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C4040-129 exam Dumps Source : IBM i 7.1 Administration
Test Code : C4040-129
Test denomination : IBM i 7.1 Administration
Vendor denomination : IBM
: 164 real Questions
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IBM IBM i 7.1 Administration
February 18, 2019 Timothy Prickett Morgan
well, that took reasonably a long time. After what appears relish eons of nudging and cajoling and pushing, IBM is making the IBM i operating system and its built-in database administration system, as neatly as the utility pile tackle and other techniques application, available on its self-branded IBM Cloud public cloud.
massive Blue previewed its plans to deliver both IBM i and AIX to the IBM Cloud at its annual feel conference in Las Vegas, on scale out machines aimed at minute and medium groups in addition to to purchasers who necessity to precipitate clusters of machines, and on scale up programs that contain NUMA electronics that greater tightly cluster them into shared recollection systems.
There are a lot of questions about how this will exist complete exist packaged up and bought below the unwieldy denomination of the IBM power programs virtual Server on IBM Cloud. however they can let you know complete that they know and fill you in as they study extra.
The vitality techniques slices served up on the IBM Cloud working IBM i and AIX were first rolled out for early access to chosen clients to verify the APIs for the manage plane, which they presuppose is in keeping with the PowerVC implementation of OpenStack. The initial shoppers who are kicking the tires contain been imagined to simplest deploy pile and check environments or protype code, under no circumstances anything operating in construction. This was totally intended to exist a proof of concept setup at this factor, as that you can survey from the rollout roadmap:
The alpha and beta checking out for the power methods slices on the IBM Cloud is on an attractive speedy song, operating from February into March, as you can see, and common availability is slated for some time in March. probably somewhere in opposition t the conclusion, we'd guess. IBM is originally making the vitality puffs – what else are you able to denomination them as shorthand? – available in the early entry assessments in its US East datacenter in the Washington DC metro enviornment; it isn't transparent what iron became used. The alpha and beta checking out is being finished on a collection of energy S922 scale out machines, that are in response to a pair of Power9 processors and which frankly is a great realm for most IBM i stores complete by course of itself, in addition to on vigour E880 scale up systems, that are being scaled up to sixteen sockets and which exercise the older Power8 processors. complete of the machines exercise ten-core processors, that are the sweet spot between performance, chip yield, and cost in the vitality techniques line. The Power9 chips within the power S922 servers precipitate at 2.8 GHz and can faster up to 3.9 GHz if there's enough thermal headroom; these chips search for relish they may contain 4 threads per core (often known as SMT4), which isn't as a entire lot threading because the tolerable Power8 chip presents in scale out machines with SMT8 threading (eight threads per core) and twice as many cores per die (24 cores versus 12 cores). Anyway, it appears relish the vitality S922 is being outfitted with the weighty cores, not the gentle ones. The Power8 chips in the power E880 are running at 4.19 GHz and additionally uphold SMT8 threading (there become handiest a weighty core here); complete sixteen cores are populated with ten-core Power8 chips, with a optimum scalability of 160 cores in a lone ratiocinative partition on the IBM Cloud.
each of the machines are being configured with 32 GB reminiscence sticks, which are again at the candy spot between skill, bandwidth, and value per bit, and IBM is allowing valued clientele to configure the machines with anyplace from 8 GB to sixty four GB of leading recollection per core. if you wish denser recollection to bolster the ability, that you may double it up the usage of sixty four GB reminiscence sticks, and then double it up once again to 128 GB sticks, however this may exist a custom order and it will suffuse considerably more, they think. IBM is offering disk and glimmer storage up to 2 TB for indigenous storage on the circumstances and growing to exist at 10 GB increments. exterior storage for applications and databases is on Storwize V7000 disk arrays within the racks near this power iron, and are linked to the servers by using Fibre Channel switches. valued clientele can share machines or employ dedicated iron, depending on their proclivities for absolute safety and coping with stentorian neighbors on the digital infrastructure.
Steve Sibley vice chairman and offering manager for the Cognitive programs division at IBM, gave me a sneak peek at the future energy cloud slices on a consult with to the Austin, Texas labs final week, forward of the respect convention. here is how the facets and functions of the ratiocinative partitions on the IBM Cloud are shaping up:
Sibley advised us that the pricing became now not yet set in stone for the digital energy skill on the IBM Cloud, but when pressed about comparing the suffuse between purchasing tackle and renting it on the cloud, Sibley estimated that for a given IBM i or AIX workload running on in a similar course configured hardware and ratiocinative partitions, running the cloud illustration would cost about 30 percent to 40 p.c extra on the instance running plenary out over three years compared to purchasing the iron and using it for 3 years. The change, of route, is that IBM is managing the power methods and paying for the datacenter, the electrical energy, the cooling, the safety, patch updates on firmware, the installing of the PowerVM hypervisor and its update, and the installing of the IBM i or AIX operating gadget. When agencies buy their own vitality programs machines, except they accumulate augmented services from IBM or a enterprise accomplice, it’s complete on them. Pricing for the digital machines, the memory, the storage, and the operating gadget and databases are complete set per hour.
incidentally, it is not transparent what IBM i and AIX releases are supported on the machines, but most likely the current IBM i 7.2 and IBM i 7.three releases are supported on the IBM Cloud in addition to AIX 7.1 and AIX 7.2, which are too latest. Linux is not mentioned as a supported platform, but SAP HANA workloads are and that implies that there's a Linux – and in particular pink Hat commercial enterprise Linux – below it on account that HANA in-reminiscence databases don't appear to exist supported on any Unix variant, or IBM i for that rely.
by course of March, IBM plans to contain the IBM i and AIX cases purchasable in creation in its US East (Washington DC) and US South (Dallas) areas, and in the 2d quarter IBM plans to roll it in its European datacenter in its european valuable (Frankfurt) vicinity, with different datacenters coming out as exact pulls and IBM pushes.
we will remove a glance on the pricing as soon as it turns into accessible and survey the course it complete stacks up, and accumulate the suggestions of other IBM i cloud avid gamers about what enormous Blue is doing.
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The X factor: Head within the Clouds
In September 2018, IBM announced a brand current product, IBM Db2 AI for z/OS. This synthetic intelligence engine monitors facts access patterns from executing SQL statements, makes exercise of laptop gaining erudition of algorithms to opt for most fulfilling patterns and passes this counsel to the Db2 query optimizer to exist used by subsequent statements.
machine studying on the IBM z Platform
In may additionally of 2018, IBM announced version 1.2 of its laptop learning for z/OS (MLz) product. here is a hybrid zServer and cloud software suite that ingests performance statistics, analyzes and builds models that characterize the health status of numerous indicators, monitors them over time and gives precise-time scoring capabilities.
several elements of this product offering are geared toward assisting a group of mannequin developers and bosses. as an example:
It helps distinct programming languages equivalent to Python, Scala and R. This permits information modelers and scientists to exercise a language with which they are ordinary;
A graphical person interface referred to as the visual mannequin Builder publications mannequin builders without requiring enormously-technical programming potential;
It comprises diverse dashboards for monitoring model consequences and scoring features, in addition to controlling the gadget configuration.
This laptop gaining erudition of suite become firstly aimed at zServer-based analytics purposes. one of the first obtrusive selections became zSystem performance monitoring and tuning. system management Facility (SMF) facts which are instantly generated by the operating device supply the uncooked statistics for tackle aid consumption corresponding to crucial processor utilization, I/O processing, reminiscence paging and so forth. IBM MLz can collect and rescue these facts over time, and construct and train fashions of tackle habits, score those behaviors, determine patterns no longer with no distress foreseen with the aid of humans, enhance key performance warning signs (KPIs) and then feed the model effects again into the device to contain an upshot on device configuration changes that can enrich efficiency.
The next step turned into to implement this suite to dissect Db2 performance statistics. One solution, called the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the machine getting to know know-how to Db2 operational statistics to benefit an figuring out of Db2 subsystem health. it may dynamically build baselines for key performance warning signs, supply a dashboard of those KPIs and provides operational group of workers precise-time insight into Db2 operations.
whereas common Db2 subsystem performance is an faultfinding aspect in basic application fitness and performance, IBM estimates that the DBA uphold workforce spends 25% or greater of its time, " ... combating entry route issues which occasions efficiency degradation and repair contain an repercussion on.". (See Reference 1).
AI comes to Db2
accept as suitable with the plight of modern DBAs in a Db2 atmosphere. In modern IT world they must uphold one or extra great facts purposes, cloud software and database services, application installation and configuration, Db2 subsystem and application efficiency tuning, database definition and management, catastrophe restoration planning, and extra. query tuning has been in actuality considering that the origins of the database, and DBAs are constantly tasked with this as well.
The coronary heart of query path analysis in Db2 is the Optimizer. It accepts SQL statements from functions, verifies authority to entry the information, stories the areas of the objects to exist accessed and develops a list of candidate records access paths. These entry paths can comprise indexes, desk scans, quite a lot of table link strategies and others. in the records warehouse and massive facts environments there are continually additional choices obtainable. One of these is the actuality of abstract tables (every now and then known as materialized question tables) that comprise pre-summarized or aggregated records, therefore allowing Db2 to avoid re-aggregation processing. one other option is the starjoin access course, regular within the facts warehouse, where the order of desk joins is changed for efficiency causes.
The Optimizer then experiences the candidate access paths and chooses the entry direction, "with the lowest can charge." cost during this context means a weighted summation of useful resource usage together with CPU, I/O, recollection and different components. at last, the Optimizer takes the bottom cost entry direction, shops it in recollection (and, optionally, in the Db2 directory) and starts off entry path execution.
big information and facts warehouse operations now comprise application suites that allow the commerce analyst to exercise a graphical interface to build and maneuver a miniature records model of the data they want to analyze. The programs then generate SQL statements in accordance with the users’ requests.
The issue for the DBA
with a view to conclude respectable analytics on your dissimilar facts stores you necessity a helpful knowing of the data necessities, an figuring out of the analytical services and algorithms accessible and a high-performance statistics infrastructure. lamentably, the quantity and location of data sources is expanding (both in dimension and in geography), records sizes are turning out to be, and applications proceed to proliferate in number and complexity. How may soundless IT managers assist this environment, specially with essentially the most skilled and mature staff nearing retirement?
take into account additionally that a enormous a allotment of reducing the total suffuse of possession of these methods is to accumulate Db2 functions to precipitate faster and more effectively. This continually interprets into using fewer CPU cycles, doing fewer I/Os and transporting less statistics throughout the network. considering that it is commonly complicated to even identify which functions could advantage from efficiency tuning, one strategy is to automate the detection and correction of tuning concerns. here's where computer learning and synthetic intelligence can exist used to incredible effect.
Db2 12 for z/OS and synthetic Intelligence
Db2 version 12 on z/OS uses the machine discovering amenities outlined above to accumulate and store SQL question text and entry course details, in addition to precise performance-related historic assistance comparable to CPU time used, elapsed times and result set sizes. This offering, described as Db2 AI for z/OS, analyzes and stores the records in laptop discovering models, with the model evaluation consequences then being scored and made available to the Db2 Optimizer. The next time a scored SQL commentary is encountered, the Optimizer can then exercise the model scoring facts as enter to its access route alternative algorithm.
The outcome should soundless exist a discount in CPU consumption because the Optimizer uses mannequin scoring input to pick more advantageous access paths. This then lowers CPU charges and speeds software response times. a significant potential is that the usage of AI software does not require the DBA to contain information science advantage or deep insights into query tuning methodologies. The Optimizer now chooses the ultimate entry paths based not only on SQL query syntax and records distribution records but on modelled and scored historical efficiency.
This will too exist primarily crucial in case you hold records in distinct locations. as an example, many analytical queries against massive statistics require concurrent access to sure information warehouse tables. These tables are commonly referred to as dimension tables, and that they comprise the information aspects usually used to manage subsetting and aggregation. for instance, in a retail atmosphere dependence a desk referred to as StoreLocation that enumerates each store and its region code. Queries against rescue sales statistics may necessity to amalgam or summarize revenue by course of region; hence, the StoreLocation table may exist used by means of some massive statistics queries. during this ambiance it is common to remove the dimension tables and copy them always to the huge records software. within the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).
Now suppose about SQL queries from both operational functions, facts warehouse users and massive facts company analysts. From Db2's point of view, complete these queries are equal, and are forwarded to the Optimizer. youngsters, within the case of operational queries and warehouse queries they may soundless surely exist directed to entry the StoreLocation desk within the warehouse. however, the question from the company analyst against great statistics tables may soundless likely access the replica of the desk there. This effects in a proliferations of skills access paths, and extra drudgery for the Optimizer. thankfully, Db2 AI for z/OS can provide the Optimizer the assistance it needs to do sapient access course decisions.
the course it Works
The sequence of events in Db2 AI for z/OS (See Reference 2) is frequently the following:
throughout a bind, rebind, rescue together or define operation, an SQL remark is handed to the Optimizer;
The Optimizer chooses the information entry direction; because the option is made, Db2 AI captures the SQL syntax, entry path option and question performance statistics (CPU used, and many others.) and passes it to a "studying assignment";
The studying assignment, which can exist carried out on a zIIP processor (a non-common-goal CPU core that doesn't aspect into utility licensing costs), interfaces with the laptop discovering utility (MLz model functions) to shop this assistance in a mannequin;
because the quantity of statistics in each mannequin grows, the MLz Scoring service (which can too exist accomplished on a zIIP processor) analyzes the mannequin information and scores the behavior;
all the course through the subsequent bind, rebind, rescue together or explain, the Optimizer now has access to the scoring for SQL models, and makes applicable adjustments to entry course choices.
There are too a considerable number of user interfaces that supply the administrator visibility to the reputation of the accumulated SQL commentary efficiency records and mannequin scoring.
IBM's computer learning for zOS (MLz) offering is getting used to notable upshot in Db2 edition 12 to enlarge the efficiency of analytical queries as well as operational queries and their linked functions. This requires management consideration, as you should examine that your enterprise is prepared to consume these ML and AI conclusions. How will you measure the charges and merits of using laptop learning? Which IT uphold team of workers necessity to exist tasked to reviewing the outcome of model scoring, and perhaps approving (or overriding) the results? How will you overview and justify the assumptions that the application makes about access path decisions?
In other phrases, how neatly were you conscious your records, its distribution, its integrity and your latest and proposed entry paths? this will assess the condition the DBAs expend their time in assisting analytics and operational application efficiency.
# # #
John Campbell, IBM Db2 distinctive EngineerFrom "IBM Db2 AI for z/OS: raise IBM Db2 utility performance with computer learning"https://www.worldofdb2.com/activities/ibm-db2-ai-for-z-os-enhance-ibm-db2-application-efficiency-with-ma
Db2 AI for z/OShttps://www.ibm.com/guide/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
Apple CEO Tim cook dinner speaks at the Anti-Defamation League's "on no account is Now" apex in current york city, manhattan, U.S., December 3, 2018. REUTERS/Brendan McDermid
(Reuters) - the manager executives of Apple Inc, Walmart Inc, IBM Corp and other most faultfinding groups are becoming a member of a Trump administration advisory board that hopes to rescue together U.S. employees to deal with job practicing considerations and the worforce shift introduced with the aid of synthetic intelligence and automation, the U.S. Commerce department said on Wednesday.
President Donald Trump signed an executive order in July creating the 25-member board, co-chaired through adviser Ivanka Trump and Commerce Secretary Wilbur Ross, to handle workforce considerations together with “automation, and synthetic intelligence it is shaping many industries.” different chief executives becoming a member of the board are those of Lockheed Martin Corp, Siemens usa, home Depot Inc and Visa Inc.
The board contributors will serve unless 2020 and will drudgery with the administration’s countrywide Council for the American worker “to develop and rescue in coerce a methodology to revamp the American team of workers to stronger meet the challenges of the 21st century.”
Trump’s executive order said “for too long, their country’s education and job training programs contain prepared american citizens for the economic system of the previous.”
Ivanka Trump stated in an announcement the administration desires “all americans to contain the capabilities and opportunities to cozy first rate paying jobs and successfully navigate technological disruptions and the unexpectedly altering nature of work.”
also becoming a member of are the manager executives of the Chamber of Commerce and country wide association of manufacturers, as well because the governors of Iowa and Indiana.
On Tuesday, the Bureau of Labor information stated U.S. job openings reached a record excessive in December at 7.3 million. The White residence says the job openings present “a mismatch between the competencies obligatory and people being taught, requiring instant consideration to uphold more americans enter the cadaver of workers.”In August 2017, Trump disbanded two high-profile company advisory councils after a lot of chief executives desist in protest over his remarks blaming violence in Virginia on anti-racism activists as well as white nationalists.
Trump dissolved the American Manufacturing Council and the Strategic and policy discussion board after eight executives, including the CEOS of Campbell Soup Co and 3M Co, desist the panels. both councils contain been relocating to disband on their personal when Trump made his announcement on Twitter.
Reporting by David Shepardson; editing by Chizu Nomiyama and David Gregorio
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Diversity and inclusion. Words repeated so often they’re virtually mantra in corporate America. Hardly a day goes by without some CEO extolling the virtues of a more diverse and inclusive workplace. But when it comes to showing their progress for the world to see, some of the biggest names in Silicon Valley are turning to a novel legal defense: asserting they’re trade secrets.
More and more, firms including Oracle and Palantir Technologies quarrel that detailed, government-mandated figures on the number of women and people of color they employ must remain confidential. Making them public, they say, would exist tantamount to giving away proprietary technology and hand competitors a “road map” to poach talent.
So while tech companies often voluntarily give the public a glimpse of their diversity strategies, the tactic complete too often lets them contain it both ways, according to Georgetown University’s Jamillah Bowman Williams, who explores the trend in the tech industry for a forthcoming paper. And as criticism grows over Silicon Valley’s bro culture and its lack of minority representation, claiming the figures are too valuable an asset to share could allow companies to pay lip service to diversity with diminutive or no accountability.
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“This is almost relish the extreme case of the commerce case for diversity,” Williams, an associate professor at Georgetown University Law Center, said in an interview. By blocking the release of these figures, she writes that “companies can exercise this tactic to cover gender and race disparities and interfere with the advancement of civil rights law and workplace equity.”
Changing the status quo has never been easy. A growing cadaver of research shows diverse perspectives bring tangible benefits to firms that uphold them, yet black and female CEOs lead just 5 percent of S&P 500 companies, a number that’s tumbled in recent years. That’s compounded by the fact that almost three-quarters of corporate leaders pick protégés of the same race or gender. Just eventual month, the Labor Department alleged Oracle shorted women and minority workers $400 million in wages by paying them less and steering them into lower-level positions. (Oracle declined to comment.)
Tech’s race and gender problem has become a focal point as the industry has risen to dominate great swaths of the U.S. economy. Everyone from Apple to Google and Facebook has publicly embraced the second of diversity and made repeated commitments to become less homogeneous. Yet thus far, the industrywide efforts contain yielded scant progress.
The latest U.S. government data, released in 2016, reinforces the point. While the harmony of women has crept up, racial disparities persist. Black employees made up less than 3 percent of the Silicon Valley workforce and Hispanic workers accounted for less than 7 percent — diminutive improvement from prior years. (Another study showed that Asians drudgery in disproportionately elevated numbers in tech, but are the least likely to accumulate leadership roles. Those, as you might expect, are occupied mostly by white men.)
At issue is the EEO-1 employment data report, which firms must submit to the Equal Employment break Commission each year. In her research, Williams looks at how tech firms responded when asked for them to exist released. Her paper cites media inquiries covering more than 200 tech companies.
She writes that the vast majority, both great and small, refused to release their data and subsequent requests were often rebuffed with the trade-secrets argument. Because diversity has become such a buzzword in tech and firms routinely vie for the same, and often limited, pool of talent, they insist releasing workforce numbers that are too specific can rescue them at a disadvantage.
Palantir, the data mining startup co-founded by Peter Thiel, for example, aired those concerns in a 2017 missive to the Labor Department, adding it risked losing its investment in recruiting a diverse workforce. Rivals could survey where it was making progress and which hiring initiatives it had undertaken, giving them a key to “raid” its growing ranks of women and minorities. Palantir didn’t respond to requests for comment.
Granted, trade-secret protection is a fairly common practice with a long history in corporate America. Applying it to diversity, though, is not only unusual, but fairly specific to tech, according to Gerardo Con Diaz, an helper professor of science and technology studies at University of California at Davis.
Tech’s exercise of the trade-secret defense goes back to at least 2011. That year, CNN asked 20 tech companies for their diversity stats. Only three — Dell, Intel and Ingram Micro — complied. When CNN submitted freedom of Information Act requests to the government, Apple, Google, Hewlett-Packard, IBM and Microsoft invoked an exemption covering trade secrets, maxim disclosure would occasions “competitive harm,” Williams says. (Apple, Google, HP and Microsoft contain since begun publicly releasing their EEO-1s and some too relent annual diversity reports. IBM, which hasn’t made its report public, declined to comment.)
The practice has only gotten more popular. eventual year, when nonprofit expose from the center for Investigative Reporting requested the EEO-1s from 211 tech companies, an overwhelming majority declined. Just 26 complied or had already released the reports. While the organization successfully sued for release of the data from federal contractors relish Oracle and Palantir over their objections, most are soundless free to exercise the trade-secret argument.
Federal regulations contain made it easier. After President Barack Obama signed a law in 2016 that let companies file trade secrets on a federal level, filings jumped 30 percent the next year, Williams found. Under the Trump administration, Labor Department officials provide firms with particular instructions on how to invoke the exemption, she said. IBM went as far as to sue its former chief diversity officer eventual year to obscure her stream to Microsoft, claiming she would remove confidential data on its diversity initiatives and strategies with her. (The case was settled out of court.)
When it comes to diversity, Williams argues the legal rationale is flimsy. To exist a trade secret, a company needs to define there’s value in keeping information confidential and that it might countenance economic harm if it becomes public. Yet firms routinely share parts of their demographic data and diversity strategies, on websites or at conferences, often for marketing and recruiting talent.
“This is not relish McDonald’s and their sauce that goes on the enormous Mac or the Google algorithm that’s the core of its commerce strategy,” Williams said. “For workforce demographics, it’s unclear what’s so economically valuable to where it would exist a detriment, other than reputational harm.”
In other words, the defense is often a convenient course for companies to shield themselves from public scrutiny and embarrassment.
Palantir’s diversity numbers, which the Labor Department made public in January, conclude diminutive to quell that perception. Its report, compiled by the center for Investigative Reporting, showed over three-quarters of its 1,328 professionals were men and just 1.4 percent were black. Palantir employed no female executives or senior officials and managers that year, either.
Tech companies, for their part, often point to the “pipeline” problem. That is, firms would relish to hire more women and people of color, if only there were enough qualified candidates. The numbers though, don’t quite add up. In 2014 to 2015, black students earning a bachelor’s degree in science, technology, engineering and mathematics accounted for 7.1 percent of graduates in those fields, according to the Department of Education. Yet in Silicon Valley, black employees made up less than half that.
That makes transparency crucial, Williams says. Without it, it’s difficult to know what the problems are — whether hiring and retention or pay and promotion — making it harder to fight workplace discrimination and change the culture. If anything, making workforce demographics public lets everyone survey what works and what doesn’t. It can exist a measuring stick to survey whether the tens of millions of dollars Silicon Valley invests to recruit and retain more women and non-white workers is money well spent.
“If they’re really concerned about equity and diversity in the long term, not only in their company but in the industry and society as a whole,” she says, “then you would hope that they would contain a broader dialogue around it.”
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