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Test Number : 70-768
Test Name : Developing SQL Data Models
Vendor Name : Microsoft
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Exam Title : Microsoft Certified Solutions Associate (MCSA) - SQL 2016 BI Development
Exam ID : 70-768
Exam Duration : 120 mins
Questions in test : 40-60
Passing Score : 700 / 1000
Official Training : 20768: Developing SQL Data Models (three days)
Exam Center : Pearson VUE
Real Questions : Microsoft Developing SQL Data Models Real Questions
VCE VCE test : Microsoft 70-768 Certification VCE Practice Test

Topic Details Design a multidimensional business intelligence (BI) semantic model (25–30%) Create a multidimensional database by using Microsoft SQL Server Analysis Services (SSAS) - Design, develop, and create multidimensional databases; select a storage model Design and implement dimensions in a cube - Select an appropriate dimension model, such as fact, parent-child, roleplaying, reference, data mining, many-to-many, and slowly changing dimension; implement a dimension type; define attribute relationships Implement measures and measure groups in a cube - Design and implement measures, measure groups, granularity, calculated measures, and aggregate functions; define semi-additive behavior Design a tabular BI semantic model (20–25%) Design and publish a tabular data model - Design measures, relationships, hierarchies, partitions, perspectives, and calculated columns; create a time table; publish from Microsoft Visual Studio; import from Microsoft PowerPivot; select a deployment option, including Processing Option, Transactional Deployment, and Query Mode Configure, manage, and secure a tabular model - Configure tabular model storage and data refresh, configure refresh interval settings, configure user security and permissions, configure row-level security Develop a tabular model to access data in near real time - Use DirectQuery with Oracle, Teradata, Excel, and PivotTables; convert in-memory queries to DirectQuery Develop queries using Multidimensional Expressions (MDX) and Data Analysis Expressions (DAX) (15–20%) Create basic MDX queries - Implement basic MDX structures and functions, including tuples, sets, and TopCount Implement custom MDX solutions - Create custom MDX or logical solutions for pre-prepared case tasks or business rules, define a SCOPE statement Create formulas by using the DAX language - Use the EVALUATE and CALCULATE functions, filter DAX queries, create calculated measures, perform data analysis by using DAX Configure and maintain SQL Server Analysis Services (SSAS) (30–35%) Plan and deploy SSAS - Configure memory limits, configure Non-Union Memory Architecture (NUMA), configure disk layout, determine SSAS instance placement Monitor and optimize performance - Monitor performance and analyze query plans by using Extended Events and Profiler, identify bottlenecks in SSAS queries, monitor processing and query performance, resolve performance issues, configure usability limits, optimize and manage model design Configure and manage processing - Configure partition processing; configure dimension processing; use Process Default, Process Full, Process Clear, Process Data, Process Add, Process Update, Process Index, Process Structure, and Process Clear Structure processing methods; configure Parallel, Sequential, and Writeback processing settings Create Key Performance Indicators (KPIs) and translations - Configure KPI options, including Associated measure group, Value Expression, Goal Expression, Status, Status expression, Trend, Trend expression, and Weight; create KPIs in multidimensional models and tabular models; create and develop translations



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Microsoft Developing Cheatsheet

Microsoft Azure: This new developer equipment helps to bust the fable that AI is tough | 70-768 Real test Questions and test Questions

Microsoft's new Azure Percept Developer package aims to make laptop vision cheaper and less complicated, bringing AI to extra corporations.

azure-percept-developer-kit.jpg

The Azure Percept building package (DK) contains vision (correct), not obligatory audio (left) and developer board (center) modules.

graphic: Microsoft

further and further sensors are being added to the edge of networks, using equipment like Azure IoT Hub to connect them to cloud capabilities, the place highest utility will also be extracted from the information they generate. but too many of those contraptions are customized, requiring big building to get that records within the right structure, within the correct area. 

more about artificial intelligence

Exploiting this growing industrial IoT to premiere effect requires software and hardware engineers to work with gadget firmware, be trained new real-time working systems, and view security at a extremely low degree. it be a posh container, where it will also be hard to get benefits except you've got the substances to deploy a dedicated construction crew. 

SEE: herbal language processing: A cheat sheet (TechRepublic)

however what if the economic IoT was basically industrial, constructed the usage of requisites, with the capacity to fit together like rugged Lego bricks? And what if there was a common construction ambiance that helped you connect APIs and capabilities to build your own laptop-researching powered IoT applications? 

Unveiling the Azure Percept DK 

it is why Microsoft lately launched Azure Percept. Like Microsoft's different IoT features, it mixes hardware, software, and the Azure cloud. on the heart of the platform is a collection of reference designs for facet hardware that takes advantage of computer researching. Hardware developers could be in a position to take those designs and build their personal instruments, whereas adding their personal features -- the use of a custom digital camera module or changing the radios, as an instance. Designs may even be tailor-made for distinctive industries, with distinctive methods in warehouses or on oil rigs. Azure Percept is meant to be a family unit of plug-and-play IoT hardware from numerous providers, the place distinct designs use the equal application platform. 

whereas having reference designs is one a part of the story, facet hardware wants application. So Microsoft is shipping an initial developer kit to kickstart the Percept ecosystem. attainable from the Microsoft shop, it includes a hub and a digital camera, with an non-compulsory audio sensor. The simple developer kit fees $349, with the audio sensor an additional $seventy nine. they're designed to fit the typical eighty/20 mounting rails found in many industrial amenities, so can also be outfitted to present rails or straight away outfitted in any area. 

The main Percept DK module is constructed around NXP's iMX8M equipment-on-module processor board, with 4GB of RAM and 16GB of storage and a TPM for protection. in addition to four Arm sixty four-bit cores, it has extra acceleration for computer-getting to know workloads with an Intel Movidius Myriad committed imaginative and prescient neural community inference processor. This enables it to dump a whole lot of Percept's on-board picture processing, saving each CPU time and energy. 

Connectivity comes via Ethernet, Wi-Fi or Bluetooth. It makes use of Microsoft's personal CBL-Mariner Linux distribution, with management and update features from Azure. The camera module connects to the leading service board over USB-C, and Microsoft suggests that you should go from opening the box to getting photos in beneath 10 minutes. 

Getting begun with Percept 

You are not looking for an eighty/20 rack to get all started, as the contraptions can be set up subsequent to your development PCs, so you can quickly see how they work. All you deserve to do is plug in energy, connect the antennae, and then join the camera unit by way of USB. as soon as it be powered up, that you can start initial configuration over Wi-Fi. a collection of web pages ebook you through connecting to a Wi-Fi network, before configuring SSH. once it be capable, it connects to Azure where it must be registered to your account, linking the Percept DK to an Azure IoT Hub (either growing a new example or joining an existing one). You must use a typical tier illustration, as Percept is not supported on Free or basic cases. 

When the Percept DK connects to Azure for the first time, it's going to replace and down load its default software modules. you can then use Azure's Percept Studio management and building ambiance to work with the hardware, firstly testing streamed video from an AI imaginative and prescient focus mannequin this is developed into Percept. 

SEE: workplace 365: A ebook for tech and business leaders (free PDF) (TechRepublic)

Getting begun straight away is a definite benefit, as which you could exhibit consequences quickly. To help go beyond the primary recogniser there are pattern vision fashions that are based on average business problems. which you can instantly set up tools for detecting people or determining empty cabinets, for example, devoid of writing a line of code. 

That low- and no-code method to purposeful AI imaginative and prescient is vital to Percept; what's critical here's what that you may do with computing device getting to know and laptop vision (and audio) for your enterprise. when you've linked your Percept gadget to Azure IoT Hub, that you could use the Azure-hosted Percept Studio building tools to build your own applications, connecting together quite a few APIs and offering code modules to your instruments. 

azure-percept-vision-models.jpg

azure-percept-vision-models.jpg

Azure Percept Studio has a couple of pattern AI fashions, comparable to these for computing device imaginative and prescient.

photo: Microsoft

building your first Percept functions 

beginning with Percept Studio is like working with any Azure tool in that every thing you create has to be assigned to a aid neighborhood and assigned to a pricing tier -- in this case for Azure's Cognitive functions, which offers the computer-getting to know APIs used by means of Percept. once you've done the basic useful resource setup, which you could quickly configure a imaginative and prescient solution. beginning by settling on even if you might be detecting or classifying objects. You don't deserve to choose a goal machine class, as this is immediately handled for you through Percept Studio. 

subsequent, beginning to instruct your mannequin, with at least 30 pictures captured from the Percept digicam module circulation. which you can automate this technique -- when you are constructing an app that is meant to monitor a space, for example. once photographs had been captured and uploaded to Percept Studio, that you may then delivery to tag them. Labels are standard for laptop researching, as they can help you tag elements of an image, making certain that your app can determine selected objects or occurrences. Manually tagging a collection of pictures and then operating them via a practicing cycle is likely probably the most time-drinking a part of constructing a basic ML application. 

Percept Studio provides tools for trying out a model and retraining where fundamental. don't predict to get things right first time; you can make your model superior with the greater examples it has to work with. when you're chuffed with the outcomes, that you could set up your model to your Percept instruments and begin it running. 

Percept is capable of a great deal, because it's constructed on properly of Azure's custom imaginative and prescient tools, that are a part of its Cognitive services computing device-gaining knowledge of suite. there may be an further pack of development equipment that can also be downloaded to aid construct greater advanced solutions, with a GitHub repository to assist you get started. This offers you entry to the software used to run the AI modules, in addition to tools to aid you instruct and installation your personal neural networks. 

Microsoft is trying something somewhat ambitious with Percept: offering a reference design for AI sensor hardware and the tools to build applications around it. there may be a delusion that AI is difficult, and or not it's clear that here's one delusion that the Percept team aims to aid you bust. No-code options get you began instantly, able to set up on particularly low-cost hardware, whereas which you can build extra advanced, custom neural networks in your personal hardware. it be a very good mix that should grow with you, as you benefit adventure with each computing device vision and audio processing. 

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