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[General] Latest Test C1000-173 Question offer you accurate Latest Braindumps Ppt | IBM IB

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【General】 Latest Test C1000-173 Question offer you accurate Latest Braindumps Ppt | IBM IB

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IBM C1000-173 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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.
Topic 2
  • 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.
Topic 3
  • 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.
Topic 4
  • 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.
Topic 5
  • Architect with Analytic Services: This domain targets a Data Analytics Architect and focuses on designing solutions using analytic components of Cloud Pak for Data. Architecting with DataStage involves creating data integration and ETL pipelines. Data Refinery architectures prepare and cleanse data for analysis. Db2 Big SQL solutions address querying large distributed data sets with SQL, facilitating analytics across diverse data sources.

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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q129-Q134):NEW QUESTION # 129
What is crucial when planning for Disaster Recovery?
  • A. Ensuring minimal communication during recovery
  • B. Defining acceptable downtime and data loss
  • C. Ignoring potential threats and vulnerabilities
  • D. Prioritizing less critical systems
Answer: B

NEW QUESTION # 130
What post-installation step is essential for enhancing Watson Assistant's performance? (Select two)
  • A. Training the AI model with domain-specific data
  • B. Increasing the physical memory of the server
  • C. Setting up feedback loops for continuous improvement
  • D. Increasing the physical memory of the server
Answer: A,C

NEW QUESTION # 131
How does IBM Cloud Pak for Data support disaster recovery and business continuity planning?
  • A. By enabling cross-region data replication
  • B. All of the above
  • C. By providing automated backup and restore capabilities
  • D. By supporting high-availability cluster configurations
Answer: B

NEW QUESTION # 132
Which statement describes MPP (Massively Parallel Processing) Database architecture?
  • A. A data warehouse that needs all compute nodes to access a shared data store simultaneously.
  • B. A transactional system which uses multiple nodes for maximum availability.
  • C. An analytics environment that improves performance by dividing the data across many nodes.
  • D. Two or more databases kept in sync via two-phase commit.
Answer: C
Explanation:
MPP, or Massively Parallel Processing, is a database architecture model where data is divided and processed across multiple compute nodes in parallel. Each node works independently on a portion of the data, dramatically improving query performance and throughput for analytics workloads. This model is ideal for big data and analytical queries, not transactional workloads. It differs from shared-disk models or replication strategies like two-phase commit. The correct definition involves distributed data and parallel query execution, as described in option C.

NEW QUESTION # 133
When planning an environment for multiple Cloud Pak deployments, what is crucial?
  • A. Combining all user data into a single deployment for ease of access
  • B. Ensuring all deployments share the same resources
  • C. Tailoring resource allocation and access controls per deployment
  • D. Ignoring the specific needs and scale of each deployment
Answer: C

NEW QUESTION # 134
......
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