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| Section | Weight | Objectives |
|---|---|---|
| Security and Compliance Architecture | 22% | - Plan certificate management and secure communications - Implement auditing and compliance controls - Design identity and access management - Secure data exchange and integration |
| Architect with Data Source and Integration Services | 18% | - Design data replication and integration architectures - Architect with watsonx.data and Db2 services - Implement data virtualization solutions |
| Architect with Data Governance Services | 20% | - Leverage knowledge accelerators and governance frameworks - Implement data privacy and regulatory compliance architectures - Design knowledge catalog and data discovery solutions |
| Operational Readiness and Scalability | 15% | - Design monitoring, logging and operational processes - Plan for scalability, performance and lifecycle management |
| Plan for a Cloud Pak for Data Implementation | 25% | - Assess requirements and select appropriate services - Plan backup, restore, high availability and disaster recovery - Design for multi-tenancy and migration - Size and design OpenShift/Kubernetes infrastructure |
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NEW QUESTION # 50
Which two Cloud Pak for Data services support the multi-tenancy mechanism of installing the service once and provisioning the workloads in tethered projects?
Answer: B,E
Explanation:
Cloud Pak for Data supports service-level multi-tenancy by enabling some services to be installed centrally and then provisioned into user "tethered" namespaces (projects). In version 4.7, Analytics Engine (Apache Spark) and IBM Data Virtualization services support this tethered#project model: they can be installed once and then instantiated per project across multiple tenants. Core services like Db2, Cognos Analytics, and Watson Pipelines in 4.7 require installation per instance and do not yet support tethered#namespace provisioning via multi#tenant model.
NEW QUESTION # 51
In the context of IBM Cloud Pak for Data, which statement best describes the purpose of using OpenShift Operators?
Answer: C
NEW QUESTION # 52
What is a primary advantage of using Watson Pipelines in AI solution architecture?
Answer: C
NEW QUESTION # 53
Which data processing engine is used for Data Privacy Masking flows?
Answer: A
Explanation:
Data Privacy Masking flows in IBM Cloud Pak for Data utilize Apache Spark as the underlying data processing engine. Spark enables large-scale, distributed data masking operations for structured data, supporting high-performance transformations and compliance with privacy regulations. While DataStage can perform similar operations, the default and recommended engine for Data Privacy flows in CP4D is Spark.
dbt and Presto are not used for this masking functionality.
NEW QUESTION # 54
How does the IBM Data Virtualization service virtualize files in shared directories?
Answer: B
Explanation:
To virtualize files that reside in shared directories (e.g., NFS, SMB, or other on-premises sources), IBM Data Virtualization uses a remote connector agent. This remote connector is installed and executed on the source server to enable secure access and metadata extraction. The service does not scan networks automatically nor rely on FTP. Directly adding file shares via the UI is not sufficient without the backend connector in place, which acts as a secure communication bridge.
NEW QUESTION # 55
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