Questions for the C1000-173 were updated on : Dec 01 ,2025
What is the purpose of profiling data in Data Refinery?
A
Explanation:
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.
How are caches defined in IBM Data Virtualization?
C
Explanation:
In IBM Data Virtualization, caches must be manually defined by administrators. While monitoring
and query performance statistics can guide where caching would be beneficial, the creation and
configuration of caches (e.g., refresh schedules and scope) are manual tasks. There is no automated
cache creation mechanism (A), nor are DataStage flows used for cache maintenance (B). Suggestions
based on statistics (D) may assist administrators, but they do not automatically create the caches.
Which of the following is watsonx.data most similar to?
C
Explanation:
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.
After importing IBM Knowledge Accelerator assets using an API endpoint, what change must be
made before the assets can be used by the appropriate users?
C
Explanation:
After importing IBM Knowledge Accelerator (KA) assets using an API (or other methods), those
assets — such as categories, terms, and relationships — are part of governance artifacts in Cloud Pak
for Data. However, to make them usable by specific users, you must assign collaborators to the
relevant categories.
This ensures users have the appropriate permissions (e.g., to view, curate, or manage terms) within
the Information Governance Catalog (IGC) or Watson Knowledge Catalog.
What is the purpose of configuring access to a Git repository associated with a project in Cloud Pak
for Data?
A
Explanation:
Configuring access to a Git repository in Cloud Pak for Data projects allows teams to collaborate on
code, notebooks, and assets while benefiting from version control and branching. This setup ensures
that all project files can be tracked, reverted, or merged, enabling collaborative development and
continuous integration workflows. It is not used for model deployment management (B) or
visualization enhancements (C). Option D is unrelated to the actual purpose of Git integration.
How does watsonx.data provide data sharing between Db2 Warehouse, Netezza, and any other data
management solution?
C
Explanation:
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.
What is a benefit of utilizing the IBM Data Virtualization service?
D
Explanation:
As mentioned previously, IBM Data Virtualization provides a single governed access point for data
residing across heterogeneous systems. It reduces data movement by creating virtualized views,
enabling organizations to query multiple sources seamlessly while adhering to centralized security
and governance policies. This capability supports analytics and reporting without duplicating or
moving data.
What is a benefit of utilizing the IBM Data Virtualization service?
D
Explanation:
IBM Data Virtualization allows users to query and combine data from multiple disparate sources
without physically moving it. One of its key benefits is that it enables central governance of access
and policies across these data sources. Rather than duplicating data, the service provides virtualized
views while maintaining source security and compliance. It does not bypass security (B), nor is its
main goal to copy data (A). Discovering and classifying sensitive information (C) is primarily the
function of IBM Knowledge Catalog.
What is a Data Refinery flow in Cloud Pak for Data?
C
Explanation:
A Data Refinery flow is an ordered sequence of data operations applied to tabular data for
preparation, cleansing, and transformation. Users can create a series of steps such as filtering,
joining, aggregating, and applying custom expressions. The flow is saved and can be rerun on
updated datasets to ensure consistency in data preparation. It is not a storage system (A), an ML
model (B), or a visualization tool (D).
A customer wants to manage Cloud Pak for Data secrets via an existing supported vault system. What
is needed to integrate any supported vault systems into Cloud Pak for Data?
B
Explanation:
To integrate a supported vault system with IBM Cloud Pak for Data, the fully qualified URL of the
external vault is a required component for establishing communication between Cloud Pak for Data
and the vault. This is typically configured in the external secrets manager settings.
While authentication credentials (like client certificates or keys) are also necessary depending on the
authentication method used, the fully qualified URL is 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
What is a Data Refinery flow in Cloud Pak for Data?
C
Explanation:
A Data Refinery flow in Cloud Pak for Data is an ordered set of data operations (transformations) that
are applied to tabular data. It is used to cleanse, shape, and prepare data for analysis or machine
learning. Users can apply filters, joins, aggregations, and custom expressions. It is not a storage
location (A), ML model (B), or a visualization tool (D), though visual previews of transformed data are
available.
What outcomes can be achieved from Match 360?
D
Explanation:
Match 360 is IBM’s master data management (MDM) service integrated into Cloud Pak for Data. It
produces master data views along with statistics, graphs, and insights that allow users to explore,
analyze, and understand their master data entities (e.g., customers, products). While it does
integrate data from disparate sources, its focus is on consolidating and providing master data
analysis rather than virtual views (B) or conversational analytics (A).
What is the purpose of the IBM Data Replication service?
C
Explanation:
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).
A Cloud Pak for Data installation was initially deployed on Microsoft Azure via the automated
deployment option. What can be used to upgrade the environment?
D
Explanation:
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.
Are there any special considerations for the client to migrate existing server jobs to DataStage in
Cloud Pak for Data?
B
Explanation:
Legacy DataStage server jobs are not automatically compatible with DataStage on Cloud Pak for Data,
which uses a parallel engine architecture. MettleCI is the recommended tool to convert server jobs
into parallel jobs before migration. This conversion allows reusability and ensures the migrated jobs
can run efficiently in the CP4D environment. Direct migration without modification (option D) is not
possible, and they do not migrate to Watson Pipelines (option A).