C1000-173題庫 - C1000-173考試題庫

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IBM C1000-173 考試大綱：

主題簡介
主題 1
  • Security Requirements: This domain targets a Security Architect and focuses on planning security for a Cloud Pak for Data deployment. It includes managing certificates that secure communications, identity management systems, and access and authorization controls to enforce secure and compliant user and service interactions. The auditing features and their integration with enterprise audit systems are crucial to ensure traceability and accountability. Asset interchange security involves safeguarding data movement between services.
主題 2
  • Architect with AI Series: This section measures the skills of an AI Solution Architect and includes designing architectures for solutions involving various IBM Watson AI services. Architecting with Watson Assistant involves creating conversational AI interfaces. Watson Discovery solutions focus on building cognitive search and content analytics applications. Watson Pipelines solutions involve orchestrating data science workflows. Watson OpenScale architectures enable AI model monitoring and governance. Architecting with Match 360 supports personalized engagement by integrating multi-channel customer insights.
主題 3
  • Architect with Data Source Services: This domain measures expertise of a Data Integration Architect and includes architecting solutions with data replication technologies to synchronize data across systems. IBM Data Virtualization architecture enables access to and querying of disparate data sources without data movement. Architecting with watsonx.data focuses on building intelligent data lakes and query engines. Architecting with Db2-related services involves designing scalable data management and processing architectures using IBM's Db2 platforms.
主題 4
  • Architect with Data Governance Services: This section evaluates skills of a Data Governance Specialist, covering the architecture of knowledge catalogs to enable data discovery and management. Data Privacy architectures ensure compliance with regulatory requirements around data protection. Knowledge Accelerators involve designing solutions that speed up data governance processes through predefined policies and templates.
主題 5
  • Plan for a Cloud Pak for Data Implementation: This section of the exam measures the skills of an Implementation Consultant and covers determining which Cloud Pak for Data services to deploy based on organizational needs. It involves sizing the Kubernetes
  • OpenShift cluster appropriately for workload demands and planning backup and restore strategies to ensure data protection. Planning for high availability and disaster recovery is essential to maintain uninterrupted service. Multi-tenancy requirements must be understood to support multiple user groups securely on shared infrastructure. Migration requirements need assessment to transition existing data and workloads smoothly.

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最新的 IBM Certified Architect C1000-173 免費考試真題 (Q56-Q61):

問題 #56
If a Cloud Pak for Data cluster is air-gapped, it is unable to reach the internet, and a private registry must be configured for installations and upgrades. Beyond container images, what other components need to be addressed?

答案：A

解題說明：
In air-gapped (offline) environments, it's not just the container images that require preparation.
Package managers like pip (for Python) and other utilities commonly attempt to pull dependencies from public internet repositories. These must be redirected to internal mirrors or handled via offline bundles. If left unaddressed, certain operations within components like Jupyter notebooks, Watson Studio, or pipelines may fail. License validation and monitoring do not inherently require internet access, and data movement between nodes is part of normal cluster function--not something to be blocked.


問題 #57
Which two features are valid only when deploying Cloud Pak for Data on-premises?

答案：A,B

解題說明：
In on-premises deployments of IBM Cloud Pak for Data:
Administrators have full control over the number of OpenShift nodes, unlike cloud-managed environments where node scaling may be automatic or abstracted. Persistent storage is always required and configured by the infrastructure team to meet service requirements and ensure data availability.
Auto-scaling of compute and automatic updates of services are not handled by IBM in on-prem setups. Network security responsibilities also lie with the deploying organization, not IBM, in an on-premises model.


問題 #58
How are Knowledge Accelerators deployed?

答案：B

解題說明：
Knowledge Accelerators are a part of IBM Knowledge Catalog in IBM Cloud Pak for Data and provide predefined industry-specific business terms and relationships. These accelerators are not included by default with sample assets nor automatically deployed through the Cloud Pak for Data marketplace. Instead, they are imported using dedicated API endpoints provided by IBM for Knowledge Accelerators. Deployment involves uploading the accelerator assets using the IBM Knowledge Accelerator API, and once imported, they can be customized and published within the governance framework. This approach provides the flexibility required for various enterprise governance models.


問題 #59
What steps are required for setting up IBM Data Replication for Cloud Pak for Data?

答案：B

解題說明：
To set up IBM Data Replication in Cloud Pak for Data, administrators must follow a sequence that begins with defining the source (such as a transactional database), then defining the target system (such as a data lake or warehouse), and finally configuring the replication process. This setup allows for near real-time data synchronization. User definitions, server setup, and data management rules are involved elsewhere in the platform but are not the essential steps for replication configuration.


問題 #60
What is a Data Refinery flow in Cloud Pak for Data?

答案：B

解題說明：
A Data Refinery flow is an ordered sequence of data operations applied to tabular data for preparation, cleansing, and transformation. Users can create a series of steps such as filtering, joining, aggregating, and applying custom expressions. The flow is saved and can be rerun on updated datasets to ensure consistency in data preparation. It is not a storage system (A), an ML model (B), or a visualization tool (D).


問題 #61
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