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[General] Valid SPLK-3002 Learning Materials, Sample SPLK-3002 Questions Pdf

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【General】 Valid SPLK-3002 Learning Materials, Sample SPLK-3002 Questions Pdf

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The SPLK-3002 certification exam covers a range of topics related to ITSI implementation, configuration, and administration. It tests the candidate's ability to use ITSI to monitor and manage IT services, perform data analysis, create dashboards and reports, and troubleshoot issues. SPLK-3002 exam also covers topics such as service modeling, key performance indicators (KPIs), and machine learning.
Splunk SPLK-3002 exam is an excellent certification for IT professionals who want to demonstrate their skills and expertise in using Splunk IT Service Intelligence to manage IT services. SPLK-3002 Exam covers essential topics in ITSI, and passing it validates an individual's ability to configure and manage ITSI in a Splunk environment. With the right preparation, candidates can pass the exam and enhance their career prospects in the IT service management field.
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Splunk IT Service Intelligence Certified Admin Sample Questions (Q62-Q67):NEW QUESTION # 62
To use Adaptive Threshholding, what is the minimum requirement for a set of KPI data?
  • A. 14 days old.
  • B. 30 days old.
  • C. 7 days old.
  • D. 10 days old.
Answer: C
Explanation:
To utilize Adaptive Thresholding in Splunk IT Service Intelligence (ITSI), the minimum requirement for a set of Key Performance Indicator (KPI) data is that it must be at least 7 days old. Adaptive Thresholding uses historical data to dynamically adjust thresholds based on observed patterns and trends. Having a minimum of
7 days worth of data allows the system to analyze a sufficient amount of information to identify normal ranges and variances in KPI behavior, thereby setting more accurate and contextually relevant thresholds. This requirement ensures that the adaptive thresholds are based on a meaningful data set that reflects the typical operational conditions of the monitored services.

NEW QUESTION # 63
Which ITSI components are required before a module can be created?
  • A. One or more entity import saved searches.
  • B. One or more datamodels.
  • C. One or more services with KPIs and their associated base searches.
  • D. One or more correlation searches and their associated entities.
Answer: B
Explanation:
Before a module can be created in Splunk IT Service Intelligence (ITSI), it is essential to have one or more datamodels established. Datamodels in Splunk provide a structured format for organizing and interpreting data, which is crucial for modules within ITSI. Modules often rely on datamodels to extract, transform, and present data in a meaningful way, especially when dealing with complex datasets across various sources. Datamodels serve as the foundation for the module's ability to categorize and analyze data efficiently, enabling the creation of KPIs, services, and visualizations that are aligned with the specific needs of the module. Having these datamodels in place ensures that the module can function correctly and provide valuable insights into the monitored IT environments.

NEW QUESTION # 64
Which of the following is a good use case for a Multi-KPI alert?
  • A. Alerting when the values of two or more KPIs go into maintenance mode.
  • B. Alerting when comparing the values of two or more KPIs indicates an unusual condition is occurring.
  • C. Alerting when two or more KPIs are deviating from their typical pattern.
  • D. Alerting when the trend of two or more KPIs indicates service failure is imminent.
Answer: B
Explanation:
A Multi-KPI alert in Splunk IT Service Intelligence (ITSI) is designed to trigger based on the conditions of multiple Key Performance Indicators (KPIs). This type of alert is particularly useful when a single KPI's state is not sufficient to indicate an issue, but the correlation between multiple KPIs can provide a clearer picture of an emerging problem. The best use case for a Multi-KPI alert is therefore when comparing the values of two or more KPIs indicates an unusual condition is occurring. This allows for more nuanced and context-rich alerting mechanisms that can identify complex issues not detectable by monitoring individual KPIs. This approach is beneficial in complex environments where the interplay between different performance metrics needs to be considered to accurately detect and diagnose issues.

NEW QUESTION # 65
Which of the following is a problem requiring correction in ITSI?
  • A. Twoormore entitieswiththe same value in a single alias field.
  • B. Twoormore entitieswiththe same entity ID.
  • C. Twoormore entitieswiththe same entity key value inanyinfo field.
  • D. Twoormore entitieswiththe same service ID.
Answer: A
Explanation:
In Splunk IT Service Intelligence (ITSI), entities represent infrastructure components, applications, or other elements that are monitored. Each entity is uniquely identified by its entity ID, and entities can be associated with one or more services through the concept of aliases. A problem arises when two or more entities have the same value in a single alias field because aliases are used to match events to entities in ITSI. If multiple entities share the same alias value, ITSI might incorrectly associate data with the wrong entity, leading to inaccurate monitoring and analytics. This scenario requires correction to ensure that each alias uniquely identifies a single entity, thereby maintaining the integrity of the monitoring and analysis process within ITSI.
The uniqueness of service IDs, entity IDs, and entity key values in info fields is also important but does not typically present the same level of issue as duplicate values in an alias field.

NEW QUESTION # 66
Which of the following items apply to anomaly detection? (Choose all that apply.)
  • A. There are 3 types of anomaly detection supported in ITSI: adhoc, trending, and cohesive.
  • B. Use AD on KPIs that have an unestablished baseline of data points. This allows the ML pattern to perform it's magic.
  • C. A minimum of 24 hours of data is needed for anomaly detection, and a minimum of 4 entities for cohesive analysis.
  • D. Anomaly detection automatically generates notable events when KPI data diverges from the pattern.
Answer: C,D
Explanation:
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/AD
Anomaly detection is a feature of ITSI that uses machine learning to detect when KPI data deviates from a normal pattern. The following items apply to anomaly detection:
B). A minimum of 24 hours of data is needed for anomaly detection, and a minimum of 4 entities for cohesive analysis. This ensures that there is enough data to establish a baseline pattern and compare different entities within a service.
C). Anomaly detection automatically generates notable events when KPI data diverges from the pattern. You can configure the sensitivity and severity of the anomaly detection alerts and assign them to episodes or teams. References: [Anomaly Detection]

NEW QUESTION # 67
......
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