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

To integrate a supported vault system withIBM Cloud Pak for Data, thefully qualified URLof the external vault is a required component for establishing communication between Cloud Pak for Data and the vault. This is typically configured in theexternal secrets managersettings.

While authentication credentials (like client certificates or keys) are also necessarydepending on the authentication method used, thefully qualified URLis universally required to locate and connect to the vault.

IBM Cloud Pak for Data supports integration with vaults such as:

HashiCorp Vault

AWS Secrets Manager

Azure Key Vault

IBM Key Protect

For more details, refer to:

IBM Cloud Pak for Data: Using external secrets managers

InIBM Cloud Pak for Data version 4.7, when configuring Dynamic Workload Management (DWM) for IBM DataStage, the system controls job concurrency based on the maximum concurrent jobs setting and auto-scaling configuration.

Withauto-scaling disabled, the system does not add or remove DataStage engine pods dynamically to handle workload changes.

Themaximum concurrent jobssetting limits the number of jobs that can run simultaneously on a single DataStage instance.

If the number of concurrent jobs reaches the maximum limit (in this case, 5), any additional job requests (such as the sixth job) willnot fail immediately; instead, these jobs are placed in aqueue.

The queued jobs remain pending until one of the running jobs completes, freeing up capacity for the next job to start.

This queuing behavior ensures workload stability and prevents resource exhaustion by enforcing the concurrency limit strictly when auto-scaling is turned off.

Exact extract from IBM Cloud Pak for Data 4.7 documentation:

"When auto-scaling is disabled, the maximum concurrency limit set on the DataStage instance controls how many jobs can run simultaneously. Jobs submitted beyond this limit are queued and wait for running jobs to complete before starting execution."

—IBM Cloud Pak for Data v4.7, DataStage Dynamic Workload Management section

watsonx.data is an open hybrid data lakehouse platform, combining the strengths of a data lake (flexibility and cost efficiency for unstructured data) with the structured query and performance features of a data warehouse. It is designed to handle both analytics and large-scale data storage, making it a hybrid solution rather than exclusively a data lake or data warehouse.

In IBM Cloud Pak for Data, a "Connection" is a configuration object that defines how to access an external data source, such as Db2, Oracle, or an S3 bucket. It does not involve moving or copying data. Instead, it acts as a reference to the external data source environment, enabling users to browse, query, and analyze the data in place. This approach supports virtualized access and governance while avoiding data duplication and ensuring data stays within its source system, aligning with enterprise data security policies.

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.

watsonx.data uses Apache Iceberg tables as the open table format for data sharing across platforms like Db2 Warehouse, Netezza, and other compatible data management solutions. Iceberg provides a transactional and schema-evolution-friendly table layer, allowing multiple engines to read and write data concurrently. This approach avoids proprietary loaders or simple file transfers and ensures efficient interoperability between different systems.

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.

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.

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.

For environments deployed via automated cloud options (such as Azure or AWS), upgrades are performed using the Command Line Interface (CLI). The CLI allows administrators to pull the latest operators, update foundational services, and upgrade installed services. The cloud marketplace is used only for initial provisioning, not for upgrades. PowerShell and GitHub repositories are not used as upgrade mechanisms within the official CP4D deployment lifecycle.

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.

The Audit Logging service in IBM Cloud Pak for Data uses Fluentd as the core log forwarding mechanism. Fluentd output plug-ins are configured to route audit logs to external SIEM systems such as Splunk or QRadar. These plug-ins are versatile and support multiple formats and transport protocols. Other options listed—like Logstash, OSS/J, or Kafka—are not the designated default forwarding mechanisms used within the CP4D Audit Logging architecture.

According to theIBM Cloud Pak for Data 4.7 Installation Guideand official IBM documentation, when deploying Cloud Pak for Data (CP4D) via a cloud provider marketplace (such as Red Hat OpenShift OperatorHub or a cloud marketplace), the deployment process offers an automated installation method that simplifies the setup. However, there are certain limitations and manual steps that may be required during the initial installation phase.

CP4D supports installation on an existing OpenShift Container Platform cluster, so option A is incorrect.

Some core services and operators, including foundational services, can be installed automatically via operators, so option B is incorrect.

Cloud Pak for Data operators and IBM foundational services operators can coexist in the same OpenShift cluster, so option C is incorrect.

Theofficial installation documentation for version 4.7specifies that the initial installation requires manual intervention for partial installation — for example, manually setting up foundational services or configuring specific operators before automated installation of the rest of the platform can continue smoothly. This partial manual setup is especially relevant when using marketplace deployment to ensure all prerequisites and configurations are met.

Exact extract from IBM Cloud Pak for Data 4.7 Installation documentation:

"When deploying Cloud Pak for Data via the OperatorHub or cloud marketplace, the initial setup requires manual installation of foundational services operators and configuration of the cluster environment before proceeding with the automated installation of Cloud Pak for Data services. Thispartial manual step ensures proper configuration and avoids conflicts during automated deployment."

—IBM Cloud Pak for Data Installation Guide v4.7, section "Installing from OperatorHub and Marketplace"

"You can create a maximum of10instances per deployment. After you reach the maximum number, the New instance button is not displayed in IBM Cloud Pak for Data."

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.