate predictions that directly impact their bottom line.

“Building on CUDA and its global ecosystem, and working closely with the open-source community, we have created the RAPIDS GPU-acceleration platform. It integrates seamlessly into the world’s most popular data science libraries and workflows to speed up machine learning. We are turbocharging machine learning like we have done with deep learning,” he said.

RAPIDS offers a suite of open-source libraries for GPU-accelerated analytics, machine learning and, soon, data visualization. It has been developed over the past two years by NVIDIA engineers in close collaboration with key open-source contributors.

For the first time, it gives scientists the tools they need to run the entire data science pipeline on GPUs. Initial RAPIDS benchmarking, using the XGBoost machine learning algorithm for training on an NVIDIA DGX-2™ system, shows 50x speedups compared with CPU-only systems. This allows data scientists to reduce typical training times from days to hours, or from hours to minutes, depending on the size of their dataset.

Close Collaboration with Open-Source CommunityRAPIDS builds on popular open-source projects — including Apache Arrow, pandas and scikit-learn — by adding GPU acceleration to the most popular Python data science toolchain. To bring additional machine learning libraries and capabilities to RAPIDS, NVIDIA is collaborating with such open-source ecosystem contributors as Anaconda, BlazingDB, Databricks, Quansight and scikit-learn, as well as Wes McKinney, head of Ursa Labs and creator of Apache Arrow and pandas, the fastest-growing Python data science library.

“RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow,” McKinney said. “NVIDIA’s collaboration with Ursa Labs will accelerate the pace of innovation in the core Arrow libraries and help bring about major performance boosts in analytics and feature engineering workloads.”

To facilitate broad adoption, NVIDIA is integrating RAPIDS into Apache Spark, the leading open-source framework for analytics and data science.

“At Databricks, we are excited about RAPIDS’ potential to accelerate Apache Spark workloads,” said Matei Zaharia, co-founder and chief technologist of Databricks, and founder of Apache Spark. “We have multiple ongoing projects to integrate Spark better with native accelerators, including Apache Arrow support and GPU scheduling with Project Hydrogen. We believe that RAPIDS is an exciting new opportunity to scale our customers’ data science and AI workloads.”

Broad Ecosystem Support and AdoptionTech-leading enterprises across a broad range of industries are early adopters of NVIDIA’s GPU-acceleration platform and RAPIDS.

“NVIDIA’s GPU-acceleration platform with RAPIDS software has immensely improved how we use data — enabling the most complex models to run at scale and deliver even more accurate forecasting,” said Jeremy King, executive vice president and chief technology officer at Walmart. “RAPIDS has its roots in deep collaboration between NVIDIA’s and Walmart’s engineers, and we plan to build on this relationship.”

Additionally, some of the world’s leading technology companies are supporting RAPIDS through new systems, data science platforms and software solutions:

“HPE is committed to advancing the way customers live and work. Artificial intelligence, analytics and machine learning technology can play a critical role in uncovering insights that can help customers achieve breakthrough results and improve the world we live in. HPE is unique in the market in that we provide complete AI and data analytics solutions from strategic advisory to purpose-built GPU accelerator technology, operational support and a strong partner ecosystem to tailor the right solution for each customer. We are excited to partner with NVIDIA on RAPIDS to accelerate the application of data science and machine learning to help our customers drive faster and more insightful outcomes.”— Antonio Neri, CEO, Hewlett Packard Enterprise

“IBM has built the world’s leading platform for enterprise AI, regardless of deployment model. We look forward to extending our successful partnership with NVIDIA, leveraging RAPIDS to provide new machine learning tools for our clients.”— Arvind Krishna, senior vice president of Hybrid Cloud and director of IBM Research

“The compute world today requires powerful processing to handle complex workloads like data science and analytics — it’s a job for NVIDIA GPUs. RAPIDS is accelerating the speed at which this processing and machine learning training can be done. We are excited to support this new suite of open-source software natively on Oracle Cloud Infrastructure and look forward to working with NVIDIA to support RAPIDS across our platform, including the Oracle Data Science Cloud, to further accelerate our customers’ end to-end data science workflows. RAPIDS software runs seamlessly on the Oracle Cloud, allowing customers to support their HPC, AI and data science needs, all while taking advantage of the portfolio of GPU instances available on Oracle Cloud Infrastructure.”— Clay Magouyrk, senior vice president of Software Development, Oracle Cloud Infrastructure

Support from other leading innovators — including Cisco, Dell EMC, Lenovo, NERSC, NetApp, Pure Storage, SAP and SAS, as well as a wide range of data science pioneers — is appended to this press release.

AvailabilityAccess to the RAPIDS open-source suite of libraries is immediately available at https://www.rapids.ai, where the code is being released under the Apache license. Containerized versions of RAPIDS will be available this week on the NVIDIA GPU Cloud container registry.

About NVIDIANVIDIA‘s (NASDAQ: NVDA) invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. More information at https://nvidianews.nvidia.com/.

Additional Supporting Quotations

Anaconda - Scott Collison, CEO“NVIDIA has made the training and deployment of complex AI models scalable and economically viable. Today’s RAPIDS announcement by NVIDIA extends the same benefits to earlier data transformation stages of the data science lifecycle. Anaconda is proud to have helped NVIDIA develop these new capabilities, which will be available to the community of 7 million users of the Anaconda Distribution through our public package repository. We’ll also be including them in Anaconda Enterprise, which, combined with NVIDIA DGX, delivers a high-performance, proven solution for business. Anaconda Enterprise on NVIDIA DGX will enable IT organizations of all sizes to accelerate data science and AI workflows.”

BlazingDB - Rodrigo Aramburu, CEO"We are thrilled to be early contributors to the RAPIDS open-source software from NVIDIA, and have built BlazingSQL, a free to use version of our distributed GPU SQL engine, on RAPIDS. Our partnership with NVIDIA has provided immense value to us as a startup as we collaborated with the RAPIDS team, joined as key contributors to cuDF, and will continue to support the RAPIDS software as we build our vision of integrating Data Lakes with AI, all using SQL.”

Cisco - Kaustabh Das, vice president of Product Management, Data Center Group“Cisco and NVIDIA are collaborating on AI/ML software stacks on NVIDIA GPU-optimized Cisco UCS platforms to simplify and accelerate AI/ML workload deployment. We are excited to learn that, with RAPIDS, NVIDIA is expanding their GPU applicability with accelerated software stacks to address traditional machine learning and big data analytics. We look forward to the possibilities for our GPU-accelerated server portfolio, including the recently launched Cisco UCS C480 ML M5 Rack Server, a best in class, purpose-built server with eight NVIDIA V100 GPUs and NVIDIA NVLink interconnect.”

Dell EMC - Ravi Pendekanti, senior vice president of Product Management and Marketing, Servers & Infrastructure Systems“Dell EMC is committed to providing our customers with world-class IT infrastructures that enable them to gain real, competitive business advantage. We work with our ecosystem partners to ensure our customers have the latest data science tools available to help them transform data insights into business outcomes. Our goal is to combine the new GPU-accelerated open-source data science software from NVIDIA with our portfolio of NVLink-enabled Dell EMC PowerEdge servers to significantly accelerate the fields of machine learning and big data analytics.”

FASTDATA.io - Alen Capalik, founder and CEO"The RAPIDS open-source project launched by NVIDIA is going to revolutionize the data science pipeline. At FASTDATA.io, we're excited that our Plasma Engine — the first software to fully leverage NVIDIA GPUs for real-time processing of infinite data in motion — will play a part in that revolution."

Georgia Tech - David Bader, professor“Georgia Tech is excited to contribute to RAPIDS, an open-source playground for NVIDIA GPU-accelerated analytics. In this age of massive data, our contribution to the RAPIDS graph libraries will help data scientists gain meaningful knowledge from ever-growing datasets.”

Graphistry - Leo Meyerovich, co-founder and CEO“Graphistry, one of the first GPU cloud startups, has been quietly bringing new levels of visibility to sensitive F500 and federal teams that must comb through records in finance, cybersecurity, operations, and sales. As an early contributor to RAPIDS and a force behind Apache Arrow, Graphistry has taken a big bet on RAPIDS. The firm is already known for having redefined the visual compute fabric to be a real-time blending of browser and cloud GPUs, and is working with the RAPIDS team to add next-level tabular analytics to its existing graph GPU visual analytics core."

H2O.ai - Sri Ambati, founder and CEO“Machine learning is transforming businesses and NVIDIA GPUs are speeding them up. With the support of the open source communities and customers, H2O.ai made machine learning on GPUs mainstream and won recognition as a leader in data science and machine learning platforms by Gartner. NVIDIA's support of the GPU machine learning community with RAPIDS, its open-source data science libraries, is a timely effort to grow the GPU data science ecosystem and an endorsement of our common mission to bring AI to the data center. Thanks to our partnership, H2O Driverless AI powered by NVIDIA GPUs has been on an exponential adoption curve — making AI faster, cheaper and easier."

INRIA (scikit-learn) - Gael Varoquaux, director of Scikit-Learn Operations"NVIDIA is demonstrating real progress in accelerating data science with new productivity tools such as RAPIDS. Combining very fast computation in a high-language is a game changer for data-analytics teams. We are excited that NVIDIA has chosen to make RAPIDS compatible with scikit-learn. We believe that it can benefit our community and look forward to collaborating with NVIDIA."

Kinetica - Nima Negahban, co-founder and CTO“The RAPIDS suite of open-source libraries is a significant improvement in enabling data scientists to leverage the power of the GPU across their model development toolchain. RAPIDS can dramatically simplify and optimize training and improve model accuracy, without any significant logical redesign effort on the part of the data scientist. We’re excited to partner with NVIDIA in this journey to democratize AI — with NVIDIA driving model development and training and Kinetica driving operationalization and deployment of those models, enabling enterprises to gain maximum insight from their data.”

Lenovo - Kirk Skaugen, president of Data Center Group“Enterprise customers and academia continue to be challenged in working with and analyzing massive amounts of data as they develop and test new strategies. The new RAPIDS open-source software promises to accelerate workflows by running them end-to-end on NVIDIA GPUs. We believe this innovation and collaboration will make a significant impact for customers.”

MapR - John Schroeder, CEO“RAPIDS is a breakthrough announcement for data science and, more importantly, the ability to directly impact an organization with data science. MapR is supporting this effort by focusing on complementary data management and deployment activities to accompany the end-to-end RAPIDS data science training and model workflow.”

NERSC - Rollin Thomas, Python data analytics lead"NERSC supports more than 7,000 researchers at universities, national labs and in industry. They increasingly want productive, high-performance ways of interacting with their data from complex science simulations or experimental and observational facilities like particle accelerators and telescopes. We look forward to working with NVIDIA to put new high-performance Python data analytics tools like RAPIDS in the hands of our users to accelerate their pace of discovery across many scientific disciplines."

NetApp - Octavian Tanase, senior vice president of ONTAP“Organizations must take advantage of new artificial intelligence capabilities to drive competitive advantage and accelerate digital transformation. The combination of RAPIDS powered by NVIDIA GPUs and NetApp’s AFF A800 cloud-connected all-flash storage will help customers confidently tap into growing data resources with virtually unlimited scalability and performance needed to feed, train and operate data-hungry AI applications.”

NumFOCUS - Andy Terrel, president of the board of directors"NVIDIA’s support of NumFOCUS represents an investment to the community. As two leaders in data science, we feel our work together will bring better tools to science and business alike."

OmniSci - Todd Mostak, CEO and co-founder“Data scientists use OmniSci on NVIDIA GPUs to accelerate data exploration and feature engineering when creating machine learning models. Now our users can interactively query and visualize data at scale in OmniSci, and then pipe the results into RAPIDS’ open-source libraries, enabling powerful end-to-end data science workflows. Together, NVIDIA and OmniSci make it much faster to build and iterate on models, resulting in increased accuracy and quicker time to deployment.”

Pure Storage - Matt Burr, general manager of FlashBlade"Our customers look to data for insights that separate them from the competition and deliver ever-increasing value for their end users. RAPIDS amplifies the impact of NVIDIA GPU acceleration and Pure Storage FlashBlade for data science and machine learning workflows to help more data scientists speed their training pipelines while maintaining optimal low-latency performance for faster time to results.”

Quansight - Travis Oliphant, NumPy and SciPy creator, co-founder and director of Anaconda, founder and CEO of Quansight"NVIDIA has long been a leader in accelerated tools for advanced analytics and has consistently offered freely available high-speed libraries for use by developers in the data-science community. I am thrilled to see their expanded open-source framework for data-science and their commitment to an end-to-end software and hardware solution. These innovations will enable a dramatic speed-up of the entire data-science workflow and unleash innovation across the broader open-source ecosystem."

SAP - Juergen Mueller, chief innovation officer“SAP has worked with NVIDIA closely over the past several years to take advantage of GPU acceleration for many SAP Leonardo Machine Learning-enabled solutions. We are furthering that collaboration now to explore the possibilities offered by RAPIDS, which promises to hypercharge data science pipelines on GPUs. This is an important step to accelerate data science and machine learning for data scientists as we bring intelligence to enterprises with SAP Leonardo and SAP HANA.”

SAS - Saratendu Sethi, head of Artificial Intelligence and Machine Learning“We are working closely with NVIDIA to contribute to the new GPU-accelerated data science library. We look forward to future SAS Viya offerings to take advantage of RAPIDS so that our customers can gain valuable insight from their data even faster.”

SQream - Ami Gal, CEO“The work NVIDIA has done on RAPIDS presents an exciting opportunity for dramatically speeding up the data science pipeline. By combining SQream DB’s capability of piping in very large amounts of data into the RAPIDS data science platform, we expect that data scientists will be able to run models faster and on more data than ever before.”

University of California, Davis - John Owens, professor and Gunrock project lead“We are delighted to be part of the RAPIDS community and look forward to working with NVIDIA and its partners in building the highest-performance, most comprehensive ecosystem for data analytics.”

For further information, contact:Kristin BrysonPR Director for Data Center AI, HPC and Accelerated ComputingNVIDIA Corporation+1-203-241-9190kbryson@nvidia.com

Certain statements in this press release including, but not limited to, statements as to: the benefits, impact, performance, and availability of the RAPIDS GPU-acceleration platform; the sizes of the server market for data science and machine learning and of the high performance computing market; the benefits and impact of NVIDIA’s collaboration with Ursa Labs; and Walmart’s relationship plans with NVIDIA are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company’s website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

© 2018 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, DGX and RAPIDS are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.



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References :


Dropmark : http://killexams.dropmark.com/367904/11587765
Wordpress : http://wp.me/p7SJ6L-Vv
Issu : https://issuu.com/trutrainers/docs/000-111
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Calameo : http://en.calameo.com/books/004923526d29f762a374d
publitas.com : https://view.publitas.com/trutrainers-inc/pass4sure-000-111-practice-tests-with-real-questions
Box.net : https://app.box.com/s/y8s0ia8x4a2sjctkyluk9f3zxq0es81w
zoho.com : https://docs.zoho.com/file/5ptno29d914de95544b28810055264631e8ab






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IBM 000-111 Exam (IBM Distributed Systems Storage Solutions Version 7) Detailed Information



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