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

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.

Knowledge Accelerators are a part of IBM Knowledge Catalog in IBM Cloud Pak for Data and provide predefined industry-specific business terms and relationships. These accelerators are not included by default with sample assets nor automatically deployed through the Cloud Pak for Data marketplace. Instead, they are imported using dedicated API endpoints provided by IBM for Knowledge Accelerators. Deployment involves uploading the accelerator assets using the IBM Knowledge Accelerator API, and once imported, they can be customized and published within the governance framework. This approach provides the flexibility required for various enterprise governance models.

IBM Knowledge Catalog’s Data Governance Express offering is a lightweight configuration meant for streamlined governance needs. It includes core capabilities like cataloging and basic lineage but does not support advanced metadata export or full-scale lineage visualization and tracking features. These advanced features are only available in the full IBM Knowledge Catalog service. The Express tier is designed for simpler use cases and quicker onboarding with limited governance overhead.

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 an OpenShift environment that hosts IBM Cloud Pak for Data, all cluster nodes—including master and worker nodes—must have access to the container registry to pull required images during deployment and runtime. In scenarios involving custom images, some nodes may also need to push to the registry. While the bastion node may initiate the setup or mirror images, it is not the only node involved. Therefore, all nodes should be configured with both pull and, where applicable, push access to the registry to ensure consistent deployment and operations.

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.

Profiling data in Data Refinery is primarily used for validating the quality, structure, and characteristics of the dataset. It provides insights such as column data types, value distributions, null counts, and patterns, enabling users to detect anomalies, inconsistencies, or data quality issues before performing transformations or analytics. It is not intended for data loading (B), backups (C), or visualization (D), although it provides basic statistical overviews as part of validation.

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.

Passthrough route configuration on OpenShift is the only route type that preserves the original TLS connection from the client to the backend service. This is essential for supporting client certificate authentication, as the certificate must be passed directly to the application without termination or inspection at the router level. Edge and Re-encrypt routes terminate the TLS connection at the OpenShift router or re-encrypt it with a new certificate, making them unsuitable for mutual TLS (mTLS) or client certificate scenarios.

What sets DataStage apart in the IBM Cloud Pak for Data ecosystem is its high-performance parallel execution engine. While other tools may offer graphical interfaces and transformation libraries, DataStage is designed for enterprise-grade ETL with scalable parallelism. It optimizes performance by processing large data volumes using parallel pipelines across multiple cores or nodes. This architecture ensures faster execution and is ideal for complex data integration tasks.

Cloud Pak for Data provides audit logging capabilities via the Audit Logging Service, which uses Fluentd to collect, process, and forward log data to external SIEM solutions. Fluentd input streams are configured to export audit records in real time. While Splunk and QRadar are common integrations, Log360 is not explicitly listed in the official supported list. Each SIEM connection can be configured independently, and while audit logging is widely supported, it may not be enabled by default for every single service—therefore only option B is correct.

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

Watson Assistant integrates with telephone systems through the Voice Gateway service. Voice Gateway captures real-time telephone voice interactions and can route them to a Watson Assistant chatbot or forward them for transcription and analytics. It enables cognitive agents to interact over voice channels and supports integration with SIP-based telephony systems. Voice Agent, Analyzer, and Transcriber are not standard Watson Assistant components within Cloud Pak for Data.

The IBM Data Replication service in Cloud Pak for Data is designed to integrate and synchronize data between various systems, ensuring that data in target systems is kept up-to-date with the source systems. It supports near real-time replication and change data capture (CDC) mechanisms, making it ideal for analytics environments that require continuous synchronization. The service is not a tool for database activity monitoring (A), creating unified virtual views (B), or performing heavy data transformations (D).

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.