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Format | C2010-653 Course Contents | C2010-653 Course Outline | C2010-653 test
Syllabus | C2010-653 test
Exam ID : C2010-653
Exam Title : Fundamentals of IBM TRIRIGA Application Platform V3.2.1 Application Development
Number of questions: 47
Number of questions to pass: 32
Time allowed: 90 mins
Section 1: Application Building
Given that IBM TRIRIGA application and platform are installed and the user has an application builder license, create a module by using the Data Modeler tool so that business objects can now be created under the new module.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, and a module is created, create a business object by using the Data Modeler tool so that fields, data sections, forms, and a state family can now be created for the new business object.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, and a business object exists, find/add a field to the business object by using the Data Modeler tool so that the field is available in the creation and modification of records of that business object type.
Given that IBM TRIRIGA application and platform are installed and the user has an application builder license, create a list by using the Lists tool so that a new list is now available for selection by a list field.
Given that IBM TRIRIGA application and platform are installed and the user has an application builder license, create a custom field in Business Object so that data is accepted in accordance with stated requirements.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, a module is created, a business object is created, and there are fields defined for this business object, create a publish name using the Data Modeler tool so that the business object may be published successfully.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, and a business object has been created, create a state family in Data Modeler so that the records of that business object type conform to a lifecycle consistent with business requirements.
Given that IBM TRIRIGA application and platform are installed and the user has an application builder license, an applicable module, business object, and state transition saved; create a new sub action to perform the specified logic when the state transition is triggered.
Given the IBM TRIRIGA application and platform are installed and the user has an application builder license, create an association between two business objects so that an association has been created between two business objects and may be used during the creation of record level associations.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, a module is created, a business object is created, create a smart section by using the Data Modeler tool so that the business object may reference data from an associated business object.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, and a business object has been created, and a published name defined, and the business object remains in a Created or a Revision In Progress state, publish the business object in Data Modeler so that forms and subsequently records, may be created and so that records can reflect any changes made in the newly published version of the business object.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license and an association has been created, add a Locator field so that the field creates an association to that record.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, a list is created, modify an existing list by using the Lists tool so that any previously configured list fields linking to a modified list will display the updated list data.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, and a business object has been published, and one or more forms published, create a query so that a set of records may be returned to fulfill a specific business requirement.
Given that IBM TRIRIGA application and platform are installed and the user has an application builder license and the extended formula agent has been started; create an extended formula field so that the system will trigger the calculation whenever one of the inputs changes.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license and an association has been created between two business objects, create an association filter on a query so that data returned by that query is filtered based on criteria define in business case.
Given that IBM TRIRIGA application and platform are installed, the user has access to Report Manager, a report is created with fields selected to display, create one or more field filter so that you may filter the result set of the report based on a pre-specified value or a user defined value at runtime of the report.
Section 2: Application Presentation
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, and a business object has been published, and a form created to support the business object, create a tab on the form and sort it in the manner desired for record presentation so that after publishing the form the new tab and/or tab order will be available throughout the application.
Given that IBM TRIRIGA application and platform are installed and the user has an application builder license, an applicable module, business object, form, and tab exist; create and sort form sections so that a new form section has been created and/or the sections are sorted in the desired order.
Given that TRIRIGA application and platform are installed, the user has access to Form Builder, a form is created with one or more tabs, and one or more form sections are created, create a section action so that this is available as a live action button for the end user at runtime.
Given that IBM TRIRIGA application and platform are installed and the user has an application builder license, configure an on change event within a form so that on change events can trigger application related processes as required when data is modified in a field.
Given that IBM TRIRIGA application and platform are installed, the user has access to Form Builder, a form is created with one or more tabs, one or more form sections are created, and one or more fields of type Form Action are created, configure an on-click event for a Form Action field so that this is available as an action button for the end user at runtime.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, and a business object has been published, and a form created to support the business object, modify the label or name of the form so that after publishing the form the new label and/or name will be available throughout the application.
Given that IBM TRIRIGA application and platform are installed, the user has access to Form Builder, a form is created with one or more tabs, import the state transition family into the form so that the form can operate through its designed state transition life cycle at runtime.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, and a business object, form, and any necessary queries have been published/created, create navigation items and collections to be available for menus and/or portals to enable a user to them.
Given that IBM TRIRIGA application and platform are installed and the user has an application builder license, and the applicable Portal exists; customize a portal so that the desired portal has been customized.
Section 3: Application Workflow
Given a completed functional design document, explain different workflow types so that the determination can be made on the type of workflow to be used.
Given a functional IBM TRIRIGA system and a properly configured user for, create a workflow so that the desired logic is performed.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, review the design of helper objects so that the user is able to perform repeatable processes without the need to design new objects.
Given that IBM TRIRIGA application and platform are installed and the user has an application builder license, create a workflow with instance logging turned on so that each time the workflow is run a history or instance of the workflow is saved for investigative purposes.
Section 4: Utilities
Given that IBM TRIRIGA application and platform are installed and the user has an application builder license, create an Object Migration Package in the Object Migration tool so that configurations can be ported from one environment to another.
Given that IBM TRIRIGA application and platform are installed, the user has an application builder license, and a business object and form has been published, create a data integrator input file so that data can be loaded or updated within the TRIRIGA system.
Given that IBM TRIRIGA application and platform are installed and the user has an application builder license, and the target object and fields of the DI file exist, import the DI file so that the specified DI file is processed and records are created or modified.
Given that IBM TRIRIGA application and platform are installed, the user has access to the Document Manager, and proper permissions on the target folder to create a document record, import a document from your local file system so that the document is available for use in the TRIRIGA system.
Section 5: Application Administration
Given that IBM TRIRIGA application and platform are installed, user with adequate security to access the Security Manager tool, configure a security group so that security group members have the desired level of access on a form's sections and actions.
Given that IBM TRIRIGA application and platform are installed, the user is a part of the Admin group, and the user has access to Admin Console, use the Cache Manager to clear cache for various platform components.
Given that IBM TRIRIGA application and platform are installed, the user has admin console access, monitor workflow from within the admin console so that Admin console user can utilize the admin console to monitor a variety of workflow activities which can be critical in evaluating workflow issues.
Given that IBM TRIRIGA application and platform are installed, the user is a part of the Admin group, and the user has access to Admin Console, use the Platform Logging tool to enable debug logging for parts of or all platform functionality.
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Hear from CIOs, CTOs, and other C-stage and senior professionals on statistics and AI ideas at the way forward for Work Summit this January 12, 2022. study greater
As AI-powered applied sciences proliferate within the commercial enterprise, the time period “explainable AI” (XAI) has entered mainstream vernacular. XAI is a collection of equipment, ideas, and frameworks intended to aid clients and designers of AI programs be mindful their predictions, including how and why the systems arrived at them.
A June 2020 IDC file found that enterprise determination-makers believe explainability is a “important requirement” in AI. To this end, explainability has been referenced as a guiding principle for AI building at DARPA, the eu fee’s high-degree knowledgeable neighborhood on AI, and the national Institute of requisites and expertise. Startups are emerging to carry “explainability as a provider,” like Truera, and tech giants reminiscent of IBM, Google, and Microsoft have open-sourced each XAI toolkits and techniques.
but whereas XAI is nearly always greater eye-catching than black-container AI, the place a device’s operations aren’t uncovered, the mathematics of the algorithms can make it intricate to acquire. Technical hurdles aside, companies from time to time fight to outline “explainability” for a given utility. A FICO record found that 65% of employees can’t interpret how AI model selections or predictions are made — exacerbating the challenge.
what is explainable AI (XAI)?
frequently talking, there are three types of explanations in XAI: international, local, and social have an effect on.
international explanations shed easy on what a device is doing as a whole as opposed to the approaches that cause a prediction or resolution. They frequently include summaries of how a device makes use of a feature to make a prediction and “metainformation,” like the category of statistics used to educate the equipment.
native explanations provide an in depth description of how the mannequin got here up with a selected prediction. These could consist of information about how a mannequin makes use of elements to generate an output or how flaws in enter information will affect the output.
Social impact explanations relate to the way that “socially significant” others — i.e., users — behave in keeping with a system’s predictions. A equipment the usage of this sort of explanation might also exhibit a document on model adoption information, or the ranking of the equipment by means of clients with similar qualities (e.g., individuals above a undeniable age).
as the coauthors of a accurate Intuit and Holon Institute of know-how research paper observe, international explanations are often low-priced and problematic to implement in actual-world programs, making them attractive in practice. local explanations, while greater granular, tend to be high priced because they ought to be computed case-through-case.
Presentation matters in XAI
Explanations, even with category, can be framed in different ways. Presentation concerns — the volume of counsel provided, as neatly because the wording, phrasing, and visualizations (e.g., charts and tables), might all have an effect on what people perceive a few device. reviews have proven that the energy of AI explanations lies as much in the eye of the beholder as within the minds of the fashion designer; explanatory intent and heuristics remember as a good deal because the meant goal.
because the Brookings Institute writes: “consider, for example, the distinctive needs of builders and clients in making an AI gadget explainable. A developer might use Google’s What-If device to overview complex dashboards that supply visualizations of a mannequin’s performance in distinct hypothetical instances, analyze the significance of different information features, and look at various different conceptions of fairness. users, however, may also decide on whatever thing more targeted. In a credit scoring system, it should be would becould very well be as simple as informing a consumer which elements, corresponding to a late charge, led to a deduction of features. diverse users and scenarios will demand diverse outputs.”
A look at authorised at the 2020 ACM on Human-computer interaction found out that explanations, written a undeniable manner, could create a false sense of safety and over-trust in AI. In a couple of linked papers, researchers locate that facts scientists and analysts perceive a gadget’s accuracy differently, with analysts inaccurately viewing certain metrics as a measure of efficiency even after they don’t bear in mind how the metrics had been calculated.
The alternative in rationalization classification — and presentation — isn’t well-known. The coauthors of the Intuit and Holon Institute of know-how design elements to accept as true with in making XAI design decisions, together with here:
Transparency: the stage of aspect provided
Scrutability: the extent to which clients can provide comments to alter the AI gadget when it’s wrong
trust: the stage of confidence within the device
Persuasiveness: the diploma to which the device itself is convincing in making users buy or try concepts given via it
delight: the degree to which the gadget is interesting to use
person figuring out: the extent a consumer understands the character of the AI provider offered
model playing cards, records labels, and reality sheets
model cards provide suggestions on the contents and habits of a system. First described by AI ethicist Timnit Gebru, cards enable developers to quickly keep in mind points like practicing records, recognized biases, benchmark and trying out effects, and gaps in ethical considerations.
mannequin cards fluctuate by means of organization and developer, however they customarily encompass technical details and records charts that display the breakdown of class imbalance or statistics skew for delicate fields like gender. several card-producing toolkits exist, but one of the crucial contemporary is from Google, which experiences on model provenance, usage, and “ethics-suggested” evaluations.
information labels and factsheets
Proposed by the meeting Fellowship, statistics labels take suggestion from dietary labels on food, aiming to highlight the important thing components in a dataset comparable to metadata, populations, and anomalous features related to distributions. statistics labels also deliver centered counsel a couple of dataset in accordance with its meant use case, including alerts and flags pertinent to that particular use.
along the identical vein, IBM created “factsheets” for methods that provide information concerning the systems’ key qualities. Factsheets reply questions starting from gadget operation and training information to underlying algorithms, check setups and results, performance benchmarks, equity and robustness assessments, supposed makes use of, maintenance, and retraining. For herbal language systems in particular, like OpenAI’s GPT-three, factsheets encompass records statements that show how an algorithm might be generalized, how it might be deployed, and what biases it may contain.
Technical strategies and toolkits
There’s a growing to be variety of methods, libraries, and tools for XAI. for instance, “layerwise relevance propagation” helps to determine which elements make a contribution most strongly to a model’s predictions. different thoughts produce saliency maps the place each and every of the facets of the enter records are scored in response to their contribution to the final output. as an example, in a picture classifier, a saliency map will expense the pixels in response to the contributions they make to the desktop discovering model’s output.
So-referred to as glassbox methods, or simplified models of techniques, make it less demanding to song how distinctive pieces of statistics have an effect on a system. while they do not operate well across domains, standard glassbox techniques work on kinds of structured statistics like facts tables. they could also be used as a debugging step to discover abilities error in more advanced, black-field techniques.
introduced three years in the past, fb’s Captum makes use of imagery to elucidate characteristic significance or operate a deep dive on models to show how their add-ons make a contribution to predictions.
In March 2019, OpenAI and Google released the activation atlases method for visualizing decisions made via computer discovering algorithms. In a weblog post, OpenAI proven how activation atlases will also be used to audit why a pc imaginative and prescient model classifies objects a undeniable manner — as an example, mistakenly associating the label “steam locomotive” with scuba divers’ air tanks.
IBM’s explainable AI toolkit, which launched in August 2019, attracts on a few other ways to explain results, similar to an algorithm that attempts to spotlight essential missing suggestions in datasets.
in addition, purple Hat recently open-sourced a equipment, TrustyAI, for auditing AI decision systems. TrustyAI can introspect fashions to describe predictions and outcomes by means of a “feature importance” chart that orders a mannequin’s inputs by using essentially the most important ones for the determination-making manner.
Transparency and XAI shortcomings
A coverage briefing on XAI through the Royal Society offers an illustration of the goals it's going to obtain. amongst others, XAI should provide users confidence that a system is an outstanding device for the aim and meet society’s expectations about how americans are afforded agency within the determination-making system. but definitely, XAI regularly falls brief, increasing the energy differentials between these growing systems and those impacted by using them.
A 2020 survey by researchers on the Alan Turing Institute, the Partnership on AI, and others printed that most of XAI deployments are used internally to aid engineering efforts rather than reinforcing have confidence or transparency with clients. analyze contributors mentioned that it become problematic to provide explanations to clients as a result of privacy risks and technological challenges and that they struggled to put into effect explainability because they lacked clarity about its objectives.
another 2020 examine, specializing in person interface and design practitioners at IBM engaged on XAI, described current XAI recommendations as “fail[ing] to live up to expectations” and being at odds with organizational desires like keeping proprietary data.
Brookings writes: “[W]hile there are numerous distinct explainability methods presently in operation, they essentially map onto a small subset of the objectives outlined above. Two of the engineering ambitions — ensuring efficacy and improving efficiency — look like the gold standard represented. different targets, including aiding person figuring out and insight about broader societal impacts, are presently unnoticed.”
approaching legislation like the European Union’s AI Act, which makes a speciality of ethics, may instantaneous companies to put in force XAI extra comprehensively. So, too, could moving public opinion on AI transparency. In a 2021 document by way of CognitiveScale, 34% of C-degree decision-makers said that probably the most important AI skill is “explainable and depended on.” And 87% of executives told Juniper in a accurate survey that they consider corporations have a accountability to undertake policies that lower the terrible affects of AI.
beyond ethics, there’s a business motivation to put money into XAI technologies. A study with the aid of Capgemini found that purchasers will reward organizations that observe ethical AI with enhanced loyalty, extra company, and even a willingness to suggest for them — and punish people who don’t.
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