IBM IBM Cloud Pak for Data V4.7 Architect C1000-173 Exam Dumps: Updated Questions & Answers (October 2025)

In Watson Pipelines, the component that enables users to define and assign values that can be referenced later in the pipeline—including by downstream components like DataStage—is Set User Variables. This component allows the user to create name-value pairs and store them as environment variables, which are accessible to DataStage and other execution blocks. This ensures dynamic parameter passing and enhances pipeline reusability. The other options listed do not correspond to valid Watson Pipeline components as defined in the official Cloud Pak for Data 4.7 release.

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.

"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."

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.

Watson Pipelines in Cloud Pak for Data support orchestration of diverse workload types including notebooks (Python or similar interactive environments) and scripts such as Bash. These pipeline components allow integration of notebook cells or shell scripts as tasks. There is no built‑in support for executing PowerShell tasks directly (unless wrapped in Bash-like containers), and Postgres is used as a data source—not a pipeline component type. While Db2 Big SQL can be invoked within a notebook or script, it is not itself a pipeline component. Therefore the supported types in pipelines are notebooks and Bash scripts.

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.

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.

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.

You can use the IBM Software Hub monitoring and alerting framework to monitor the state of the platform. You can set up events to alert when action is needed, based on thresholds that you define.

By default, IBM Software Hub is initialized with one monitor that runs every ten minutes. The diagnostic monitor records the status of deployments, StatefulSets, and persistent volume claims. It also tracks your system usage of virtual processors (vCPUs) and memory. The data that is collected can be used for analysis and to alert customers in a production environment based on set alert rules.

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.