매달, 우리는 1000명 이상의 사람들이 시험 준비를 잘하고 시험을 잘 통과할 수 있도록 도와줍니다.
Certified Implementation Specialist - Platform Analytics 온라인 연습
최종 업데이트 시간: 2026년02월14일
당신은 온라인 연습 문제를 통해 ServiceNow CIS-PA 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 CIS-PA 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 60개의 시험 문제와 답을 포함하십시오.
정답:
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.
정답:
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.
정답:
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.
정답:
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.
정답:
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.
정답:
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.
정답:
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.
정답:
Explanation:
When a Bucket Group is used as a Breakdown Source in Platform Analytics, the required Facts table is Bucket [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 the pa_buckets table, which makes it the authoritative facts table for bucket-based breakdowns.
The pa_bucket_groups table only stores configuration metadata for bucket definitions. The sys_choice table 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 the pa_buckets table to ensure accurate historical analysis and proper breakdown rendering. Therefore, option C is the only correct answer.
정답:
Explanation:
When Collect records is enabled on an Automated Indicator, Platform Analytics stores the sys_ids of the records that contributed to each indicator score at collection time. This capability enables drill-down functionality, allowing users to view the exact records behind a score directly from analytics widgets and dashboards. Importantly, Platform Analytics does not store full copies of records―only the identifiers―ensuring historical accuracy while maintaining storage efficiency.
Option A describes the preview feature available when defining indicator conditions, not record collection.
Option B refers to manual 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.
정답:
Explanation:
Platform Analytics helps reduce IT spending by enabling cost visibility, trend analysis, and optimization insights. Breaking down incident resolution costs allows 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 supports strategic objectives such as cost reduction by revealing inefficiencies through indicators, breakdowns, and historical trend analysis― making option C the correct answer.
정답:
Explanation:
In Responsive Canvas dashboards, when a Time Series widget contains multiple elements, the Show multiple elements as property controls how those elements are visualized. Setting this property to Aggregate combines the values of all returned elements into a single trend line, which is exactly the desired outcome when viewing a combined trend for High and Critical priority incidents. Applying an elements filter (option D) limits which elements are displayed but does not combine them into one trend. Setting the property to Separate (option C) explicitly shows individual trend lines for each element. Manually adding elements (option B) still results in multiple distinct series unless aggregation is enabled. According to ServiceNow Platform Analytics documentation, aggregation is the correct method for consolidating multiple indicator elements into one unified visualization on a dashboard.
정답:
Explanation:
An Automated Indicator in Platform Analytics is defined as a series of measurements collected over time that represent the performance of a process. These measurements are stored as time series data, allowing organizations to analyze trends, patterns, and historical performance. Automated indicators rely on indicator sources and scheduled data collection jobs to collect data at defined intervals, such as daily or hourly.
Option B describes a snapshot report, which represents data at a single point in time and does not support trending.
Option C refers to breakdowns, which categorize indicator scores for deeper analysis but do not define the indicator itself.
Option D describes the data 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.
정답:
Explanation:
When creating a breakdown based on the age of a task, the correct Facts table for the Breakdown Source is Bucket [pa_buckets]. In Platform Analytics, age-based breakdowns (such as 0C5 days, 6C10 days, etc.) are not derived directly from the Task table. Instead, they use bucketed data, which is generated by bucket groups during data collection.
The pa_buckets table 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 the pa_buckets table to function correctly and produce accurate time series analytics.
정답:
Explanation:
The Visualization Library is the Platform Analytics feature that enables visualizations to be reused and shared across any dashboard. When a visualization (such as a time series, scorecard, or breakdown visualization) is saved to the Visualization Library, it becomes a reusable analytics component that 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, controls who 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.
정답:
Explanation:
In Platform Analytics, dashboards are shared through the dashboard Share action. In the Share Dashboard dialog, the Grant access to field explicitly allows you to enter one or more users, groups, or roles to share the dashboard with. This sharing controls whether recipients can view the dashboard or edit it, 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 as admin, 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 the Roles [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