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IBM C1000-173 Exam Syllabus Topics:
Topic
Details
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
- 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 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.
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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q34-Q39):
NEW QUESTION # 34
Which set of DataStage features are primarily intended to improve reusability and flexibility?
- A. Macros, DataStage APIs, and DataStage jobs
- B. DataStage components, parameters, parameter sets, and environment variables.
- C. DataStage connectors, Qualitystage stages, and DataStage Pipeline components.
- D. Schema drift support, Dynamic Workload Management, and ELT run mode.
Answer: B
Explanation:
DataStage promotes modularity and reusability through the use of components such as job stages, parameters, parameter sets, and environment variables. Parameters and parameter sets allow dynamic configuration of jobs, enabling reuse across environments and reducing hardcoding. Environment variables allow users to define global job behavior across projects. These elements significantly enhance development efficiency and pipeline portability. Other features like schema drift and ELT modes support execution flexibility, but they are not primarily focused on reusability.
NEW QUESTION # 35
Which option is supported for backing up and restoring Cloud Pak for Data on a FIPS-enabled cluster?
- A. Online backup and restore to the same cluster
- B. Snap and Restore Tool
- C. Online backup and restore to a different cluster
- D. Watson Backup Assistant
Answer: A
Explanation:
For Cloud Pak for Data deployed on a FIPS-enabled cluster, the supported method for backup and restore is the online backup and restore to the same cluster. This method ensures that the data and configuration remain compliant with FIPS encryption standards. Offline and cross-cluster restore methods are not supported under FIPS constraints due to strict requirements around key handling and encryption. The Snap and Restore Tool and Watson Backup Assistant are not applicable to this context.
NEW QUESTION # 36
Which architectural consideration is crucial when deploying IBM Cloud Pak for Data on Red Hat OpenShift to ensure high availability?
- A. Network latency
- B. Resource quotas
- C. Storage class performance
- D. Pod colocation
Answer: C
NEW QUESTION # 37
When creating a Db2 Big SQL service instance, which two service resource items should be taken into account when sizing the cluster?
- A. Number of virtual cores
- B. Number of physical cores
- C. Amount of memory
- D. Number of workers
- E. Maximum expected throughput
Answer: C,D
Explanation:
When provisioning a Db2 Big SQL service instance in IBM Cloud Pak for Data, key considerations include the amount of memory available per worker node and the total number of worker nodes. These factors directly affect query execution parallelism and system capacity. The system does not distinguish between physical and virtual cores at the configuration level. Throughput is an outcome of sizing, not a direct sizing parameter.
Therefore, memory and number of workers are the two most critical sizing metrics.
NEW QUESTION # 38
How do Cloud Pak for Data administrators obtain access to Match 360?
- A. Each user is automatically granted read access.
- B. Administrative users automatically have access.
- C. Each user who is assigned to a service group has access.
- D. Administrative users must belong to the appropriate service group.
Answer: D
Explanation:
Access to Match 360 within IBM Cloud Pak for Data is role-based and governed by service groups. Even administrative users are not granted automatic access unless they are explicitly assigned to the appropriate Match 360 service group. This allows fine-grained control over who can access master data management capabilities. Service group membership defines the roles and privileges needed for interacting with Match 360 functionalities like entity resolution and golden record management.
NEW QUESTION # 39
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