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IBM C5050-287 : Foundations of IBM Cloud Reference Architecture V5 Exam

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Exam Number : C5050-287
Exam Name : Foundations of IBM Cloud Reference Architecture V5
Vendor Name : IBM
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C5050-287 exam Format | C5050-287 Course Contents | C5050-287 Course Outline | C5050-287 exam Syllabus | C5050-287 exam Objectives

Exam Title : IBM Certified Solution Advisor - Cloud Reference Architecture V5
Exam ID : C5050-287
Exam Duration : 90 mins
Questions in exam : 60
Passing Score : 38 / 60
Official Training : Cloud Computing Fundamentals
Exam Center : Pearson VUE
Real Questions : IBM Foundations of IBM Cloud Reference Architecture Real Questions
VCE Practice Test : IBM C5050-287 Certification VCE Practice Test

Cloud Computing Concepts and Benefits
- Define the cloud computing business advantages.
- Define cloud architecture.
- Describe considerations such as risk, cost and compliance around cloud computing.
- Define automation as it pertains to cloud computing.
- Define why standardization is important to cloud computing.
- Define service catalog as it pertains to cloud computing.
- Define a public cloud.
- Define a private cloud.
- Define a hybrid cloud.
- Define the difference between a private cloud, a public cloud, and a hybrid cloud.
- Define Software as a Service (SaaS).
- Define Platform as a Service (PaaS).
- Define Infrastructure as a Service (IaaS).
- Define DevOps as it pertains to cloud computing.
- Explain Maturity as it relates to SaaS, PaaS, and IaaS.
- Explain the benefits of patterns as description of cloud services.
- Define software defined environments as they relate to cloud computing.
- Summarize how business processes can be automated in a cloud environment.

Cloud Computing Design Principles
- Demonstrate base knowledge needed to advise on creating a cloud infrastructure.
- Explain Cloud networking principles.
- Explain Cloud storage principles such as block object file and storage area networks
- Describe security strategies in a cloud computing environment.
- Design principle for cloud ready applications including patterns, chef, puppet, and heat templates.
- Design principles for cloud native applications such as open standards and 12-Factor app.
- Design principles for DevOps.
- Designing consumable applications for the cloud.
- Define hybrid integration capabilities.
- Define API Economy in Cloud Computing.
- Define how solutions in the cloud can be more effective.
- Explain to the customer how some popular billing models work and how they pertain to the software the customer has.
- Describe principles for governance, compliance and service management.

IBM Cloud Reference Architecture
- Explain the four defining principles of IBM Cloud.
- Explain the benefits of using the IBM Cloud Reference Architecture (ICRA).
- Explain the Cloud Platform Services for ICRA including containers, foundational services, etc.
- Explain the Cloud Service Provider Adoption Pattern for ICRA.
- Describe the ICRA Building SaaS cloud adoption pattern.
- Explain the Hybrid patterns for ICRA
- Describe the solution integration process detailed in the ICRA to take an existing environment to an IBM Cloud Computing environment.
- Design a secure cloud service model using ICRA.
- Describe high availability and disaster recovery as it pertains to cloud computing.
- Describe actors and roles as defined in ICRA (Cloud Service Consumers, Cloud Service Creators, Cloud Service Provider, Cloud Services and the Common Cloud Management Platform).
- Describe how IBM Service Management can effectively manage a customer's cloud environment.
- Describe the IBM API management capabilities.
- Describe the role of governance in the ICRA.
- Describe non-functional requirements (NFRs) as described by ICRA.
- Explain the role of mobile as part of the ICRA.
- Explain the Cognitive pattern as part of the ICRA.
- Explain the IOT pattern as part of the ICRA.
- Explain the DevOps pattern as part of the ICRA.
- Explain the Big Data and Analytics pattern as part of the ICRA.

IBM Cloud Solutions
- Describe the IBM capabilities for Cloud Managed Services.
- Describe the IBM capabilities for hybrid integration.
- Describe the IBM capabilities for video services.
- Describe the IBM capabilities for cloud brokerage.
- Describe the IBM capabilities for DevOps.
- Describe the IBM capabilities for cloud native applications.
- Describe the IBM capabilities for service management.
- Describe the IBM capabilities for storage.
- Describe the IBM capabilities for business process management.
- Describe the IBM capabilities for the IBM Marketplace.

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IBM of real questions

‘We should boost the tempo of discovery’: Ed Pyzer-Knapp, AI lead at IBM | C5050-287 Practice Test and Practice Questions

worldwide research lead for AI Enriched modelling and simulation at IBM, Ed Pyzer-Knapp says that the ‘need for science has under no circumstances been greater pressing’, whereas the key to solving future international challenges lies in ‘accelerated discovery’.

“To deal with the challenges their world is dealing with, they must enhance the tempo of discovery,” says Dr Edward O Pyzer-Knapp, international analysis lead for AI enriched modelling and simulation at multinational expertise service provider IBM. a person on a mission, in case you take only one component faraway from assembly the 33-yr-old British scientist, it might be that there's an imperative firmly positioned on the shoulders of the STEM community to do issues stronger and quicker.

“The need for science has certainly not been more urgent,” says Pyzer-Knapp, and the way to get that message throughout is to talk about it in simple phrases. A passionate suggest for the general public understanding of science, he talks perpetually about “precise-​world applications”. You get the sensation that he’d be happier with a a bit of less intimidating job title, which he accepts is “a little convoluted”, while his enthusiasm and power within the AI space looks to be limitless. aside from his publish at IBM, Pyzer-Knapp is additionally the editor-in-chief on the journal applied AI Letters, as well as visiting Professor in Industrially utilized AI at the tuition of Liverpool. “I work more suitable when there’s a great deal to do,” he says.

Pyzer-Knapp’s work makes a speciality of artificial intelligence, which he described as “one in all a collection of equipment that helps me to convey what i am in reality enthusiastic about, which is what’s now called accelerated discovery”. This time period is of IBM coinage and, whereas it brings with it greater than a hint of twenty first-century company policy newspeak, Pyzer-Knapp says that it may also be translated into which means effectively “investigating how they use present know-how and strategies to make us stronger at science”. What this extra means for the piano-taking part in cricket enthusiast is “the blending of AI with HPC [high-performance computing] – together with dabbling in quantum – and Cloud, to help us do discovery quicker”.

Pyzer-Knapp is additionally obsessed with communicating the price of science to the non-scientist and is eager to describe what he does in easy terms whereas referencing actual-world purposes. here's the founding philosophy of applied AI Letters, which he explains is “as the title implies, all about applications”. He says it’s all correct to have abstract knowledge and analysis, but you’ve obtained to do anything with it. As an analogue, he refers to Damien Hirst’s artwork installing The real Impossibility of demise within the mind of a person living, explaining that “you’d get americans asserting ‘any one may have put a shark in a glass tank crammed with formaldehyde’. but Hirst did it.” Pyzer-Knapp changed into “enraged adequate” by using the shortcoming of knowing of what AI might do within the true world that, as with Hirst, he went forward and made whatever thing ensue. And whereas Hirst pickled a tiger shark, Pyzer-Knapp deploy a journal with the editorial principle that it needed to be about something: “So I assembled a solid of editors and it’s going truly well.”

“i admire to think of the realm splitting up into three materials,” he says. “there's the real world: you build a bridge. Then there's the records world, which is an old agglomeration of what’s long gone on in that true world. Then there is the digital world, which contains the simulation of the real world via perhaps taking the legal guidelines of physics and saying: ‘I’m building a bridge. before I do it, can i produce a grounded theory according to science of what’s going to ensue?” during this means, AI has revolutionised bridge-building, says Pyzer-Knapp, “as a result of no person builds one at the present time devoid of simulating wind conditions”. He illustrates what occurs should you don’t through regarding the crumple of the pre-AI Tacoma Narrows Bridge as a result of self-interesting aeroelastic flutter created via apparently innocuous 40mph (64km/h) wind conditions. “The complete element fell down. these days, that couldn’t turn up since you’d have simulated every thing in advance.”

‘Accelerated discovery ability the use of existing expertise and methods to make us more suitable at science.’

The problem with simulation, says Pyzer-Knapp, is that it's costly. It’s also complex because, “you need to ask the right questions. when I discuss AI-enriched modelling and simulation, I’m asking: ‘how can they get AI to support us reply these questions in regards to the digital world so that they are able to do the optimal job that they can in the real world?’ It should be would becould very well be through suggesting the right protocols to use or replacing a few of that expensive physics with neural networks.” When requested what his crew does to make that occur, Pyzer-Knapp says that they “exist to work throughout the spectrum. they have achieved simple mathematical development, computing device science algorithm deployment optimisation, in addition to stringing this all together into complicated heterogeneous workflows for applications.”

whereas this may sound abstract, it capacity that if a fashion designer on, say, a components 1 motor vehicle should optimise a spoiler profile, Pyzer-Knapp can support, peculiarly at IBM’s new Hartree national Centre for Digital Innovation. announced in June this year, the centre is a £210m collaboration with the uk govt’s Science and know-how facilities Council (STFC) that, based on IBM’s blog ‘the discovery Accelerator comes to Europe’, will “turn into a hub of engagement with collaborators across the uk’s industrial and research ecosystem searching for to pressure innovations in life sciences, new materials building, environmental sustainability, and advanced manufacturing”. more than 100 scientists from IBM research and STFC will work collectively over the subsequent 5 years applying AI and quantum computing, “to produce improvements in materials, existence sciences, climate, agriculture and manufacturing”.

Pyzer-Knapp has mixed feelings over his family teasing him “mercilessly” in regards to the fact that his first interaction with a laptop came at the smooth age of five. “It became a BBC Micro, and i changed into the most effective person that may wire it up appropriately.” His first task into programming become to jot down a search protocol for the faculty computing device’s encyclopaedia: “If I’d had any sort of prescience, I’d have remembered that later in existence and developed search algorithms once they have been plenty extra monetisable.” He describes himself as “very logical”, and it changed into the corresponding good judgment at the coronary heart of computing that changed into its early enchantment. whereas studying chemistry at Durham school, a chance sequence of movements that included a application software licence expiring on him led Pyzer-Knapp to put in writing his own application. It became the moment when “I started to comprehend that I loved working with the data greater than getting the information. My wife is a proper chemist, however for me there become only a specific amount of time I could spend scrubbing down a lab because whatever’s exploded before i used to be dissuaded from chemistry.”

His hands still fluorescing from the mishaps that led him to suspect that he was “horrific” at lab-based mostly chemistry, he retreated to the school of Cambridge to do a merely theoretical PhD about the means molecules pack into solids. The title of his thesis changed into ‘Exploring the crystal energy landscapes of porous molecular crystals’, which turned into to deliver out the latent periodical editor in Pyzer-Knapp, who continues to be annoyed at the bumpy scansion and repetition of ‘crystal’. “at least it didn’t have the be aware ‘against’ in it,” he displays. “That at all times sounds like you didn’t rather get there.”

Impatient to get onto true-world functions, he wrote his PhD in two-and-a-half years (it normally takes as a great deal as thrice longer to gain a doctorate), acquired on a airplane, crossed the Atlantic and headed for Harvard, the place he turned into in charge of the everyday running of the university’s clean power task: a White house mission below the Obama administration in collaboration with IBM which combined huge disbursed computing, quantum-mechanical simulations and computing device learning to “speed up discovery of the subsequent technology of biological solar cells. The theory become that they understood that these are crucial, however challenging to find good ones. So could we, via realizing the molecule, predict how respectable the mobilephone it generated would be: and in that case, may they display via a big variety of molecules? the place IBM got here in become with expertise known as the world neighborhood Grid, which really observed that for those who weren’t using your computer you could either get it to show you images of your cat, otherwise you could get it to do some thing advantageous. 1 / 4 of a million individuals signed up for this and they had the equivalent of a 7,000-node cluster working just for me, each day. And that’s one hell of a beast to retain fed.

“The reason I received into AI became that I couldn’t do that manually. I essential some intelligence to drive this element: some thing that might assemble these molecules and prioritise them. here is what took me into the enviornment called Bayesian optimisation, which is what I do most of my work in now.”

‘Bayesopt’, because the AI community calls it, is not simplest named after the 18th-century Presbyterian minister and statistician Thomas Bayes, but is also in accordance with Pyzer-Knapp, “a extremely underappreciated family of algorithms”, that he explains for the layperson in terms of hunting for a lost set of keys in the real world. “How do you look for keys?” he asks rhetorically, before explaining that the one element you don’t do is seem in a focused approach all over the place. “What you do is consider about where you remaining saw them and you retrace your route. and you have some sort of theory about possibilities.” What bayesopt does is look for ‘respectable options’ in the method that you just search to your keys. “It’s checking and updating what it thinks about the world now and it’s discovering the entire time.”

I put it to Pyzer-Knapp, the logical loser of keys would hardly ever assign a good deal value in looking for lost items in the ‘undoubtedly’ places on the foundation that in the event that they were latest in any of those, you’d be challenging-pushed to describe them as misplaced. this is “precisely what makes bayesopt greater than the rest because the optimisation a part of it knows what it doesn’t know. It could make the alternatives attainable for the subsequent circulate in finding your keys sit on a scale between basically exploitative (‘here is where I suppose it's’) and merely exploratory (‘I’m going there to rule this out’). Then there’s a sliding scale in between where you are buying and selling off these two things. What I’ve been doing for the previous five years is inventing greater strategies for how you do this in quite a few cases.” Such occurrences may encompass a situation where there are five clones looking on the identical time or constraints that you should take a look at however now not be aware (‘you can’t seem to be to your keys in there since the door’s locked from the different facet...’).

Pyzer-Knapp is eager to aspect out how this could be used within the precise world: “I’ve received 20,000 molecules and i are looking to recognize with a constrained budget the way to discover the gold standard one. Or I’m designing a front wing for an F1 motor vehicle: i will be able to deform it in a few techniques, however I’ve obtained a budget. How can i discover the optimum approach? We’ve achieved work with experimental design where we’re assisting individuals making enormously cool know-how which outlets advice on DNA. however they deserve to tune their machines so that the method is greater effective. They use the know-how in all places.”

To make this work, “there are two belongings you need to believe about: quasi-philosophical and quasi-functional. First, there’s how you have interaction with the know-how from the person standpoint, and 2d, how the system is there to be interacted with. I might build the most excellent GUI on earth, but when the device in the back of it isn’t built to be interacted with in any type of sensible method, I’m confined.” Pyzer-Knapp says these issues had been understood early on, “and so they spent a while establishing ‘explainable AI’ models. Explainability and transparency is essential for the long-term success of those fashions.”

As their time involves an in depth, Pyzer-Knapp introduces the theory that the unifying thread it really is woven through every little thing he does is “the range of notion and adventure. in case you only seek advice from one set of individuals, you’ll only ever get one set of ideas. It’s easy to operate in that echo chamber, but now not have an influence on the true world. And the way to do this, essentially the most essential thing at this aspect in time, is accelerated discovery.”

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