IBM C1000-173 - IBM Cloud Pak for Data V4.7 Architect

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.

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.

Data Refinery is the dedicated data preparation service in IBM Cloud Pak for Data. It enables users to cleanse, shape, filter, and enrich tabular datasets through a graphical interface. Users can create data preparation flows that integrate seamlessly with Watson Studio and other services. Data Manager and Data Wrangler are not services available in CP4D, and Watson Data is not a recognized component. Data Refinery is the officially supported tool for this purpose.

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.

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.

Applications interact with Db2 Big SQL using industry-standard protocols. The most common and supported interface is through a REST (Representative State Transfer) API endpoint. REST endpoints allow for external applications to query, manage, and manipulate data within Big SQL using simple HTTP calls. None of the other options—SLEEP, SNORE, or DREAM—are valid or recognized interfaces in IBM Cloud Pak for Data or Db2 Big SQL documentation.

The Data Engineer role in IBM Cloud Pak for Data includes permissions necessary for managing the data pipeline lifecycle. This includes the ability to create projects, manage data assets, and configure data protection rules, which are critical for ensuring data privacy and governance. The role does not include platform-level administrative privileges like managing the platform or monitoring all workloads. Workflow management is typically assigned to users with broader project or orchestrator roles.

During the upgrade to Cloud Pak for Data version 4.7, significant changes were made to the underlying metadata repository, which is powered by Db2. These changes affect how governance data is stored and accessed, and an export/import is necessary to ensure compatibility and data integrity. The export step extracts governance metadata using the supported utility, and after the upgrade, it is re-imported into the upgraded structure. This is not due to container ephemerality or storage changes, but because of schema and format changes in Db2.

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.