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70-475 Design and Implement sizable Data Analytics Solutions

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70-475 exam Dumps Source : Design and Implement sizable Data Analytics Solutions

Test Code : 70-475
Test appellation : Design and Implement sizable Data Analytics Solutions
Vendor appellation : Microsoft
real questions : 42 actual Questions

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Microsoft Design and Implement Big

Microsoft (70-475): Designing and implementing massive statistics Analytics solutions | actual Questions and Pass4sure dumps

MeasureUp’s 70-475: Designing and enforcing huge information Analytics options result examine is designed to aid candidates prepare for and lope the Microsoft 70-475 examination.

The Microsoft 70-475 examination is supposed for statistics experts who've journey designing big data analytics options on Microsoft Azure. Candidates should noiseless be in a position to design sizable records batch processing and interactive solutions in addition to sizable records actual-time processing solutions. Candidates may noiseless even be in a position to design computer-researching options and create and manipulate end-to-end cloud analytics solutions.

Certification: This exam counts as credit toward privilege here certifications:specialist

Watch Microsoft's MWC 2019 reside rush remedy privilege here | actual Questions and Pass4sure dumps

MWC 2019

MWC 2019

Microsoft is making its first sizable appearance at mobile World Congress (MWC) in quite ages this yr, and you'll be in a position to circulation the business's keynote as it happens. Set to kick off on February 24 at 9 a.m. PT (12 p.m. ET / 6 p.m. CET), the keynote will duty "new innovations for the future of computing" with comments from Microsoft CEO Satya Nadella and HoloLens chief Alex Kipman.

which you can observe along with the keynote are living, tune in with the video embed above or at Microsoft's adventure web page.

As for what we're anticipating Microsoft to announce, outright signs point to a 2d iteration of the company's HoloLens augmented verity headset. We're truly not expecting any shock mobile bulletins, however Kipman's involvement strongly tips at a HoloLens 2 exhibit. it truly is far from definite, of route, but outright they ought to fade off of is a slick, cryptic teaser video posted to YouTube ultimate week by means of Kipman himself.

The bonafide HoloLens has more often than not loved success as an enterprise tool, bolstering manufacturing tactics, along with design and scientific purposes. The headset has even led to a lucrative $480 million shrink with the U.S. militia, which could lead on the military to eventually purchase upwards of 100,000 HoloLens devices.

when you regard that the bonafide HoloLens launched in 2016, a followup is hotly predicted. it's already been tested that the HoloLens 2 will recreation a custom Holographic Processing Unit (HPU) with an AI coprocessor, enabling it to natively attach in obligate profound neural networks to research objects and statistics without sending the repose to the cloud. although, they call several other advancements, together with, potentially, a change to an ARM processor and aid for LTE.

sooner or later, we'll must wait and spot what Microsoft has in withhold for its MWC 2019 keynote.

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AMD Navi GPUs could espy sizable performance extend With Variable expense Shading Tech | actual Questions and Pass4sure dumps

A these days posted patent software means that AMD can be infusing a technology called Variable expense Shading (VSR) into its upcoming Navi GPU architecture, which is expected to arrive later this yr. The patent software does not really specify Navi, though it's definitely a probability, given when the patent changed into filed and subsequent date of booklet. AMD filed the patent in August 2017, and it became published the day prior to this. AMD had already been establishing its Navi GPU architecture in 2017, and had additionally begun taping out its 7-nanometer FinFET manufacturing system around the identical time (a tape out is when a company fondness AMD finalizes its semiconductor design before sending it off for manufacturing).

VSR is a rendering approach designed to lessen the tribulation on the GPU and other elements, for introduced efficiency without successful to the perceived visual constancy of a scene. It accomplishes this by means of breaking up a frame into distinctive sections and rendering handiest parts of a frame in complete aspect, those being the enviornment(s) you are most concentrated on, and using coarser pixels across the fringe of a person's imaginative and prescient.

VR headsets stand to the benefit the most from this tech, the Place your eyes are concentrated on the core. That talked about, VSR might conceivably be used in alternative routes for gaming on the desktop. This may especially be really useful at greater resolutions which are greater taxing on a GPU, equivalent to 4K, and eventually 8K if they want to view that far down the street. AMD is not by myself in seeing the advantage of VSR. NVIDIA carried out a edition of the technology into its Turing GPU architecture. here's NVIDIA's explanation of VSR:

Variable cost Shading is a brand new, smooth to attach into result rendering system enabled with the aid of Turing GPUs. It raises rendering efficiency and satisfactory by artery of applying varying quantity of processing power to distinctive areas of the picture. VRS works with the aid of various the variety of pixels that may furthermore be processed through a single pixel shader operation. single pixel shading operations can now be applied to a screen of pixels, permitting applications to effectively disagree the shading expense in diverse areas of the monitor. Variable rate Shading can furthermore be used to render extra efficaciously in VR through rendering to a surface that closely approximates the lens corrected picture it is output to the headset monitor. This avoids rendering many pixels that could otherwise be discarded earlier than the photograph is output to the VR headset.

there is actually widespread activity in VSR. concerning that, Microsoft furthermore owns a VSR patent and is working to implement it into the DirectX API. no matter if it extends past VR headsets and into the realm of desktop monitors, notwithstanding, they are able to exigency to wait and spot.

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Design and Implement sizable Data Analytics Solutions

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Reporting & Analytics - sizable Data Developer# 125072 | actual questions and Pass4sure dumps

eFinancialCareers jobs: Reporting & Analytics - sizable Data Developer# 125072 in Credit Suisse -, Raleigh, NC, USA
  • Salary: Competitive
  • Location: Raleigh, NC, USA
  • Job Type: Permanent, complete time
  • Company: Credit Suisse -
  • Updated on: 02 Mar 19
  • We OfferCredit Suisse is seeking a Data Engineer in the Enterprise Services -

    SMO - Enterprise Services are amenable for providing innovative ways through maintaining learning of industry gauge methodology, technology trends and product availability. Developing business and technical requirements through consultation with key client business partners and ensuring consistency to corporate standards for risk and security compliance.

    The Analytics & Reporting team works as allotment of a globally distributed team that will be the primary provider of IT Operational Reports to the firm. At any given time you will ally with team members across outright regions and time zones while reporting to a line manager in novel York. You will collaborate with key clients to develop dashboards that will drive business decisions across the firm.


  • Design/Maintain/Support Bigdata environment, including ETL.
  • Work directly with key business partners to understand data requirements and deliver on timely manner.
  • Analyze big data sets.
  • Design very involved SQL statements, including Impala.
  • Scripting automation solutions for Data and Reports.
  • Adhere with Agile methodology and result the process to maintain/ uphold and to document the details.
  • Ability to easily communicate involved problem solving ideas to senior management and peers
  • Find opportunities on automation for any manual process.
  • In this function, you will be designing and implementing data solutions via Bigdata and python. You will furthermore build automation scripts to embolden minimize manual involvement when building reports or dashboards. You will furthermore embolden migrate existing involved SQL tables into Bigdata, and uphold reporting team to integrate the migrated tables in Tableau.

    You should furthermore be well versed with data design and maintain ETL process experience.

    Credit Suisse maintains a Working Flexibility Policy, theme to the terms as set forth in the Credit Suisse United States Employment Handbook.

    You Offer

  • You maintain a degree or higher education in Information Technology or equivalent drudgery experience
  • You maintain at least 3 to 4 years of experience building Bigdata environment
  • You maintain demonstrable experience with Data Engineering
  • You maintain profound learning in Bigdata impala/Hadoop
  • You maintain skills in Python for building functions using Pyspark and other packages related to Python
  • Database architect for bigdata environment on how the ETL process works will be an advantage
  • Good interpersonal skills as you will be involved in the interaction with clients to understand or present the requirements
  • Experience in Service Now ITIL process would be an added advantage
  • For more information visit Technology Careers . eFinancialCareers
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    Big data's growing pains | actual questions and Pass4sure dumps


    By Lisa Harrington, Dr. Zac Rogers, Dale Rogers, Richard Sharpe and Susan Lacefield | From the Quarter 1 2019 issue

    Big data analytics implementations are up, but satisfaction with those implementations is down, according to a recent survey. Why are companies frustrated with their attempts to harness the power of big sets of supply chain data?

    We are at the nascence of a supply chain technological revolution. Everything—from pallets and trucks to a bag of Romaine lettuce—has the potential to collect, store, and transmit information. If only companies could consume this data and feed it into analytics and artificial intelligence systems, they could gain better, faster decisions than ever before, driving down waste and increasing value. That, at least, is the vision of what sizable data analytics could achieve.

    But now, reality is nascence to set in. As more companies try to wrap their arms around their "big data" and implement more involved analytic tools, they are nascence to realize that achieving this vision is difficult work. It takes significant investment in information technology (IT) systems as well as change and process management. Companies are furthermore finding that the key data that they exigency is often missing or inaccurate. The swear of sizable data analytics is coming, but perhaps not as easily as initially thought. That was the main message conveyed by the results of the "Second Annual sizable Data Analytics Study" conducted by the analytics company Competitive Insights LLC; the consultancy lharrington group; CSCMP's Supply Chain Quarterly; and two prominent supply chain management schools, Arizona condition University and Colorado condition University.

    This annual study is designed to provide companies with a benchmark that they can expend to understand the current condition of supply chain data analytics and learn what analytical strategies organizations are adopting to harness the power of sizable data. The intent is to expose the levels of progress that companies are making in addition to the obstacles impeding that progress.

    A survey was conducted in both 2017 and 2018 with readers of Supply Chain Quarterly, subscribers to a newsletter produced by Competitive Insights, and a contact list generated by Arizona condition and Colorado condition University researchers. A total of 125 usable responses were compiled for the 2018 survey, comparable to 2017's total of 133 usable responses.

    Here are the findings and some suggested best practices that could embolden companies overcome their initial frustration and gain positive momentum with their sizable data analytics implementations.

    More implementations, less satisfaction

    A comparison of 2018's and 2017's results declare an absorbing story. In both 2017 and 2018, they asked people: "How would you characterize your supply chain organization's maturity in regard to its expend of sizable data analytics?" (See pattern 1.) In general, the survey results expose that more companies maintain begun implementing sizable data analytics initiatives. There was a 14-percent extend in the number of sizable data implementations between 2017 and 2018, and far fewer people reported that they had not adopted supply chain analytics at outright (only 10 percent in 2018 as opposed to 23 percent in 2017). And yet, pattern 1 furthermore shows that there were fewer people reporting that their implementations were "transformational" or "advanced." Instead, the majority of respondents were in the "early" or "developing" stages of adoption, indicating that most companies are either noiseless conducting proof-of-concept testing or maintain only rolled out initial implementations.

    Why was there a drop off in the number of "transformational" and "advanced" responses in 2018 from 2017? They achieve not believe that firms are necessarily less successful with their sizable data analytics implementations this year as opposed to 2017. Instead, responding firms' definition of "transformational" may maintain evolved since eventual year. As more companies implement sizable data analytics in earnest, they are developing a better understanding of what it entails and how much farther they maintain to go. In other words, companies now maintain a more realistic assessment of where they actually tumble on the maturity curve and the extent of the obstacles they must overcome. The widespread excitement of 2017 has been replaced by the realization that, fondness almost outright novel technologies, it takes a lot of drudgery to gain sizable data analytics useful. Companies, as a result, are more realistic about their progress along the maturity curve.

    We furthermore espy this perspective in respondent satisfaction with the data they achieve have. In general, companies are less satisfied with the attribute and availability of their data in 2018 than they were in 2017. On average, respondents in 2018 were 4 percent less satisfied with their data availability, 8 percent less satisfied with data usability, and 7 percent less satisfied with their data's integrity than respondents in 2017. Satisfaction with data reliability furthermore dropped 5 percent between 2018 and 2017. It seems unlikely that data attribute actually dropped from 2017 to 2018. Rather, they believe that as companies trap deeper into their sizable data analytics implementations, they are becoming more awake of their existing data issues and maintain a better understanding of the magnitude of the pains and commitment required to address them.

    It stands to understanding that if respondents are less satisfied with the attribute of their data, they are going to be less satisfied with their data analytics results as well. Indeed, the 2018 survey showed a slight drop in respondents' assessment of the realized benefits of their sizable data analytics efforts in 2018 relative to 2017. In both years, survey respondents were asked to expend a seven-point scale to quantify the repercussion they maintain already realized from sizable data analytics in a variety of areas, such as profitability, inventory management, and visibility to total cost-to-serve. A score of 1 equals no impact, and 7 equals a transformative impact. pattern 2 shows a slight drop in perceived repercussion across outright of the potential benefits in 2018 versus 2017.

    The nature of analytics matters

    When assessing the benefits that can be expected from a sizable data analytics implementation, it is crucial to understand that there are many types of analytics. For the survey, they defined the different types of data analytics as follows:

  • Descriptive—what is happening/has happened
  • Diagnostic—why it's happening
  • Predictive—what will happen
  • Prescriptive—what should be done
  • Cognitive—uses machine learning to declare what should or could be done
  • Survey respondents were again asked to select from a seven-point scale (where a score of 1 equals no use, and a score of 7 equals weighty use) the extent that their company currently uses each of those types of analytics to uphold supply chain conclusion making. On average, respondents gave descriptive analytics a score of 4.61 (indicating between "some use" and "regular use"), diagnostic analytics received a score of 4.02, predictive analytics a score of 3.16 (indicating between "some use" and "occasional use"), 3.56 for prescriptive analytics, and 2.27 (or "infrequent use") for cognitive analytics. (See pattern 3.) These scores are similar or slightly less than what was seen eventual year. The survey results testify that more sophisticated types of analytics are noiseless used less than more rudimentary methods in many real-life supply chains.

    Further investigation using regression analyses on the responses identified a significant correlation between the nature of analytics that firms expend and the benefits that they report achieving.1 Generally, more sophisticated analytics are associated with a wider range of benefits. pattern 4 shows that the expend of descriptive analytics is only correlated with improvements in customer service. This finding makes sense, as knowing what is happening in their supply chain can embolden companies better inform their customers of the problems they are facing before these issues become visible to them. While expend of diagnostic analytics was slightly associated with claim planning and highly correlated with collaboration, no correlation could be establish with any of the other realized benefits. (By "slight," they matter that they are greater than 90 percent inevitable there is a correlation, instead of greater than 95 percent sure there is a correlation.) Predictive analytics, however, could be linked to many benefits, such as improvements in claim planning, risk management, and collaboration.

    Somewhat counterintuitively, prescriptive analytics was not positively related with any of these realized benefits. In fact, it was slightly negatively correlated to improvements in claim planning. It's workable that by the time firms trap to the flat of prescriptive analytics, they not only maintain made some improvements to their claim planning but furthermore are more awake of their issues. Without the capabilities of more advanced analytics, they may maintain anxiety addressing these issues which they now maintain more insight into. In many ways, this negative correlation is a microcosm of the trends they espy throughout the 2018 report. Firms are nascence to implement more sophisticated protocols, and, in the process, are nascence to realize the obstacles they maintain yet to overcome.

    Finally, they establish that the most helpful nature of analytics was cognitive, with a stalwart correlation to risk management and productivity and a slight correlation to customer service, visibility, and collaboration. In other words, the more sophisticated and forward-looking the nature of analytics that are being used, the more benefits companies are realizing.

    Similar results can be observed when considering the types of software tools that companies are employing to fulfill their analytics. Survey respondents were again asked to expend a seven-point scale (where a score of 1 equals "no use," a score of 4 equals "some use," and a score of 7 signifies "heavy use") to measure the extent that their company currently uses a variety of analytics tools. Those tools included: Microsoft outdo or similar spreadsheet programs; operational point solutions (OPS) such as warehouse management systems and transportation management systems; advanced analytical tools associated with enterprise resource planning (ERP) systems; and business intelligence (BI) tools. As was the case in eventual year's survey results, outdo spreadsheets continue to carry the day and are by far the most widely used analytics instrument with an unconcerned score of 5.80, indicating "frequent use." OPS received an unconcerned score of 4.64, and ERP and business intelligence tools received scores of 3.97 and 3.88 respectively.

    The survey team furthermore ran multiple analyses to espy if there were any links between realized tangible sizable data analytical benefits and the expend of these platforms. (See pattern 5.) While Microsoft outdo was the most widely used of these platforms, it furthermore was the least useful. In fact, no correlation was established between the expend of either outdo or an OPS as the primary sizable data analysis platform and any tangible improvement in customer service, claim planning, risk management, supply chain visibility, collaboration, or overall productivity. This demonstrates that for many companies, the current methods for analyzing sizable data are ineffective. They may be useful for maintaining the status quo, but they are unlikely to lead to many benefits. Without a change in methods, it is unlikely firms will espy a change in results.

    Conversely, although ERP and BI systems were the least used platforms, the regression analysis suggests that they were the most beneficial. ERP expend was significantly correlated with realized benefits in customer service, claim planning, risk management, inventory management, visibility, and increased profitability. business intelligence users reported the greatest levels of performance improvement in customer service, supply chain visibility, end-to-end supply chain collaboration, and overall productivity. These results seem to demonstrate that to gain sizable data analysis work, companies exigency to be using the privilege tools.

    Still hope?

    Although most of the survey respondents maintain only implemented the less sophisticated types of analytics, they are noiseless hopeful about the results that they are going to achieve. Companies reported that they anticipate moderately significant improvements in customer service, supply chain visibility, productivity, and profitability in the next 12 months. However, only 40 percent of survey respondents are planning to gain a moderate to very big investment in cognitive analytics in the next 12 months, and only 52 percent strategy to gain a significant investment in predictive analytics. In contrast, almost two-thirds (63 percent) of survey respondents strategy to invest in descriptive analytics.

    The 2018 survey results point to a disconnect between reality and anticipated results. Companies seem to be focusing their investments in baseline solutions but are hoping for results that are correlated to more sophisticated solutions.

    In spite of these discouraging signs, there is hope. Survey respondents generally reported facing fewer roadblocks to implementing sizable data analytics than they did eventual year. On a seven-point scale, outright of the potential impediments that they asked about dropped from an unconcerned score between 5 (moderately significant) and 4 (neither significant nor insignificant) to scores between 4 and 3 (moderately insignificant). (See pattern 6.)

    After analyzing the 2017 data versus the 2018 data, they determined that managerial uphold was the key dissimilarity between this year and eventual year. Companies that had stalwart managerial uphold did not espy getting their firms to invest in additional software or hardware or understanding the value proposition as significant barriers to sizable data analytics implementation. They furthermore did not believe that concerns about security risks posed an impediment to adoption. However, they did noiseless believe that talent acquisition, integrating siloed legacy systems, and gaining competency in novel tools were significant barriers to further implementations.

    The fact that more companies are reporting having the uphold of top management is an encouraging sign, as this is essential for continuing to push successful sizable data analytics initiatives when they kisser difficulties or missteps. It furthermore ensures that the implementations will receive the funding they exigency to gain the required tools, staff, and training.

    The road ahead

    It would be smooth to view at the results from this year's survey and feel discouraged. While the number of companies implementing sizable data analytics programs has increased, their satisfaction with the data they are working with and with the overall results of these initiatives has dropped. Companies seem frustrated with their need of progress and return on investment.

    However, this emotion of frustration and even disillusionment is not surprising. It is actually fairly typical for the implementation of any novel technology, as described by the analyst group Gartner in its "Hype Cycle." Many technology experts maintain observed that when a novel technology is introduced there is often a epoch of increasing hype and building expectations. When the technology does not initially live up to those overinflated expectations, there is often a epoch that Gartner calls "the trough of disillusionment." During this period, companies' expectations for the technology drop rapidly as they fade through the ache of actual implementation. Then, as companies discover what the technology can actually do, expectations mount again. During that time, companies regain some of the expectations that they lost in the trough of disillusionment, but they never again achieve the height seen during the start of the hype cycle. The ache that many firms seem to be experiencing with their sizable data analytics implementations is a normal, and often essential, allotment of the maturity process.

    It's workable that sizable data analytics has entered that "trough of disillusionment," as companies realize how much drudgery they exigency to achieve to clean up their data and expand their expend of additional types of analytics. To trap dependable and trusted data, companies exigency to maintain not just a central data repository, but furthermore an executive-sponsored, cross-organizational approach to data collection and analysis. This process needs to factor in accountability, repeatability, and subject-matter validation. This takes time and work. To embolden lead companies out of this "trough of disillusionment," espy the sidebar on "Suggested best practices."

    Companies are furthermore in the process of learning that the analytical tools they are currently using are not sophisticated enough to provide them with the timely, accurate, and specific insights needed to gain smart decisions. Companies exigency solutions that can quickly reply the following questions: What happened? Why did it happen? If they gain this change, what could happen? These tools furthermore exigency to be able to track the fiscal and operational impacts of the changes that were made. These types of solutions are out there, but companies will exigency to gain investments in novel software and address the change management issues of fully utilizing the insights gained. They will furthermore exigency to invest in their employees to gain sure they maintain the skill sets required to drudgery with these systems.

    While the initial shine and optimism might maintain worn off sizable data analytics, companies maintain a more realistic assessment of the road ahead. They more clearly understand the gaps they kisser in data attribute and accessibility and the limitations of their existing tools. But sizable data analytics, combined with artificial intelligence methods, noiseless holds grandiose swear for the future. With the privilege flat of executive uphold and investment, companies can achieve the point where they maintain the valid data that they exigency for analytics tools to gain better decisions more efficiently. But until then, companies must prepare themselves to drudgery their artery through a epoch of uncomfortable growing pains.


    1. Regression analysis is a set of statistical processes for estimating the relationship among variables. For example, regression analysis could be used to establish whether there is a relationship between respondents who expend descriptive analytics and those who reported improvements in customer service.


    Suggested best practices

    The aptitude to gain the most value from sizable data analytics is a journey. You can accelerate your company's progress by following proven best practices that address the people involved, the processes and data employed, and the technology utilized.


  • Involve cross-functional teams, with key stakeholders from sales, finance, and marketing, early in any implementation effort. The solutions or root causes that sizable data analytics may expose will repercussion multiple areas and not always be confined to the supply chain. Likewise, be sure to partake any successes with the other functions involved.
  • Link your sizable data analytics efforts to your company's key initiatives. Using the tools to better something that is Important to your business will embolden you build momentum and gain uphold across the organization.
  • Make sure that you maintain senior-level uphold and ideally a project champion for your sizable data analytics efforts. Garnering this uphold will be easier if you can tie it back to fiscal performance.
  • Process:

  • Avoid "one-off," single design efforts. Recognize that sizable data analytics is not meant to be a single project. Instead, you are building an ongoing organizational capability. Integrating siloed projects is costly and time-consuming. The cost of coordinating implementation efforts early on will pay off down the road.
  • Be intentional about the focus of a specific analytics effort. Clearly communicate what question the sizable data analytics project will seek to reply and who the key stakeholders are. Otherwise you risk scope creep, with the project broadening to the point where it becomes unmanageable and difficult to measure.
  • Adopt a "crawl, walk, run" mentality. Start with a petite pilot project first. Getting small, achievable wins early will embolden build momentum for bigger projects down the road.
  • Measure direct fiscal impact. A key metric for judging the success of a sizable data analytics project is how much it contributed to the bottom line.
  • Data:

  • Gain cross-functional organizational consensus on what data sources should be used.
  • Make sure that you expend the best sources of data and that the data itself is specific and accurate. This may involve the establishment of gauge operating procedures for data collection a few months prior to the start of an implementation.
  • Have sound data governance and repeatable data validation to ensure that the entire organization trusts the data.
  •  Technology:

  • Design solutions to be used cross-functionally. Ultimately, sizable data analytics should be applied across the organization, beyond the supply chain. gain sure the technology you expend has this finish goal in mind.
  • Apply an agile evolution approach. As you implement sizable data analytics in more areas and to address more problems, your approach will change. gain sure that your technology can easily reconcile to any changes.
  • Make sure that the technology is scalable.
  • Lisa Harrington is President of lharrington group LLC. Dr. Zac Rogers is an helper professor in the managment department at Colorado condition University's College of Business.  Dale Rogers is a professor of logistics and supply chain management at Arizona condition University's W. P. Carey School of Business. Richard Sharpe is CEO of Competitive Insights LLC, a provider of cost-to-serve and profit-analytical solutions. Susan Lacefield is Executive Editor of CSCMP’s Supply Chain Quarterly.

    lisa at at

    Dale.Rogers at

    rsharpe at

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    We Want to Hear From You! They invite you to partake your thoughts and opinions about this article by sending an e-mail to ?Subject=Letter to the Editor: Quarter 2019: sizable data's growing pains"> . They will publish selected readers' comments in future issues of CSCMP's Supply Chain Quarterly. Correspondence may be edited for clarity or for length.

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    Microsoft (70-475): Designing and Implementing sizable Data Analytics Solutions | actual questions and Pass4sure dumps

    MeasureUp’s 70-475: Designing and Implementing sizable Data Analytics Solutions practice test is designed to embolden candidates prepare for and pass the Microsoft 70-475 exam.

    The Microsoft 70-475 exam is intended for data professionals who maintain experience designing sizable data analytics solutions on Microsoft Azure. Candidates should be able to design sizable data batch processing and interactive solutions as well as sizable data real-time processing solutions. Candidates should furthermore be able to design machine-learning solutions and create and manage end-to-end cloud analytics solutions.

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