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000-M602 - IBM Banking Industry Solutions Sales(R) Mastery Test v1 - BrainDump Information

Vendor Name : IBM
Exam Code : 000-M602
Exam Name : IBM Banking Industry Solutions Sales(R) Mastery Test v1
Questions and Answers : 30 Q & A
Updated On : March 15, 2019
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000-M602 exam Dumps Source : IBM Banking Industry Solutions Sales(R) Mastery Test v1

Test Code : 000-M602
Test Name : IBM Banking Industry Solutions Sales(R) Mastery Test v1
Vendor Name : IBM
Q&A : 30 Real Questions

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IBM IBM Banking Industry Solutions

ICBC Argentina Strikes a $63M services contract with IBM to Reimagine customer capabilities in Argentinian Banking business with AI and Cloud | killexams.com Real Questions and Pass4sure dumps

SAN FRANCISCO and BUENOS AIRES, Argentina, Feb. 12, 2019 /PRNewswire/ -- ICBC Argentina, subsidiary of the greatest financial institution on the planet, with 119 branches, serving 1,000,000 households, is a key participant within the modernization of fiscal capabilities in Argentina. The financial institution is dedicated to continuing its digital transformation experience with a five-12 months US $63 million settlement with IBM (NYSE: IBM) functions.

As a part of the agreement, ICBC Argentina is redefining the financial institution's client journey with IBM AI and Cloud, which will permit the financial institution to enhance response time by means of predicting seventy five% of particular system disasters, bear in mind online consumer behavior and preferences in its home banking platform, reduce up to 30% of customer queries via on-line services powered by way of Watson, and innovating in personalized experiences via using the convergence between physical and digital branches.

"Over the final 5 years, ICBC Argentina has persisted to develop after setting up in the nation, driven through the constant development of our customers' experience and an accelerated innovation technique that we applied along with IBM," spoke of Guillermo Tolosa, CIO of the financial institution. "Working with IBM capabilities enables us to focus on our company strengths and leverage IBM's experience in artificial Intelligence, Cloud and capabilities with open and relaxed necessities, to consolidate our growth and present customers a straightforward, attainable and convenient experience, while at the equal time complying with the wants of our particularly regulated business."

Banks are faced with altering purchaser behaviors and expectations, in areas such as real-time banking, mobile services and access to a more complete view of their price range. To handle purchasers' expectations, ICBC Argentina has undertaken a plan designed to accelerate its digital transformation for extra customized banking adventure. In doing so, ICBC's consumers may be in a position to extra right away evaluation their funds, habits transactions and inquire about further services with out ever having to stroll into a branch.

"ICBC is accelerating its transformation with cutting-edge know-how to take a step ahead within the disruption of the banking trade in Argentina", says Roberto Alexander, country supervisor of IBM Argentina. "IBM and ICBC will proceed developing innovative solutions, presenting deeper perception into its consumers' behaviors, enabling them to make smore informed decisions with advice, and launch items and capabilities sooner."

IBM will also continue managing the bank's infrastructure, with a purpose to aid speed up the bank's digital transformation plan as well as to raise innovation and increase operational efficiency through and IBM Cloud Public credit and upgraded capabilities of its IBM Mainframe z14 technology.

About ICBC Argentina

ICBC Argentina is a popular financial institution that offers financial services via 119 branches and 877 ATMs and Self-service Terminals in 18 provinces and more than 3700 personnel. It serves 1,000,000 households, forty,700 SMEs and corporations, and 1,821 company shoppers. It is one of the market leaders in pledge loans, digital banking, leasing and overseas alternate. It has more than 4,900 million bucks in property and 2,500 million bucks in deposits, ranking among the many 7 first private banks in Argentina in both rankings.

internet: www.icbc.com.ar  Twitter: @ICBCArgentina Instagram: @icbc.argentina facebook: /ICBCArgentina  LinkedIn: /icbc-argentina

About IBM ThinkAt feel 2019, IBM will outline new offerings, customer engagements, partnerships, technology breakthroughs and developer equipment that underscore how IBM and companions are changing the way the area works. For more information, visit the IBM consider 2019 Newsroom: https://newsroom.ibm.com/consider. follow the conference on Twitter at #think2019 and @ibmlive, and go to https://www.ibm.com/events/consider/ for the total agenda and are living streaming agenda.

Media RelationsScott prepare dinner IBM Media Relationsscotty@us.ibm.com 1-720-395-2360

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country have confidence bank Has $289,000 place in IBM (IBM) | killexams.com Real Questions and Pass4sure dumps

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IBM Extends $3B Cloud Streak, indications Santander to five-12 months Deal | killexams.com Real Questions and Pass4sure dumps

source: shutterstock 94867786

IBM persisted to make inroads into the massive bank sector, saying this morning at its annual think convention in San Francisco that Banco Santander, the fifth biggest bank in Europe with approximately US$1.four trillion in complete property (as of 2017), has signed a $700 million, five-yr world expertise digital transformation contract concentrated round imposing the financial institution’s hybrid cloud approach.

Santander joins a listing of fiscal associations that have shrunk with IBM for hybrid cloud choices and features, including bank of the Philippine Islands ($260 million), BNP Paribas (an eight-12 months-deal for an undisclosed quantity), Westpac, Lloyds Banking neighborhood, Crédit Mutuel, Krungsri, Royal financial institution of Canada, and Mizuho financial institution. moreover, ICBC Argentina has announced a five-year, US $63 million contract with IBM services, “concentrated on redefining the bank’s client journey with IBM Watson and IBM Cloud,” large Blue stated, adding that 19 out of the properly 20 Fortune 500 banks and economic institutions use IBM Cloud.

In other business sectors, IBM closing week announced a multi-million greenback settlement with wise energy Water, which gives SAAS consumer engagement and analytics tools to utility groups. It follows IBM Cloud contracts with Vodafone ($550 million) and France's biggest financial institution, BNP Paribas.

In total, IBM says it has secured greater than $three billion in cloud deals in 2019.

IBM noted Santander is constructing a brand new IT structure as a foundation for its hybrid cloud atmosphere and has fashioned its own Cloud Competence center. “IBM is participating with the core in the definition of the methodologies and processes to speed up that transformation event.” additionally, the financial institution is using IBM DevOps options and IBM API join, “aimed to strengthen, iterate and launch new or upgraded purposes and digital features a good deal more all of a sudden.”

The bank will also use IBM Watson to contain AI capabilities geared toward improving customer experience, raise department advisory expertise and enhance worker productiveness.

“here is a part of a a lot larger trend taking place across the banking industry,” IBM noted in its announcement. “Banks nowadays face a couple of pleasing challenges: consumers are annoying more custom-made features, whereas security and regulatory guidelines are making facts governance greater complicated, and records and workloads are unfold across a variety of cloud and on-premises environments. but a new-found confidence is emerging as extra banks circulate to adopting hybrid cloud solutions…, the secret sauce to swift innovation.” IBM mentioned a discovering, with out attributing the source, that banks that have outperformed their friends are 88 % extra more likely to encompass hybrid cloud adoption as a part of their ordinary company approach.

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IBM Banking Industry Solutions Sales(R) Mastery Test v1

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Kubernetes Powered Central Go Modules | @KubeSUMMIT @JFrog @LeonStigter #Serverless #Containers #Docker #Kubernetes | killexams.com real questions and Pass4sure dumps

Building a Kubernetes Powered Central Go Modules Repository

Register for this session Here

Today, Kubernetes is the defacto standard if you want to run container workloads in a production environment. As we set out to build our next generation of products, and run them smoothly in the cloud, we needed to move to Kubernetes too! In the process of building tools like KubeXray and GoCenter we learned a whole bunch.

Join this talk to learn how to get started with Kubernetes and how we got started at JFrog building our new tools. After the session you will know:How we got to Kubernetes (and why we chose it)

How to know what you have in your apps and containers (and how we built ours)

How we're running GoCenter at scale.

Speaker Bio:

Leon is a Developer Advocate at JFrog where his focus is on helping developers get the most out of their DevOps tools with the end goal of enabling those developers to achieve the Liquid Software vision. As an outspoken supporter of developers Leon advocates for the needs of developers within JFrog and helps to drive open source tooling and Developer Relations strategy.Leon is passionate about serverless and container technologies and enjoys writing code and speaking and blogging about that. In his personal life, he's on a mission to taste cheesecake in every city he visits (suggestions are welcome @LeonStigter).

At CloudEXPO Silicon Valley, June 24-26, 2019, Digital Transformation (DX) is a major focus with expanded DevOpsSUMMIT and FinTechEXPO programs within the DXWorldEXPO agenda. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throughout enterprises of all sizes.

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    The Feed | killexams.com real questions and Pass4sure dumps

    Sunday Oct 22ndBuilding the Products Your Customers Need — Alluvium

    Company focus:- 1. We help them understand stability- 2. Perfect production

    Software is there to help, support, and enable experts in the all parts of the system.

    Under the hood, fundamentally about dimensionality reduction…folks at the edge face high dimensionality problems day after day. Started with online learning off streaming data for the edge….but faced cultural and organizational friction.

    Risk of adoption stalled conversations and POCs. Turned to, hey, what happens if we evaluate your historical data and immediately provide value/build longterm trust.

    Historical event analysis — grounding it in real problem (stability score + real sensor data). Using shallow NN (lstm), unsupervised.

    Saturday Oct 21st

    Listening primarily to Andrew Ng.- Today, the money is in supervised learning, structured data, and ads. He’s spoken before about the revenue Baidu was seeing (even a couple years ago) using deep learning for ad targeting, and while it’s not the sexiest use case, it’s certainly a prudent one.- Ng said he thinks of building a business around AI as a multi-year “chess game,” where the goal is to strategically gather the best data, build models, build the product, and then learn even more from user data — ultimately creating a virtuous cycle.- True AI company has these components.  — ‘strategic data acquisition’ — ‘centralized data warehouse’ — ‘pervasive automation’ — ‘new job descriptions'

    Merlon with Bradford. Similar to skymind with financial industry fraud — crimes analysis. - wanted to be here because of how unsexy, real biz problem it is- catalysts: execs loosing jobs, amount of spending, amount of fines- humans in the loop and ML product to help signal if someone opening a bank account should be doing that, notifying staff of users in the news for — potentially bad developments- companies spending $10B+ each year on this problem- risk ranking vs interest ranking (comps to his own startup data crawling, learn to rank implementations)- provide a UI, full stack product- generate some of their own data- what is defensible. Product that ties within prop data, prop ui. Learning cross competitors. - People/HR side is defensible too

    Bradford speaking with Matt Turck. - Largely the same pitch around vertical AI and why MLaaS/horizontal has — limited potential- Focus on solving higher level business problem as opposed to low level task that will become commoditized pretty quickly- Revenue - “There is need to help people understand how machine learning can solve their problems and if you don’t come to them with a solution…but you kind of come with a really great piece of technology that could solve their solution…your ultimately drop the problem back in their lap.”https://www.youtube.com/watch?v=mYu2MnPnCCY

    New (to me) commentary on ideal team structures:- Mostly see polar (very technical team…7 top kids from stanford) or (some guy from trucking business, domain expertise but no clue around tech)- You see very little of very smart ML founder and smart business founder that realize they need to work together and also get PM, designer, subject matter expert, etc- Sometimes you can overweight the domain expertise in first 1–2…startup commercial sense and savvy is more important in the beginning

    Friday Oct 20th Command C + Command V for the future tech IPOs. S-1

    Thursday Oct 19th

    Pod: https://overcast.fm/+HzyVPL4iU — AlluviumNotes:

    Manufacturing, broadly speaking, falls into two buckets. - Discrete — monorail (car + plane assembly)- Process/Additive — focus for Alluvium- Producing a substance or material (refining oil, chemical processing), this has highly interdependent systems.- Think of a visual aid…thousands of pipes and boilers wrangled together

    Who the user is falls into two groups:

    - someone working in oil refinery…typically relying on traditional threshold based results (alarm going off)- team of individuals looking back at historical (corporate or incident resolution)

    Product goal is to consolidate that and provide collective intelligence — system level analysis in real time…humans in the loop looking at feedback. + — labels

    Not necessarily considered anomaly detection (thresholds have already been hit), rather — series of smaller changes and heads up

    Technical under the hood:- time series data of different frequency- asset emitting state or sensor sharing temperature- used unsupervised or semi machine learning (user)- reality is most folks aren’t in a position to have that data..need to start from scratch and start injecting human expertise into the loop- traditional ML (forecasting) and shallow NN (fluffy answer here, wonder if most folks worry about being perceived as not having deep tech)- built deep empathy and humility for operators (most team lacked industrial DNA and domain knowledge)

    General fears of a solution of this type

    Corporate execs: fear that these tools that don’t actually know the problem and fear of how do we retain domain knowledge amongst workers that are aging / retire- looking ways at software can build institutional knowledge

    Front line side: machines are coming to replace job- stress and messaging that system has to work alongside the human

    Wednesday Oct 18th

    and Part 2. Very well received by /r/machinelearing

    - Neuron — thing that holds a number (between 0–1)…function - Learning: computer finding right weights and biases - Relu replaced sigmoid

    - Cost function high when not training well, low when performant- local minimum: doable- global minimum: very hard

  • find downhill direction and steepest descent- a network learning is minimizing a cost function using gradient descent
  • Tuesday Oct 17thConversation with software engineer spending last year seriously exploring the field.Draw to DL:- Potential impact over the longterm- Can play a role in bringing software eng practices to ml workflows- Find ML problems to be more interesting than APIs/backend plumbing- Business reality that folks are using ML right now

    Shallow and new field~1 yr of in-depth study can bring you to the edge of the field.

    Commentary on the state of tooling being completely lacking…state of javascript 10 years ago - model training, deployment, tracking, reproducibility

    Monday Oct 16th

    PodcastNotes:- We have a lot of resources, this is a big field that we’re well poised to play in.- A lot of corporate speak oddly concluded with a candid and genuine comment that they don’t know of how some (non-silicon) business models will develop

    Found this helpful in trying to understand automatic speech recognition vs natural language processing.

    ASR — attempt to determine what, exactly, was said- also known as speech to textNLP —tries to determine the intent of what was said

    Saturday October 14th

    Post from Erik Bernhardsson who I’ve come to really respect from afar for discussions about ML, data, and org building.

    This piece does a nice job of setting the scene. MOAR DATA =/ right or useful

    #anotherone

    Post.A tweet from a month prior

    again…real candid and grounded thoughts from someone who has actually built these systems. valuable to get this perspective.

    Anecdotally found his reasoning as to why he took the CTO position to be one of the better I’m sharing a development in my professional life kind of updates.

    Friday October 13th

    Pod.Notes — ProductAI

    Applied computer vision - Special focus in object detection- Matt is former longtime MSFT researcher. SEA → Beijing office- 2012 mainstream debut / breakthrough for DL on-stage…NYT front page press- ‘I still think it’s the early days of DL’ — Supervised is a bit more mature, semi/weakly semi supervision is needed

    Applications- Textiles Quality control (much higher interval testing vs random human sampling) material classification, fiber classification, and color analysis — acquired data via human labels (microscope hooked up to inspection)- Object detection within x-rays for safety within transportation- Fashion — style/cust/other attributes- Ecommerce — dealing with billions of images/products on marketplaces and auction sites — semi supervised learning is needed to scale with this volume…models trained via this. - Semi-supervised learning is our key technical innovation - “semi-supervised learning = subset of your data which is labeled with reliable quality and then using that to inform the rest of the data set”- “weakly, in our definition….when you essentially have labels for all of your data but it’s unreliable (unbalanced, noisy) so that’s where actually find the biggest value…because the internet is full of it.”- Scalability…moving away from dependence on human labeling

    - China landscape is a bit different in perception. Far more open that folks think. Everyone is sharing notes. Rising tide overall. Information reaches the east but research (published in mandarin) in china doesn’t flow back through to US. Andrew Ng highlighted this on same pod.

    Thursday October 12thBox movements in ML space the past year.

    2013…

    Box has now done technical integrations / biz partnerships with - IBM (text, audio)- MSFT (video+audio)- Goog (mostly image)

    tl;dr~ Machine learning as a service technology is effective and robust enough~ Customers can see value in better searching, discovering, organizing enriched media. Far more efficient than humans (or no interaction at all)~ Box can leverage best in class APIs…simply build out the plumbing on top of content management platform. ~ Said APIs are commoditized and become plug+play modular. Box here is an aggregator (HT Ben T) with end user relationship (billing, legal, UX, sales force).

    Good Q&A with Aaron this summer.

    PostThe more content you have and the more you use the system, the harder it gets to use. It’s counter-intuitive that this should keep progressing this way. The way to solve that problem, that scale, is through machine learning

    Wednesday October 11thSkymind — deep learning for the enterprise…an org and CTO I’ve been very intrigued by.

    Notes from variety of sources.- Podcast on Archillect.- Tech talk, talk 2- HN and /r/ML

    Business:- Largely serving China (healthcare) and Japan (banking, tech)-US is more stable but conservative line of business- 20 distributed people. - Long sales in suits…working with systems integrators to scale selling to these large organizations. - Tech agnostic…go to where data and dollars are. JVM systems.- Open-core

    Applications and solutions:- Fraud detection (credit cards, online banking)- Detecting illegal mining activity via satellite- Network intrusion (packet analysis)- Predicting machine failure- Stock market trend prediction (up/down)- Simbox fraud- Finding similar parts for large vehicles

    DL can be good at time series data, good at finding patterns in behaviorProblems largely revolving around analytics, churn prediction, recommendation engines

    Technology:- Created DL4J - Main thing is production deployment software and a service level agreement for models we build.- Not necessarily always DL, whatever the problem calls for- Have built a lot of the software stack

    Tuesday October 10thhttps://www.youtube.com/watch?v=IMoQPvXMkJw

    Building robust machine learning systems- Ravelin — Protect merchants from credit card fraud- 1–3% of revenue…cost for the merchant- Serious precision/recall tradeoff. FP as a consumer (just checking, we think there may be fraud…False Neg — you don’t spot the fraud)- I think most engineers would be horrified at the state of machine learning tooling (training, testing, deploying)- ML systems — High complexity — Current decisions impact future performance — Loss function doesn’t represent true goal

    - Training models — How do we make sure the world we present to the model is the view of the world we actually want to learn?

    - Labels & truth — Delayed labels….takes many weeks/months to acocunt for so if you just train on last 2–3mos…you actually don’t have that captured view of labels.

    - Testing — pull of set of examples you should *always* get right — use as unit test — generate synthetic data for worst case scenarios (loading up credit cards in 10min, spending 100k)

    Deploying- Model provenance — who trained the model…when…what git hash….on what data…which hyperparameters…..train/test performance — Shipping model…share as much code as possible between offline and online — Release gradually. A/B test.  — Score in live with both models. Inspect biggest disagreements.

    Automation:- Unfortunately end up training on *data_1_final_edited_really_final(1)(2).csv*

    Monday October 9th

    #AI methods ranked by econ value Supervised Learning Transfer Learning Unsupervised Learning Reinforcement Learning

    Older notes to be backfilled


    Chris Deering looks back at Sony | killexams.com real questions and Pass4sure dumps

    Deering stresses that he's giving "no Sony views but personally ideas moving sideways out-of-the-box."(sic)

    "As Sony's portfolio of businesses diversifies, then Sony might move toward services in order to be more efficient in terms of capital employed," he began. "They have their ups and downs, but..."

    Deering clearly thinks Sir Howard Stringer, the recently appointed new honcho at Sony worldwide - the first non-Japanese leader Sony has ever had - is going to be a big success. "He's bringing in new ideas and new engineers are coming along.

    "Of course the major trend-line seems to be moving from devices to services, particularly as chips become cheaper and more available. Bandwidth is increasing in both wireless and fixed-line so the pipelines are growing fast. I don't think I invented these truths and anybody can see that anything that can go wireless, will."

    Sony has over the last 20 years moved aggressively into the content arena with Sony/BMG Music, Sony Pictures and surprisingly even has a Sony Bank, Sony Assurance and Sony Life divisions.

    "Sony is an entertainment concern like a Viacom or Disney without the devices," he says, alluding to the increasing Sony content focus."

    He says: "iTunes won't play on Windows devices and Sony songs won't work on iPods, so we launched Sony Connect which is, as are the others, more track-based and less album-based sales of music."

    How much autonomy did Deering have from the Tokyo executives at Sony?

    "Tokyo let us run and expand the business while giving us the resources to do it. We had a good amount of self-determination. SCE was set-up to be a JV between Sony Music and Sony Electronics, so generally the type of response I would get from them was 'Please get on with it - we're busy!'

    "Japan was kind of the Hollywood of games, and particularly game devices, though the command and control within the electronics industry is traditionally much stronger."

    Why hasn't Deering started his own business?

    "Because I like things that make a big impact - like millions of people using the Gillette product, and the same at Atari. I'm not sure I could have built a company with that kind of impact. But who wouldn't have wanted to start eBay or Google?"

    Now that he's retired, does Deering think he'll take the entrepreneurial plunge?

    "If I do," he said, "it'll be around a concept where application software helps you find what you want - I call it 'concierge'.

    "If Google's a 'librarian' whom you ask for a particular book title, then concierge is a librarian who discriminates and makes selections for you based on learning what you like. So, everybody's around the dining-room table snacking - and more and more we're snacking on digital content. The 'entertainment concierge' comes to you, on your mobile or whatever, and says 'there's a live event going on right now that you're gonna love'." ®

    Bill Robinson may be reached at: bill@relentlessmarketing.com



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