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Vendor : IBM
Exam Code : 000-775
Exam Name : Applying Fundamentals of IBM Tivoli On Demand Automation
Questions and Answers : 150 Q & A
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000-775 Applying Fundamentals of IBM Tivoli On Demand Automation

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000-775 exam Dumps Source : Applying Fundamentals of IBM Tivoli On Demand Automation

Test Code : 000-775
Test Name : Applying Fundamentals of IBM Tivoli On Demand Automation
Vendor Name : IBM
Q&A : 150 Real Questions

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IBM Applying Fundamentals of IBM

IBM Watson: The interior story of how the Jeopardy-profitable supercomputer turned into born, and what it wants to do next | killexams.com Real Questions and Pass4sure dumps

IBM Watson wowed the tech trade and a nook of U.S. pop lifestyle with its 2011 win in opposition t two of Jeopardy's gold standard champions. here's how IBM pulled it off and a look at what Watson's real profession goes to be.

Between them, they'd racked up over $5 million in winnings on the tv quiz reveal Jeopardy. They were the premier avid gamers the demonstrate had produced over its a long time-long lifetime: Ken Jennings had the longest unbeaten run at 74 winning appearances, whereas Brad Rutter had earned the greatest prize pot with a complete of $3.25 million.

Rutter and Jennings have been Jeopardy-winning machines. And in early 2011, they agreed to an exhibition suit towards an opponent who'd in no way even stood at the back of a Jeopardy podium earlier than.

but this Jeopardy unknown had spent years making ready to take on both giants in the $1m suit, playing one hundred video games in opposition t previous winners in an effort to increase his chances of profitable.

That opponent didn't smile, provided all his solutions within the same emotionless tone, and wouldn't sit down in the equal room as his fellow contestants. He had to work too tough at keeping his cool and became so noisy, it become idea he was too disruptive to take the podium in person. He become saved in a back room, his solutions piped into the studio.

You would not understand by way of him what he become pondering - might be you'd spot only a tinge of coloration when he become difficult over a very hard query.

The contender all started out with a run of profitable answers - he knew his Beatles songs, Olympic heritage, literary criminals. certain, he wasn't too regular with his Harry Potter, but he stretched out a lead nevertheless, leaving Rutter and Jennings trailing hundreds of bucks behind.

but questions about decades tripped him up, and Rutter fought back, piling up satisfactory cash to unsettle anyone who'd wager on the outcome of the healthy. by means of the end of the first of the special exhibition fit indicates, you'll had been tough pushed to work out which turned into safest along with your money.

but then Double Jeopardy happened. The upstart powered throughout the huge questions, winning even with guesses he turned into far from convinced about, and putting unusual bets that got here first rate.

by the conclusion of the second episode, the unknown had $25,000 greater than his closest opponent, Rutter. Rutter and Jennings looked increasingly uncomfortable because it begun to look like they'd get a pasting from the brand new boy, bobbing in frustration as their opponent buzzed in before them time and time again.

"I, for one, welcome our new computer overlords" Ken Jennings' response to losing to an exhibition Jeopardy match to Watson

Jennings managed a late fightback in the third episode, however the new opponent gradually clawed returned sufficient cash to make it an in depth run.

All three correctly answered the last question 'William Wilkinson's 'An account of the principalities of Wallachia and Moldavia' impressed this creator's most noted novel' with 'who is Bram Stoker?' but Jennings appended his response with: "I for one welcome our new desktop overlords".

He, and Rutter, had lost to Watson - a room-sized beast of a desktop made by IBM and named after the enterprise's founder Thomas J Watson.

Watson, including ten racks of ten vigor 750 servers, needed to be saved apart from the human contestants as a result of the roar of its cooling system and became represented at the podium with the aid of an avatar of IBM's Smarter Planet brand, whose moving lines would go eco-friendly when Watson had cracked a thorny issue, orange when the reply became incorrect.

whereas Watson had the questions delivered in text in place of through paying attention to the quizmaster, he played the video game like his human counterparts: puzzle over the query, buzz in, give the reply it's definitely to be appropriate, tot up some prize money.

And Watson became right loads of the time. He won the video game with $seventy seven,147 leaving Rutter and Jennings within the grime with $21,600 and $24,000 respectively.

It became out that the real Jeopardy winning desktop was, smartly, a machine.

Three nights, two individuals, one laptop and $1 million: the victory of IBM's Watson over two human contestants on Jeopardy became the first, and possibly only, time the laptop impressed itself on the prevalent public's consciousness.

watson-jeopardy

IBM Watson defeated two of Jeopardy's gold standard champions.

photo: IBM

but even before Watson secured its now-noted win, IBM changed into working on how to turn the lovely quiz show-dominating laptop into a significant business contender.

Watson began life 5 years before its tv appearance, when IBM analysis execs have been looking for the subsequent "Grand challenge" for the enterprise. IBM periodically runs these Grand Challenges, selected tasks that pit man against computing device, have international attraction, are handy to draw close and attract americans into working in science and maths fields. along with Watson, the Grand Challenges have spawned Deep Blue, the computing device that famously beat grand master Garry Kasparov at chess, and the Blue Gene supercomputer.

within the mid-2000s, IBM became in search of its next Grand challenge. Paul Horn, then director of IBM research, was in favour of attempting to develop a desktop that could win the Turing check, a way to measure machine intelligence through having a equipment try and fool a human into thinking that they're having a conversation with a different adult.

As challenging as passing the Turing look at various is - no computer has yet completed it - it become felt that it wouldn't in all probability light up the public's creativeness as different projects had. but had been there any connected challenges that could nevertheless convey those aspects of competing towards people and realizing human speech together?

"They at the beginning pointed out no, it's a silly assignment to work on, it's too gimmicky, it's not a true computer science verify, and we probably can not do it anyway" IBM researchers' first tackle building a laptop that may win Jeopardy

"Beating a human in Jeopardy is a step in that course - the questions are advanced and nuanced, and it takes a different classification of laptop to have a chance of beating a human by answering these category of questions. i used to be operating the research division and that i turned into bugging people in the corporation, in particular [former EVP in IBM's software group] Charles Lickel," Horn spoke of.

Lickel turned into impressed to take on the problem of building a Jeopardy-successful laptop after having dinner together with his crew. "We have been at a steak condo in Fishtail, ny. within the center of dinner, all of a sudden the complete restaurant cleared out to the bar - I became to my group and requested 'what's occurring?'. It became very atypical. I hadn't in reality been following Jeopardy, nevertheless it grew to become out it became when Ken Jennings changed into having his lengthy profitable streak, and everyone desired to find out if he would win once more that evening, and they'd gone to the bar to look," Lickel stated. Jennings gained as soon as again that evening, and nevertheless holds the longest unbeaten run on Jeopardy with 74 appearances undefeated.

The concept of a quiz champion machine didn't automatically win his team round, with lots of Lickel's most useful personnel announcing they didn't trust a laptop could compete with, let alone beat, flesh and blood champions.

"They originally stated no, it's a foolish assignment to work on, it's too gimmicky, or not it's now not a true computing device science look at various, and we doubtless can not do it anyway," referred to Horn.

nevertheless, a group sufficiently adventuresome to take on the challenge of building a Jeopardy winner was found.

It became nevertheless a small undertaking and options of commercialisation weren't uppermost in any individual's intellect - Grand Challenges were demonstration initiatives, whose return for the company become more within the buzz they created than in a contribution to the bottom line. If commercialisation happened, first rate - however for now, Watson became just just a little of a moonshot for IBM.

because of the preliminary size of the trouble, it became funded from the research neighborhood's normal budget and failed to require sign-off from big Blue's bigger-ups, which means it could operate freed from the industrial pressures of most projects.

Jeopardy's quirk is that as a substitute of the quizmaster atmosphere questions and contestants featuring the answer, the quizmaster provides the answers, known as 'clues' in Jeopardy-speak, to which contestants give a query. no longer best would the computing device need to be able to produce questions for the feasible clues that might come its means on Jeopardy, it could should be in a position to first pull aside Jeopardy's tricksy clues - work out what was being requested - before it might even supply the appropriate response.

trebeck-jeopardy

Jeopardy host Alex Trebeck and the IBM crew talk concerning the exhibition fit with Watson.

photograph: IBM

For that, IBM developed DeepQA, a vastly parallel software architecture that examined herbal language content in both the clues set via Jeopardy and in Watson's own stored facts, together with looking into the structured counsel it holds. The part-primarily based device, developed on a series of pluggable add-ons for looking and weighting information, took about 20 researchers three years to reach a level the place it might tackle a quiz reveal efficiency and are available out looking greater than its human opponents.

First up, DeepQA works out what the question is asking, then works out some feasible solutions in accordance with the tips it has handy, creating a thread for each. each thread makes use of hundreds of algorithms to look at the evidence, taking a look at factors including what the counsel says, what category of counsel it is, its reliability, and the way possible it is to be primary, then developing an individual weighting in keeping with what Watson has in the past realized about how probably they're to be right. It then generated a ranked list of answers, with proof for each and every of its alternatives.

The information that DeepQA would at last be in a position to question for Jeopardy became 200 million pages of suggestions, from plenty of sources. all of the counsel needed to be in the community kept - Watson wasn't allowed to connect to the internet right through the quiz - and understood, queried and processed at a fair clip: in a Jeopardy's case, Watson needed to spit out an solutions in a count number of seconds to be sure it become first to the buzzer.

"when I left IBM in end of 2007, Watson turned into an embryonic project," stated Horn. "It had three individuals in Charles Lickel's enviornment that bought the statistics from the old Jeopardy programmes and have been starting to educate the machine. It could barely beat a 5 12 months historical at that time. The projection changed into 'god is aware of how long it will take to beat an adult, let alone a grand champion'. Then over time when it appeared like they all started to have an opportunity, Dave beneath the leadership of John Kelly grew the challenge into whatever thing monstrous," said Horn.

while there is nonetheless debate over precisely when the thought of creating Watson pay its approach eventually took form at IBM, when Watson took to the stage for its Jeopardy-successful performance, the demonstrate featured IBM professionals speakme about viable makes use of for the device in healthcare, and strikes to set up a Watson business unit began now not lengthy after the Jeopardy exhibit aired.

IBM's then-CEO Sam Palmisano and its latest CEO Ginni Rometty, under whose remit Watson fell at the time, began discussions in the weeks after the win, and the task turned into moved from beneath the wing of IBM research and into the IBM software neighborhood.

In August of 2011, the Watson enterprise unit suitable came into being, headed up by Manoj Saxena, who'd joined IBM some years prior when the business he labored for, Webify, became got by IBM.

Saxena turned into the unit's worker number 1. within three months, he had been joined with the aid of 107 new Watson staffers, generally technologists within the fields of natural language processing and laptop learning.

Healthcare had already been cautioned because the first trade Watson should still target for business offerings, however there were no plans to confine it simply to drugs. Any guidance-intensive trade become reasonable video game, any place had been there have been large volumes of unstructured and semi-structured records that Watson could ingest, have in mind and system sooner than its human counterparts. Healthcare may be a place to begin, but banking, insurance, and telecoms were all in the firing line.

however how do you turn a quiz show winner into anything extra company-like? First job for the Watson team become to familiarize yourself with the laptop they'd inherited from IBM research, consider the forty one separate subsystems that went into Watson, and figure out what vital to be fixed up before Watson may put on its suit and tie.

within the Watson unit's first yr, the equipment obtained sped up and slimmed down. "We serialised the threads and the way the application labored and drove up the efficiency," Saxena talked about. "The gadget nowadays in comparison to the Jeopardy system is approximately 240 % sooner and it is one-sixteenth the dimension. The system that become the dimension of a master bedroom will now run in a gadget the size of the vegetable drawer in your double-drawer refrigerator."

yet another manner of taking a look at it: a single energy 750 server, measuring 9 inches high, 18 inches extensive and 36 inches deep, and weighing in at round a hundred pounds. Having bought the device to a extra manageable size for agencies, it set about finding purchasers to take it on.

IBM had healthcare pegged as its first vertical for Watson from the time of the Jeopardy win. youngsters, whereas Jeopardy Watson and healthcare Watson share a typical heritage, they're distinct entities: IBM forked the Watson code for its commercial incarnation.

"The equipment that changed into the dimension of a grasp bedroom will now run in a system the measurement of the vegetable drawer on your double-drawer fridge" Watson VP Manoj Saxena on the shrinking Watson

Jeopardy Watson had one assignment - get an answer, have in mind it, and discover the query that went with it. It turned into a single consumer device - had three quizmasters put three solutions to it, it could have thrown the machine right into a spin. Watson had to be retooled for a situation where tens, lots of, although many, clinicians could be asking questions directly, and not single questions both - complicated dialog with a few connected queries one after the other, all asked in non-usual formats. And, of path, there was the English language itself with all its messy complexity.

"there were simple areas of innovation that had to be executed to move past Jeopardy - there changed into a massive quantity of pre-processing, submit-processing and tooling that we now have added around the core engines," brought Saxena. "it be the equivalent of getting a Ferrari engine then making an attempt to construct an entire race car around it. What we inherited became the core engine, and we noted 'ok, let's construct a brand new element that does all kind of things the common Jeopardy device wasn't required to do'."

To get Watson from Jeopardy to oncology, there were three approaches that the Watson group went through: content material adaptation, working towards adaptation, and functional adaptation - or, to position it yet another way, feeding it medical information and having it weighted appropriately; testing it out with some follow questions; then making any technical changes mandatory - tweaking taxonomies, for instance.

The content adaptation for healthcare adopted the equal direction as getting Watson up to velocity for the quiz show: feed it advice, demonstrate it what right seems like, then let it guess what correct appears like and proper it if it's wrong. In Jeopardy, that meant feeding it with hundreds of question and answer pairs from the demonstrate, after which demonstrating what a right response seemed like. Then it was given just the solutions, and asked to get a hold of the questions. When it went wrong, it turned into corrected. through laptop learning, it will begin to get a handle on this answer-query factor, and modify its algorithms accordingly.

watson-on-phone

Watson has moved on to options that may power searches from smartphones. photograph: IBM

"it will be fed many circumstances where the heritage changed into regularly occurring and relevant medication was ordinary, after which, analogous to training for Jeopardy, or not it's been given circumstances after which it suggests therapies," Kohn spoke of.

Some statistics came from what IBM describes as a Jeopardy-like video game known as medical professional's quandary, whose questions include 'the syndrome characterised with the aid of joint pain, abdominal ache, palpable purpura, and a nephritic sediment?'. (The answer, of path, is Henoch-Schönlein purpura.)

The practicing, says Kohn, "is an ongoing technique, and Watson is rapidly improving its capacity to make cost effective suggestions the oncologists suppose are constructive."

via 2012, there have been two healthcare businesses that had started piloting Watson.

Wellpoint, one of the vital US largest insurers, changed into one of the pair of businesses that helped define the application of Watson in fitness. The different turned into Memorial Sloan-Kettering melanoma center (MSKCC), an organization IBM already had a relationship with and which is discovered now not far from both IBM's own Armonk headquarters and the analysis laboratories in York Heights, long island that still residence the primary Watson.

And it turned into this relationship that helped spur Watson's first commercial move into working within the field of cancer treatment options. whereas using Watson as a diagnosis tool could be its most glaring software in healthcare, using it to help in choosing the right remedy for a cancer affected person made much more experience. MSKCC turned into a tertiary referral centre - by the time sufferers arrived, they already had their diagnosis.

So Watson turned into destined first to be an oncologist's assistant, digesting reams of statistics - MSKCC's personal, clinical journals, articles, patients notes and greater - along with patients' preferences to come up with suggestions for medication alternate options. each would be weighted consequently, counting on how significant Watson calculated they were.

in contrast to its Jeopardy counterpart, healthcare Watson additionally has the capability to go browsing - not all its statistics must be stored. And whereas Watson had two million pages of scientific facts from 600,000 sources to swallow, it could still make use of the well-known expertise garnered for Jeopardy - details from Wikipedia, for example. (What it doesn't use, however, is the urban Dictionary. Fed into Watson late closing 12 months, it become reportedly removed after answering a researcher's query with the word "bullshit". "We did discover some interesting responses, so we had to shut that down," Saxena referred to diplomatically. "That isn't to be repeated, since it could be viewed as very flawed in definite situations, and we needed to teach Watson the right company behaviour.")

watson-info-box

This chart, carried out a 12 months after Watson's Jeopardy win, suggests a few of its swift development.

picture: IBM

As such, the sources at the moment are scientific publications like Nature and the British medical Journal. And there are different safeguard nets too.

"in the educating part, a doctor - a cancer care expert in this case - sits down and asks questions of Watson and corrects the desktop discovering. The medical professional and an information scientist are sitting next to every other, correcting Watson. Spurious material, or conflicted material or whatever from a pharmaceutical business that the doctor feels may be biased - that's caught all the way through the practising cycle," delivered Saxena.

WellPoint and MSKCC used Watson because the foundation for methods that might study and keep in mind volumes of medical literature and different assistance - sufferers' medication and family unit histories, as an instance, in addition to medical trials and articles in clinical journals - to assist oncologists through recommending courses of treatment.

A 12 months of working with each enterprises has produced industrial items: Interactive Care Insights for Oncology, and the WellPoint Interactive Care e book and Interactive Care Reviewer. Interactive Care Insights for Oncology gives information for medicine plans for lung cancer sufferers, whereas New WellPoint Interactive Care guide and Interactive Care Reviewer reviews clinicians' suggested treatments towards their patients' plans and is expected to be in use at 1,600 healthcare suppliers this year.

Watson has larger ambitions than a clinician's assistant, youngsters. Its medical expertise is round that of a first yr scientific pupil, in keeping with IBM, and the enterprise hopes to have Watson circulate the general clinical licensing board assessments in the not too far away future.

"Our work nowadays is in the very early degrees round observe of medicine, around persistent care illnesses. We're starting with melanoma and we will quickly add diabetes, cardiology, intellectual fitness, different chronic illnesses. and then our work is on the charge facet, where we're streamlining the authorisation and approval technique between hospitals, clinics and insurance organizations," Saxena talked about.

The most desirable intention for Watson is to be an assist to analysis - in preference to simply suggesting treatments for melanoma, because it does nowadays, it may assist medical doctors in making a choice on the ailments that bring people to the clinics within the first area.

earlier than then, there is figure to be performed. while massive information companies frequently trumpet the increase of unstructured statistics and the forsaking of relational databases, for Watson, or not it's these older sources of facts that current more of an issue.

"Watson works exceptionally with herbal language - free text or textual content-like assistance - and that's the reason about 80 percent of the huge volumes of healthcare information attainable to us," noted Kohn. "Then there may be the 20 p.c it really is structured statistics - actually, numerical facts - or pictures, like MRIs, CAT scans, and so forth. Watson doesn't method structured information at once and it would not interpret images. it might interpret the file attached to an image, however now not the photograph itself."

additionally, IBM is working on making a broader healthcare providing which will take it past its oncology roots.

"in spite of the fact that Watson is working with these two establishments, what the designers and computing device scientists are specializing in [is] that whatever thing they strengthen is generalisable, or not it's now not just niche for melanoma therapy and especially for the a number of cancers we're working with. We're the use of it as a discovering technique to create algorithms and methodologies that might be comfortably generalisable to any enviornment of healthcare. They don't have to need to say, right, we have oncology below control, now let's start again with family unit follow or cardiology," Kohn pointed out.

ibm-citiback-ebrain

Citi and IBM had been participating on company systems due to the fact that the times of the early mainframes.

image: IBM

Watson has also already discovered some hobby in banking. Citi is the use of Watson to improve client experience with the financial institution and create new capabilities. it's convenient to see how Watson may well be put to use, say, finding out no matter if a borderline-risk business consumer is likely to repay the personal loan they've applied for, or used to decide upon circumstances of fraud or identification theft before shoppers may well be mindful they're happening.

Citi continues to be early in its Watson experiments. A spokeswoman mentioned the enterprise is currently simply "exploring use cases".

From here on in, instead of being standalone items, the subsequent Watson choices to hit the market might be embedded into items within the IBM Smarter Planet product line. they're expected to appear within the second half of the 12 months.

the first such Smarter Planet product appeared in may: IBM Engagement consultant. The concept in the back of the Engagement guide, geared toward contact centres, is that consumer service agents can query their employers' databases and different suggestions sources the usage of natural language whereas they're conducting helpline conversations with their consumers. one of the crucial companies trying out out the service is Australia's ANZ financial institution, the place it should be helping call centre group of workers with making financial functions concepts to individuals who ring up.

watson-question-phone

Ask Watson goes far past what Apple's Siri can do, IBM believes.

graphic: IBM

Watson could presumably one day scour available proof for the premiere time to find a person able to speak and choose the verbal exchange channel undoubtedly to generate a positive response, or pore over social media for disgruntled shoppers and provide answers to their problems in herbal language.

There are additionally plans to exchange how Watson's delivered, too. in its place of just interacting with it by way of a name centre employee, consumers will quickly be capable of familiarize yourself with the Engagement consultant. rather than have some call centre agent examine out Watson generated tips to a consumer with, say, a fault with their new washing computing device or a stock-trader looking assistance on updating their portfolio, the client and trader could just quiz Watson at once from their cellphone or pill, by typing their query straight right into a company' app. Apps with Watson below the hood may still be out in the latter half of this 12 months, in line with Forbes.

IBM professionals have additionally prior to now counseled that Watson might come to be a supercharged edition of Siri, where individuals may be able to speak at once into their phone and pose a complex question for Watson to reply - a farmer protecting up his smartphone to take video of his fields, and asking Watson when to plant corn, as an example.

IBM is raring to spell out the differences between Watson and Siri. "Watson knows what it knows - and via listening, learning and the use of human-like thinking capabilities uncovers insights from massive records, Watson also right now ascertains what it does not comprehend. Siri, even so, quite simply looks for key terms to search the internet for lists of options that it chooses one from," the business says. but, the comparison holds: Watson may certainly have a future as your infinitely a professional very own assistant.

while adding voice-recognition capabilities to Watson should still be no top notch shakes for IBM given its present partnerships, such a stream would require Watson to be capable of know photos (whatever IBM's already working on) that might require Watson to query all types of sources of counsel together with newspapers, books, photos, repositories of statistics that have been made publicly purchasable, social media and the information superhighway at tremendous. That Watson should still take on such a job in the coming years, exceptionally if the processing goes on in an IBM datacentre and never on the cellular itself, as you could are expecting, is actually in the realms of the possible.

As IBM seeks to embed Watson's capabilities into further and further products, how a long way does the company feel Watson will unfold in the coming years? it'll only say gnomically, "as we proceed to scale our capabilities, we want to make Watson obtainable as a collection of features in lots of industries." desire a more robust answer? superior ask Watson.

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Cloudy weather forward for IBM and red Hat? | killexams.com Real Questions and Pass4sure dumps

the area is buzzing concerning the utility business’s greatest acquisition ever. This “video game changing” IBM acquisition of red Hat for $34 billion eclipses Microsoft’s $26.2 billion of LinkedIn, which set the previous record. And it’s the third largest tech acquisition in historical past at the back of Dell purchasing EMC for $64 billion in 2015 and Avago’s buyout of Broadcom for $37 billion the same 12 months.

Wall street certainly gets fearful when it sees these lofty expense tags. IBM’s inventory turned into down four.2 p.c following the announcement, and there are probably more considerations over a broader IBM selloff around how plenty IBM is paying for pink Hat.

This sets the stage for large expectations on IBM to leverage this asset as a vital turning factor in its historical past. due to the fact that IBM’s Watson AI poster infant has did not create sustainable boom, might this be their most fulfilling probability to appropriate the ship as soon as and for all? Or is this mega merger a sophisticated clash of cultures and items on the way to make it difficult to recognise the complete talents?

big Blue’s been in massive concern

When the chips are down, it’s time to go all in. massive Blue certainly stunned the expertise world when it announced it would do its greatest deal ever and purchase purple Hat for a big 11x premium. The reality is that pink Hat was no longer necessarily looking to be received, so overpaying was the simplest achievable choice. And if IBM didn’t pay, Google, Amazon, VMWare or even Alibaba would have.

determined instances demand determined measures. IBM has been struggling to demonstrate boom in new markets for somewhat some time. before 2018, it had 22 straight quarters of salary decline. And it has misplaced over $28 billion in income over the last six years. Its income on the conclusion if 2017 changed into $79.14 billion, the bottom in twenty years and the more severe annual quantity in view that 1997, when IBM revenues have been $seventy eight.51 billion, excluding inflation.

In early 2018, IBM became capable of produce three consecutive quarters of income increase, however that become exceptionally as a result of the introduction of a new line of IBM Z mainframe computers.

IBM has been a company in decline for a long time. It’s difficult to sustain a enterprise with shrinking income.

Too historic to grow?

IBM is greater than a hundred years historical and definitely suffers from comparisons to younger and nimbler agencies reminiscent of Amazon, Google, fb, and Apple which have posted listing growth in recent times. Amazon’s recent profits have surpassed $2 billion, for instance.

if you contrast IBM to Microsoft, an additional historical world application business, it’s startling to look the change in how Microsoft has been capable of reposition itself as boom company in keeping with the cloud.

In 1990, when Microsoft liberate home windows 3.0, IBM had revenues of $sixty nine billion (handiest $10 billion shy of what it has these days), whereas Microsoft had around $800 million. Microsoft surpassed IBM in income in 2015 and crossed the $100 billion annual revenue mark in 2018.

during the last a few years, as IBM’s income shrank, Microsoft invested in its “commercial cloud” business that encompasses Azure, workplace 365, and Dynamics 365, bringing in over $23 billion in new revenues. Microsoft has these days been firing on all cylinders while IBM skilled boom stalls.

slow to get to the cloud

IBM’s success within the hardware company, principally it’s Z-sequence mainframes, pressured it to protect its turf and distracted it from seeing the future have an effect on of cloud. AWS begun offering public cloud functions returned in 2006. As late as 2011, IBM become barely mentioning the note “cloud” in its annual experiences or salary calls. The business at last realized in 2013 that cloud computing changed into the long run and made a hail-Mary purchase of SoftLayer to bridge the hole, paying $2 billion and then investing an additional $1 billion to combine the platform.

It’s complicated to set up significant market share if you’re late to the birthday celebration. Softlayer’s worldwide market share continues to be fifth at the back of AWS, Microsoft, Google, and even more energizing newcomer Alibaba, which exceeded IBM’s cloud revenues in June of 2018.

IBM made a number of other cloud-connected acquisitions, including Gravitant (a cloud brokerage and administration application), Bluebox (a non-public cloud as a carrier platform in response to OpenStack), Sanovi (a hybrid cloud restoration and migration software), Lighthouse and CrossIdeas (both cloud protection structures), and CSL foreign (a cloud virtualization platform).

regardless of these acquisitions in the cloud market, IBM has did not definitely monetize those items and benefit market share within the cloud.

The company has didn't capitalize on innovations earlier than: Watson AI turned into on the exact of its game when it debuted on Jeopardy in 2011 to beat human contestants but without delay fell in the back of Amazon, Google, and Microsoft.

Will pink Hat be the savior?

crimson Hat is the realm’s biggest issuer of open-source commercial enterprise utility options. crimson Hat’s bread and butter Linux business continues to convey increase principally as it powers many modern AI and analytics workloads. Its mannequin has evolved from basically on-premise to a healthy subscription business used on public cloud structures similar to Amazon net features (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

red Hat has also multiplied into open middleware options reminiscent of OpenStack, a cloud infrastructure platform, and OpenShift, a platform for managing application containers. OpenShift has long been a smartly-stored secret as Cloud Native Computing basis (CNCF) has grabbed many of the headlines with its Kubernetes container orchestration platform. IBM has an opportunity to leverage its advertising and global attain to motivate mainframe and legacy customers to adopt OpenShift. These systems were enormously leveraged in deepest and hybrid cloud deployments, above all in industries like telecommunications.

There is not any doubt that crimson Hat gives IBM a a whole lot extra credible cloud story. however the query definitely is, is it too late?

The acquisition is certainly good information for agencies trying to shift classic container-primarily based functions and virtual machines to the cloud. however, Amazon has already captured a big part of that market.

whereas the acquisition of red Hat gives IBM a powerful place in the hybrid-cloud market, which will be well-known for companies that aren't taking the time to decommission or re-architect legacy applications, the speedy-starting to be public cloud market should be the battleground of the longer term.

Will the integration get messy?

IBM has had a spotty checklist when it comes to integrating and capitalizing on large acquisitions.

while the vast majority of IBM’s M&A has been in the area of software, revenue within the section has been disappointing. most likely what is regarding is that adjusting for acquisitions, IBM’s utility business continues to decline — on the whole due to the indisputable fact that these big acquisitions have develop into part of the IBM material and business as standard.

Can IBM integrate some thing as big as pink Hat without interfering with its core cost proposition? Many fear that massive Blue will try to “blue wash” their platform of option.

And there’s the question of whether these two diverse company cultures can come together – IBM, a sluggish boom company no longer making a great deal development in the cloud space, and pink Hat, an innovative, open source enterprise it's constructing foundational accessories for operating in the cloud.

We’ve seen subculture clashes derail many different high profile mergers comparable to HP/Compaq, HP/Autonomy, Microsoft/Nokia, AOL/Time Warner, dash/Nextel and Alcatel/Lucent. IBM will should embrace the open supply community and strategy.

The joint company will face crucial platform selections on the cloud front. IBM has a public cloud that competes with AWS and Microsoft. but builders use red Hat’s Linux on many public clouds. whereas that multi-cloud approach will help IBM herald income throughout the public clouds, it will create conflict with its own Softlayer cloud offering. IBM has struggled to control this type of channel and product conflict efficaciously during the past.

after which there's the way forward for IBM’s personal AIX working gadget vs. Linux — not to mention the SCO-IBM Unix lawsuit nevertheless lingering in the courts.

also to be aware are the lesser normal crimson Hat storage items like crimson Hat Ceph (an object file storage) and pink Hat Gluster (a NAS product). As red Hat integrates into IBM’s hybrid cloud group, these storage items could be separated from IBM, which might create confusion and conflict.

So while IBM definitely faces lots of possibility with the acquisition, there is no assure this big bet will repay. IBM vital a daring move. but within the short term, we are unlikely to peer any unexpected circulation of IBM’s position within the public cloud space. All eyes might be on its ability to catapult into the hybrid cloud market. For that, the business will deserve to make certain it doesn’t get in its personal method.

Frank Palermo is the govt vice chairman at Virtusa’s global Digital business, where he's liable for technology practices in UX, mobility, social, cloud, analytics, massive records, and IoT.


Seagate, IBM team up for counterfeit challenging power mission | killexams.com Real Questions and Pass4sure dumps

IBM will follow its blockchain technology to a mission with tough disk power (HDD) maker Seagate that aims to handle counterfeiting.

Seagate is one of the premiere known HDD manufacturers and like lots of its peers out there has been affected by counterfeiters who customarily are attempting to circulate off an ancient drive – which has been re-programmed to file that the skill is better than it actually is – as a genuine product. apart from misplaced income, HDD producers also have to deal with fakes being inveigled into its official give chain via product returns.

The business is now fighting lower back, and intends to mix IBM’s blockchain platform with its own digital fingerprinting technology in a multi-layered method that it says will provide “product monitoring and support to give provenance over a tough power’s existence cycle,”

The blockchain is virtually a digital database of time-stamped records or hobbies, which follows the supply chain and transactions of items to assess provenance, during this case tracking HDDs from production, assembly and installation to the conclusion of life. It’s being piloted in the mean time however should be utterly operational via the end of the yr, and should ultimately be accelerated to include other deliver chain companions.

Seagate will register product authentication records according to its comfortable digital identity (eID) electronic fingerprinting tech, generated at the aspect of HDD manufacture and serving because the ‘crypto-anchor’ during this challenge, on IBM’s cloud-based mostly blockchain equipment. Blockchain will even be used to collate Seagate’s licensed Erase process, which wipes facts off drives cryptographically and produces a digital certificate of records purge.

IBM has also begun making use of its Hyperledger-based blockchain to a variety of supply chain purposes, together with monitoring meat from slaughterhouses and container shipments carried with the aid of delivery massive Maersk.

in line with figures from UK economics consultancy CEBR, customer electronics counterfeiting changed into a $169bn market in 2016. computer systems (and their add-ons) are high on the record of most-faked items, together with telephones, tablets and tune gamers.


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Applying Fundamentals of IBM Tivoli On Demand Automation

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Creating Value In The Supply Chain Through TransportTech | killexams.com real questions and Pass4sure dumps

Transportation and logistics is a difficult industry for generating consistent growth in free cash flow: it tends to be highly capital-intensive with weak returns on invested capital, and companies are exposed to boom-and-bust economic and capacity cycles. For these reasons, the sector is structurally discounted compared to the S&P 500, trading at enterprise multiples well below the stock market as a whole. A leading tech-enabled freight brokerage, for example, is currently trading at 10x EV/EBITDA, compared to 13x for the entire S&P 500.

Nevertheless, investor appetite for transportation companies that can meaningfully differentiate themselves with technology has grown rapidly in the past five years. According to a new FreightWaves study, since 2014, annual venture capital investment in "TransportTech" companies grew 24x to $2.68 billion year-to-date. It has become clear that the market increasingly views digitization as one of the most important means of creating value and improving the financial fundamentals of companies operating in the supply chain.

Digitizing your business—and that means building products, services, and internal processes on technology rather than simply tacking on technology to a pre-existing model——can be risky. In our view, the risk of becoming a technology laggard is relatively well understood: from Eastman Kodak to Blockbuster and Sears, recent history is replete with stories of companies that failed because they did not innovate. Less well understood, though, is the risk of over-investing in technology and occupying the ‘bleeding edge' of an industry where companies burn through cash developing technology that may never have a commercial outcome or solve a problem. Think of Segway, which is still unprofitable 17 years after its product was introduced, or Samsung's baffling recent decision to make WiFi-enabled washing machines.

Fortunately, transportation and logistics companies don't have to reinvent themselves overnight to create value through digitization. There is plenty of low-hanging fruit that can be captured by incrementally applying digital technologies where they can do the most good today. The immediate opportunities in the supply chain space fall into two broad categories: eliminating inefficiencies and improving decision-making. FreightWaves and Slync see predictive analytics, artificial intelligence (AI), intelligent workflows, and blockchain as four key technologies that will eliminate inefficiencies and improve decision-making in the supply chain by enabling more effective collaboration between partners.

Enterprises generate more data than ever before and are looking for ways to leverage it to see their business in new ways and make better decisions. Predictive analytics extracts information from data sets and uses it to predict trends and behavior patterns, allowing supply chain participants to mitigate risk in down cycles and seize opportunities in growth periods. Freight forwarders can use historical data to predict drayage capacity crunches on the East Coast due to cyclical and seasonal factors; consignees in the United States can proactively manage their order flows in response to models forecasting container rates on the volatile Transpacific lane. Shippers who can accurately predict freight transit times will be able to continue reducing inventory in a period of rising interest rates. In each of those cases, sharing insights between partners creates a data-driven collaborative process producing better decisions and more value.

"This is the digital feedback loop: use the insights, ideas, and innovation generated by the team or your customer as an accelerator for improving the capability and product and service that you already have," said Ibrahim Gokcen, chief digital officer at A.P. Moller-Maersk, in a September interview with McKinsey.

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"Predictive analytics are one of the most powerful things in expediting decision-making in logistics," said Chris Kirchner, co-founder and CEO of Slync. "If decision makers can proactively solve problems or mitigate potential problems instead of having to fix things after they break, it will radically change how they conduct business on a daily basis."

AI technologies including natural language processing, virtual assistants, and advanced machine learning algorithms that can execute tasks in addition to making recommendations, are poised to make an even larger impact on corporations' enterprise values. A recent study predicted that AI frontrunners could double their cash flows by 2030, while non-adopters will see cash flows contract by 20%. In the supply chain, the ability of AI to ingest information in multiple formats—email, bills-of-lading, exception alerts, and weather data—and act on it will shrink administrative and clerical headcounts while reducing error. Repetitive tasks like data entry, routine emails, and track-and-trace communications will be automated, freeing up supply chain managers to concentrate on strategic, high-value decisions. Ultimately, AI-powered collaboration will allow supply chain partners to share higher volumes of more accurate information on demand.

"An enterprise AI platform… can be trained with legislative materials, regulatory documents, customs brokerage SME knowledge, and customer and industry handbooks to learn how to automate customs declarations," a 2018 report by DHL and IBM found. "Using natural language processing and the self-learning capabilities of deep learning, a customs brokerage AI could ingest customs documents in myriad formats, extract relevant information using its collected body of knowledge, and present an automatic declaration."

"A.I has tremendous opportunity to solve key problems in logistics and supply chain. Companies have a wealth of data, still untapped, that could drive major optimizations in their business," said Raj Patel, Slync's Chief Product Officer.

Blockchain, a technology that creates distributed, cryptographically secured ledgers, is being deployed in the supply chain to help eliminate wasteful dispute resolution and audit processes. Blockchains were first invented to record cryptocurrency transactions, but the technology is well suited to track the movement of other digital assets, including images of damaged or delivered shipments, documents, and temperature and location data. Automated smart contracts run on the blockchain can speed payments and simplify accessorial compensation. Blockchain's most basic value proposition is a tamper-proof multi-party view into a series of records that creates trust between companies and makes doing business easier.

Over and over, in transportation and logistics we hear two refrains from companies trying to differentiate themselves from their competitors: "we have superior technology" and "we have superior customer service." Too often, the automation of manual processes and the reduction in workforces means that customers are offered a generic product and little support. But a focus on technology does not have to come at the expense of collaborative partnerships with your customers.

"Visibility is one of the most common asks from a customer throughout the supply chain today," said Kirchner. "However, I believe the best solutions are a step further--leveraging visibility with intelligent workflows to streamline the time-to-decision for a human. This is where the most impressive and impactful efficiencies are generated."


Big bang theory? Not for cloud-native development | killexams.com real questions and Pass4sure dumps

Know your apps, know your workloads, know your team. In the third installment of this occasional series on cloud migration, Don Boulia, GM of IBM Cloud Developer Services, and guest Chris Condo, principal analyst at Forrester Research, discuss how cloud-native development teams can find more success through innovation, automation, and culture shifts.

How can cloud-native development teams help overcome the complexity of owning different parts of the application?

Chris Condo: Innovation. Teams don’t have to start from scratch. Cloud-native development provides things like serverless computing, elasticity, and scalability already built in.  Gone are the days when development teams are bogged down creating and maintaining every component of an application — cloud-native development allows teams to focus on what they’re good at, while leveraging necessary components, like seamless scalability, from cloud providers.

The caveat here is preparing your organization for this way of working. Because everyone is in a cloud environment, things are far more transparent than in the past when each team had their own dedicated systems, set up on premise by various developers who owned those pieces. Each team understood their environment but didn’t know what other teams were working on. In a cloud environment, you get a topographical view across the board, with the environments illuminated for coordination.

Don Boulia: I couldn’t agree more, especially when working with microservices. There’s a shift in the ability for teams to work in parallel and deliver updates and features to parts of a larger whole, wherein the monolith generation, features would get rolled in during long development cycles, a big bang effect. Today’s development promises 5-10 teams working independently but rolling out features in a coordinated way so we all make progress. There are rules that come along with that — it’s not a no-rule world, but the agility you get is paramount in a cloud world where users assume updates are always happening.

CC: Constant updates are possible because teams can now create environments on demand as necessary, scale up or down, and create and destroy test environments so there can always be a fresh start. There’s less friction for daily processes. Teams can share a single platform, collaborate and share components easily, and coordinate activities in a much more agile manner, thanks to the single dashboard view that’s now available.

How do you set up your teams to succeed? Where to start?

DB: I think automation is one of the fundamentals to most cloud-based environments, so you really have to foster a team culture that allows it to infiltrate. Manual steps and interventions become the killer to the agility, speed, and reproducibility of environments, so the expectation now and going forward is to plan for applications or workloads that can be easily recreated and torn down and brought back up.

A big piece of fostering that culture is how you organize your team. At IBM, we have a model of fairly small, complete teams with people defining the capabilities of a service from an offering management perspective, the features incorporated into development, and owning overall upkeep and availability. Teams are tightly clustered with the end-to-end responsibility for a component, which enables rapid delivery and the ability to respond quickly to customer feedback. And teams are doing this in parallel across an organization. That’s exactly the kind of invisible support people are looking for out of cloud.

CC: Good point. The fact that you’re developing and deploying in the same infrastructure enables the “you build it, you own it” mindset, which moves development agility along. More importantly, it focuses on customer needs, which in turn improves product quality and understanding where to focus your automation efforts. The environment you’re working in is no longer a distant land you have little access to — now you have ability to comb the logs and gain insights into what users are doing, so teams are much happier because they have evidence of the overall performance of their app.

DB: That’s the biggest shift in developing software and running cloud services: the end-to-end set of skills.  Developers can now understand both how to build an original feature set but also maintain and improve it over time, gaining customer feedback for constant improvement. As the old adage goes, “great power comes with great responsibility,” cloud-native development enables the business to drive forward and requires team accountability to take ownership of their part of the whole.

For a deeper dive into how companies are leveraging cloud native, explore the research.


A Product Overview of DB2 9 for Linux, UNIX, and Windows | killexams.com real questions and Pass4sure dumps

In this chapter you will be introduced to the DB2 family of products that run on the Linux, UNIX, and Windows operating systems.

This chapter is from the book 
  • DB2 PRODUCTS and PACKAGING
  • DB2 CONNECTIVITY
  • DB2 TOOLS
  • DB2 ADMINISTRATION
  • In this chapter you will be introduced to the DB2 family of products that run on the Linux, UNIX, and Windows operating systems. This version of DB2 is often referred to as the distributed version to differentiate it from the DB2 for z/OS® version that runs on an IBM mainframe.

    DB2 has the ability to store all kinds of electronic information. This includes traditional relational data, data encoded within XML as well as structured and unstructured binary information, documents and text in many languages, graphics, images, multimedia (audio and video), information specific to operations like engineering drawings, maps, insurance claims forms, numerical control streams, or any type of electronic information. This chapter illustrates some of the ways to access data in a DB2 database using some of the interfaces provided within the DB2 family. A description of each of the DB2 products are provided to illustrate some of DB2's features and functions.

    The DB2 Data Server is an important part of IBM's Information as a Service software portfolio that serves as the atomic level for the broader IBM On Demand architecture.

    In Figure 1-1 you can see that the IBM software portfolio has really evolved into a collection of high value services provided by various IBM software portfolio offerings. The backbone fabric of this IBM reference architecture is the Enterprise Service Bus (ESB) that is used to facilitate communications across this rich set of services.

    Figure 1-1

    Figure 1-1 IBM Service Framework for an On Demand business

    IT Service Management is mostly provided by various Tivoli® products. The Tivoli portfolio is built around four key disciplines or pillars:

  • Security Management
  • Storage Management
  • Performance and Availability
  • Configuration and Operations
  • Services from these pillars can be used to collectively manage your entire IT framework. For example, Tivoli Storage Resource Manager services can be used enterprise-wide to monitor and report on heterogeneous storage resources to increase storage utilization, identify and resolve potential problems, and ensure application availability through policy-based automation.

    Development Services are the culmination of various Rational-based products that are built on the open source Eclipse platform. For example, Rational® ClearCase® provides source control services, and Rational Application Development empowers application developers with a rich set of services that can be used to develop applications, Web pages, and extended custom services for implementation in a Services Oriented Architecture (SOA) or loosely coupled application framework.

    Services that enable interaction are typically part of the Lotus® suite of products that enhance collaboration and idea sharing across the enterprise and beyond. Products like Lotus Sametime® Connect can be used for messaging and more.

    A number of services in the framework illustrated in Figure 1-1 are provided by the WebSphere® portfolio. For example, a product like WebSphere Integration Developer helps you define business process flows in the standard Business Process Execution Language (BPEL), which are used to implement process services that in turn help you define, orchestrate, and automate business policies. The Enterprise Service Bus (ESB) is provided by the WebSphere ESB product that provides your enterprise services with transformation, transport switching, and routing remediation among other services. Perhaps the most famous product of the WebSphere brand is the WebSphere Application Server that provides a runtime framework for J2EE®-based operations that are part of the Infrastructure Services component.

    Finally there's the Information Services which represent the superset of the capabilities you'll learn about in this book. The specific set of services that are typically found in this part of the IBM reference architecture are shown in Figure 1-2.

    The services shown to the right in Figure 1-2 are hierarchical in nature. In other words, as you work from bottom to top, the services provided become richer and more business oriented.

    For example, Master Data Management services are provided by the WebSphere Product Center and WebSphere Customer Center products. Master data are facts that describe your core business entities: customers, suppliers, partners, products, bill of materials, employees, and so on. The discipline of Master Data Management seeks to decouple master information from individual applications spread across the enterprise and create a central, application independent, resource. The end result is a simplification of ongoing integration tasks and new application development. This discipline addresses key issues such as data quality and consistency proactively rather than "after the fact"; for example, in a data warehouse (a lower service in this taxonomy). There is also a set of entity resolution services that fit within the Master Data Management service tier.

    Business Intelligence services are provided by the DB2 Data Warehouse editions that you'll learn more about later in this chapter. Content Manager services are provided by the set of Content Management products and are used for document management, archiving, regulatory retention, and are a basis by which unstructured information (such as FAXes, video, voicemail, and so on) can be searched and folded into the information asset.

    Information Integration services seek to provide enterprises with ways to share, place, publish, cleanse, and enrich data found in the lower-level data management services. WebSphere Federation Server and its parent WebSphere Information Server are two such products that help implement these services.

    Finally, the Data Management services tier is the foundation upon which the other services are built. IBM has a number of data servers that fit into this tier, including DB2, Informix®, IBM Cloudscape™, U2, and IMS™.

    This book is specifically about DB2 in this service tier. Specifically, you'll learn how DB2 can provide any number of the high-value data-centric services shown in Figure 1-3.

    Figure 1-3

    Figure 1-3 The data services provided by DB2, the focus of this book

    For more information on the entire IBM software portfolio and how they are mapped to the illustrated services shown Figures 1-1 and 1-2, refer to the IBM Web site at www.ibm.com for more details.

    The DB2 family of data servers executes on Windows, Linux (which can be run on the entire spectrum of IBM's hardware: System i™, System z™, System x™, and System p™), Solaris™ (both SPARC®-based and Intel®/AMD™-based installations), HP-UX™ (both PA-RISC™-based and Itanium-based installations), i5/OS®, VSE/VM, z/OS, and on pervasive platforms (like Windows Mobile Edition, BlueCat® Linux, Symbian®, Palm OS®, J2ME® platforms like the RIM® Blackberry®, and more).

    The DB2 code base is optimized for each platform to ensure maximum performance and integration. DB2 for Linux, UNIX, and Windows shares about a 98 percent common code base with platform-specific optimizations at the operating system interface (OSI) level (Figure 1-4).

    Figure 1-4

    Figure 1-4 The DB2 code for Linux, UNIX, and Windows is virtually the same

    This means that once you've learned how to administer a DB2 for AIX system, for the most part you'll know how to manage DB2 for Linux or DB2 for Windows; this is the reason why there is a single DB2 certification for all the distributed platforms.

    DB2 for i5/OS and DB2 for z/OS are optimized for their respective environments. For example, DB2 for z/OS is based on a shared-everything disk architecture where the hardware-assisted Coupling Facility is used to serialize access to the shared disk. No such hardware exists for Linux, UNIX, and Windows, and therefore DB2 on these platforms uses a shared-nothing architecture. For this reason, administration tends to vary between these platforms (though many concepts and features are similar). However, the SQL API is 98% common to all the platforms where DB2 runs, allowing applications written on one platform to be easily ported to another. This means that you can build an application on DB2 for Windows and port it effortlessly to DB2 for z/OS. If you build your application according to the SQL Reference for Cross-Platform Development handbook, your application will be 100% portable across the DB2 family.

    There are other synergies among DB2 running on Linux, UNIX, and Windows, as well as the other DB2 family members. For example, the JDBC driver used for DB2 for z/OS is exactly the same code as is used for DB2 for Linux, UNIX, and Windows. So while there may be variations in specific data definition language (DDL)-based tasks, the data manipulation language (DML) and client APIs are similar.



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