CIS-PA Certified Implementation Specialist - Platform Analytics Question and Answers

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.

In Platform Analytics, aBreakdownis used togroup or filter indicator scoresbased on specific attributes, such as priority, category, assignment group, or age ranges. Breakdowns allow users to analyze how different segments contribute to overall performance and to compare trends across those segments over time.

Breakdowns operate onindicator scores, not on raw report data. While reports can also be grouped or filtered, that functionality is separate from Performance Analytics. A Breakdown does not define the source table itself (that is the role of the Breakdown Source), nor is it merely a choice list. ServiceNow documentation clearly defines Breakdowns as a core analytics concept used to slice indicator data for deeper performance insight, making option D 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 Platform Analytics, thecolor of the score displayed in a Score widgetis determined by theIndicator’s relationship to its targetin combination with theDirection setting(Maximize or Minimize) of the Indicator. This behavior is part of the KPI evaluation logic and is consistent across dashboards and KPI Details.

When an indicator has a defined target, Platform Analytics compares thecurrent scoreagainst that target. Based on whether the indicator is configured tomaximize(higher is better) orminimize(lower is better), the platform automatically assigns a visual status—such as green (on track), yellow (warning), or red (off track). This status directly controls thecolor of the score valueshown in the widget.

Chart colors, field styles, or widget-specific settings donotinfluence the score color. Those options may affect line charts or visual styling, but not KPI status coloring. ServiceNow documentation clearly states that KPI status and score coloring are driven bytarget evaluation logic, makingoption Athe correct and verified answer.

Dashboard filters retain their values across page refreshes and user navigation throughsession-based persistence. When this option is enabled, filter selections are stored for the duration of the user’s session and automatically reapplied as the user navigates or refreshes the page.

Favorites and global defaults define starting values, not persistence behavior. Applying a filter to the entire dashboard controls scope, not retention. ServiceNow documentation explains that session persistence is the mechanism that maintains filter state, making option C the correct answer.

In KPI Details, Platform Analytics usesforecastingto predict whether an indicator is likely to meet its defined target within the specified timeframe. When the forecast shows that the indicator is expected to reach the target, theKPI status is automatically set to “On Track.”

This status update helps stakeholders quickly assess performance without manual interpretation of trend lines. Platform Analytics does not automatically create new targets, generate comments, or prompt target owners to change review dates as part of the forecasting process. Those actions remain manual and governed by performance review practices. ServiceNow documentation explains that KPI status reflects the relationship between current performance, targets, and forecasted trends, making option C the correct answer.

In Platform Analytics, aTargetdefines the desired performance level for an indicator. TheReview Dateproperty specifies therecommended date to evaluate progresstoward that target. This date is used for governance and performance review purposes, helping stakeholders determine when to assess whether the indicator is trending appropriately toward its goal.

TheBaseline Datemarks the starting point for measurement, while theEnd Datedefines when the target expires or is no longer valid. TheImprovement Dateindicates when improvement efforts begin, not when performance should be evaluated. ServiceNow documentation clearly identifies the Review Date as the point in time when the target’s effectiveness and achievement should be assessed, making option D the correct answer.

Formula Indicators in Platform Analytics arepre-calculated during data collectionand their results arestoredin the Indicator Facts table, just like automated indicators. They are not calculated in real time when a dashboard is viewed. Instead, they use the previously collected scores of their component indicators to compute a derived value at collection time.

This design ensures consistent historical analysis, predictable performance, and accurate trending. Real-time calculation would undermine historical integrity and dashboard performance, which is why ServiceNow explicitly avoids it for formula indicators. The stored results can then be reused across dashboards and reports without recalculation. ServiceNow documentation clearly states that formula indicators behave like other indicators in that they arepersisted for future analysis, making option C correct.

InPerformance Analytics Formula Indicators, indicator references must followstrict syntax rules:

Indicator names must be enclosed insingle square brackets:[Indicator name]

Mathematical operators must be valid

Parentheses must be properly paired

Curly braces { } and double square brackets [[ ]] arenot validfor indicator references

Let’s evaluate each option:

Option A – IncorrectUses indicator nameswithout square brackets, which is invalid syntax.

Option B – IncorrectUsescurly braces { }, which are not supported for indicator references, and also has ambiguous operator precedence.

Option C – CorrectUses propersingle square brackets, valid arithmetic operators, and correctly nested parentheses. This is a common and documented formula pattern for calculating percentage improvement against a baseline.

Option D – CorrectUses valid square-bracket indicator references and proper arithmetic. Even though it is logically redundant, it issyntactically valid, which is what the question asks.

Option E – IncorrectUsesdouble square bracketsand mismatched braces { ), which is invalid syntax.

ServiceNow best practice recommends running Performance Analytics data collections using a dedicated service account specifically created for analytics processing. This ensures consistent permissions, predictable access to data, and improved auditability.

Running collections as individual admins can introduce permission inconsistencies if roles change. The maint account is intended for system upgrades and maintenance, not scheduled analytics jobs. A dedicated Performance Analytics service account minimizes operational risk and aligns with ServiceNow security and governance guidelines.

ServiceNow documentation emphasizes the importance of service accounts for background processing, making option C the correct and recommended choice.

In ServiceNow Platform Analytics, aBreakdown Sourceis automatically disabled when the number of generated breakdown elements exceeds themaximum element limitdefined in system properties (for example, properties controlling the maximum allowed breakdown elements during data collection). This safeguard exists to protect system performance and prevent excessive storage and processing during data collection jobs.

When this error occurs, therecommended and documented resolutionisnotto increase the system property arbitrarily. Increasing the global limit can negatively impact performance across all analytics workloads and is discouraged unless there is a strong architectural justification.

Instead, ServiceNow best practice is torefine the Breakdown Source conditions—for example, by adding filters, narrowing scope, or excluding unnecessary records—so that the number of returned elements fallsbelow the configured system threshold. Once the conditions are optimized, you can safely re-enableDC active, and the breakdown will participate in data collection successfully.

Simply re-enabling DC active without reducing elements will cause the breakdown to be disabled again, and deleting/recreating the source does not address the root cause. Therefore,option Dis the correct and fully supported resolution according to ServiceNow Platform Analytics documentation and best practices.

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.

AnAutomated Indicatorin Platform Analytics is defined as aseries of measurements collected over timethat represent the performance of a process. These measurements are stored astime series data, allowing organizations to analyze trends, patterns, and historical performance. Automated indicators rely onindicator sourcesandscheduled data collection jobsto collect data at defined intervals, such as daily or hourly.

Option B describes asnapshot report, which represents data at a single point in time and does not support trending. Option C refers tobreakdowns, which categorize indicator scores for deeper analysis but do not define the indicator itself. Option D describes thedata collection job, which is a mechanism used by automated indicators but not the indicator definition. ServiceNow documentation explicitly states that indicators represent performance over time, making option A the correct and most complete description of an Automated Indicator.

Ascripted Breakdown Mappingis required when there isno direct field relationshipbetween the Indicator source data and the Breakdown source table. In such cases, standard field mapping cannot resolve how indicator records should be categorized, so a script is needed to programmatically determine the correct breakdown value.

Mapping to a Sys ID field (option B) is supported through standard mappings. Database views (option C) can still be mapped if fields are accessible. Dot-walked fields (option D) are commonly supported without scripting. According to ServiceNow Platform Analytics documentation, scripted mappings are specifically intended forcomplex or indirect relationships, 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.