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Test Number : C9060-528
Test Name : IBM Spectrum Protect V8.1 Administration
Vendor Name : IBM
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C9060-528 test Format | C9060-528 Course Contents | C9060-528 Course Outline | C9060-528 test Syllabus | C9060-528 test Objectives

Exam Title : IBM Certified Administrator - Spectrum Protect V8.1
Exam ID : C9060-528
Exam Duration : 90 mins
Questions in test : 66
Passing Score : 62%
Official Training : IBM Spectrum Protect 7.1.6 Advanced Administration, Tuning, and Troubleshooting
Exam Center : Pearson VUE
Real Questions : IBM Spectrum Protect Administration Real Questions
VCE practice questions : IBM C9060-528 Certification VCE Practice Test

IBM Spectrum Protect V8.1 Concepts
- List IBM Spectrum Protect V 8.1.0 server components
- Describe Spectrum Protect V8.1 client components
- Describe the different IBM Spectrum Protect 8.1 data management methodologies such as: progressive incremental, differential, full
- Describe IBM Spectrum Protect 8.1 server (critical - most typical/relevant) processes
- Describe Spectrum Protect V8.1 Operations Center tasks/functions
- Describe IBM Spectrum Protect V8.1 monitoring and alerting
- Explain IBM Spectrum Protect V 8.1.0 deduplication (inline/client side)
- Describe the purpose of IBM Spectrum Protect for Databases and IBM Spectrum Protect for Mail
- Describe the data protection methods available in IBM Spectrum Protect for virtual environments
- Describe IBM Spectrum Protect 8.1 Windows/UNIX backup/archive client automatic deployment.
- Explain Node Replication feature in IBM Spectrum Protect V8.1
- Describe IBM Spectrum Protect V8.1 multiple data streams for database backup and restore
- Explain IBM Spectrum Protect 8.1.0 Policy management

IBM Spectrum Protect V8.1 Daily Operations
- Monitor ongoing health of the IBM Spectrum Protect V8.1 environment using the IBM Spectrum Protect V8.1 Operations Center
- Monitor/ schedule client operations
- Monitoring and scheduling housekeeping operations
- Rotate tapes offsite (DR media management)

IBM Spectrum Protect V8.1 Server Management
- Monitor and manage IBM Spectrum Protect database and recovery log usage
- Protect the storage pools
- Manage Node Replication
- Manage device classes and storage pools including active data pools, deduplication pools, simultaneous write during storage pool migration, and data shredding
- Manage policy domain, policy sets, management class, and copy groups
- Manage library, drives, and paths
- Configure, monitor and manage server-to-server communication tasks in a IBM Spectrum Protect server netrwork
- Manage disaster recovery (DR) components, DR plan file, and off-site media
- Describe client node management in IBM Spectrum Protect 8.1
- Protect the IBM Spectrum Protect Database
- Manage administrative users (add, delete and provide privileges).
- Monitor the data reduction ratio
- Repair damaged data in a directory container
- Manage replicated data with policies that are defined on the targetreplication server
- Configure daily or monthly reports regarding server operations.

IBM Spectrum Protect V8.1 Client Management
- Install IBM Spectrum Protect backup/archive clients and configure the option files
- Manage IBM Spectrum Protect client component and services (e.g., configure and manage a BA client scheduler, web access, journal daemon or journal engine service etc.)
- Manage Spectrum Protect V8.1 client security and encryption (e.g., configure Spectrum Protect V8.1client behind a proxy server or firewall, password encryption, client data encryption, Secure Socket Layer (SSL), etc.)
- Configure alternative IBM Spectrum Protect backup/archive methodologies (e.g., configure IBM Spectrum Protect journal based backup, Image backup, Inline deduplication, configure data archive, etc.)
- Configure IBM Spectrum Protect LAN-free backup restore
- Manage and configure IBM Spectrum Protect for NDMP communication
- Configure an IBM Spectrum Protect Client for Node Replication
- Manage IBM Spectrum Protect client/node authorization
- Recover IBM Spectrum Protect V8.1 client data (Backup/Archive data)
- Manage client data (Decommission a client node; move data between storage pools; move node data)

IBM Spectrum Protect V8.1 Performance and Problem Determination
- Identify/analyze IBM Spectrum Protect Server problems
- Identify/analyze errors reported in IBM Spectrum Protect client logs
- Tune IBM Spectrum Protect V8.1 server for better performance (e.g., reclamation, expiration, migration, IBM Spectrum Protect V8.1 server database backup, IBM Spectrum Protect V8.1 server settings and recommend network changes to support and tune IBM Spectrum Protect DB2 table and index reorgs, etc.).
- Tune IBM Spectrum Protect Client for better performance (e.g., resourceutilization, memoryefficient, compression, deduplication, recommend network changes to support and tune IBM Spectrum Protect, etc.)
- Troubleshoot and tune IBM Spectrum Protect LAN-free client configuration
- Troubleshoot Spectrum Protect V8.1 client using Spectrum Protect V8.1 Client Management Services
- Analyze Operating System error reporting or perfmon logs

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IBM V8.1 Free test PDF

using Analytics To cut back Burn-in | C9060-528 braindump questions and Practice Questions

Silicon suppliers are the use of adaptive examine flows to reduce burn-in costs, some of the many tactics aimed at stemming cost raises at superior nodes and in superior programs.

no person likes it when their cell fails in the first month of possession. however the issues are lots more urgent when the key accessories in facts warehouse servers or cars fail. Reliability expectations of complicated SoCs have handiest grown over the last few decades, and demand for normal good die in applications is expanding, peculiarly with the emphasis on chiplets.

Semiconductor makers try to fulfill these expectations on the end of the manufacturing move by means of accelerating defect mechanisms. Burn-in has been a common test step that displays out early-lifestyles failures. The issue is that it’s costly. So by using combining adaptive check flows and statistical publish-processing statistics analytics, engineering teams can tremendously reduce that charge — sometimes to zero.

during the last two decades engineers have exploited records from wafer probe to:

  • regulate burn-in recipe to in the reduction of burn-in times;
  • display for reliability failures at wafer type, and
  • in the reduction of the percent of gadgets receiving burn-in.
  • These innovations have been utilized to both ASIC devices and sophisticated SoCs. Work all started lower back within the early 2000s, triggered by Iddq testing fitting less advantageous due to the increase in quiescent historical past latest with each and every tick on the CMOS manner node roadmap. in consequence, the capability to have a single Iddq flow/fail restrict created anxiety between yield loss and escapes. Engineers became to adaptive check flows and statistical post-check analysis to stability the scales.

    identical to the performance binning adaptive check flows in the Nineteen Nineties, those early burn-in reduction test flows concerned lots of custom development. during the past decade, records analytic groups have eased the customization burden for engineers by using offering standardized statistical evaluation stories, equipment that execute the dynamic examine limits on ATE, and the capability to connect statistics between wafer, burn-in, and kit-check steps. This makes it possible for even small companies to leverage manufacturing facts and adaptive test flows to cut back burn-in costs.

    “we have a fabless AI startup that's a user of their whole analytics platform, and that they chose it notably since it allowed them to focal point their efforts on constructing the highest quality chips and techniques. They relied on their products and capabilities to assemble, clean, and control all of their records, and quickly convey advice and insights to their engineers,” said Greg Prewitt, director of Exensio solutions at PDF options. “There is not any reason any semiconductor business, IDM or fabless, public or deepest, could not take full skills of massive information and adaptive test these days.”

    To respect the growth made in making use of adaptive trying out to expanding IC reliability and cutting back burn-in cost, one must take into account why burn-in has been a necessary cost for advanced digital gadgets.

    Accelerating failure ratesAll devices under enough stress will wear out. Seven years has been a typical lifestyles-time spec for the microprocessors that AMD and Intel enhance. An IC device may also ultimate longer than seven years, but it’s never a assure.

    No chipmaker waits seven years before launching a more moderen and enhanced version, but that’s the generally authorised lifespan for servers. Electronics reliability engineers use the excessive-temperature operating existence (HTOL) manner to take into account the early life screw ups and the practical lifetime of the half, as measured in months and years. Reliability engineers commonly seek advice from this because the bathtub curve. HTOL makes use of the indisputable fact that strong-state device wear-out mechanisms (aka getting older mechanisms) can be accelerated by means of applying temperatures and voltages above their standard working range for extended intervals of time. growing old mechanisms for CMOS include bad bias temperature instability (NBTI), scorching carrier injection (HCI), electromigration (EM), and time-based dielectric breakdown (TDDB).

    Fig. 1: The ‘bathtub curve’ hazard function (blue, upper solid line) is a combination of a decreasing hazard of early failure (red dotted line) and an increasing hazard of wear-out failure (yellow dotted line), plus some constant hazard of random failure (green, lower solid line). Source: Wikimedia

    Fig. 1: The ‘bathtub curve’ hazard function (blue, upper solid line) is a combination of a decreasing hazard of early failure (red dotted line) and an increasing hazard of wear-out failure (yellow dotted line), plus some constant hazard of random failure (green, lower solid line). Source: Wikimedia

    Fig. 1: The ‘bathtub curve’ hazard function (blue, upper solid line) is a combination of a lowering hazard of early failure (red dotted line) and an increasing hazard of wear-out failure (yellow dotted line), plus some steady hazard of random failure (eco-friendly, decrease solid line). supply: Wikimedia

    Defects that only show up themselves early within the contrast procedure are called infant mortality failures. In CMOS, oxide pin-holes and narrowed metal strains for electromigration are examples of physical defects that frequently outcomes in newborn mortality disasters.

    For all silicon items, engineers use HTOL for brand spanking new product introduction evaluations. For giant SoC devices, using HTOL as a production step has been a part of doing business. within the latter context, engineers consult with this test move step as the burn-in.

    Burn-in module incorporates a temperature handle chamber and PCBs that can manage the vigour to the IC devices. To vicinity a burn-in step right into a creation examine procedure effects in here prices gadget, factory footprint, energy, and manufacturing time. Burn-in chambers have tremendously lower via-put than the verify cells (combination handler, ATE, and linked software) used for wafer probe and ultimate look at various as there exists much less parallelism. The check circulate firstly requires ATE trying out prior and after burn-in- see determine 2. expenses of ATE and burn-in chambers run at 7 and 6 figures respectively.

    Fig. 2: Production burn-in flow. Source: Anne Meixner/Semiconductor Engineering

    Fig. 2: Production burn-in flow. Source: Anne Meixner/Semiconductor Engineering

    Fig. 2: creation burn-in circulation. source: Anne Meixner/Semiconductor Engineering

    Such costs have influenced engineering groups to cut back these expenses, or get rid of them altogether.

    settling on what and the way to burn-inUsing statistics from wafer test, engineers have modified the burn-in recipe, identified the ingredients surely to fail after burn in, and absolutely eliminated burn-in. To guide such choices in CMOS, devices look at various engineers essentially relied on Iddq test measurements. To keep in mind its relationship to early life disasters requires an figuring out of Iddq checking out.

    A defect in silicon can take place electrically in diverse ways. Relying upon burn-in to accelerate a failure accredited caught-at-fault (S@0, S@1) trying out to detect the failures afterwards. As CMOS grew to be the predominant procedure for computing gadgets, the use of Iddq checking out to reveal for disasters grew to become part a verify engineer’s toolbox. It detected failure modes that caught-at-fault checking out overlooked, and this protected early-existence reliability failure modes.

    Iddq is the dimension of quiescent present. it's measured after an input stimulus has been applied, but not right through its application. Defects effect in elevated Iddq values. beginning around 1985, product and fine engineers all started the use of Iddq testing at wafer test to achieve 0% production burn in. For these system nodes, defects resulted in at the least one order of magnitude larger Iddq values than defect-free instruments. So with relative ease, engineers might set a circulate/fail limit to successfully display reliability disasters and never trigger massive yield loss.

    As mentioned, shrinking procedure nodes made it much less beneficial since the quiescent present expanded and the distribution of quiescent present became wider. Engineers responded to those statistics in creative easy methods to retain using this size as a reveal and as a minimum one engineering crew cleverly used the increased current to in the reduction of burn-in times. both used adaptive verify methods and flows to obtain their dreams.

    Leakier ingredients imply higher vigour and therefore, better thermal resistance, which in translates into decrease burn-in times. Intel engineers used this property to lessen burn-in times. In a 2006 ITC paper, Intel researchers described evaluating each and every die’s static present and other wafer examine measurements to check the most fulfilling burn-in recipes (time, temperature, voltage). next, an automatic feed-ahead look at various move directed the die into several diverse buckets, each with an optimized burn-in recipe. Segregation into buckets with the aid of their static power reduces the mandatory stress time and reduces the basic model in stress temperature within each bucket.

    Intel finished an exquisite discount in burn-in time – superior than ninety% for a excessive-volume 90nm product. Yet the feed-ahead check circulation become now not the only real contributor to this reduction time. a brand new burn-in machine cellphone enabled this stage of segregation per burn-in board with a slot structure, which authorised individual burn-in manage of vigor and times in the burn-in chamber. For the latter, the new cellphone obviated batch processing, so the continual handling of burn-in boards additional optimized the burn-in recipe buckets.

    Fig. 3: Intel’s adaptive burn-in recipe flow. Source Anne Meixner/Semiconductor Engineering

    Fig. 3: Intel’s adaptive burn-in recipe flow. Source Anne Meixner/Semiconductor Engineering

    Fig. 3: Intel’s adaptive burn-in recipe stream. supply Anne Meixner/Semiconductor Engineering

    The huge model in Iddq currents posed a problem for verify engineers who wanted to enhance its effectiveness. Even with the introduction of delta-Iddq latest examine concepts through the early 2000s, it grew to be significantly complicated to steadiness yield and first-class.

    unlike stuck-at assessments, with Iddq engineers have a numerical price to investigate in opposition t a restrict. With a numerical price, then you could follow greater superior statistical learn how to determine defects.

    Fig. 4: Adaptive test flow to downgrade die that are highly likely to fail burn-in. Source Anne Meixner/Semiconductor Engineering

    Fig. 4: Adaptive test flow to downgrade die that are highly likely to fail burn-in. Source Anne Meixner/Semiconductor Engineering

    Fig. 4: Adaptive examine stream to downgrade die which are particularly likely to fail burn-in. supply Anne Meixner/Semiconductor Engineering

    In its 2002 VLSI check Symposium paper, LSI engineers and a PSU researcher shared how they used publish-processing of wafer test records and wafer style maps to determine possible reliability disasters and customer visible escapes. For burn-in related trying out, they checked out Iddq data to determine materials that could definitely fail burn-in. Reporting the results on 0.18µm products, they described a verify flow that required making selections related to burn-in after wafer model and previous to remaining examine.

    The writers referred to that having a single-threshold verify limit for Iddq resulted in area die being marked as fails. youngsters, they have been simply sooner die instead of defective. Plotting Iddq versus velocity measurements, they observed, “obviously the outliers are seen however atmosphere the restrict on the tester with out causing high yield loss turns into complicated.” They resolved their catch 22 situation with put up-processing the wafer-level look at various statistics with a few statistical analytical learn how to examine limits (aka, virtual verify).

    using statistical strategies, they downgraded die that handed the entire simple examine limits to constituents deemed suspicious primarily based upon their wafer place and wafer look at various outcomes population. For each burn-in and yield their effects were compelling. To examine the development on burn-in discount, they ran an experiment the usage of 14 lots and a total of 60,one zero five die passing wafer variety, subjecting all downgraded die and a pattern of non-downgraded die to a 24-hour burn-in. Of the 171 burn-in failures, their statistical downgrading formula recognized 168 of them.

    As these skills of these methods unfold, engineers at other agencies similar to IBM and Texas gadgets all started to apply them to their products. They discovered them alluring because of the can charge discounts by way of getting rid of burn-in or cutting back the percent of product going through burn-in. They did so regardless of the engineering investment to observe the advanced statistical analytics and to create the customized tools to manage the product movement in the course of the manufacturing unit.

    Balancing overkill and underkillSo how did the LSI crew do it? They used assorted variables and rigorous statistical analytic find out how to distinguish between respectable and dangerous die.

    organising a single variable circulate/fail restrict from a parametric measurement all the time has the statistical chance of classification I and sort II blunders.

    In IC manufacturing. engineering teams by no means use the statistical phrases. equal phrases one would hear are:

  • category I error = OverKill, failing good constituents, yield loss.
  • type II mistakes = UnderKill, passing bad elements, escapes.
  • Minimizing overkill and underkill in a look at various manufacturing procedure respectively pits yield versus excellent and reliability. With superior CMOS method nodes, surroundings the a single restrict at creation launch neglects two statistics- distributions shift with the fitness and maturity of a manufacturing method and there exists a far better adaptation around particular person measurements. For the latter, engineers could name it the issue of dealing with noisy records. using statistical analytical methods allows for the usage of numerous measurements and die attributes (geo-spatial) to identify outliers. still overkill and underkill is still existing with these greater refined statistical flow/fail decisions, the risks become smaller.

    Burn-in effects mixed with wafer and final check information enabled engineers at Texas contraptions to construct very focused statistical analytic models as described of their IRPS 2006 paper co-authored with Rob Daasch of PSU.

    “Burn-in statistics combined with the Iddq records is a very prosperous supply of tips. no longer simplest simply when it comes to voltage stress and response to a burn-in stress, however above all since you can do issues with outlier identification techniques. you could do pre-stress and submit-stress Iddq measurements, compute deltas, and search for movement,” said Ken Butler, IEEE fellow and former verify techniques architect at Texas instruments. “Then that you can run all that statistics via an outlier algorithm to decide upon the refined mechanisms because these are those which are going to pop up for those who get into burn-in. with a view to dispose of your burn-in you need to predictively dispose of the contraptions that are more likely to fail burn-in.”

    This isn’t excellent, of route. “You by no means catch every thing,” Butler observed. “in the early days (circa 2002 to 2006) when their intention turned into burn-in avoidance or burn-in minimization for huge digital SoC contraptions they may use interesting die identification to tune the die all of the means through burn-in and final examine. If it failed after burn-in, you appeared on the wafer records to develop a screen. as an example, here’s 5 burn-in disasters that took place on this wafer, go and discover a correlating parameter that would permit me to predict those failures.”

    Others agree. “The accelerated complexity of producing statistics and its volume, at the side of the need to boost nice, outcomes not simplest within the want for productive data administration platform, but additionally require advanced analytical solutions,” spoke of Alon Malki, head of facts science at national contraptions. “Correlating burn-in results to a small set of testing parameters has develop into inadequate for screening functions. The engineers now need to consider analyzing thousands of parameters from multiple ranges of the manufacturing system. To contend with these new challenges, they ought to seem to be on the complete product’s lifecycle of records.”

    For advanced system nodes (< 14nm) Malki stated that making use of advanced analytical the way to the huge statistics that manufacturing procedure can reduce burn-in charges with the aid of as much as 40% (keep in mind that for 90nm Intel executed 90% with easier analytics and adaptive check movement). but to be achieve those savings requires taking into consideration the total existence cycle of a device, from mannequin introduction via allotted deployment to continual monitoring of model efficiency, and it must be in a position to directly adapt to change.

    while IDMs have the engineering materials to strengthen custom tools, this funding requires chronic protection and construction. So agencies that specialise in presenting the framework and equipment for these evaluation methods, and sensible manufacturing flows, have persevered to develop.

    “The manufacturing method is increasingly complex and spread throughout numerous amenities and operational agencies,” talked about PDF’s Prewitt. “simply as this distribution creates logistic challenges, it additionally complicates the timely collection and alignment of data sources and types. fixing these challenges benefits from collecting data without delay at the processing device, automatic timely data transport, and organising a single information repository for these connected but disparate statistics sources to be coalesced right into a single source of certainty for product engineering.”

    ConclusionFor complex SoC devices, the burn-in step has been required to meet the high reliability demands of conclusion clients. It’s a expensive manufacturing step that test engineers like to get rid of, yet engineers responsible for reliability metrics cautiously watch-over such removing. This tension between yield loss and quality runs all the way through the complete manufacturing test circulation manner.

    meeting both metrics in an economically manner is the third point in the triangle of yield/high-quality/cost. collectively, test and reliability engineers can use adaptive test flows and complicated statistical analytics to effectively meet their respective metrics of activity. traditionally, these engineering efforts best may be done by means of silicon businesses with big engineering teams.

    “One could argue that it’s even probably worse in a low quantity circumstance, since you’ve acquired all the overhead of creating the look at various setup, maintaining the gadget and everything like that,” mentioned Butler. “probably that’s sustainable should you can amortize that over a a lot higher extent of material. but now if you’re a small, you have to create all that stuff.”

    Yet every person should still improvement from these strategies, and that has changed. in the past 5 to 10 years, analytic systems that understand silicon manufacturing check expertise carry the analysis tools to engineers.

    complicated manufacturing designs, coupled with complicated IC designs, necessitate using a couple of variable to see that one of those issues isn't just like the different. Engineers equipped with information, analytic tools, and extra automatic check procedures now can try this for burn-in.

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