ServiceNow CIS-DF Exam With Confidence Using Practice Dumps

To specifically improve data quality related to CSDM, the most effective and prescribed action is to use the CSDM Data Foundations Dashboard. In ServiceNow, this dashboard is purpose-built to assess and improve CSDM alignment, not just general CMDB hygiene.

The CSDM Data Foundations Dashboard focuses on service modeling readiness, highlighting gaps such as missing service ownership, incomplete relationships between Business Applications and Application Services, unmanaged services, and misaligned lifecycle states. It provides Get Well Playbooks that guide administrators through structured remediation using Analyze Data, Fix Data, and Govern Data plays—directly tied to CSDM outcomes.

Option C (default CMDB Health Dashboard) is valuable, but it measures generic CMDB data quality dimensions (completeness, correctness, compliance) and does not specifically evaluate CSDM constructs or service modeling maturity. Option B (ServiceNow Health Scan) provides platform-wide configuration and performance recommendations, but it is not focused on CMDB or CSDM data quality.

Therefore, to improve CSDM-specific data quality, the administrator should use the CSDM Data Foundations Dashboard, making Option A the correct answer.

In ServiceNow, the Duplicate CI Remediator is designed to resolve duplicate records while preserving the most authoritative data from each source. Data Foundations guidance clearly states that automated discovery is the system of record for technical attributes, such as IP address, hostname, and operational status, while manually maintained records often contain valuable business context, such as relationships to business applications or services.

In this scenario, the discovered CI contains the most accurate and up-to-date technical data, making it the correct CI to retain as the primary record. However, the manually imported CI has a critical relationship to a business application, which is essential for impact analysis, incident prioritization, and CSDM alignment. Deleting this CI without preserving the relationship would result in loss of business context and reduced CMDB value.

The Duplicate CI Remediator supports selective merging, allowing administrators to retain one CI while merging specific attributes or relationships from the duplicate. Option C reflects this best practice by retaining the discovered CI and merging the relationship from the manually imported CI, ensuring both technical accuracy and business relevance are preserved.

Options A and D would result in the loss of important relationship data, while Option B would discard the discovered CI, violating the principle that discovery should be the authoritative source for technical attributes.

Therefore, Option C is the correct and Data Foundations–aligned answer.

In Data Foundations, “Ingest” is not limited to Discovery. It includes establishing reliable sources and mechanisms to populate and maintain CMDB attributes—especially those that Discovery cannot provide. Warranty expiration dates are a classic example of non-discoverable data: they typically come from procurement systems, asset repositories, vendors, or contract tools rather than from device interrogation.

The correct approach is to maintain these attributes via a scheduled data import from an authoritative external source. This ensures ongoing accuracy and consistency at scale. A scheduled import allows you to define a repeatable integration pattern, apply data quality checks, and keep warranty data current as vendors and procurement records change—without relying on manual updates that inevitably drift over time.

Option A is incorrect because the CMDB Reconciliation Engine resolves conflicts between multiple data sources and applies precedence rules; it does not create the warranty values out of thin air. Reconciliation helps decide which source wins when updates occur, but you still need a source feed providing the warranty attribute values. Option B is also not recommended because creating a new CI class just to hold non-discoverable attributes fragments the model and complicates reporting and processes. Best practice is to keep the attributes on the appropriate hardware CI classes (or related normalized structures, when applicable) and manage them through governed ingestion from authoritative systems. Therefore, C is the best method.