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C2010-024 IBM Tivoli plane 2 back Tools and Processes

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C2010-024 exam Dumps Source : IBM Tivoli plane 2 back Tools and Processes

Test Code : C2010-024
Test designation : IBM Tivoli plane 2 back Tools and Processes
Vendor designation : IBM
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IBM IBM Tivoli plane 2

IBM Counts On Its Cloud And Platform For The Turnaround | killexams.com true Questions and Pass4sure dumps

abstract

IBM has been pushing ahead on its platform and cloud-pushed method.

Its Platform as a service providing, previously known as Bluemix, is now universal as the IBM Cloud.

IBM also has been investing in including capabilities similar to cloud migration services and cloud optimized systems.

IBM (NYSE:IBM) has been specializing in its strategic imperatives side to accommodate the subsequent stage of increase. The side contains the cloud, analytics, mobile, social, and security functions. however the turnaround is taking a all lot longer than anticipated. After three consecutive quarters of income increase, IBM revenues seem to subsist back on the decline. In an past streak, IBM had stated revenue declines for 5 straight years. The market is losing persistence and publish the influence announcement, IBM's stock fell 2% in the after hours trading session.

IBM's Financials

Revenues for the primary quarter fell four.7% to $18.18 billion, missing the road's forecast of $18.46 billion. Its net income also declined to $1.fifty nine billion, or $1.seventy eight per share, compared with $1.sixty eight billion, or $1.81 per share, a year prior. On an adjusted basis, IBM earned $2.25 per participate and turned into forward of the market's expectations of $2.22 per share. IBM attributed the omit in revenues to its declining hardware enterprise and exotic money headwinds.

via section, cognitive options and know-how services and cloud platform revenues came in at $eleven.91 billion, falling short of the market's anticipated $12.43 billion. within the phase, cloud and cognitive options revenues, which encompass analytics, cybersecurity, and synthetic intelligence, fell 1.5% to $5.04 billion, but managed to beat the analyst estimates of $four.18 billion.

global company capabilities revenues fill been flat at $4.12 billion and additionally fell wanting the analyst estimates of $4.19 billion. programs revenues fell to $1.33 billion, lacking the market's forecast of $1.37 billion. It ended the quarter with global financing revenues coming in flat at $406 million. IBM is divesting a few of its different agencies such because the mortgage servicing enterprise. It pronounced revenues from these segments as half of other revenues that ended the quarter with $417 million.

IBM didn't report the revenues from its strategic imperatives. but it surely did mention that the annual exit revenue bustle rate for cloud-delivered features improved 10% to $eleven.7 billion.

IBM expects to conclusion the current yr with EPS of as a minimum $13.ninety, which became short of the market's forecast income of $13.91 per share.

IBM's Cloud method

IBM has been pushing ahead on its platform and cloud-driven strategy. Its Platform as a service providing, previously referred to as Bluemix, is now widely used because the IBM Cloud. Bluemix become released in 2014 and within a year it had a collection of greater than 100 cloud-based construction tools. these days IBM's Cloud Foundry is the open-source PaaS providing on IBM Cloud that permits businesses to installation and scale apps. IBM makes it viable for organizations to bustle the Cloud Foundry apps on a public cloud, commerce environment or a non-public cloud. The Cloud Foundry now has heaps of applications that give capabilities similar to compute, storage, networking, analytics, and IoT. A contemporaneous record by means of Transparency research estimates the revenues from IBM Bluemix functions to develop to $38.85 billion with the aid of 2026, starting to subsist at an annualized augment cost of nearly 25% over the next seven years.

anyway PaaS, IBM is riding its cloud commerce through numerous improvements. over the last few months, IBM has been investing in including capabilities akin to cloud migration services and cloud-optimized methods. throughout the final quarter, it introduced Watson anywhere that makes IBM Watson available on-premises and on any private or public cloud. It released the IBM cloud integration platform that gives a typical route to integrate functions and purposes across distinct cloud environments.

IBM's cloud company also is anticipated to score a robust thrust from its $34 billion acquisition of pink Hat. IBM plans to leverage the acquisition to aid its customers shift their company functions to hybrid cloud, while addressing the concerns of portability, administration consistency and protection. The acquisition continues to subsist pending regulatory approval, but IBM plans to complete the acquisition by using the second quarter of the year.

IBM will should proceed to thrust its cloud and platform approach to deliver the a all lot-necessary turnaround. i want to know from IBM what apps on its platform does it observe as offering essentially the most traction? What are the apps that IBM builders exigency to focal point on to pressure more revenues? Which of these apps and its builders carry out you feel IBM should quiet acquire?

IBM's inventory is currently buying and selling at $139.eleven, with a market capitalization of $123.eight billion. It touched a 52-week inordinate of $162 in September closing year. When the tech stocks tumbled last December, its inventory too had fallen to a 52-week low of $105.ninety four.


IBM CFO: pink Hat deal is on the privilege track to near | killexams.com true Questions and Pass4sure dumps

No influence found, are trying new keyword!"we can constantly try this to subsist positive we’re optimizing the confiscate stage of ... to the company. IBM said its As-a-carrier annual exit bustle cost for cloud revenue changed into at $11.7 billion, up 10 ...

A Dividend Hike From IBM Is Coming, but carry out not are expecting a lot | killexams.com true Questions and Pass4sure dumps

foreign enterprise Machines (NYSE:IBM) has raised its dividend fee yearly for 23 consecutive years, and it's paid a quarterly dividend devoid of fail because 1916. With that nature of music record, an additional dividend hike later this month is near a guarantee.

That dividend raise doubtless might not subsist total that huge, although. IBM has already been slowing down its pace of dividend boom during the last few years because the company's transformation took a bite out of earnings and earnings. With the mega-acquisition of crimson Hat set to load the steadiness sheet with debt, IBM will likely play it safe and opt for a petite dividend hike.

IBM's Global  focus for Watson IoT in Munich, Germany

image supply: IBM.

a superior and growing to subsist dividend

IBM expects the $34 billion purple Hat acquisition to shut within the 2d half of this year. The company will finance the contend with a blend of money and debt, so the wait result could subsist a more debt-encumbered stability sheet. on the wait of 2018, IBM had $12.2 billion of cash and $14.6 billion of corporate debt unrelated to the international financing enterprise.

The route is to suspend participate buybacks in 2020 and 2021 to assist shore up the steadiness sheet. IBM has spent between $three billion and $5 billion annually on participate buybacks due to the fact that 2015, so so that you can actually back the cause.

whereas IBM will forego participate buybacks for a while, it remains dedicated to growing its dividend. IBM expects the crimson Hat deal to enhance cash skedaddle within the first year, and to enhance adjusted profits per participate in the second 12 months. with a purpose to back sheperd continued dividend increases.

The present quarterly dividend of $1.fifty seven per participate represents simply 45% of IBM's adjusted profits suggestions for this 12 months on an annualized groundwork, so there's definitely room for IBM to develop its dividend. or not it's viable IBM will shock investors with a larger-than-anticipated dividend augment -- a ten% raise would leave the payout ratio simply demure of fifty%. however a smaller boost would permit IBM to carve back its debt at a sooner fee, which may quiet subsist the priority.

remaining 12 months, IBM raised its dividend through just 5%. i would expect a similar mid-single-digit enhance this 12 months.

A carve charge stock

whereas IBM is rarely featuring the identical stage of dividend augment in comparison to a number of years ago, the inventory remains a infrequent cost within the tech sector. Shares trade for just about 10 instances adjusted salary assistance for this yr, regardless of key competitive merits that construct IBM intricate to disrupt.

The purple Hat deal will construct stronger IBM's hand within the hybrid cloud-computing market. IBM has long identified that colossal businesses with knotty IT operations were not going to depart all-in on the common public cloud. while Amazon and Microsoft dominate the public cloud market, hybrid cloud looks to subsist the belt the trade is headed. it truly is safe information for IBM.

IBM stock yields about four.4%. that is down from just about 6% total over the inventory market sell-off late last yr, however's nevertheless greater than double the capitulate of the S&P 500. With any such inordinate yield, even gradual dividend augment makes IBM an excellent dividend inventory.


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IBM Tivoli plane 2 back Tools and Processes

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IBM Improves IT Operations with ersatz Intelligence | killexams.com true questions and Pass4sure dumps

Artificial Intelligence in IT Today

Many IT departments fill implemented software solutions that depart beyond simple transaction and analytical processing. These packages hold models that characterize inescapable data behaviors, and these models consume current data to observe if these patterns of data deportment exist. If so, operational systems can use this information to construct decisions. A safe specimen of this is fraud detection. IT data engineers use analytics on historical data to determine when fraud occurred, code this into a model, and deploy the model as a service. Then, any operational system can invoke the model, pass it current data and receive a model “score” that represents the probability that a transaction may subsist fraudulent.

The common term for these new packages is ersatz intelligence (AI). They consist of a combination of search, optimization and analytics algorithms, statistical analysis techniques and template processes for ingesting data, executing these techniques and making the results available as services called models. The subset of AI that deals with model creation and implementation is sometimes called machine learning (ML).

Machine Learning and ersatz Intelligence

IT departments implement ML and AI solutions in the broader context of their data and processing footprint. This is usually depicted as the following four-layer hierarchy.

Layer 1: The Data.

This layer contains the data distributed across the enterprise. It includes mainframe and distributed data such as product and sales databases, transactional data and analytical data in the data warehouse and any mammoth data applications. It also may comprise customer, vendor and supplier data, perhaps at remote sites, and even extends to public data such as twitter, advice feeds and survey results. Another viable source of data is server performance logs that comprise resource usage history.

Note that these data exist across diverse hardware platforms including on-premises and cloud-based. As such, various data elements can exist in multiple forms and formats (e.g. text, ASCII, EBCDIC, UTF-8, XML, images, audio clips, etc.). In addition, at this plane will exist hardware and software that manage the data, including high-speed data loaders, data purge and archive processes, publish-and-subscribe processes for data replication, as well as those for touchstone backup and recovery and calamity recovery planning.

Layer 2: The Analytics Engines.

In this layer exist a blend of hardware and software that executes commerce analytics against the data layer. There are several common players in this space. They include:

  • The IBM Db2 Analytics Accelerator (IDAA) than can subsist implemented as standalone hardware or fully integrated within inescapable z14 servers;
  • Spark on z/OS;
  • Spark Anaconda on z/OS;
  • Spark clusters on distributed platforms.
  • Just as the data layer occurs across multiple hardware platforms and distributed sites, so will the analytics engines layer. The major role of this layer is to provide an optimized data access layer against the underlying data as a service for AI and operational applications.

    Layer 3: The Machine Learning Platform.

    IT implements machine learning software in this layer. It accesses the data through one or more of the analytics engines. It is in this layer that IBM delivers its latest offering, Watson Machine Learning for z/OS (WMLz). WMLz provides a basic machine learning workflow consisting of the following steps:

  • Data Ingestion and Preparation — Inputting data, filling in missing values, encoding category data, creating indexes and normalizing numeric values;
  • Model structure and Training — An interface for the data scientist to create a model of data deportment based on historical analytics, train the model to detect data patterns and validate the model;
  • Model Deployment — Implement the model as a production process, including procedures for updating models in-place and monitoring model results;
  • Feedback Loops — Processes that allow automated model learning by feeding model results back into the model training process to update models or capitulate new ones.
  • Data scientists know that one of the greatest benefits of machine learning is to use the results in operational systems; for example, having an ML model dissect financial data to determine the possibility of fraud. This means that you will achieve best performance when you deploy ML in the hardware environment where transaction processing occurs. For many large organizations this means the IBM zServer environment.

    Layer 4: Machine Learning Solutions.

    Now that they fill the machine learning platform available as ML services, they can create combined AI/ML solutions that invoke those services. IBM has several ready-made solutions for this layer, including the following:

  • Db2 AI for z/OS (Db2ZAI) -- Using Db2 SMF data for analysis, Db2ZAI monitors and analyzes Db2 operations in a Z/OS environment. It can provide improved query access path information to the Db2 optimizer to augment SQL performance, diagnose Db2 performance abnormalities and recommend corrective action and detect Db2 statistics anomalies and provide performance tuning recommendations;
  • IBM Z Operations Analytics (IZOA) -- This product analyzes z/OS SMF data and detects changes in subsystem use and forecasts changes that may subsist required in the future, does automatic problem analysis and provides problem insights from known problem signatures.
  • Watson Machine Learning on Z

    Let’s engage a deeper dive into how Watson Machine Learning on Z (WMLz) works and what services it can provide.

    Key Performance Indicators (KPIs). WMLz does not inherently know what performance factors are significant to you. However, once these KPIs are defined (either by a user or by implementing one of the machine learning solutions celebrated above), WMLz can dissect KPI data to glance for correlations. For example, when one KPI (say, I/O against a censorious database) goes up, another KPI (say CPU usage) may depart up as well. As another example, several KPIs may subsist behaviorally similar, so WMLz can cluster them as a group and achieve further analysis across groups. WMLz can also determine KPI baseline behaviors based on time-of-day, time zone of transactions or seasonal activity.

    Anomaly Detection. Once correlations are discovered, WMLz can glance contradictory effects and report them as anomalies. In their I/O specimen above, an anomaly would subsist reported if I/O against a censorious database increased but CPU usage decreased.

    Pattern Recognition. As with many machine learning engines, WMLz will glance for patterns among KPIs and data identifiers. For example, CPU may augment when processing inescapable categories of transactions.

    KPI prediction. An extension of basic KPI processing, WMLz can use the past behaviors of groups of KPIs to prognosticate the future. reckon their I/O specimen once again. The product may detect that inescapable transactions become more numerous during a particular time period, and these transactions consume significantly more CPU cycles. The product may then prognosticate future CPU spikes.

    Batch workload analysis. Many IT shops fill a large contingent of batch processing that is tightly scheduled and includes job and resource dependencies. Some jobs must wait for their predecessors to complete, some use significant shared resources (such as tape drives or specialty hardware) and some are so resource-intensive that then cannot subsist executed at the same time. WMLz can dissect the workload data, including resource usage, and provide recommendations for balancing resources or tuning elapsed times.

    MLC cost pattern analysis and cost reduction. Some IBM software license charges are billed monthly, and the license amount may depend upon maximum CPU usage during peak periods. WMLz can dissect CPU usage across time, glance for patterns and construct predictions and recommendations for software license cost reduction.

    Watson Machine Learning for z/OS — Features

    IBM’s Watson Machine Learning for z/OS allows IT its selection of progress environments to develop models including IBM SPSS Modeler. These environments assist data scientists by using notebooks, data visualization tools and wizards to speed the progress process. Several quick-start application templates are also incorporated in the toolset for common commerce requirements such as fraud detection, load approval and IT operational analytics. The latest version of WMLz (version 2.1.0) includes back for Ubuntu Linux on Z, java APIs, simplified Python package management and several other features.

    Interested readers should reference the links below for more circumstantial technical information.

    # # #

    See total articles by Lockwood Lyon

    REFERENCES

    Machine Learning and ersatz Intelligencehttps://en.wikipedia.org/wiki/Machine_learning

    Data and AI on IBM Zhttps://www.ibm.com/analytics/z-analytics

    Using Anaconda with Spark — Anaconda 2.0 documentationhttps://docs.anaconda.com/anaconda-scale/spark/

    Watson Machine Learning - Overviewhttps://www.ibm.com/cloud/machine-learning

    Watson Machine Learning - Resourceshttps://www.ibm.com/cloud/machine-learning/resources


    IBM and Ricoh Unveil Printer Management appliance | killexams.com true questions and Pass4sure dumps

    IBM and Ricoh last week unveiled a new software product that allows companies to monitor and manage multi-function printers (MFPs) from a central location. Reducing squander and limiting carbon footprints are goals of the new software, which will also subsist used for managed remote printing services.

    The new offering, which was not given a formal name, is based on Tivoli software from IBM, and will subsist pre-loaded onto MFPs and production printers from Ricoh and, presumably, from InfoPrint Solutions Company, the joint IBM-Ricoh operation based at IBM’s former printing headquarters in Boulder, Colorado.

    Last week’s announcement, which was made at the Pulse industry conference in Las Vegas, was total about cost savings. The new Tivoli software can restrict the printing capabilities available to wait users, and track the energy usage and carbon footprints of individual users, as well as at device and departmental levels.

    IBM and Ricoh quoted a Gartner study that organize companies can reduce their printing-related costs by 10 to 30 percent by monitoring and managing their printers more pro-actively.

    The new software is fraction of IBM and Ricoh efforts to bring a new plane of “smart” to offices, says Bruce Anderson, common manager of IBM Electronics Industry. “As CIOs travail to drive down costs, printers and other office devices offer an attractive target for improvement,” he states in a press release.

    It is unclear how much the new Tivoli software will cost. Additional product information was not available as this newsletter reached its deadline.

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    IBM marries on-premises, private and public-cloud data | killexams.com true questions and Pass4sure dumps

    Cloud

    (Image: Stockfresh)

    Cloud Integration Platform allows customers to better integrate data securely no matter where it resides in on-premises, private, hybrid or public cloud.

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    Read More: cloud computing hybrid cloud IBM private cloud public cloud

    IBM has taken the wraps off of a new multi-cloud integration platform it hopes will back customers manage, secure and integrate data no matter where it resides – on-premises, private-cloud or public-cloud applications.

    Enterprise customers are faced with the daunting stint of bridging legacy applications with latest cloud service, and many cannot just elevate and shift, said Juan Carlos Soto, IBM vice president of hybrid cloud integration. On top of that many businesses are already trying to manage five or more cloud environments, often from multiple vendors, and they cannot maintain up, he said.

    IBM’s Institute for commerce Value estimates that by 2021, 98% of organisations route to adopt hybrid architectures, but just 38% will fill the procedures and tools they exigency to operate that environment.

    Specifically the ICP runs on mammoth Blue’s IBM Cloud Private environment which can manage Kubernetes and other containers. The ICP includes a number of existing IBM offerings that comprise the following.

    IBM MQ is software that simplifies and accelerates the integration of applications and commerce data across multiple platforms. It uses message queues to enable the exchanges of information and offers a unique messaging system for cloud, mobile, Internet of Things (IoT) and on-premises environments. IBM says by connecting virtually everything from a simple pair of applications to the most knotty commerce environments, MQ helps organisations help commerce responsiveness, control costs, reduce risk and gain real-time awareness from mobile, IoT and sensor data.

    Enterprise-gradeEnterprise-grade Kafka: Specifically IBM Event Streams is a platform built on the approved open source Apache Kafka mammoth data technology and a key open-source technology that underpins responsive and quick-witted applications. Event Streams helps organisations focus on writing applications rather than deploying middleware. It enables customers to deploy mission-critical workloads and helps them connect new applications to existing infrastructure, IBM says.

    The platform also includes back for integrating siloed application on premises and in the cloud with back for key APIs such as REST, IBM said.

    Coding timeThe credence is that with total the integration components of the platform customers can carve coding time and costs by reusing assets they are chummy with, Soto said.

    Offering a multi-cloud, portable hybrid integration solution is significant for IBM in a few ways, said Cassandra Mooshian, a senior analyst with Technology commerce Research. It greatly reduces the perception of vendor or platform lock-in, which in the world of hybrid IT is attractive, Mooshian said.

    “It underscores that IBM is willing to play in a multivendor world (rather than promoting IBM IaaS as the technology underpinning ICP and solutions atop it), it can back bring IBM to the table more often in enterprise and midmarket organisations now that it is ‘playing nice’ more often with peers, and it addresses a fundamental pang point that IT departments are facing, linking on-prem apps and data to cloud apps and data such that processes can become more efficient and customers can score the most commerce value,” Mooshian said.

    The Cloud Integration Platform was just one of a number of IBM cloud announcements that emanated from its reflect 2019 gathering this week in San Francisco.  Others included:

    Watson AI for all: Until now IBM’s Watson ran only on mammoth Blue’s Cloud Private system but that changed this week. The company announced that customers running Amazon, Google, Microsoft and other cloud services will subsist able to engage advantage of Watson’s intelligence by being able to bustle IBM Watson services, including Watson second and Watson OpenScale.  IBM said with Watson, customers will subsist able to apply AI to data wherever it is hosted and with its set of applications, progress tools, machine-learning models and management services. Watson can back organisations mine data, prognosticate outcomes and automate time- and resource-sensitive processes. Companies can also develop conversational services such as virtual customer-service agents, IBM said.

    Hyper-security: Built on IBM LinuxONE technology, which features pervasive encryption support, IBM Cloud Hyper Protect Services is aimed at securing mammoth Blue’s public-cloud offering. The service will provide encryption-key management with a dedicated cloud hardware security module (HSM) built on FIPS 140-2 plane 4-based technology.

    Cloud at your service: IBM rolled out a service customers can consult with to build hybrid-cloud environments. IBM Services is establishing dedicated teams of consultants certified in the latest services and technologies across multiple cloud platforms.

    Multi-cloud management: The company has added IBM Services for Multicloud Management to provide a unique system to back enterprises manage IT resources across multiple cloud providers, on-premises environments and private clouds. The service lets customers track costs and monitor service levels among other tasks.

    IDG advice Service

    Read More: cloud computing hybrid cloud IBM private cloud public cloud

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