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| Section | Weight | Objectives |
|---|---|---|
| Overview and Capabilities | 31% | - Workflow capability - Foundational services - Decision Management capability - Process Mining capability - Value proposition of Cloud Pak for Business Automation - Document Processing capability - Insights capability - Common tools - Robotic Process Automation capability - Content Services capability |
| Integration | 21% | - Integration between internal products - External integration options |
| Plan and Configure | 26% | - Architecture, deployment and consumption models - Component setup and configuration - Best practices for component usage |
| Business Requirements and Use Cases | 22% | - Understanding business requirements - Selecting appropriate components for requirements and use cases |
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質問 # 58
A state government benefits agency wants to implement a new platform to manage the processing of benefits applications from its constituents. The solution must support the following requirements:
- Required and optional activities and tasks
- Activities/tasks can start automatically or manually by a case worker
- A case is completed when all required and currently running activities are completed
- Work can be assigned and prioritized automatically based on business rules.
Which component would be best suited for this use case?
正解：A
解説：
The correct answer is A: Business Automation Workflow. The use case is a classic case-management and workflow-automation scenario: a benefits application is a case folder containing documents, data, tasks, activities, milestones, routing rules, and human work. Case management supports activities that can be required or optional, can start automatically or manually by a case worker, and can complete a case when all required and currently running activities are complete. This maps directly to the requirements in the question. Business Automation Workflow provides case and process capabilities, task routing, role-based assignment, activity management, integration services, and business-rule-driven work handling. Automation Document Processing could classify and extract data from application documents, but it does not manage the end-to-end benefits case lifecycle by itself. Workflow Process Service is narrower and not the best answer for full case management. Automation Decision Services can automate eligibility decisions, but it does not provide the primary case orchestration platform. Reference/topics: Business Automation Workflow, case management, required/optional activities, automatic/manual activity start, case completion, work assignment.
質問 # 59
Which configuration should be prioritized for a highly available Cloud Pak for Business Automation environment?
正解：D
解説：
For a highly available Cloud Pak for Business Automation deployment, the priority is to deploy control planes and worker nodes across multiple availability zones. This design reduces single-zone failure risk and enables the OpenShift platform to reschedule workloads, maintain quorum, and recover services when infrastructure in one zone becomes unavailable. Keeping hardware in one availability zone improves locality but creates a fault-domain concentration risk, so option A is the opposite of the recommended HA posture. IBM HTTP Server is relevant to traditional WebSphere-based topologies, not the primary load-balancing mechanism for Cloud Pak container deployments. Node agents are also associated with traditional WebSphere Network Deployment rather than Kubernetes-native pod supervision. In containerized CP4BA, high availability is achieved through pod distribution, resilient storage, OpenShift scheduling, router/load-balancer behavior, replicas, and fault-domain separation. A correct architecture separates failure domains across control-plane and worker infrastructure. Reference/topics: High availability topology, Red Hat OpenShift fault domains, Storage HA, CP4BA HA versus traditional on-premises HA.
質問 # 60
What is the business value of IBM Automation Document Processing?
正解：D
解説：
The correct answer is A. IBM Automation Document Processing delivers business value by applying AI-powered services to capture, classify, extract, validate, and prepare document data for downstream business automation. It is a cloud-native set of AI-powered services that automatically reads and corrects data from documents, with a no-code designer for training classification, extraction, and enrichment models. This positions ADP as an end-to-end document intelligence capability rather than a narrow extraction tool. Option B is partially accurate, but it understates the larger business value by describing only part of the processing chain. Option C is wrong because human-in-the-loop validation is used to improve confidence and data quality, not to increase manual processing. Option D is also too absolute: AI reduces training effort and improves extraction, but successful enterprise document processing still requires modeling, validation, tuning, and governance. The strategic value is reducing manual capture work, accelerating document-heavy operations, improving data quality, and enabling workflow or content applications to act on trusted extracted data. Reference/topics: Automation Document Processing, intelligent document processing, AI-powered services, classification, extraction, validation, no-code document processing designer.
質問 # 61
Which elements are part of the Decision Modeling in Automation Decision Services?
正解：A
解説：
The correct answer is D. Automation Decision Services decision modeling uses a decision diagram composed of nodes that represent the structure and dependencies of an automated decision. The primary diagram elements are decision nodes, input data nodes, function nodes, prediction nodes, and links. Decision nodes contain the logic that processes input and supporting decisions. Input data nodes represent the data required to make the decision. Function nodes provide values computed by other decision models, enabling model reuse and composition. Prediction nodes provide values computed by predictive models, allowing machine-learning outputs to participate in decision logic. Option A describes task-model or ruleflow concepts rather than ADS decision modeling. Option B mixes model categories but does not list the actual diagram elements. Option C describes ODM-style artifacts such as business rules, decision tables, and ruleflow nodes; business rules and decision tables can express logic inside a decision, but they are not the decision-diagram element set being asked for. Reference/topics: Automation Decision Services, Decision Designer, decision diagrams, decision nodes, input data nodes, function nodes, prediction nodes.
質問 # 62
What can be reviewed during a conformance check in Process Mining?
正解：B
解説：
The correct answer is A: Model fitness. IBM Process Mining conformance checking compares a data-derived process model with a reference model to determine how closely real process execution follows the expected process design. The Model Conformance panel visualizes fitness and conformance indicators, including similarity, fitness, minimum fitness, and maximum fitness. Fitness represents the percentage of represented cases in the data-derived model, while minimum and maximum fitness reflect the least and most similar cases when compared with the reference model. This is exactly the type of measurement reviewed during a conformance check. Option B, input data model, refers to the structure of input data but not the conformance result. Option C, system diagram, is not a Process Mining conformance artifact. Option D, input data quality, is important during ingestion and preparation, but it is not the specific conformance-check result being tested. In practice, model fitness helps analysts identify deviation, nonconforming paths, missing activities, unexpected activities, and gaps between designed and actual execution. Reference/topics: Process Mining, model conformance, reference model, fitness indicators, deviation analysis.
質問 # 63
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