Free IBM C1000-173 Exam Dumps June 2026

IBM Knowledge Catalog is the core governance and cataloging service within Cloud Pak for Data that enables the delivery of trusted, business-ready data to AI and analytics pipelines. It provides data lineage, metadata management, access policies, and quality scores, ensuring data consumers use curated and compliant data. Watson Machine Learning and its accelerator are focused on model training and inference, while IBM Data Catalog is a former term replaced by Knowledge Catalog in recent versions.

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.

To enable IBM Cloud Pak for Data to trust certificates from internal servers using a custom Certificate Authority (CA), the correct method is to create a Kubernetes ConfigMap that contains the CA certificate. This ConfigMap is referenced by the platform's foundational services to include the CA in the trusted root store. Secrets are typically used for storing sensitive data like private keys and TLS certificates but are not used for adding trusted root CAs at the platform level. A ConfigMap is explicitly required by the platform to inject the CA trust into the certificate validation chain.

In Watson Pipelines within IBM Cloud Pak for Data, error management is handled by the Error Handling component. This feature allows developers and pipeline administrators to define how pipeline failures are processed---whether to stop execution, continue, or trigger alternate flows. It ensures controlled behavior in response to job failures, particularly in complex ETL pipelines like those built with DataStage. Error Handling is a configurable element of pipeline orchestration and is typically used to enhance fault tolerance and control error propagation in production workflows.