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70-776 - Performing Big Data Engineering with Microsoft Cloud Services - Dump Information

Vendor : Microsoft
Exam Code : 70-776
Exam Name : Performing Big Data Engineering with Microsoft Cloud Services
Questions and Answers : 69 Q & A
Updated On : October 22, 2018
PDF Download Mirror : 70-776 Brain Dump
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70-776 Questions and Answers

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70-776 Performing Big Data Engineering with Microsoft Cloud Services

Study Guide Prepared by Killexams.com Microsoft Dumps Experts


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70-776 exam Dumps Source : Performing Big Data Engineering with Microsoft Cloud Services

Test Code : 70-776
Test Name : Performing Big Data Engineering with Microsoft Cloud Services
Vendor Name : Microsoft
Q&A : 69 Real Questions

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Microsoft Performing Big Data Engineering

Analyze Elasticsearch statistics in R | killexams.com Real Questions and Pass4sure dumps

Java-based mostly (JDBC) information connectivity to SaaS, NoSQL, and massive statistics. down load Now.

that you may entry Elasticsearch data with pure R script and common SQL on any computing device the place R and Java can also be put in. which you could use the CData JDBC Driver for Elasticsearch and the RJDBC kit to work with faraway Elasticsearch statistics in R. by using the CData Driver, you're leveraging a driver written for industry-confirmed requirements to entry your statistics in the popular, open-source R language. this text suggests the way to use the driver to execute SQL queries to Elasticsearch and visualize Elasticsearch data via calling ordinary R capabilities.

install R

which you can match the driving force's performance beneficial properties from multithreading and managed code by using running the multithreaded Microsoft R Open or by operating open R linked with the BLAS/LAPACK libraries. this text uses Microsoft R Open 3.2.3, which is preconfigured to install packages from the Jan. 1, 2016 snapshot of the CRAN repository. This photo ensures reproducibility.

Load the RJDBC package

to make use of the driver, down load the RJDBC package. After setting up the RJDBC kit, right here line masses the package:

library(RJDBC) connect to Elasticsearch as a JDBC records supply

you'll need right here counsel to hook up with Elasticsearch as a JDBC statistics supply:

  • Driver class: Set this to cdata.jdbc.elasticsearch.ElasticsearchDriver.
  • Classpath: Set this to the location of the motive force JAR. with the aid of default, here's the lib subfolder of the installation folder.
  • The DBI capabilities, similar to dbConnect anddbSendQuery , deliver a unified interface for writing statistics access code in R. Use right here line to initialize a DBI driver that can make JDBC requests to the CData JDBC Driver for Elasticsearch:

    driver <- JDBC(driverClass = "cdata.jdbc.elasticsearch.ElasticsearchDriver", classPath = "MyInstallationDir\lib\cdata.jdbc.elasticsearch.jar", identifier.quote = "'")

    that you would be able to now use DBI features to hook up with Elasticsearch and execute SQL queries. Initialize the JDBC reference to the dbConnect function. beneath is a typical JDBC connection string:

    conn <- dbConnect(driver,"Server=127.0.0.1;Port=9200;consumer=admin;Password=123456;")

    Set the Server and Port connection properties to connect. To authenticate, set the person and Password residences, PKI (public key infrastructure) homes, or both. to use PKI, set the SSLClientCert, SSLClientCertType, SSLClientCertSubject, and SSLClientCertPassword properties.

    The information provider makes use of X-Pack protection for TLS/SSL and authentication. To connect over TLS/SSL, prefix the Server value with 'https://'. word: TLS/SSL and customer authentication need to be enabled on X-Pack to use PKI.

    once the statistics provider is related, X-Pack will then function user authentication and provide function permissions based on the nation-states you've got configured.

    Schema Discovery

    the driver fashions Elasticsearch APIs as relational tables, views, and saved approaches. Use right here line to retrieve the listing of tables:

    dbListTables(conn) Execute SQL Queries

    which you can use the dbGetQuery function to execute any SQL query supported by means of the Elasticsearch API:

    orders <- dbGetQuery(conn,"opt for Orders.Freight, consumers.ContactName FROM valued clientele inner be a part of Orders ON valued clientele.CustomerId=Orders.CustomerId")

    which you can view the outcomes in a knowledge viewer window with the following command:

    View(orders) Plot Elasticsearch facts

    that you would be able to now analyze Elasticsearch information with any of the statistics visualization programs attainable within the CRAN repository. that you may create primary bar plots with the constructed-in bar plot characteristic:

    par(las=2,ps=10,mar=c(5,15,4,2)) barplot(orders$Freight, leading="Elasticsearch Orders", names.arg = orders$OrderName, horiz=genuine)

    issues:

    big records ,elasticsearch ,r ,records evaluation ,tutorial


    HireVue Appoints Microsoft AI executive Eric Boyd to Board of directors | killexams.com Real Questions and Pass4sure dumps

    SALT LAKE city, Oct. 8, 2018 /PRNewswire/ -- HireVue, issuer of the most complete AI-pushed talent evaluation suite and video interviewing solutions, has named Eric Boyd, CVP AI Platform at Microsoft,  to the HireVue Board of administrators.

    "i'm extremely joyful to welcome Eric to the HireVue Board of administrators," stated Kevin Parker, Chairman and CEO at HireVue. "His wealth of adventure and wonderful viewpoint in AI and analytics may be of large value as we proceed to accelerate HireVue's know-how for our world valued clientele."

    Boyd has spent pretty much a decade at Microsoft main gigantic-scale efforts such because the Bing advertisements engineering group, together with the relevance AI, excessive-efficiency serving methods, and large-facts analytics systems. In his latest role as CVP AI Platform, he oversees building of the AI equipment, infrastructure, hardware, big information systems and key data units powering the computing device-getting to know techniques using Bing, Bing advertisements, and Microsoft office.

    past in his career, he served as vice chairman of Engineering for Mochi Media and spent over 9 years in engineering leadership roles at Yahoo!, the place he was finally promoted to vice chairman of Platform Engineering. He holds a B.S. in laptop Engineering and mathematics from the Massachusetts Institute of expertise.

    "it be striking to be part of the HireVue board at a time of such large boom," Boyd talked about. "Few organizations carry on a vision the manner that HireVue has delivered on the thought of democratizing the recruiting system. With such a robust Board in region, i am looking forward to helping e book HireVue to the next degree as a corporation."

    Boyd's appointment follows a 12 months of vast boom:

  • In may additionally, HireVue received London-based MindX, a online game-based assessments company, to build out probably the most potent AI-driven pre-appoint assessments platform available on the market.
  • The business is on track to boost new business via nearly 35 percent year over 12 months.
  • HireVue has hosted over seven million video interviews so far, up from the business's milestone of five million interviews executed in October 2017.
  • overseas earnings have accelerated more than 50 p.c yr over 12 months and now contain very nearly a fifth of complete revenue bookings.
  • The variety of HireVue personnel grew with the aid of virtually 50 p.c in two years to over 300 employees, 90 of whom are committed to product construction and facts security and privacy.
  • About HireVueAcross the globe, HireVue is reworking the style businesses find, employ and enhance the choicest ability with Hiring Intelligence. using a combination of validated industrial/organizational science and fastidiously demonstrated, predictive artificial intelligence, recruiting consultants are hiring greater nice ability, quicker. HireVue is available worldwide in additional than 30 languages and has hosted basically eight million interviews for greater than seven-hundred valued clientele worldwide, together with over one-third of the Fortune a hundred and leading manufacturers reminiscent of Vodafone, Intel, Hilton, HealthSouth, Qantas and Carnival Cruise strains. For greater tips, visit www.hirevue.com.

    HireVue Social NetworksTwitter:  www.twitter.com/HireVue LinkedIn:  www.linkedin.com/enterprise/HireVue  fb: www.fb.com/HireVue   YouTube: www.YouTube.com/user/HireVue Instagram:  www.instagram.com/hirevue/

     

    Cision View original content:https://www.prnewswire.com/information-releases/hirevue-appoints-microsoft-ai-government-eric-boyd-to-board-of-administrators-300725516.html

    source HireVue


    Microsoft is hiring former Qualcomm engineers to work on its quantum computing crew | killexams.com Real Questions and Pass4sure dumps

    beginning in June, Microsoft started hiring a couple of former Qualcomm engineers and bosses who at the moment are engaged on Redmond's quantum-computing crew. a couple of these engineers had been formerly working on Qualcomm's ARM-based mostly server efforts, as first said on October eight through The information.

    msquantumqualcomm.jpg credit score: Microsoft

    during the last couple of years, Microsoft publicly has talked up its intentions to be a player in quantum computing, however officers say the company has been working on scalable quantum computing for very nearly two decades. Microsoft officials say they plan to use a quantum desktop as a co-processor, with builders deploying their quantum applications from within Microsoft Azure.

    Quantum computers are designed to process in parallel, as a consequence enabling new sorts of applications throughout loads of workloads. they're designed to harness the physics of subatomic particles to provide a special technique to store information and solve complications in comparison to universal computers. The influence is that quantum computers, developed around custom quantum processors, are anticipated to be capable of resolve definite excessive-performance-computing complications more correctly.

    The suggestions referred to names of a couple of former Qualcomm personnel who grew to be Microsoft personnel this summer. I discovered up-to-date LinkedIn profiles for 4 of the individuals they cited:

  • Muntaquim Chowdhury became vp of Tech at Qualcomm in Raleigh-Durham, N.C., and worked as chief architect of Server CPU at Qualcomm from February 2016 to July 2018. (He turned into at Intel for 24 years earlier than that.) He joined Microsoft (in Bellevue, Wash.) as a associate Architect.
  • Thomas Speier was a 15-year Qualcomm worker. He turned into Senior Director at Qualcomm and an architect for customized ARM cores. In June he grew to become fundamental Architect at Microsoft in Raleigh-Durham.
  • Michael McIlvaine turned into Senior Director of Tech, and a important CPU architect at Qualcomm, where he worked for just about 15 years, including a stint engaged on "subsequent gen server CPU." he's now a primary architect with Microsoft in Raleigh-Durham.
  • Michael Underkoffler, who joined Microsoft in June, is a partner Engineering manager in Raleigh-Durham. His profile says he's "building a control equipment for quantum computing it truly is based on a classical laptop architecture." earlier than that, he become a Senior Director of Engineering at Qualcomm in Raleigh Durham, where he worked for 14 years on ARM processor design, among other jobs.
  • In July 2018, Microsoft appointed former Microsoft unusual Engineer and researcher Doug Burger because the head of its Azure Hardware Engineering crew. (Burger became a Microsoft Technical Fellow on the equal time.) Burger's mission, I've heard, is to spearhead a few different, non-consumer-focused hardware efforts across the enterprise, including Microsoft's field-programmable gate-array work, its quantum computing efforts, new datacenter work like its "challenge Natick" underwater datacenter and greater. Org-chart-intelligent, Burger studies to Todd Holmdahl, who is company vice president of Quantum Computing.

    Microsoft officers firstly referred to they'd speak greater concerning the business's growth on quantum computing at the recent Ignite IT professional convention, however ended up asserting very little at that event about quantum. possibly Microsoft moved its update on quantum to a different of its activities later this year (?)


    70-776 Performing Big Data Engineering with Microsoft Cloud Services

    Study Guide Prepared by Killexams.com Microsoft Dumps Experts


    Killexams.com 70-776 Dumps and Real Questions

    100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



    70-776 exam Dumps Source : Performing Big Data Engineering with Microsoft Cloud Services

    Test Code : 70-776
    Test Name : Performing Big Data Engineering with Microsoft Cloud Services
    Vendor Name : Microsoft
    Q&A : 69 Real Questions

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    Google provides new options for cloud data storage locations and access | killexams.com real questions and Pass4sure dumps

    In one of several additions to its cloud computing offerings today, Google LLC announced new ways make data more accessible in its Cloud Storage service and control where it’s stored around the world.

    Introduced early Thursday at the company’s Cloud Next conference in London, the new options for storage across multiple regions are intended to help customers for applications such as content delivery and big-data workloads.

    For one, Google announced a dual-regional option in beta test mode, allowing customers to store data in a pair of specific Google Cloud regions. Often with many cloud storage services, Google Cloud product manager Geoffrey Noer said in an interview, users have to create multiple locations for their data and then use replication tools to manage the synchronization of that data.

    Google’s new option, by contrast, provides the availability and backup of geographical replication of storage with low-latency storage access. “You write just one copy and we do the rest,” Noer said. “In standard enterprise offerings, if you lose your primary site, you need to manage getting your data up again.”

    The option is especially useful for analytics or big-data workloads, for which having data and computing close together provides faster performance, and for serving content such as video.

    For example, Twitter Inc. plans to use the dual-region option for its data platform, in particular for one large log analysis project the microblogging site just completed. Derek Lyon, Twitter’s director of engineering, will outline at Cloud Next this week how it’s moving 300 petabytes of data to Google’s cloud, but for this particular project it had to run queries on a massive 15 petabytes of compressed log files. That was going to be difficult ingesting data from only one region.

    “We chose dual-region buckets because they provide us with the ingest performance and multiregional availability of multiregion buckets, combined with the data locality of regional buckets,” he said.

    The dual-regional option is an addition to current options for regional — data stored in a specific region with replication across that region’s availability zones — and multi-regional, where data is distributed across the U.S., the E.U. or Asia (below).

    gcpregions

    Google also introduced higher availability for so-called nearline data, which is data that needs to be overwritten, say, once a month, and coldline data, for “cold” storage of data that may not change more than once a year. Both are lower-cost archival options than “hot” storage of oft-accessed or overwritten data.

    Now, both options are stored redundantly in multiregional locations so they’re more available, with millisecond latency. “Most data has a lifecycle where its relevance (and subsequently its access frequency) is highest immediately after it’s been created, and then tails off as it ages,” Noer explained in a blog post written with product manager Ben Chong and Chris Talbott, head of Cloud Storage product marketing. “We believe that as content is accessed less frequently, you should benefit from lower-tier storage costs while maintaining a consistent customer experience and data availability and reliability.”

    The new services, along with user interface and other tweaks coming soon, are part of a continuing push by Google to become more of a leader in the cloud computing business.

    One analyst noted that Google is playing catch-up generally to larger rivals Amazon Web Services Inc. and Microsoft Azure. “But it’s the automation and ease of use that determines the uptake,” said Holger Mueller, vice president and principal analyst at Constellation Research Inc.

    “Google is making good progress on its storage portfolio, and cloud storage matters for enterprises as they build their next-generation applications in the cloud,” he added. “So redundancy, performance and resilience capabilities like the ones that Google is adding are key for CxOs to consider the cloud as storage for their next-gen applications.”

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    SAP's road to cloud-native data platforms in a hybrid world | killexams.com real questions and Pass4sure dumps

    At SAP's 2018 TechEd conference that has just wrapped up Las Vegas, it made several announcements for general availability and betas of SAP HANA and data integration products with an eye toward the cloud. SAP has offered HANA in the cloud for some time; for instance, you can go to AWS Marketplace and order up an instance, taking your existing license.

    SAP also announced general availability of SAP Data Hub 2.3, which was part of our SAPPHIRE coverage back in the spring. To recap, Data Hub manages data pipelines, providing the flexibility to move data and/or push down compute to the data source. The notion with data pipelines is chaining together operations related to sourcing, ingesting, cleaning, integrating, and in some cases, executing ML processes and performing light in-line analytic and/or filtering operations on data. Version 2.3 takes important steps toward unifying metadata management and data exploration.

    SAP is designing Data Hub with an eye on cloud deployment, support of multi-cloud data landscapes, and on managed cloud service delivery. For now, it is a bring your own license service - not yet a managed service. But there is precedent with SAP Data Network, providing a managed service for data discovery, enrichment, and analytics. The plot thickens with the announcement at TechEd last week of the beta for SAP HANA database as a service. Like Data Hub, you could already run HANA in the cloud, but it was targeted at lift and shift where you managed HANA in the cloud rather than your own data center.

    There's little shock or awe about SAP's HANA as a service announcement. (Postscript: This was announced at SAPPHIRE last spring.) It's more an inevitability where SAP's rivals are already running there. We've been big proponents of managed cloud database services because, unlike infrastructure as a service (which is the modern, multi-tenanted equivalent of hosting), managed cloud services fully deliver on the promise of simplification. Set the instance, size of the footprint, and service level that you want, and leave the messy, non-value-added jobs of provisioning, patching, and maintenance to the database provider. And by the way, the same goes with any form of packaged software; we can all thank Salesforce.com for raining awareness of the benefits of SaaS (and PaaS) services when it comes to allowing IT and the business to focus on more value-added tasks.

    Of course, the rub with managed cloud services is the lock-in factor, but that's where cloud challengers like SAP come in: depending on where they make their managed service available, they are not necessarily locking you into the usual suspects: AWS, Azure, or Google Cloud.

    But that's where the balancing act comes in. If you're Amazon or Google, it is much easier to provide a consistent experience to your managed database service, whether it be Aurora, Cloud Spanner, DynamoDB, or Cloud Firestore, because it's only delivered on their environment.

    For incumbents like SAP (and Microsoft, for that matter), who have the on-premise legacy, there is the challenge and opportunity of giving customers the best of both worlds. Adapt your on-premise product to give a consistent experience with the cloud counterpart, and you have a powerful wedge against cloud-only providers, not to mention a ready answer for those demanding private cloud deployments. This is an area where IBM, Oracle, and Microsoft are staking preemptive claim.

    But to deliver that common experience, the devil is in the details. At the base, most cloud-native platforms separate compute from storage to provide elasticity and ease of scaling. At first blush, that sounds like a nice idea for on-premise, except that data center deployments won't have the virtually infinite scalability and multi-availability zone replication for HA/DR (and improved local performance for reads and, for multi-master databases, writes). As we pointed out last week, Microsoft is beginning to perform this balancing act with SQL Server 2019 and Azure SQL Database.

    The flip side of cloud native is containerization and microservices. By deconstructing monolithic on-premise databases and applications, they can utilize cloud resources far more efficiently. If you are running a change data capture log, for instance, you don't have to fire up the whole database engine and therefore reduce the amount of compute footprint to consume.

    Containerization is not an all-or-nothing deal. You can put the entire database in a container, like SAP has already done with SAP Data Hub which includes SAP Vora (which allows you to easily deploy on different clouds seamlessly using Kubernetes), or you can go further and deconstruct the platform into multiple containers that in the long run could really exploit the efficiencies and scale of cloud. For now, SAP has taken the first option, but it's still a key step forward in promoting its hybrid, cloud-agnostic advantage and in making HANA ready to be deployed as a container on Data Hub.

    Now we're waiting for the other shoe to drop. SAP Data Hub is a product that's begging for cloud deployment because so much of the data that SAP customers will be working with will reside in the cloud. SAP has not announced any plans yet, but watch this space. And indirectly related to this is how SAP supports data scientists performing advanced analytics, modeling and machine learning. Given SAP's all-in support for Leonardo, its venture to develop industry expertise in AI, and the embedding of the fruits of those labors in SAP enterprise applications, we are expecting that SAP will plant its stake there soon. And yes, a lot of that (especially IoT) will require running in the cloud. So, we're looking for SAP to do two things: get the wheels in motion for a cloud-native data science platform that in turn will reuse much of the data engineering capabilities of Data Hub.


    Cloud buoys data microservices -- for on-premises systems, too | killexams.com real questions and Pass4sure dumps

    It shouldn't be too startling to learn that cloud architectures are coming to data centers. Even if they are stuck on an organization's premises, today's enterprise architects want to get in on the action as data microservices evolve.

    The cloud architectures they are bringing down to earth come largely via containers that are based on a microservices approach akin to old-time SOA -- which stands for service-oriented architecture, not Sons of Anarchy, the outlaw biker club from the TV show of the same name. Data microservices are like SOA, only more micro.

    Improving big data support for containers was one of the engineering to-do list items cited by Hadoop vendors Cloudera and Hortonworks when they announced plans to merge this month. That and a move to wider use of cloud object storage represent a big shift coming to big data.

    Kubernetes orchestration

    While containers were born as stateless vessels, work is underway to make them more stateful, with support for persistence of data in applications. If successful, this could simplify the use of multiple for-purpose analytics engines and frameworks -- think Hive, Presto, Druid, TensorFlow and so on.

    Used together with the increasingly popular Kubernetes container orchestration technology, data microservices will soon emerge as an alternative style of data processing, whether on the cloud or off.

    It's early, and there is a lot of learning to do, as evidenced by Kubernetes sessions that were often full at last month's Strata Data Conference in New York. After learning comes building, much of which is necessary before the new architecture can take root.

    Enter Cloud Private for Data

    For IBM and its customers, finding a balance between on-premises and cloud data architectures is an ongoing quest. The company's vigorous embrace of both Hadoop and Spark has been noted before, but IBM is still working to neatly package those and other big data mainstays for cloud and on-premises deployments.

    Near the end of the Strata conference, we spoke with Rob Thomas, general manager of IBM Analytics, as he prepared for a panel discussion on the company's IBM Cloud Private for Data platform and the role it can play in bringing AI to fruition. The Cloud Private for Data package includes Db2, Db2 Warehouse and Db2 Event Store, and it supports various interfaces for data scientists and other predictive analytics users, both on and off the cloud.

    "If you built a model on private cloud, you can deploy it on public cloud. Or, if you built it on public cloud, you can deploy it on private cloud," Thomas said. The data preparation and governance tools are the same whether the deployment is public or private, he added.

    Thomas said the company recently followed through on plans to add support for MongoDB, EnterpriseDB's EDB Postgres and IBM Data Risk Manager to IBM Cloud Private for Data, and it is working to certify the platform to work with Red Hat's OpenShift container application technology.

    Chart showing differences between monolithic and microservices Monolithic vs. microservices

    IBM's traditional data-side competitors are also looking to connect data analytics on premises with data analytics in the cloud. Oracle's efforts center on its Oracle Cloud at Customer undertaking. And Microsoft recently previewed a version of SQL Server 2019 that can bring the Azure versions of Spark and Hadoop down into the data center.

    IBM has significant cloud development and data alliances with Red Hat and Hortonworks, both of which touch on data microservices. Both companies combined with IBM just before Strata to launch an Open Hybrid Architecture Initiative. The project's purpose is to meld Kubernetes, containers and Hadoop workloads, and there is much work to be done in that regard, Thomas said.

    Remaking the monolithic

    After Strata, we checked in with Tom Phelan, co-founder and chief architect at BlueData Software, maker of the software platform BlueData Epic, which is short for Elastic Private Instant Clusters. He said he liked the goals of the Open Hybrid Architecture Initiative, but added that there must be more work before it can move forward.

    "A big issue is that, when Hadoop was written, it was written as a monolithic architecture," Phelan said.

    That is the case even though Hadoop can be broken down into services such as the name node, the YARN resource manager, Hive, Hadoop Distributed File System services and so on.

    When Hadoop was written, it was written as a monolithic architecture. Tom Phelanchief architect, BlueData

    "None of those are microservice-architected," Phelan said. "Those services have to be started in a certain order. They have to interact with each other service in a very particular way. There is, for example, no way to restart one of those services and not have it impact other services that are running."

    For its part, BlueData this month released KubeDirector, an open source project for developing stateful application clusters on Kubernetes.

    New scheme coming?

    Whether data moves to the cloud or not, the influence of cloud architecture looms as substantial.

    "Companies obviously have a lot of on-premises data going into the cloud, but not all the data is going to the cloud," said Forrester analyst Noel Yuhanna. "Sometimes, when the cloud bill comes in, it may be huge -- maybe more than on premises."

    Moves such as the Open Hybrid Architecture Initiative address the need to harmonize views in the two domains, according to Yuhanna, and set the stage for common management policies for both.

    "You want an architecture that can manage both cloud and on-premises data with common policies," he added.

    Back when SOA walked the earth, it brought about a reworking -- or rewrapping -- of legacy systems. New data microservices could make existing big data systems akin to legacy systems. That would be nothing more than progress.

    It's a good bet that, as hybrid data processing becomes more real, data will be shuttling from here to there and back. And, of course, YANA -- Yet Another New Architecture -- will be waiting in the wings.



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