Free Practice Questions for IBM C1000-173 Exam

Training Data Statistics:Watson OpenScale needs to understand the characteristics of the data the model was trained on. This includes things like the distribution of features, sensitive attributes (for fairness monitoring), and how the model performed on this initial data. These "training data statistics" are crucial for:

Fairness Configuration:Recommending fairness attributes, reference, and monitored groups.

Bias Detection:Calculating fairness metrics (like disparate impact) by comparing runtime behavior to the learned training data distribution.

Explainability:Generating explanations by understanding the distribution of values in the training data to create meaningful perturbations.

Drift Detection:Building a drift detection model that compares runtime data to the training data to identify shifts.

While Watson OpenScale also consumespayload data(the data sent to the deployed model for predictions) at runtime to calculate various metrics and perform monitoring, the initial setup and the ability to generate meaningfulstatisticsfor things like fairness and drift fundamentally rely on understanding thetraining 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.

Collaborators in IBM Knowledge Catalog are granted access to data assets that have been properly governed and made available through connections. Once a connection is established by an administrator or asset owner, users with collaborator roles can access the data without needing to re-enter credentials. This simplifies secure data consumption and aligns with enterprise access control policies. They do not see underlying credentials, and access is not limited to document types like PDFs.

Within IBM Knowledge Catalog as part of IBM Cloud Pak for Data, governing data lakes is enabled via integration with Data Virtualization. This approach supports automated data discovery, cataloging, tagging, and virtualization, allowing users to access enterprise data virtually—without physical movement. Policies and governance metadata are applied automatically to virtualized assets, enabling secure and efficient data consumption. Manual processes are not required for discovery, and data is masked selectively based on policies—not completely masked without user intervention. Thus automation and virtualization are central, making statement B correct.

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).

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.

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.

Data Refinery is the dedicated data preparation service in IBM Cloud Pak for Data. It enables users to cleanse, shape, filter, and enrich tabular datasets through a graphical interface. Users can create data preparation flows that integrate seamlessly with Watson Studio and other services. Data Manager and Data Wrangler are not services available in CP4D, and Watson Data is not a recognized component. Data Refinery is the officially supported tool for this purpose.

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.