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P2020-007 exam Dumps Source : IBM Optimization Technical Mastery Test v1

Test Code : P2020-007
Test designation : IBM Optimization Technical Mastery Test v1
Vendor designation : IBM
: 30 actual Questions

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There comes a degree where respectable concepts don't appear to exist enough; potential, proven solutions should hold flight.

here's the station the station cloud computing finds itself nowadays, specially when the conversation turns towards immoderate performance computing (HPC).

The mammoth majority of companies these days are leveraging the agility, charge merits, and console provided by using the public cloud capabilities mannequin. HPC amenities are additionally exploring the advantages of cloud, however most likely now not for the same motives. Whereas greater ordinary businesses are most regularly seeking to public cloud services providers for backup and catastrophe recovery solutions or the alternative to lessen storage expenses through parking seldom-accessed data sets on less expensive cloud storage, HPC amenities usually tend to exist exploring the possibilities of accommodating spikes renowned with the aid of dynamically including and subtracting cloud-based mostly materials.

These each and every sound relish very decent ideas, but making them work, effectively and reliably, has been a actual problem. Cloud compute materials, unlike cloud storage, may besides exist extra costly than on-premises infrastructure. safety is each and every the time a consideration. relocating huge HPC information units over networks may besides exist prohibitively sluggish and cumbersome. and besides you deserve to own should own functions that may duty acceptably on cloud-based mostly elements.

When off-site cloud materials are integrated with on-premises computing, the influence is referred to as a hybrid cloud structure. There is no one redress technique to construct an HPC hybrid cloud computing cluster. each website is different, and clients dash discrete workloads which own very distinctive aid consumption patterns. for instance, some abysmal gaining scholarship of practicing consumes huge quantities of information to provide a small model. Others are exceedingly CPU intensive, or GPU intensive. The hybrid cluster respond really carried out will of direction exist driven by means of the workloads HPC users want to run, but two hybrid architectures regularly rise to the distinguished when IT architects wield the challenges of constructing cloud soar for HPC – the “stretch cluster” configuration and the “multi-cluster” configuration. To accommodate spikes favourite, organizations operating typical HPC environments are increasingly hybrid clouds to give the further potential when it’s truly obligatory.  Stretch and multi-cluster hybrid cloud solutions can assist wield this challenge by using enabling dynamic entry to cloud resources.

A stretch cluster capacity that you just extend or augment your on-premises HPC compute cluster with further cloud-based compute nodes, when obligatory. This structure assumes that you've a compute cluster in another location – both on-premises or configured in a cloud. This structure is formally defined as a single cluster stretched over a wide district network (WAN) in order that compute nodes on the other region or within the cloud speak with a grasp scheduling host on the originating region. even though lots more straightforward in understanding than a multi-cluster, this capability that each and every verbal exchange and coordination with the grasp scheduler happens over the WAN, which can exist a supply of added can charge or reduced reliability.

A multi-cluster hybrid cloud configuration is a more complex structure, since it adds a grasp scheduler running within the cloud. through together with this additional grasp scheduler within the cloud, the architecture eliminates lots of the communique from cloud compute nodes to the on-premises master. the two grasp schedulers as a substitute exchange project meta-data in a “job forwarding” model. in this model, users on-premises post utility workloads to a queue on-premises, which in turn forwards that workload to the cloud for execution. Upon job completion, the scheduler running within the cloud synchronizes the job popularity with the on-premises environment and a notification is provided to the person.

The question turns into, what are the deployment, configuration, and optimization particulars for HPC stretch and multi-cluster hybrid cloud solutions? How accomplish I in fact manufacture this thing fly? A fresh announcement from IBM provides the solutions. In a crucial step along the pathway to doable, legit, effortless-to-use hybrid cloud options, IBM has developed and released a deployment ebook that helps using IBM Spectrum LSF with Amazon net capabilities (AWS). This LSF cloud deployment e-book builds on an existing relationship with AWS. IBM is presenting expertise, services, and management capabilities on the way to provide IBM Spectrum LSF users quickly, bendy entry to AWS choices.

the original deployment e-book gives suggestions for structure a wide range of customizable stretch and multi-cloud IBM Spectrum LSF cluster configurations that allow clients to more comfortably and without problems profit the agility, charge merits, and console provided by way of cloud computing.

IBM Spectrum LSF is a share of a comprehensive Suite of solutions aiding natural HPC and immoderate throughput environments, as well as massive statistics, synthetic Intelligence, GPU, desktop gaining scholarship of, and containerized workloads, amongst many others. IBM Spectrum LSF, the core of the Suite, is an impressive workload administration platform for traumatic, dispensed HPC environments. It provides an entire set of intelligent, policy-pushed scheduling aspects that support maximize utilization of compute infrastructure components while optimizing utility performance. LSF is the HPC workload management regular, with probably the most complete set of capabilities – from license scheduling and session scheduling to advanced analytics.

IBM Spectrum LSF Suite comes in three editions and contains a number of extra add-ons comparable to IBM Spectrum LSF useful resource Connector, which makes it possible for policy-pushed cloud bursting to each and every essential cloud functions, including IBM Cloud, AWS, Google, and Azure. And now, IBM is providing variable consume licensing for IBM Spectrum LSF which ability that LSF users can exist in a position to dash the solution nearly anywhere, and pay simplest for what they use.

The cloud offers many merits, however constructing valuable hybrid cloud solutions isn’t so simple – before. With the leading-side functionality offered through IBM Spectrum LSF and the aid of the brand original LSF deployment pilot for AWS, HPC amenities are a whole lot greater ready to manufacture cloud soar for them.

learn about main-area HPC and AI options at the IBM booth next week at SC18 (sales space #3433) in Dallas, Texas. Register here for technical briefings and consumer neighborhood sessions.

study greater about dynamic hybrid cloud with IBM Spectrum LSF on the IBM IT Infrastructure weblog – IBM Spectrum LSF Goes Multicloud.

a more in-depth appear at the deployment e-book and helping materials for structure IBM Spectrum LSF hybrid-cloud configurations with Amazon internet services will besides exist discovered here.


P2020-007 IBM Optimization Technical Mastery Test v1

Study pilot Prepared by Killexams.com IBM Dumps Experts


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P2020-007 exam Dumps Source : IBM Optimization Technical Mastery Test v1

Test Code : P2020-007
Test designation : IBM Optimization Technical Mastery Test v1
Vendor designation : IBM
: 30 actual Questions

Nice to hear that Latest dumps of P2020-007 exam are available.
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Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, share 2 | killexams.com actual questions and Pass4sure dumps

Ricardo Balduino and Tim Bohn

Early Flight, Creative Commons Introduction

As they described in share 1 of this series, their objective is to succor call the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. They consume historical flights data and historical weather data to manufacture predictions for upcoming flights.

Over the course of this four-part series, they consume different platforms to succor us with those predictions. Here in share 2, they consume the IBM SPSS Modeler and APIs from The Weather Company.

Tools used in this consume case solution

IBM SPSS Modeler is designed to succor ascertain patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a range of advanced algorithms and analysis techniques, including text analytics, entity analytics, determination management and optimization to deliver insights in near real-time. For this consume case, they used SPSS Modeler 18.1 to create a visual representation of the solution, or in SPSS terms, a stream. That’s right — not one line of code was written in the making of this blog.

We besides used The Weather Company APIs to retrieve historical weather data for the ten airports over the year 2016. IBM SPSS Modeler supports calling the weather APIs from within a stream. That is accomplished by adding extensions to SPSS, available in the IBM SPSS Predictive Analytics resources page, a.k.a. Extensions Hub.

A proposed solution

In this blog, they submit one possible solution for this problem. It’s not meant to exist the only or the best possible solution, or a production-level solution for that matter, but the discussion presented here covers the typical iterative process (described in the sections below) that helps us accumulate insights and refine the predictive model across iterations. They hearten the readers to try and Come up with different solutions, and provide us with your feedback for future blogs.

Business and data understanding

The first step of the iterative process includes understanding and gathering the data needed to train and test their model later.

Flights data — We gathered 2016 flights data from the US Bureau of Transportation Statistics website. The website allows us to export one month at a time, so they ended up with 12 csv (comma separated value) files. They used IBM SPSS Modeler to merge each and every the csv files into one set and to select the ten airports in their scope. Some data clean-up and formatting was done to validate dates and hours for each flight, as seen in motif 1.

Figure 1 — gathering and preparing flights data in IBM SPSS Modeler

Weather data — From the Extensions Hub, they added the TWCHistoricalGridded extension to SPSS Modeler, which made the extension available as a node in the tool. That node took a csv file listing the 10 airports latitude and longitude coordinates as input, and generated the historical hourly data for the entire year of 2016, for each airport location, as seen in motif 2.

Figure 2 — gathering and preparing weather data in IBM SPSS Modeler

Combined flights and weather data — To each flight in the first data set, they added two original columns: root and DEST, containing the respective airport codes. Next, flight data and the weather data were merged together. Note: the “stars” or SPSS super nodes in motif 3 are placeholders for the diagrams in Figures 1 and 2 above.

Figure 3 — combining flights and weather data in IBM SPSS Modeler Data preparation, modeling, and evaluation

We iteratively performed the following steps until the desired model qualities were reached:

· Prepare data

· discharge modeling

· Evaluate the model

· Repeat

Figure 4 shows the first and second iterations of their process in IBM SPSS Modeler.

Figure 4 — iterations: prepare data, dash models, evaluate — and accomplish it again First iteration

To start preparing the data, they used the combined flights and weather data from the previous step and performed some data cleanup (e.g. took charge of null values). In order to better train the model later on, they filtered out rows where flight cancellations were not related to weather conditions (e.g. cancellations due to technical issues, security issues, etc.)

Figure 5 — imbalanced data institute in their input data set

This is an thoughtprovoking consume case, and often a difficult one to solve, due to the imbalanced data it presents, as seen in motif 5. By “imbalanced” they exist substantive that there were far more non-cancelled flights in the historical data than cancelled ones. They will discuss how they dealt with imbalanced data in the following iteration.

Next, they defined which features were required as inputs to the model (such as flight date, hour, day of the week, root and destination airport codes, and weather conditions), and which one was the target to exist generated by the model (i.e. call the cancellation status). They then partitioned the data into training and testing sets, using an 85/15 ratio.

The partitioned data was fed into an SPSS node called Auto Classifier. This node allowed us to dash multiple models at once and preview their outputs, such as the district under the ROC curve, as seen in motif 6.

Figure 6 — models output provided by the Auto Classifier node

That was a useful step in making an initial selection of a model for further refinement during subsequent iterations. They decided to consume the Random Trees model since the initial analysis showed it has the best district under the curve as compared to the other models in the list.

Second iteration

During the second iteration, they addressed the skewedness of the original data. For that purpose, they chose one of the SPSS nodes called SMOTE (Synthetic Minority Over-sampling Technique). This node provides an advanced over-sampling algorithm that deals with imbalanced datasets, which helped their selected model work more effectively.

Figure 7 — distribution of cancelled and non-cancelled flights after using SMOTE

In motif 7, they notice a more balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.

As mentioned earlier, they picked the Random Trees model for this sample solution. This SPSS node provides a model for tree-based classification and prediction that is built on Classification and Regression Tree methodology. Due to its characteristics, this model is much less prostrate to overfitting, which gives a higher likelihood of repeating the same test results when you consume original data, that is, data that was not share of the original training and testing data sets. Another edge of this method — in particular for their consume case — is its ability to wield imbalanced data.

Since in this consume case they are dealing with classification analysis, they used two common ways to evaluate the performance of the model: confusion matrix and ROC curve. One of the outputs of running the Random Trees model in SPSS is the confusion matrix seen in motif 8. The table shows the precision achieved by the model during training.

Figure 8 — Confusion Matrix for cancelled vs. non-cancelled flights

In this case, the model’s precision was about 95% for predicting cancelled flights (true positives), and about 94% for predicting non-cancelled flights (true negatives). That means, the model was redress most of the time, but besides made wrong predictions about 4–5% of the time (false negatives and deceptive positives).

That was the precision given by the model using the training data set. This is besides represented by the ROC curve on the left side of motif 9. They can see, however, that the district under the curve for the training data set was better than the district under the curve for the testing data set (right side of motif 9), which means that during testing, the model did not discharge as well as during training (i.e. it presented a higher rate of errors, or higher rate of deceptive negatives and deceptive positives).

Figure 9 — ROC curves for the training and testing data sets

Nevertheless, they decided that the results were still distinguished for the purposes of their discussion in this blog, and they stopped their iterations here. They hearten readers to further refine this model or even to consume other models that could solve this consume case.

Deploying the model

Finally, they deployed the model as a ease API that developers can convene from their applications. For that, they created a “deployment branch” in the SPSS stream. Then, they used the IBM Watson Machine Learning service available on IBM Bluemix here. They imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can besides convene The Weather Company APIs directly from their application code to retrieve the forecast data for the next day, week, and so on, in order to pass the required data to the scoring endpoint and manufacture the prediction.

A typical scoring endpoint provided by the Watson Machine Learning service would perceive relish the URL shown below.

https://ibm-watson-ml.mybluemix.net/pm/v1/score/flights-cancellation?accesskey=<provided by WML service>

By passing the expected JSON cadaver that includes the required inputs for scoring (such as the future flight data and forecast weather data), the scoring endpoint above returns if a given flight is likely to exist cancelled or not. This is seen in motif 10, which shows a convene being made to the scoring endpoint — and its response — using an HTTP requester tool available in a web browser.

Figure 10 — actual request URL, JSON body, and response from scoring endpoint

Notice in the JSON response above that the deployed model predicted this particular flight from Newark to Chicago would exist 88.8% likely to exist cancelled, based on forecast weather conditions.

Conclusion

IBM SPSS Modeler is a powerful tool that helped us visually create a solution for this consume case without writing a single line of code. They were able to succeed an iterative process that helped us understand and prepare the data, then model and evaluate the solution, to finally deploy the model as an API for consumption by application developers.

Resources

The IBM SPSS stream and data used as the basis for this blog are available on GitHub. There you can besides find instructions on how to download IBM SPSS Modeler, Get a key for The Weather Channel APIs, and much more.


The Week In Review: Design | killexams.com actual questions and Pass4sure dumps

M&AIoT-focused memory chipmaker Adesto Technologies acquired S3 Semiconductors, a provider of mixed-signal and RF ASICs and IP. Based in Ireland, S3 Semiconductors was founded in 1986. S3 Semiconductors will become a commerce unit of Adesto and will continue to operate under its current model in the $35 million deal. S3 Semiconductor’s parent company, S3 Group, will continue as a separate company focused on smart medical devices.

ToolsCadence debuted a original design-for-reliability tool for analog and mixed-signal IC design. The Legato Reliability Solution incorporates analog defect analysis and simulation to evaluate the ability to eradicate die with manufacturing defects and resulting test escapes that antecedent sphere failures, as well as electro-thermal and aging analysis to obviate thermal overstress and call wear-out.

Aldec made several updates to its Riviera-PRO verification platform, adding a UVM Register Generator for automated register model creation. The UVM Register Generator supports input of IP-XACT component files and CSV files. Other additions comprise a Unit Linting duty that runs ALINT-PRO in the background and support for the latest extensions to VHDL.

XJTAG uncorked free DFT software for users of Mentor’s Xpedition product to extend the design for test and debug capabilities of the schematic capture and PCB design environment. XJTAG DFT assistant helps validate redress JTAG chain connectivity by finding and repairing errors.

IPArasan Chip Systems uncorked its MIPI I3C Host Controller Interface (I3C HCI) Master IP core compliant to the recently released MIPI I3C HCI Specification 1.0. The IP kit includes a Linux software stack compatible with the I3C HCI Master IP Core, an I3C Hardware progress Kit to enable prototyping and compliance testing of I3C products, and the I3C Slave IP Core.

Chips&Media launched its first computer vision IP, which implements a convolutional neural network capable of processing 4K resolution at 60 frames per second input in real-time for kick detection. The IP targets edge devices for automotive and surveillance applications.

Chipus taped out a set of its ultra-low power analog IP on SilTerra’s 0.18um I18L IoT Platform. The test chip includes a temperature sensor, RTC oscillators, 6MHz oscillators, and an ultra-low power bandgap reference.

StandardsThe Si2 Compact Model Coalition published the Open Model Interface, a C-language application programming interface that supports SPICE compact model extensions. OMI allows for simulation and analysis of physical effects such as self-heating and aging as well as design optimization. It is based on TMI2, the TSMC Model Interface, which was donated to Si2 by TSMC in 2014. So far, OMI supports BSIM, BSIM-CMG, HiSIM2, and BSIM-SOI.

The MIPI Alliance released MIPI Touch, a family of publicly available specifications for the design and implementation of touch applications, such as in smartphones. The application ties together several MIPI specifications for touch, sensor, and software integration: MIPI Touch Command Set (MIPI TCS) for writing device drivers across operating systems; MIPI Touch Adaptation Layer for I3C (MIPI ALI3C) to translate touch commands for consume on the MIPI I3C protocol; MIPI I3C Host Controller Interface (MIPI I3C HCI), which enables touch commands and multiple data streams for advanced systems; and MIPI I3C v1.0, which connects an application processor to a touch sensor.

DealsRambus and GigaDevice formed a joint venture, Reliance Memory, in China to focus on progress and commercialization of Resistive Ram (RRAM), a non-volatile memory that works by changing the resistance of materials when different voltages are applied. The joint venture has investment funding from THG Ventures, West Summit Capital, Walden International, and Zhisland Capital.

Esperanto Technologies inked deals for both NetSpeed Systems’ network-on-chip IP and design environment and UltraSoC’s embedded analytics and debug IP for its Supercomputer-on-a-Chip for AI applications. The 7nm chip will integrate thousands of RISC-V compatible processors each with vector/tensor accelerators.

NetSpeed Systems and Northwest Logic are teaming up on a tall throughput memory subsystem solution that uses NetSpeed’s interconnect IP and Northwest Logic’s HBM2 and GDDR6 memory controllers for customers designing SoCs for hyperscale and automotive applications. The companies will provide a common platform to extend NetSpeed’s dynamic-priority elevation approach to the memory controller so that system-level traffic-flow priorities are handled in an end-to-end solution.

NumbersIntel Capital placed investments in two firms: Movellus and SiFive. Movellus provides generator tools enabling automatic creation and implementation of PLL, DLL, and LDO digital IP and will consume the funds to accelerate market deployment and adoption of its technology. SiFive is a provider of RISC-V IP cores, SoCs, and progress boards, as well a RISC-V-focused IP catalog.

Rambus released monetary results for first quarter 2018 with revenue of $46.4 million. On a GAAP basis, there was a loss per share of $0.36; on a non-GAAP basis there was a loss per share of $0.10. Rambus changed to a original reporting standard this year; under the previous reporting standard, revenue for Q1 2018 was $100.5 million, up 4% from the same quarter terminal year. GAAP income per share for the quarter was $0.05, up 66.7% from $0.03 in Q1 2017; non-GAAP income per share was $0.21, up 23.5% from $0.17. The company posted record revenue for its memory and interface IP cores.

Jesse Allen   (all posts)Jesse Allen is the scholarship focus administrator and a senior editor at Semiconductor Engineering.

Cisco refused to participate in NSS Labs report on SD-WAN | killexams.com actual questions and Pass4sure dumps

Cisco refused to activate the Viptela software-defined WAN product NSS Labs bought for testing, leaving the research...

firm with a noticeable pocket in its recent comparative report on SD-WAN vendors.

Cisco did not provide a judgement for refusing to activate the product NSS Labs had purchased for between $30,000 and $40,000, NSS Labs CEO Vikram Phatak said this week. "There was no judgement given other than, effectively, they didn't want to exist tested (for the NSS Labs report)."

Cisco's action marked the first time a vendor had refused to turn on a product NSS Labs had bought for evaluation, Phatak said. Cisco's Viptela team had initially told NSS Labs it would support the test, which led the firm to buy the product.

"That's a first for us, candidly," Phatak said. "And given Cisco's ethical rules and so on -- rules of conduct -- I'm in shock because normally, they're pretty straightforward to work with."

Cisco's response

After initially refusing to discuss the matter, Cisco released a statement that said NSS Labs's proposed testing methodology "fell well short" of reflecting "the enterprise-grade nature of customer SD-WAN requirements."

"We shared actual customer deployment requirements with them, but their feedback wasn't incorporated," Cisco said. "At that point, they decided not to participate in the test."

Not participating, however, usually doesn't exist substantive the vendor will obviate the test from going forward. Typically, when a company refuses to connect a test, NSS Labs will conduct it alone.

"If someone says they don't want to exist tested, they say, 'That's great, but if a product is distinguished enough to exist sold to the public, it's distinguished enough to exist tested,'" Phatak said. "We're going to buy it, and we'll report to the public."

NSS Labs wants a refund

NSS Labs wants Cisco to refund the money spent on Viptela. It is hoping it can Get the money back without going to court.

"I hope it doesn't Come to that," Phatak said. "We haven't talked to any lawyers. I'm assuming that we'll exist able to own the conversation and Get their money back."

Typically, NSS Labs buys products, and the vendors turn them on relish they would for any other customer.

That's a first for us, candidly. And given Cisco's ethical rules and so on -- rules of conduct -- I'm in shock. Vikram PhatakCEO, NSS Labs

NSS Labs renowned Cisco's refusal to activate the Viptela purchase in its SD-WAN Comparative Report, which was the company's first SD-WAN test. Not having Cisco in the evaluation left out one of the largest SD-WAN vendors and a major tech company.

In the first quarter, London-based IHS Markit listed Cisco as No. 4 in the SD-WAN market, just behind Silver Peak. VMware was first with a 19% share, followed by Aryaka with 18%.

The NSS Labs report, released this month, compared the products of nine vendors, including VMware's NSX SD-WAN, formerly VeloCloud. VMware is Cisco's largest competitor.

NSS Labs had besides planned to comprise Silver Peak in the comparison but renowned it was unable to obtain the product in time for testing.

Tech companies often cite recommended ratings in NSS Labs reports in marketing materials. In April, Cisco highlighted in a blog post the organization's "recommended" rating for the Cisco Advanced Malware Protection for Endpoints product.

Based on its recent SD-WAN tests, NSS Labs recommended products from VMware, Talari Networks and Fortinet and listed products from Citrix Systems, FatPipe Networks, Forcepoint and Versa Networks as "verified." Tech buyers should consider recommended and verified products as candidates for purchase, according to NSS Labs.

The company issued "caution" ratings for Barracuda Networks and Cradlepoint, which means companies should not deploy their products without a comprehensive evaluation, NSS Labs said.



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Blogspot : http://killexams-braindumps.blogspot.com/2017/10/just-study-these-ibm-p2020-007.html
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