 Microsoft MB-300 : Microsoft Dynamics 365 Unified Operations Core ExamExam Dumps Organized by Corwin
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Exam Number : MB-300
Exam Name : Microsoft Dynamics 365 Unified Operations Core
Vendor Name : Microsoft
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Format | MB-300 Course Contents | MB-300 Course Outline | MB-300 test
Syllabus | MB-300 test
Objectives
Skills Measured
Use common functionality and implementation tools (20-25%)
Identify common Microsoft Dynamics 365 Finance features and functionality
determine when to use workspaces
identify use cases for Power Platform apps including Power Apps, Power BI and Microsoft Flow
identify and differentiate between the global address book and other address books
demonstrate Work Items functionality
demonstrate Microsoft Dynamics 365 Finance navigation techniques
identify Inquiry and Report types available in a default installation
Implement Lifecycle Services (LCS) tools
identify opportunities to re-use existing assets
analyze Business Process Modeler results and identify gaps in functionality
including creating an Acceptance Testing BPM library and analyzing the results
use the LCS tools including Issue Search and analyze results
Configure security, processes, and options (45-50%)
Implement security
identify and distinguish between the various standard security roles in Finance and Operations
distinguish between duties, privileges, and permissions
assign users to security roles based on given scenarios
Design and create workflows
identify opportunities for automation and controls based on customer workflows
configure workflow properties and elements
troubleshoot workflows
Configure options
set up and configure legal entities
configure base number sequences
import or create all necessary startup data including Zip/Postal Code data, customers,vendors, and products
configure the calendars and date intervals
configure units of measure and conversions
configure posting profiles and definitions
create organization hierarchies
apply purposes and policies
describe and apply user options
Implement Microsoft Dynamics 365 Finance common features
configure Microsoft Office integration with Microsoft Dynamics 365 Finance
configure email (SMTP/Exchange)
create and maintain email and record templates
integrate Power BI with Entity store
create, export, and import personalizations
set up network printing
Implement business processes for the solution
define use case scenarios
participate in phase-based planning processes and the solution design
design and create workflows
set up batch Jobs and alerts
use business process workspaces
Perform data migration (15-20%)
Plan a migration strategy
identify common migration scenarios and tools in Microsoft Dynamics 365 Finance
determine migration scope
identify relevant data entities and elements based on given scenarios
establish migration strategy processes including migration scope
Prepare data for migration and migrate data
identify and extract source data
generate field mapping between source and target data structures
support the transition between the existing and migrated systems
perform a test migration and validate output from the process
Validate and support the solution (15-20%)
Implement and validate the solution within Microsoft Dynamics 365 Finance
perform user acceptance testing (UAT)
prepare and validate to Go live
build test scripts to test business functionality
automate test case automation by using the Regression Suite Automation Tool (RSAT)
demonstrate the correlation between test scripts and business requirements
monitor validation test progress and make ad hoc changes during validation testing to correct identified issues
Support Application Lifecycle Management (ALM) by using LCS
perform a solution gap analysis
use LCS tools to identify, report, and resolve issues
manage Microsoft Dynamics 365 One Version
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Microsoft Dynamics real Questions
bring up your enterprise data expertise and approach at transform 2021.
one of the most largest highlights of construct, Microsoftâs annual software building convention, became the presentation of a device that makes use of deep discovering to generate supply code for workplace applications. The device uses GPT-3, a huge language mannequin developed by OpenAI ultimate year and made accessible to opt for developers, researchers, and startups in a paid application programming interface.
Many have touted GPT-3 as the subsequent-era synthetic intelligence technology which will bring in a new breed of applications and startups. in view that GPT-threeâs free up, many developers have discovered exciting and inventive makes use of for the language model. And a couple of startups have declared that they can be the usage of GPT-three to construct new or augment existing items. but making a ecocnomic and sustainable company round GPT-3 remains a problem.
Microsoftâs first GPT-3-powered product offers vital guidelines in regards to the company of enormous language models and the future of the tech significantâs deepening relation with OpenAI.
just a few-shot gaining knowledge of mannequin that need to be first-rate-tuned?
Above: Microsoft makes use of GPT-3 to translate herbal language instructions to records queries
picture credit score: Khari Johnson / VentureBeat
according to the Microsoft blog, âfor example, the new AI-powered aspects will permit an worker building an e-commerce app to explain a programming intention the use of conversational language like âfind items where the identify starts with âkids.ââ A fine-tuned GPT-3 model [emphasis mine] then offers choices for transforming the command right into a Microsoft energy Fx formulation, the open supply programming language of the energy Platform.â
I didnât discover technical particulars on the high-quality-tuned version of GPT-three Microsoft used. however there are generally two reasons you could possibly satisfactory-tune a deep getting to know mannequin. in the first case, the mannequin doesnât function the goal project with the favored precision, so you should excellent-tune it through practicing it on examples for that selected project.
in the second case, your mannequin can function the supposed project, but it surely is computationally inefficient. GPT-three is a very significant deep discovering model with a hundred seventy five billion parameters, and the expenses of operating it are huge. for this reason, a smaller version of the mannequin can also be optimized to function the code-era task with the equal accuracy at a fraction of the computational charge. a likely tradeoff could be that the model will operate poorly on different initiatives (corresponding to question-answering). but in Microsoftâs case, the penalty may be beside the point.
In either case, a first-rate-tuned edition of the deep studying mannequin looks to be at odds with the customary conception discussed within the GPT-3 paper, aptly titled, âLanguage models are Few-Shot learners.â
hereâs a quote from the paperâs summary: âhere we demonstrate that scaling up language fashions vastly improves task-agnostic, few-shot efficiency, every now and then even achieving competitiveness with prior state-of-the-paintings finetuning processes.â This really skill that, if you build a big adequate language model, you might be in a position to perform many projects devoid of the need to reconfigure or modify your neural community.
So, whatâs the point of the few-shot computing device researching model that have to be best-tuned for brand spanking new initiatives? here is the place the worlds of scientific research and applied AI collide.
academic analysis vs commercial AI
Thereâs a clear line between educational research and commercial product construction. In tutorial AI analysis, the intention is to push the boundaries of science. here's exactly what GPT-three did. OpenAIâs researchers showed that with sufficient parameters and practicing statistics, a single deep learning mannequin may operate a few projects devoid of the need for retraining. and that they have proven the mannequin on a number of generic herbal language processing benchmarks.
but in commercial product building, youâre not running in opposition t benchmarks equivalent to GLUE and SQuAD. You must remedy a particular difficulty, clear up it ten instances better than the incumbents, and be able to run it at scale and in a cost-beneficial manner.
therefore, in case you have a large and expensive deep gaining knowledge of mannequin that may operate ten different initiatives at ninety percent accuracy, itâs a fine scientific fulfillment. but when there are already ten lighter neural networks that operate each and every of those tasks at ninety nine % accuracy and a fraction of the fee, then your jack-of-all-trades model should not capable of compete in a income-pushed market.
hereâs an interesting quote from Microsoftâs weblog that confirms the challenges of applying GPT-3 to true enterprise complications: âThis discovery of GPT-threeâs giant capabilities exploded the boundaries of whatâs feasible in herbal language gaining knowledge of, talked about Eric Boyd, Microsoft company vp for Azure AI. but there have been still open questions about even if such a huge and complicated mannequin may be deployed cost-conveniently at scale to meet precise-world business wants [emphasis mine].â
and those questions have been answered with the optimization of the mannequin for that particular task. when you consider that Microsoft wanted to remedy a extremely selected issue, the full GPT-3 model can be an overkill that would waste expensive supplies.
hence, the undeniable vanilla GPT-three is greater of a scientific achievement than a legitimate platform for product construction. but with the right supplies and configuration, it may possibly develop into a helpful device for market differentiation, which is what Microsoft is doing.
Microsoftâs competencies
In a great world, OpenAI would have launched its own items and generated earnings to fund its own analysis. however the truth is, establishing a profitable product is a whole lot greater problematic than releasing a paid API service, however your companyâs CEO is Sam Altman, the former President of Y Combinator and a product development legend.
And this is why OpenAI enrolled the aid of Microsoft, a decision if you want to have lengthy-term implications for the AI research lab. In July 2019, Microsoft made a $1 billion funding in OpenAIâwith some strings connected.
From the OpenAI blog put up that declared the Microsoft investment: âOpenAI is producing a series of more and more effective AI applied sciences, which requires loads of capital for computational vigour. probably the most evident way to cowl charges is to build a product, but that could mean changing our center of attention [emphasis mine]. as an alternative, we intend to license some of our pre-AGI technologies, with Microsoft becoming our favourite associate for commercializing them.â
by myself, OpenAI would have a tough time discovering a method to enter an latest market or create a brand new marketplace for GPT-three.
then again, Microsoft already has the pieces required to shortcut OpenAIâs course to profitability. Microsoft owns Azure, the 2nd-largest cloud infrastructure, and it is in a suitable place to subsidize the prices of coaching and operating OpenAIâs deep discovering fashions.
but more importantlyâand here is why I feel OpenAI chose Microsoft over Amazonâis Microsoftâs attain across distinct industries. hundreds of businesses and millions of users are the use of Microsoftâs paid purposes similar to office, teams, Dynamics, and vigour Apps. These purposes provide ideal structures to integrate GPT-three.
Microsoftâs market potential is completely evident in its first utility for GPT-three. it's a really essential use case centered at a non-technical audience. Itâs no longer supposed to do complex programming logic. It just converts herbal language queries into data formulation in energy Fx.
This trivial software is beside the point to most seasoned developers, who will find it a whole lot simpler to without delay category their queries than describe them in prose. but Microsoft has a number of shoppers in non-tech industries, and its power Apps are constructed for clients who donât have any coding event or are researching to code. For them, GPT-three can make a major change and support lessen the barrier to developing fundamental applications that resolve business problems.
Microsoft has a further component working to its advantage. It has secured unique entry to the code and architecture of GPT-3. while other groups can handiest interact with GPT-3 through the paid API, Microsoft can customise it and integrate it at once into its purposes to make it effective and scalable.
by means of making the GPT-three API obtainable to startups and developers, OpenAI created an atmosphere to find all sorts of purposes with tremendous language models. in the meantime, Microsoft become sitting again, looking at all of the distinct experiments with transforming into pastime.
The GPT-3 API truly served as a product analysis venture for Microsoft. something use case any business finds for GPT-3, Microsoft should be in a position to do it faster, more affordable, and with enhanced accuracy thanks to its exclusive access to the language mannequin. This gives Microsoft a distinct abilities to dominate most markets that take form around GPT-three. And here's why I feel most companies which are building items on suitable of the GPT-3 APIÂ are doomed to fail.
The OpenAI Startup Fund
Above: Microsoft CEO Satya Nadella (left) and OpenAI CEO Sam Altman (correct) at Microsoft construct 2021
photograph credit score: Khari Johnson / VentureBeat
And now, Microsoft and OpenAI are taking their partnership to the next stage. at the build conference, Altman declared a $100 million fund, the OpenAI Startup Fund, through which it'll put money into early-stage AI corporations.
âWe plan to make massive early bets on a comparatively small number of organizations, doubtless not more than 10,â Altman pointed out in a prerecorded video played on the conference.
What form of agencies will the fund invest in? âWeâre hunting for startups in fields the place AI can have essentially the most profound high-quality have an impact on, like healthcare, climate exchange, and schooling,â Altman noted, to which he added, âWeâre also excited about markets the place AI can force big leaps in productivity like very own assistance and semantic search.â the first half looks to be in accordance with OpenAIâs mission to make use of AI for the betterment of humanity. however the 2nd half appears to be the type of earnings-generating applications that Microsoft is exploring.
also from the fundâs web page: âThe fund is managed via OpenAI, with funding from Microsoft and different OpenAI companions. apart from capital, corporations in the OpenAI Startup Fund will get early entry to future OpenAI techniques, assist from our group, and credits on Azure.â
So, definitely, it appears like OpenAI is becoming a marketing proxy for Microsoftâs Azure cloud and will help spot AI startups that may qualify for acquisition by using Microsoft in the future. this will deepen OpenAIâs partnership with Microsoft and make sure the lab continues to get funding from the tech large. but it will also take OpenAI a step nearer toward becoming a commercial entity and eventually a subsidiary of Microsoft. How this may have an effect on the research labâs long-time period goal of scientific research on synthetic standard intelligence remains an open question.
Ben Dickson is a application engineer and the founding father of TechTalks. He writes about technology, enterprise, and politics.
This story at the start regarded on Bdtechtalks.com. Copyright 2021
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