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70-475 - Design and Implement Big Data Analytics Solutions - BrainDump Information

Vendor Name : Microsoft
Exam Code : 70-475
Exam Name : Design and Implement Big Data Analytics Solutions
Questions and Answers : 42 Q & A
Updated On : March 22, 2019
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70-475 exam Dumps Source : Design and Implement Big Data Analytics Solutions

Test Code : 70-475
Test Name : Design and Implement Big Data Analytics Solutions
Vendor Name : Microsoft
Q&A : 42 Real Questions

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Microsoft Design and Implement Big

Microsoft (70-475): Designing and implementing massive statistics Analytics solutions | killexams.com Real Questions and Pass4sure dumps

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

The Microsoft 70-475 examination is supposed for statistics experts who've journey designing large data analytics options on Microsoft Azure. Candidates should still be in a position to design big records batch processing and interactive solutions in addition to big records actual-time processing solutions. Candidates may still 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 right here certifications:specialist


Watch Microsoft's MWC 2019 reside flow correct right here | killexams.com Real Questions and Pass4sure dumps

MWC 2019

MWC 2019

Microsoft is making its first big 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 function "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, all signs point to a 2d iteration of the company's HoloLens augmented truth 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 all we ought to go off of is a slick, cryptic teaser video posted to YouTube ultimate week by means of Kipman himself.

The normal 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 contract with the U.S. militia, which could lead on the military to eventually purchase upwards of 100,000 HoloLens devices.

when you consider that the normal 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 put in force deep neural networks to research objects and statistics without sending the rest to the cloud. although, we predict 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 keep for its MWC 2019 keynote.

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AMD Navi GPUs could See big performance increase With Variable price Shading Tech | killexams.com Real Questions and Pass4sure dumps

A these days posted patent software means that AMD can be infusing a technology called Variable price 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 method around the identical time (a tape out is when a company like AMD finalizes its semiconductor design before sending it off for manufacturing).

VSR is a rendering approach designed to decrease the burden 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 full 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 we want to look 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, easy to put into effect rendering method enabled with the aid of Turing GPUs. It raises rendering efficiency and satisfactory by way 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 also be processed through a single pixel shader operation. Single pixel shading operations can now be applied to a block of pixels, permitting applications to effectively differ the shading price in diverse areas of the monitor. Variable rate Shading can also 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 also 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, we are able to need to wait and spot.

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Design and Implement Big Data Analytics Solutions

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Reporting & Analytics - Big Data Developer# 125072 | killexams.com real questions and Pass4sure dumps

eFinancialCareers jobs: Reporting & Analytics - Big Data Developer# 125072 in Credit Suisse -, Raleigh, NC, USA
  • Salary: Competitive
  • Location: Raleigh, NC, USA
  • Job Type: Permanent, Full 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 responsible for providing innovative ways through maintaining knowledge of industry standard 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 part of a globally distributed team that will be the primary provider of IT Operational Reports to the firm. At any given time you will partner with team members across all regions and time zones while reporting to a line manager in New York. You will collaborate with key clients to develop dashboards that will drive business decisions across the firm.

    Duties:

  • Design/Maintain/Support Bigdata environment, including ETL.
  • Work directly with key business partners to understand data requirements and deliver on timely manner.
  • Analyze large data sets.
  • Design very complex SQL statements, including Impala.
  • Scripting automation solutions for Data and Reports.
  • Adhere with Agile methodology and follow the process to maintain/ support and to document the details.
  • Ability to easily communicate complex 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 also build automation scripts to help minimize manual involvement when building reports or dashboards. You will also help migrate existing complex SQL tables into Bigdata, and support reporting team to integrate the migrated tables in Tableau.

    You should also be well versed with data design and have ETL process experience.

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

    You Offer

  • You have a degree or higher education in Information Technology or equivalent work experience
  • You have at least 3 to 4 years of experience building Bigdata environment
  • You have demonstrable experience with Data Engineering
  • You have deep knowledge in Bigdata impala/Hadoop
  • You have 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
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    Big data's growing pains | killexams.com real questions and Pass4sure dumps

    Technology

    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 large sets of supply chain data?

    We are at the beginning 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 take this data and feed it into analytics and artificial intelligence systems, they could make better, faster decisions than ever before, driving down waste and increasing value. That, at least, is the vision of what big data analytics could achieve.

    But now, reality is beginning to set in. As more companies try to wrap their arms around their "big data" and implement more complex analytic tools, they are beginning to realize that achieving this vision is hard work. It takes significant investment in information technology (IT) systems as well as change and process management. Companies are also finding that the key data that they need is often missing or inaccurate. The promise of big 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 Big 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 State University and Colorado State University.

    This annual study is designed to provide companies with a benchmark that they can use to understand the current state of supply chain data analytics and learn what analytical strategies organizations are adopting to harness the power of big data. The intent is to show 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 State and Colorado State 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 help companies overcome their initial frustration and gain positive momentum with their big data analytics implementations.

    More implementations, less satisfaction

    A comparison of 2018's and 2017's results tell an interesting story. In both 2017 and 2018, we asked people: "How would you characterize your supply chain organization's maturity in regard to its use of big data analytics?" (See Figure 1.) In general, the survey results show that more companies have begun implementing big data analytics initiatives. There was a 14-percent increase in the number of big data implementations between 2017 and 2018, and far fewer people reported that they had not adopted supply chain analytics at all (only 10 percent in 2018 as opposed to 23 percent in 2017). And yet, Figure 1 also 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 still conducting proof-of-concept testing or have only rolled out initial implementations.

    Why was there a drop off in the number of "transformational" and "advanced" responses in 2018 from 2017? We do not believe that firms are necessarily less successful with their big data analytics implementations this year as opposed to 2017. Instead, responding firms' definition of "transformational" may have evolved since last year. As more companies implement big data analytics in earnest, they are developing a better understanding of what it entails and how much farther they have to go. In other words, companies now have a more realistic assessment of where they actually fall 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, like almost all new technologies, it takes a lot of work to make big data analytics useful. Companies, as a result, are more realistic about their progress along the maturity curve.

    We also see this perspective in respondent satisfaction with the data they do have. In general, companies are less satisfied with the quality 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 also dropped 5 percent between 2018 and 2017. It seems unlikely that data quality actually dropped from 2017 to 2018. Rather, we believe that as companies get deeper into their big data analytics implementations, they are becoming more aware of their existing data issues and have a better understanding of the magnitude of the effort and commitment required to address them.

    It stands to reason that if respondents are less satisfied with the quality 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 big data analytics efforts in 2018 relative to 2017. In both years, survey respondents were asked to use a seven-point scale to quantify the impact they have already realized from big 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. Figure 2 shows a slight drop in perceived impact across all of the potential benefits in 2018 versus 2017.

    The type of analytics matters

    When assessing the benefits that can be expected from a big data analytics implementation, it is crucial to understand that there are many types of analytics. For the survey, we 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 tell 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 heavy use) the extent that their company currently uses each of those types of analytics to support supply chain decision 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 Figure 3.) These scores are similar or slightly less than what was seen last year. The survey results indicate that more sophisticated types of analytics are still 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 type of analytics that firms use and the benefits that they report achieving.1 Generally, more sophisticated analytics are associated with a wider range of benefits. Figure 4 shows that the use 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 help companies better inform their customers of the problems they are facing before these issues become visible to them. While use of diagnostic analytics was slightly associated with demand planning and highly correlated with collaboration, no correlation could be found with any of the other realized benefits. (By "slight," we mean that we are greater than 90 percent certain 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 demand 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 demand planning. It's possible that by the time firms get to the level of prescriptive analytics, they not only have made some improvements to their demand planning but also are more aware of their issues. Without the capabilities of more advanced analytics, they may have trouble addressing these issues which they now have more insight into. In many ways, this negative correlation is a microcosm of the trends we see throughout the 2018 report. Firms are beginning to implement more sophisticated protocols, and, in the process, are beginning to realize the obstacles they have yet to overcome.

    Finally, we found that the most helpful type of analytics was cognitive, with a strong 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 type 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 perform their analytics. Survey respondents were again asked to use 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 Excel 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 last year's survey results, Excel spreadsheets continue to carry the day and are by far the most widely used analytics tool with an average score of 5.80, indicating "frequent use." OPS received an average score of 4.64, and ERP and business intelligence tools received scores of 3.97 and 3.88 respectively.

    The survey team also ran multiple analyses to see if there were any links between realized tangible big data analytical benefits and the use of these platforms. (See Figure 5.) While Microsoft Excel was the most widely used of these platforms, it also was the least useful. In fact, no correlation was established between the use of either Excel or an OPS as the primary big data analysis platform and any tangible improvement in customer service, demand planning, risk management, supply chain visibility, collaboration, or overall productivity. This demonstrates that for many companies, the current methods for analyzing big 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 see 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 use was significantly correlated with realized benefits in customer service, demand 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 make big data analysis work, companies need to be using the right tools.

    Still hope?

    Although most of the survey respondents have only implemented the less sophisticated types of analytics, they are still hopeful about the results that they are going to achieve. Companies reported that they expect 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 make a moderate to very large investment in cognitive analytics in the next 12 months, and only 52 percent plan to make a significant investment in predictive analytics. In contrast, almost two-thirds (63 percent) of survey respondents plan 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 big data analytics than they did last year. On a seven-point scale, all of the potential impediments that we asked about dropped from an average score between 5 (moderately significant) and 4 (neither significant nor insignificant) to scores between 4 and 3 (moderately insignificant). (See Figure 6.)

    After analyzing the 2017 data versus the 2018 data, we determined that managerial support was the key difference between this year and last year. Companies that had strong managerial support did not see getting their firms to invest in additional software or hardware or understanding the value proposition as significant barriers to big data analytics implementation. They also did not believe that concerns about security risks posed an impediment to adoption. However, they did still believe that talent acquisition, integrating siloed legacy systems, and gaining competency in new tools were significant barriers to further implementations.

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

    The road ahead

    It would be easy to look at the results from this year's survey and feel discouraged. While the number of companies implementing big 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 lack of progress and return on investment.

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

    It's possible that big data analytics has entered that "trough of disillusionment," as companies realize how much work they need to do to clean up their data and expand their use of additional types of analytics. To get reliable and trusted data, companies need to have not just a central data repository, but also 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 help guide companies out of this "trough of disillusionment," see the sidebar on "Suggested best practices."

    Companies are also 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 make smart decisions. Companies need solutions that can quickly answer the following questions: What happened? Why did it happen? If we make this change, what could happen? These tools also need to be able to track the financial and operational impacts of the changes that were made. These types of solutions are out there, but companies will need to make investments in new software and address the change management issues of fully utilizing the insights gained. They will also need to invest in their employees to make sure they have the skill sets required to work with these systems.

    While the initial shine and optimism might have worn off big data analytics, companies have a more realistic assessment of the road ahead. They more clearly understand the gaps they face in data quality and accessibility and the limitations of their existing tools. But big data analytics, combined with artificial intelligence methods, still holds great promise for the future. With the right level of executive support and investment, companies can reach the point where they have the valid data that they need for analytics tools to make better decisions more efficiently. But until then, companies must prepare themselves to work their way through a period of uncomfortable growing pains.

    Notes:

    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 use descriptive analytics and those who reported improvements in customer service.

     

    Suggested best practices

    The ability to gain the most value from big 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.

     People

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

  • Avoid "one-off," single design efforts. Recognize that big 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 big data analytics project will seek to answer 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 small pilot project first. Getting small, achievable wins early will help build momentum for bigger projects down the road.
  • Measure direct financial impact. A key metric for judging the success of a big 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 use the best sources of data and that the data itself is specific and accurate. This may involve the establishment of standard 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, big data analytics should be applied across the organization, beyond the supply chain. Make sure the technology you use has this end goal in mind.
  • Apply an agile development approach. As you implement big data analytics in more areas and to address more problems, your approach will change. Make sure that your technology can easily adapt to any changes.
  • Make sure that the technology is scalable.
  • Lisa Harrington is President of lharrington group LLC. Dr. Zac Rogers is an assistant professor in the managment department at Colorado State University's College of Business.  Dale Rogers is a professor of logistics and supply chain management at Arizona State 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 lharringtongroup.com

    zac.rogers at colostate.edu

    Dale.Rogers at asu.edu

    rsharpe at ci-advantage.com

    After you comment, click Post. If you're not already logged in, you will be asked to log in or register.

    Please enable JavaScript to view the comments powered by Disqus.

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

    Want more articles like this? Subscribe to CSCMP's Supply Chain Quarterly.


    Microsoft (70-475): Designing and Implementing Big Data Analytics Solutions | killexams.com real questions and Pass4sure dumps

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

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

    Certification: This exam counts as credit toward the following certifications:Specialist



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