CIS-PA Exam dumps PDF | Exam-Dumps.net (Page: 2)

QUESTION: 9

Which method in ServiceNow can be used to calculate the rate of performance per reporting period using time series aggregations?

pa.getChange()
pa.getIndicator()
gs.getDuration()
pa.getRate()

Answer(s): D

Explanation:

The pa.getRate() method is used in Platform Analytics to calculate rates of performance over time, such as incidents resolved per day, requests closed per week, or changes per reporting period. This method works on time series data and applies aggregation logic to derive a rate rather than a raw count or sum.

pa.getChange() is used to calculate the difference between two data points, not a rate.

pa.getIndicator() retrieves indicator metadata and does not perform calculations. gs.getDuration() is a general-purpose GlideSystem utility for calculating durations and is unrelated to analytics time series processing. ServiceNow documentation clearly identifies pa.getRate() as the appropriate API for rate-based calculations using historical indicator scores, making option D the correct answer.

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QUESTION: 10

Which scenario requires a scripted Breakdown Mapping?

There is no direct mapping between the Indicator field and the Breakdown table
The field to map to is of type Sys ID
The table being mapped is a database view and not an actual table
The value needed for the Breakdown is available only as a dot-walked field

Answer(s): A

Explanation:

A scripted Breakdown Mapping is required when there is no direct field relationship between 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 for complex or indirect relationships, making option A the correct answer.

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QUESTION: 11

Which Indicator should be excluded from a Historic Data Collection because its scores cannot be accurately collected?

Number of new requests
Number of incidents resolved in time
Summed age of open problems
Number of open problems not updated in the last 90 days

Answer(s): C

Explanation:

Historic Data Collection is designed to accurately reconstruct past indicator scores based on historical records. Indicators that rely on calculated age values, such as summed age of open problems, cannot be accurately reconstructed because age is a time-relative value that depends on the exact moment of calculation.

Count-based indicators (options A, B, and D) can be recalculated historically by evaluating record states at specific points in time. However, summing age values requires knowing the precise age of each record at each historical interval, which is not reliably reproducible. ServiceNow documentation explicitly warns against using historic data collection for age-based and duration-sum indicators, making option C the correct exclusion.

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QUESTION: 12

Which statements describe the respective Performance Analytics object behavior?

Indicator Sources with a Monthly frequency can still be collected in a Daily job
Breakdowns require a Breakdown Mapping to be added to an Automated Indicator
The frequency of an Indicator can differ from that of an Indicator Source
Data collection must be completed before assigning Breakdowns to an Indicator

Answer(s): A,C

Explanation:

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

Option A is correct.

Indicator Sources define how and when raw data is queried, but they can be reused by multiple data collection jobs. Even if an Indicator Source is configured with a Monthly frequency, it can still be executed by a Daily 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.

The Indicator 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 incorrect because Breakdowns require a Breakdown Mapping, but they are not inherently tied only to Automated Indicators, nor is this statement describing object behavior accurately in isolation.

Option D is incorrect because Breakdowns can be assigned to an Indicator before or after data collection; they are applied when the next collection runs.

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QUESTION: 13

What should the target for the Index and its supporting indicators be set to when creating an Index Indicator?

0% and Maximize
100% and Minimize
0% and Minimize
100% and Maximize

Answer(s): D

Explanation:

An Index Indicator in Platform Analytics represents a composite score calculated from multiple supporting indicators. According to ServiceNow best practices, both the Index and its supporting indicators should be normalized so that higher values represent better performance. Therefore, the correct configuration is a target of 100% with a Maximize direction.

This standardization ensures consistent weighting and scoring logic across all contributing indicators. If supporting indicators were set to Minimize or had inconsistent targets, the index calculation would produce misleading or inverted results. Options involving a 0% target are incorrect because index scores are designed to trend toward full achievement, represented as 100%. ServiceNow documentation clearly states that index indicators assume maximization logic for proper normalization and aggregation, making option D the correct and documented choice.

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QUESTION: 14

Breakdown element security is configured in the properties of which object?

Automated Indicator
Manual Breakdown
Breakdown Source
Automated Breakdown

Answer(s): D

Explanation:

Breakdown element security determines which users are allowed to see specific breakdown elements (such as certain categories or values) when viewing analytics data. In Platform Analytics, this security is configured directly on the Automated Breakdown record.

Automated Breakdowns include properties that allow administrators to define element-level access control, typically by specifying roles that are required to view certain breakdown elements. This ensures sensitive analytics data is only visible to authorized users. The Breakdown Source defines how data is mapped and categorized but does not control visibility. Automated Indicators control score collection and aggregation, not breakdown element security. Manual Breakdowns are static and do not support dynamic element security in the same way.

ServiceNow documentation explicitly states that breakdown element security settings--such as restricting elements by role--are part of the Automated Breakdown configuration, making option D the correct answer.

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QUESTION: 15

Which configuration confirms that an Automated Breakdown is using a Bucket Group?

The Facts table of the Breakdown is set to [pa_buckets]
The Default elements filter of the Breakdown specifies the Bucket Groups
The Facts table of the Breakdown Source is set to [pa_buckets]
The Related list conditions of the Breakdown Source identify the Bucket Groups

Answer(s): C

Explanation:

An Automated Breakdown is confirmed to be using a Bucket Group when the Facts table of the Breakdown Source is set to Bucket [pa_buckets]. Bucket Groups define how numeric or duration values are grouped, but the actual bucketed analytics data is stored in the pa_buckets table during data collection.

The Breakdown Source is responsible for defining where the breakdown facts originate. If its Facts table is pa_buckets, this indicates that the breakdown is based on bucketed values generated by a Bucket Group. The Breakdown record itself does not define the facts table, and default element filters or related list conditions do not establish the use of bucket data.

ServiceNow Platform Analytics documentation clearly states that all bucket-based breakdowns must reference pa_buckets at the Breakdown Source level, making option C the correct and definitive answer.

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QUESTION: 16

Which scenarios require the use of a scripted Breakdown Mapping?

Categorizing requests based on the number of times they were updated
Categorizing incidents based on their category
Categorizing incidents based on their priority
Categorizing the length of time since tasks were updated into age ranges

Answer(s): A

Explanation:

A scripted Breakdown Mapping is required when the breakdown value cannot be obtained through a direct field mapping or simple reference relationship. Categorizing requests based on the number of times they were updated requires 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 uses age ranges, which are handled through Bucket Groups and the pa_buckets table, not scripted mappings. ServiceNow documentation clearly states that scripted mappings are intended for calculated, derived, or indirect values, making option A the correct answer.

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ServiceNow CIS-PA Exam (page: 2) ServiceNow Certified Implementation Specialist - Platform Analytics Updated on: 31-Mar-2026

QUESTION: 9

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QUESTION: 10

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QUESTION: 11

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QUESTION: 12

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QUESTION: 13

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QUESTION: 14

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QUESTION: 15

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QUESTION: 16

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ServiceNow CIS-PA Exam (page: 2)
ServiceNow Certified Implementation Specialist - Platform Analytics
Updated on: 31-Mar-2026