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IBM C9530-519 : IBM API Connect v. 5.0.5 Solution Implementation Exam

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Test Number : C9530-519
Test Name : IBM API Connect v. 5.0.5 Solution Implementation
Vendor Name : IBM
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C9530-519 exam Format | C9530-519 Course Contents | C9530-519 Course Outline | C9530-519 exam Syllabus | C9530-519 exam Objectives


Exam Title : IBM Certified Solution Implementer - API Connect V5.0.5
Exam ID : C9530-519
Exam Duration : 90 mins
Questions in exam : 72
Passing Score : 69%
Exam Center : Pearson VUE
Real Questions : IBM API Connect Solution Implementation Real Questions
VCE Practice Test : IBM C9530-519 Certification VCE Practice Test

Architectural Overview of IBM API Connect
- Articulate the architectural requirements to support a given IBM API Connect topology.
- Compare the different IBM API Connect deployment options.
- Differentiate between the two types of organizations in IBM API Connect.
- Demonstrate the various stages in the lifecycle of an API, including Create, Run, Manage, and Secure. The API lifecycle provides the foundation of an API strategy.
- Distinguish between the various roles involved in the lifecycle of an API within IBM API Connect.
- Implement the OpenAPI specification in IBM API Connect.
- Identify typical use cases for IBM API Connect across industry. 18%
Cloud Manager Role
- Install and administer collectives and gateways.
- Install and configure the IBM API Connect Cloud.
- Install, configure, and customize the Developer Portal.
- Manage the IBM API Connect Cloud using the REST interface.
- Use the IBM API Connect Command Line Interface.
- Backup and restore IBM API Connect configuration data.
- Backup and restore APIs and Products.
- Analyze logs to identify problems within the IBM API Connect Cloud.
- Secure the IBM API Connect Cloud.
- Integrate with an external user registry.
- Configure the API Gateway extensions.
- Manage IBM API Connect catalogs.
- Manage the IBM API Connect Cloud. 32%
API Developer Role
- Design a LoopBack application using IBM API Connect.
- Use the Unit Testing tools to test APIs.
- Implement user-defined policies.
- Utilize built-in policies.
- Manage error handling.
- Utilize API properties.
- Create an API assembly.
- Create and configure a SOAP API.
- Create and configure a REST API.
- Apply a security definition to an API.
- Use the IBM API Connect Developer Toolkit Command Line Interface. 28%
Product Manager Role
- Distinguish between the various lifecycle stages of APIs and Products.
- Gain business insight from analytics information.
- Show the relationship between Products and Plans and APIs.
- Design Products and Plans.
- Administer Consumer Access. 11%
App Developer Role
- Use the Developer Portal. 11%



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The direction to actual-world synthetic intelligence | C9530-519 boot camp and exam Questions

specialists from MIT and IBM held a webinar this week to focus on the place AI technologies are nowadays and advances for you to assist make their usage greater functional and common.

Artificial Intelligence project creating. Abstract concept of cyber technology, machine learning.Brain of AI. Futuristic Innovative technology in science concept

picture: Sompong Rattanakunchon / Getty photographs

extra about synthetic intelligence

synthetic intelligence has made significant strides in contemporary years, however up to date AI options continue to be restrained, a panel of MIT professors and IBM's director of the Watson AI Lab stated during a webinar this week.

Neural networks can perform certain, neatly-described tasks but they combat in actual-world instances that go beyond demo cognizance and latest obstacles like constrained facts, reliance on self-practicing, and answering questions like "why" and "how" versus "what," the panel spoke of.

The future of AI depends upon enabling AI methods to do anything once regarded unimaginable: be trained via demonstrating flexibility, some semblance of reasoning, and/or by transferring skills from one set of initiatives to another, the group said. 

SEE: Robotic system automation: A cheat sheet (free PDF) (TechRepublic)

The panel discussion was moderated with the aid of David Schubmehl, a analysis director at IDC, and it started with a question he posed asking about the existing barriers of AI and laptop gaining knowledge of.

"The amazing success right now in specific, in computing device discovering, is in problems that require interpretation of indicators—images, speech and language," pointed out panelist Leslie Kaelbling, a pc science and engineering professor at MIT. 

For years, people have tried to solve complications like detecting faces and images and directly engineering solutions that failed to work, she spoke of.

we have turn into good at engineering algorithms that take statistics and use that to derive a solution, she mentioned. "that's been an awesome success." nonetheless it takes lots of records and a lot of computation so for some complications formulations don't seem to be available yet that could allow us to learn from the volume of records purchasable, Kaelbling mentioned.

SEE: 9 super-sensible difficulty solvers tackle bias in AI, microplastics, and language instructions for chatbots (TechRepublic)

one in every of her areas of focus is in robotics, and it be tougher to get working towards examples there because robots are expensive and elements ruin, "so they definitely need to be capable of learn from smaller quantities of statistics," Kaelbling stated.

Neural networks and deep gaining knowledge of are the "latest and surest option to frame those sorts of problems and the successes are many," brought Josh Tenenbaum, a professor of cognitive science and computation at MIT. 

however when speaking about regular intelligence and how to get machines to bear in mind the world there remains a huge gap, he spoke of.

"however on the analysis side … definitely enjoyable things are beginning to occur to are trying to trap some steps to greater regularly occurring kinds of intelligence [in] machines," he noted. In his work, "we're seeing techniques by which they can draw insights from how humans be aware the realm and taking small steps to position them in machines."

despite the fact people suppose of AI as being synonymous with automation, it is highly labor intensive in a means that does not work for most of the issues they want to clear up, cited David Cox, IBM director of the MIT-IBM Watson AI Lab.

Echoing Kaelbling, Cox noted that leveraging equipment these days like deep discovering requires huge quantities of "carefully curated, bias-balanced information," to be in a position to use them smartly. moreover, for many problems they are attempting to remedy, they would not have those "large rivers of information" to build a dam in entrance of to extract some cost from that river, Cox observed.

these days, corporations are more focused on solving some category of 1-off issue and even when they've large information, it be infrequently curated, he pointed out. "So most of the issues they like to solve with AI—we would not have the correct equipment for that."

it's as a result of they now have issues with bias and interpretability with humans the usage of these tools and that they have to bear in mind why they're making these decisions, Cox spoke of. "they are all limitations." 

besides the fact that children, he said, there's gigantic possibility looking in any respect these different fields to chart a course forward. 

That comprises using deep learning, which is good for pattern focus, to aid clear up difficult search problems, Tenenbaum spoke of. To advance intelligent brokers, scientists should use the entire purchasable equipment, spoke of Kaelbling. as an example, neural networks are obligatory for notion in addition to higher degree and greater summary forms of reasoning to make a decision, for instance, what to make for dinner or to make a decision how to disperse materials.

"The essential component technologically is to understand the sweet spot for each piece and work out what it's good at and never good at. Scientists should remember the position every piece plays," she referred to.

The MIT and IBM AI experts additionally discussed a new foundational components known as neurosymbolic AI, which is the capacity to combine statistical, statistics-pushed studying of neural networks with the potent competencies illustration and reasoning of symbolic processes.

Moderator Schubmehl commented that having a combination of neurosymbolic AI and deep gaining knowledge of "might really be the holy grail" for advancing precise-world AI.

Kaelbling agreed, including that it could be now not just those two strategies but consist of others as well.

one of the most subject matters that emerged from the webinar is that there is a really constructive confluence of all kinds of AI that are now being used, observed Cox. The next evolution of very practical AI is going to be understanding the science of discovering things and constructing a equipment they are able to reason with and develop and be taught from, and assess what is going to turn up. "That could be when AI hits its stride," he observed. 

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