ServiceNow CIS-PA Dumps

In Platform Analytics, dashboards are shared through the dashboardShareaction. In theShare Dashboarddialog, theGrant access tofield explicitly allows you to enterone or more users, groups, or rolesto share the dashboard with. This sharing controls whether recipients canviewthe dashboard oreditit, depending on whether you add them as a viewer or editor. Sharing can also optionally allow recipients to manage (add/edit/remove) sharing permissions if that option is enabled. ServiceNow further notes that only certain privileged roles (such asadmin, dashboard_admin, pa_admin, or pa_power_user) can see roles in the sharing panel in some configurations, and sharing with roles may require read access to theRoles [sys_user_role]table. This means access can be granted at the individual level (user), team level (group), or permission level (role), making “user, group, or role” the correct and complete choice.

Ascripted Breakdown Mappingis required when the breakdown value cannot be obtained through adirect field mappingor simple reference relationship. Categorizing requests based onthe number of times they were updatedrequires calculating a value dynamically (for example, counting updates from the audit history), which is not stored as a single field on the record. Because this value must be derived programmatically, a scripted mapping is necessary.

Options B and C use existing fields (category, priority) that can be mapped directly without scripting. Option D usesage ranges, which are handled throughBucket Groupsand thepa_bucketstable, not scripted mappings. ServiceNow documentation clearly states that scripted mappings are intended forcalculated, derived, or indirect values, making option A the correct answer.

When creating a breakdown based on theage of a task, the correctFacts tablefor the Breakdown Source isBucket [pa_buckets]. In Platform Analytics, age-based breakdowns (such as 0–5 days, 6–10 days, etc.) are not derived directly from the Task table. Instead, they usebucketed data, which is generated bybucket groupsduring data collection.

Thepa_bucketstable stores the calculated bucket values for records at collection time, making it the authoritative facts table for age, duration, and numeric range breakdowns. Bucket Groups define how values are grouped, while the Bucket table stores the actual bucket assignments used in analytics. The Task table itself cannot be used as the facts table for age breakdowns because Platform Analytics requires pre-aggregated, time-aware bucket data to ensure historical accuracy. The Choice table is only used for choice list values and is unrelated to numeric or age-based breakdowns. ServiceNow documentation clearly states that bucket-based breakdowns must reference thepa_bucketstable to function correctly and produce accurate time series analytics.

A single Breakdown can be reused across multiple indicators—even when those indicators are based on different facts tables—by creating aseparate Breakdown Mapping for each indicator facts table.

The Breakdown defineswhatis being analyzed, while the Breakdown Mapping defineshowthe indicator facts relate to the breakdown source. Because different facts tables may store data differently, each requires its own mapping configuration. A scripted mapping is only required when no direct relationship exists, not simply because facts tables differ. ServiceNow documentation clearly states that reuse across facts tables is achieved through multiple mappings, making option D the correct answer.

During data collection, Platform Analytics first applies theIndicator Source conditionsto retrieve the base dataset. Once the source data is identified, the system then evaluates theAdditional conditionsdefined on the Indicator itself.

This separation allows indicator-specific filtering without duplicating logic in the indicator source, supporting reuse and performance optimization. Additional conditions do not run in parallel with source conditions and are not evaluated at visualization time. ServiceNow documentation explicitly explains that Indicator conditions refine the datasetafterthe source query executes, making option D the correct answer.

Platform Analytics optimizes historical data collection by executingone query per indicator source per day, regardless of how many indicators reference that source. If 10 indicators share the same daily indicator source, the system executesonly one SQL query per day, not one per indicator.

Over a 30-day historical collection period, this results in30 SQL queries total. This optimization significantly reduces database load and improves performance. ServiceNow documentation explicitly highlights this behavior as a best-practice advantage of reusing indicator sources. Therefore, option C is the correct and documented answer.

WhenCollect recordsis enabled on an Automated Indicator, Platform Analytics stores thesys_ids of the recordsthat contributed to each indicator score at collection time. This capability enablesdrill-down functionality, allowing users to view the exact records behind a score directly from analytics widgets and dashboards. Importantly, Platform Analytics doesnot store full copies of records—only the identifiers—ensuring historical accuracy while maintaining storage efficiency.

Option A describes thepreview featureavailable when defining indicator conditions, not record collection. Option B refers tomanual data collection, which is triggered separately. Option C is incorrect because Platform Analytics does not retain full record snapshots. ServiceNow documentation clearly states that enabling Collect records allows analytics users to drill into the contributing records for any given score, making option D the correct and precise answer.

When aBucket Groupis used as a Breakdown Source in Platform Analytics, the requiredFacts tableisBucket [pa_buckets]. Bucket Groups define how numeric or duration values (such as age, time, or cost ranges) are grouped, but they do not store analytics facts themselves. The actual bucketed values generated during data collection are stored in thepa_bucketstable, which makes it the authoritative facts table for bucket-based breakdowns.

Thepa_bucket_groupstable only stores configuration metadata for bucket definitions. Thesys_choicetable is used exclusively for choice list values and is unrelated to bucket analytics. The Indicator Facts table stores indicator scores but does not contain bucket-level breakdown data. ServiceNow documentation explicitly states that any breakdown based on bucket groups must reference thepa_bucketstable to ensure accurate historical analysis and proper breakdown rendering. Therefore, option C is the only correct answer.

Platform Analytics helps reduce IT spending by enablingcost visibility, trend analysis, and optimization insights. Breaking downincident resolution costsallows organizations to identify high-cost incident categories, inefficient processes, or teams with unusually long resolution times. By correlating cost data with performance indicators, leaders can make data-driven decisions to streamline workflows, reduce rework, and optimize resource allocation.

User satisfaction surveys (option A) provide qualitative feedback but do not directly measure or reduce costs. Importing asset cost reports (option B) is a reporting or data integration activity, not an analytics-driven optimization approach. Automating password resets (option D) is an operational improvement but does not directly leverage Platform Analytics capabilities. ServiceNow documentation emphasizes that Platform Analytics supportsstrategic objectivessuch as cost reduction by revealing inefficiencies through indicators, breakdowns, and historical trend analysis—making option C the correct answer.

A Breakdown Source relies on theFacts tableand aunique identifier field (typically the Sys ID)to define how breakdown elements are generated and linked to indicator scores. The Facts table specifies where the breakdown data originates, while the Sys ID field uniquely identifies each breakdown element.

Related list conditions and labels do not define uniqueness. Indicator configuration and targets are unrelated to breakdown structure. Manual indicators and scoresheets are not involved in breakdown sourcing. ServiceNow documentation clearly states that the Facts table and unique identifier are foundational to Breakdown Source configuration, making option B the correct answer.

Fiscal Calendarsallow Platform Analytics to aggregate and visualize indicator data usingfiscal periodssuch as fiscal years, quarters, and accounting periods. This is essential for organizations that do not follow standard calendar years for reporting and performance measurement.

Gregorian calendars use standard date boundaries, while Business Calendars define working and non-working time for duration calculations. Timecard calendars are unrelated to analytics aggregation. ServiceNow documentation clearly states that fiscal period-based analysis is enabled through Fiscal Calendars, making optionBthe correct answer.

In ServiceNow Performance Analytics,Indicator SourcesandIndicatorsare distinct objects with different responsibilities, and understanding their behavior is essential for correct architecture and deployment.

Option A is correct.

Indicator Sources definehowandwhen raw data is queried, but they can be reused by multiple data collection jobs. Even if an Indicator Source is configured with aMonthly frequency, it can still be executed by aDaily data collection job. The job frequency controls execution timing, not the source frequency itself. This reuse is a documented performance optimization in Platform Analytics.

Option C is correct.

TheIndicator frequency is independent of the Indicator Source frequency. For example, an Indicator Source may collect daily raw data, while the Indicator aggregates and stores scores weekly or monthly. This separation allows flexible aggregation strategies and is explicitly supported by Platform Analytics design.

Option B is incorrectbecause Breakdowns require aBreakdown Mapping, but they are not inherently tied only to Automated Indicators, nor is this statement describing object behavior accurately in isolation.

Option D is incorrectbecause Breakdowns can be assigned to an Indicatorbefore or after data collection; they are applied when the next collection runs.

By default, a newly created Platform Analytics indicator isVisible to Just Me, andVisible by All Rolesis set toFalse. This ensures that indicators are private to their creator until explicitly shared.

No role is required by default, and visibility must be intentionally expanded by updating the access control settings. This design supports governance and prevents incomplete or experimental indicators from being exposed prematurely. ServiceNow documentation confirms this default behavior, making option A the correct answer.

When a Performance Analytics dashboard is opened, ServiceNow loadsonly the widgets configured on the currently active tab. Widgets on other tabs are not loaded until the user clicks those tabs. This behavior is intentional and supportsperformance optimization, especially for dashboards containing many analytics widgets or complex queries.

Widgets are not limited by view rules alone, and ServiceNow does not preload widgets from all tabs. Additionally, widgets that require scrolling are still loaded as long as they are part of the active tab. ServiceNow documentation confirms that dashboard widgets are loadedper tab, reducing unnecessary data processing and improving load times. Therefore, option A is the correct and documented behavior.

TheIn-Line Dashboard Editoris designed primarily forbusiness userswho need to make quick, simple adjustments to dashboards without deep technical knowledge. It allows users to edit layouts, move widgets, and make basic configuration changes directly from the dashboard view.

System administrators and developers typically use the full dashboard configuration interfaces for advanced customization, scripting, or governance. External contractors are not a defined target audience for this feature. ServiceNow documentation emphasizes that the in-line editor empowers non-technical users to personalize dashboards safely and efficiently, making option A the correct answer.

TheDashboards modulein Analytics Center provides full lifecycle management forNext Experience Dashboards. Users with appropriate permissions cancreate, update, certify, share, and view dashboardsfrom this module.

Certification is an important governance feature that marks dashboards as trusted and production-ready. Sharing controls access, while update and creation enable continuous improvement. Viewing alone is insufficient for analytics governance. ServiceNow documentation clearly defines the Dashboards module as the central place for full dashboard management, making optionDthe correct answer.

TheVisualization Libraryis the Platform Analytics feature that enables visualizations to be reused and shared acrossany dashboard. When a visualization (such as a time series, scorecard, or breakdown visualization) is saved to the Visualization Library, it becomes areusable analytics componentthat can be added to multiple dashboards without duplicating configuration. This ensures consistency in metrics, reduces maintenance overhead, and supports centralized governance of analytics content.

Dashboard Sharing, by contrast, controlswho can view or edit a dashboard, not how individual visualizations are reused across dashboards. Roles such as pa_kpi_signals_admin or report_admin provide administrative capabilities but do not enable cross-dashboard visualization reuse. According to ServiceNow Platform Analytics documentation, the Visualization Library is specifically designed to store, manage, and distribute analytics visualizations so they can be embedded in dashboards throughout the platform. This feature is essential in enterprise analytics implementations where the same KPIs and indicators must appear consistently across multiple dashboards and user audiences.

Historical Data Collection is intended to accurately reconstruct past indicator scores. Age-based breakdowns must be excluded because age is a time-relative value that cannot be reliably recalculated for historical points in time.

For the indicator Number of open and overdue incidents, breakdowns such as Location, Category, and State can be historically recalculated by evaluating record attributes at specific points in time. However, Age depends on the exact moment of evaluation and changes continuously, making it unsuitable for historical backfilling.

ServiceNow documentation explicitly warns against using age or duration-based breakdowns in historical data collection jobs, as they lead to inaccurate or misleading results. Therefore, option A is the correct and documented exclusion.