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Test Number : 1Z0-321
Test Name : Oracle Communications BRM Elastic Charging Engine 2017 Implementation Essential
Vendor Name : Oracle
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Exam Title : Oracle Communications BRM Elastic Charging Engine 2017 Implementation Essentials
Exam ID : 1Z0-321
Exam Duration : 120
Questions in test : 73
Passing Score : 60
Format : Multiple Choice
Exam Center : Pearson VUE - Oracle
Recommended Practice : 1Z0-321 Online VCE Practice Test

Functional and Architectural Overview of the Revenue Management Suite (RMS) - Describe the Revenue Management System (RMS) components and architecture
- Explain Billing and Revenue Management (BRM) functionality and the integration points with the other RMS components
- Explain Elastic Charging Engine (ECE) functionality and integration points with other RMS components
- Explain Pricing Design Center (PDC) functionality and integration points with other RMS components
- Explain Oracle Communications Offline Mediation Controller (OCOMC) functionality and integration points with other RMS components
- Explain Convergent Charging Controller (CCC) functionality and integration points with other RMS components Pricing Configuration using Pricing Design Center (PDC) - Describe pricing and charging concepts and terminology
- Explain the PDC Pricing Model
- Create pricing profiles
- Translate business offerings into PDC pricing objects using the PDC UI
- Set up a product offering using PDC; including charge offers, discount offers, bundles, packages, and selectors Customer and Partner Management - Create and manage customer data
- Explain the Subscriber Lifecycle Management functionality Elastic Charging Engine (ECE) - Configure ECE
- Implement Rating, Discounting, and re-rating functionality using ECE
- Integrate ECE with BRM and PDC
- Modify ECE business logic using custom code
- Configure a notification using ECE Usage Charging - Troubleshoot Rating and Discounting processes
- Perform re-rating in BRM
- Configure and run the BRM Suspense Manager
- Explain content partner settlement and remittance functionality Billing and Finance - Configure flexible cycles and run billing scripts
- Configure a bill cycle that includes account groups and customer segments
- Configure, execute, and monitor the billing time processes
- Configure trial billing, trial invoicing, corrective billing, and corrective invoicing
- Configure a BRM general ledger
- Describe the different BRM tax procedures
- Describe BRM payment processes
- Configure a Collections Scenario
- Analyze and correct suspended payments Reporting and Analytics - Configure and run the Business Intelligence (BI) Publisher for reporting
- Explain how BRM is integrated with Oracle Communications Data Mining (OCDM) for analytics
- Describe the BRM Revenue Assurance function Operation, Administration and Optimization - Identify, troubleshoot, and debug common BRM problems
- Analyze and Boost BRM Connection Manager (CM), Data Manager (DM), and Billing performance
- Partition event tables in order to purge BRM data
- Explain the Oracle recommended method for planning and implementing an upgrade
- Explain the integration and management of RMS components with Oracle Enterprise Manager (OEM) , using the Application Management Pack (AMP) Extensibility and Integrations - Extend Policy Opcodes and Customer Center using the BRM Software Development Kit (SDK)
- Describe the features of Enterprise Application Integration (EAI) Manager, WebServices Manager, and Java 2 Platform Enterprise Edition (J2EE) Connector Architecture (JCA) Resource Adapter
- Program BRM using PIN and PCM Macros
- Configure an input and output Flist for a specified BRM Opcode
- Customize the registration process, which includes identifying the opcodes used, and changing fields validation
- Customize event creation and enable event notification
- Configure and deploy a new service
- Set up configurable bill items and display them on invoices
- Describe Application Integration Architecture (AIA) for Communications



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desktop imaginative and prescient | 1Z0-321 PDF obtain and Free test PDF

at the Lawrence J. Ellison Institute for Transformative medication of USC, scientists have knowledgeable a neural community to identify different types of breast melanoma on a small data set of under 1,000 photos. as an alternative of teaching the AI equipment to distinguish between agencies of samples, the researchers taught the network to respect the visual “tissue fingerprint” of tumors in order that it might work on lots bigger, unannotated records units.

midway throughout the nation in suburban Chicago, Oracle’s construction and engineering community is working with video-digicam and software corporations to construct a synthetic intelligence system that may tell from live video feeds—with up to 92% accuracy—even if construction laborers are wearing complicated hats and protective vests and training social distancing.

Such is the promise of desktop imaginative and prescient, whereby machines are knowledgeable to interpret and be aware the physical world around them, commonly recognizing and comparing quality visual cues the human eye can pass over. The fusion of computer imaginative and prescient with deep studying (a branch of artificial intelligence that employs neural networks), together with advances in portraits processors that run many calculations in parallel and the provision of big facts sets, has led to leaps in accuracy.

Now, a generation of GPUs geared up with much more circuitry for parsing photos and video and wider availability of cloud records centers for working towards statistical prediction methods have quickened construction in self-using cars, oil and gasoline exploration, assurance assessment, and other fields.

“Devoting more money to huge statistics facilities makes it feasible to teach problems of any size, so the decision can develop into simply an financial one: how many dollars should still be devoted to finding the most excellent solution to a given information set?”

David Lowe, Professor Emeritus of computer Science, college of British Columbia

“laptop discovering has absolutely modified laptop imaginative and prescient due to the fact 2012, because the new deep-learning methods readily perform far superior than what changed into possible prior to now,” says David Lowe, a professor emeritus of laptop science at the tuition of British Columbia who works on computerized driving and developed a computer vision algorithm that ended in advances in robotics, retail, and police work within the 2000s.

“just about all laptop vision issues at the moment are solved with deep gaining knowledge of the use of huge amounts of training records,” he says. “This ability the main difficulty and fee are gathering very colossal records sets consisting of pictures that are correctly labeled with the favored outcomes.”

56% of enterprise and IT executives say their organizations use laptop imaginative and prescient technologies.1

Oracle is making servers obtainable on its Oracle Cloud Infrastructure that run Nvidia’s existing A100 GPUs. moreover quicker processing cores, bulked-up reminiscence, and quicker statistics shuttling among processors, the GPUs consist of circuitry and application that make practicing AI programs on pictures and video faster and extra correct.

powerful however static

There are still limits to these days’s vision systems. independent cars need to clear safety hurdles stemming from the immense variety of unpredictable pursuits that arise when people and animals get near vehicles; a neighborhood that’s difficult to train laptop gaining knowledge of systems to respect. computers still can’t reliably predict what will turn up in definite instances—reminiscent of when a motor vehicle is ready to swerve—in a method that people intuitively can. Many applications are confined of their usefulness by way of the provision or can charge of generating massive units of naturally labeled practising facts.

“these days’s AI is powerful, however it’s static,” observed Fei-Fei Li, codirector of Stanford’s university’s Human-headquartered AI Institute, all the way through a contemporary corporate speak. “The next wave of AI analysis ought to focus on this more active perspective and interplay with the precise world as an alternative of the passive work we’ve been doing.”

Neural networks use successive layers of computation to be aware more and more complex ideas, then arrive at an answer. running deep discovering systems on GPUs permits them to train themselves on enormous volumes of statistics that involve multiplying statistics elements via their statistical weights in parallel on portraits chips’ many small processors. In laptop imaginative and prescient, the ideas have ended in the potential to straight away establish individuals, objects, and animals in pictures or at streetlevel; construct robots that may see and work more advantageous alongside people; and increase cars that force themselves.

“training can use such gigantic amounts of computation that there are some issues limited simply by way of the velocity of processors,” says computer scientist Lowe. “youngsters, practicing is extremely parallel, which means that simply devoting extra money to giant data facilities makes it viable to train problems of any size, so the determination can develop into conveniently an financial one: how many greenbacks should still be dedicated to finding the premiere solution to a given statistics set?”

hundreds of chips

For video evaluation, for instance, every new Nvidia A100 GPU incudes 5 video decoders (compared with one in the previous-generation chip), letting the performance of video decoding match that of AI practicing and prediction application. The chips consist of expertise for detecting and classifying JPEG photos and segmenting them into their element components, an energetic enviornment of desktop imaginative and prescient research. Nvidia, which is acquiring cell chip maker Arm Holdings, additionally offers utility that takes capabilities of the A100’s video and JPEG capabilities to hold GPUs fed with a pipeline of picture statistics.

the usage of Oracle Cloud, companies can run purposes that connect GPUs by way of a excessive-velocity faraway direct reminiscence access community to build clusters of heaps of photographs chips at speeds of 1.6 terabits per second, says Sanjay Basu, Oracle Cloud engineering director.

An oil and gasoline reservoir modeling enterprise in Texas makes use of Oracle Cloud Infrastructure to categorise images taken from interior wells to check promising drilling websites, Basu says. It additionally employs so-known as AI “inference” to make selections on actual-world facts after practising its desktop researching equipment.

94% of executives say their organizations are already the use of it, or plan to within the subsequent yr. 1

An auto insurance claims inspector runs a cluster of computer systems in Oracle’s cloud that instruct a computing device getting to know gadget to appreciate pictures of vehicles broken in accidents. Insurers can make quick restoration estimates after drivers, the usage of an insurer-supplied app, ship them photos snapped with their phones.

Oracle is additionally in discussions with European automakers about applying its cloud computing infrastructure to educate computerized riding programs based on photos and video of traffic and pedestrians captured right through check runs.

In a Deloitte survey of more than 2,seven hundred IT and enterprise executives in North the us, Europe, China, Japan, and Australia published this 12 months, 56% of respondents mentioned their corporations are already the usage of desktop vision, while a further 38% referred to they plan to in the next yr. in response to analysis firm Omdia, the global laptop imaginative and prescient software market is anticipated to develop from $2.9 billion in 2018 to $33.5 billion through 2025.

1 source: Deloitte Insights “State of AI within the commercial enterprise” report, 2020.


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