Splunk IT Service Intelligence Certified Admin Exam - SPLK-3002 Exam Dumps

When onboarding data into a Splunk index, assuming that ITSI will need to use this data, you should consider the following:
B. Check if the data could leverage pre-built KPIs from modules, then use the correct TA to onboard the data. This is true because modules are pre-packaged sets of services, KPIs, and dashboards that are designed for specific types of data sources, such as operating systems, databases, web servers, and so on. Modules help you quickly set up and monitor your IT services using best practices and industry standards. To use modules, you need to install and configure the correct technical add-ons (TAs) that extract and normalize the data fields required by the modules.
The other options are not things you should consider because:
A. Use | stats functions in custom fields to prepare the data for KPI calculations. This is not true because using | stats functions in custom fields can cause performance issues and inaccurate results when calculating KPIs. You should use | stats functions only in base searches or ad hoc searches, not in custom fields.
C. Make sure that all fields conform to CIM, then use the corresponding module to import related services. This is not true because not all modules require CIM-compliant data sources. Some modules have their own data models and field extractions that are specific to their data sources. You should check the documentation of each module to see what data requirements and dependencies they have.
D. Plan to build as many data models as possible for ITSI to leverage. This is not true because building too many data models can cause performance issues and resource consumption in your Splunk environment. You should only build data models that are necessary and relevant for your ITSI use cases.

Explanation:
ITSI provides a kvstore_to_json.py script that lets you backup/restore ITSI configuration data, perform bulk service KPI operations, apply time zone offsets for ITSI objects, and regenerate KPI search schedules.
When you run a backup job, ITSI saves your data to a set of JSON files compressed into a single ZIP file.
C and D are correct answers because ITSI backup and restore functionality uses kvstore_to_json.py as a command line script or as part of custom scripts to backup ITSI datafor full or partial backups. ITSI backups are also stored as a collection of JSON formatted files that contain KV store objects such as services, KPIs, glass tables, etc. A is not a correct answer because there is no pre-configured default ITSI backup job provided. You can create your own backup jobs or use the command line script or custom scripts to backup ITSI data. B is not a correct answer because ITSI backup is not inclusive of index dependencies. ITSI backup only includes KV store objects and optionally some .conf files. You need to use other methods to backup index data.

A base search is a search definition that can be shared across multiple KPIs that use the same data source. Base searches can improve search performance and reduce search load by consolidating multiple similar KPIs. One of the characteristics of base searches is that it is possible to filter to entities assigned to the service for calculating the metrics for the service’s KPIs. This means that you can use entity filtering rules to specify which entities are relevant for each KPI based on the base search results.

Explanation: Time policies are user-defined threshold values to be used at different times of the day or week to account for changing KPI workloads. Time policies accommodate normal variations in usage across your services and improve the accuracy of KPI and service health scores. For example, if your organization’s peak activity is during the standard work week, you might create a KPI threshold time policy that accounts for higher levels of usage during work hours, and lower levels of usage during off-hours and weekends. The statement that applies when configuring time policies for KPI thresholds is:
B. They are great if you expect normal behavior at 1:00 to be different than normal behavior at 5:00. This is true because time policies allow you to define different thresholdvalues for different time blocks, such as AM/PM, work hours/off hours, weekdays/weekends, and so on. This way, you can account for the expected variations in your KPI data based on the time of day or week.
The other statements do not apply because:
A. A person can only configure 24 policies, one for each hour of the day. This is not true because you can configure more than 24 policies using different time block combinations, such as 3 hour block, 2 hour block, 1 hour block, and so on.
C. If a person expects a KPI to change significantly through a cycle on a daily basis, don’t use it. This is not true because time policies are designed to handle KPIs that change significantly through a cycle on a daily basis, such as web traffic volume or CPU load percent.
D. It is possible for multiple time policies to overlap. This is not true because you can only have one active time policy at any given time. When you create a new time policy, the previous time policy is overwritten and cannot be recovered.

A is the correct answer because a service must define entity rules if the intention is for the KPIs in the service to filter to only entities assigned to the service. Entity rules are filters that match entities to services based on entity aliases or entity metadata. If you enable the Filter to Entities in Service option for a KPI, you need to define entity rules for the service to ensure that the KPI search results only include the relevant entities for the service. Otherwise, the KPI search results might include entities that are not part of the service or exclude entities that are part of the service.

Explanation: Notable events in Splunk IT Service Intelligence (ITSI) are primarily generated through scheduled correlation searches. These searches are designed to monitor data for specific conditions or patterns defined by the ITSI administrator, and when these conditions are met, a notable event is created. These correlation searches are often linked to specific services or groups of services, allowing for targeted monitoring and alerting based on the operational needs of those services. This mechanism enables ITSI to provide timely and relevant alerts that can be further investigated and managed through the Episode Review dashboard, facilitating efficient incident response and management within the IT environment.

Explanation: In Splunk IT Service Intelligence (ITSI), creating teams for each department and assigning services to those teams is an effective way to segregate data and ensure that information remains private between different groups within an organization. Teams in ITSI provide a mechanism for role-based access control, allowing administrators to define which users or groups have access to specific services, KPIs, and dashboards. By setting up teams corresponding to each department and then assigning services to these teams, ITSI canaccommodate multi-departmental use within the same instance while maintaining strict access controls. This ensures that each department can only view and interact with the data and services relevant to their operations, preserving confidentiality and data integrity across the organization.

Explanation:
It's a deduplicated group of notable events occurring as part of a larger sequence, or an incident or period considered in isolation.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/EA/EpisodeOverview
An episode is a deduplicated group of notable events occurring as part of a larger sequence, or an incident or period considered in isolation. An episode helps you reduce alert noise and focus on the most important issues affecting your IT services. An episode is created by an aggregation policy, which is a set of rules that determines how to group notable events based on certain criteria, such as severity, source, title, and so on. You can use episode review to view, manage, and resolve episodes in ITSI. The statement that defines an episode is:
C. A notable event group. This is true because an episode is composed of one or more notable events that are related by some common factor.
The other options are not definitions of an episode because:
A. A workflow task. This is not true because a workflow task is an action that you can perform on an episode, such as assigning an owner, changing the status, adding comments, and so on.
B. A deep dive. This is not true because a deep dive is a dashboard that allows you to analyze the historical trends and anomalies of your KPIs and metrics in ITSI.
D. A notable event. This is not true because a notable event is an alert generated by ITSI based on certain conditions or correlations, not a group of alerts.

B is the correct answer because value over time is a valid type of Multi-KPI Alert in ITSI. A Multi-KPI Alert is a type of alert that triggers when multiple KPIs from one or more services meet certain conditions within a specified time range. Value over time is a condition that compares the current value of a KPI to its previous values over a specified time range. For example, you can create a Multi-KPI Alert that triggers when the CPU usage and memory usage of a service are both higher than their average values in the last 24 hours.

Explanation: In Splunk IT Service Intelligence (ITSI), administrators can manually control the grouping of notable events using aggregation policies. Aggregation policies allow for the definition of criteria based on which notable events are grouped together. This includes configuring rules based on event fields, severity, source, or other event attributes. Through these policies, administrators can tailor the event grouping logic to meet the specific needs of their environment, ensuring that related events are grouped in a manner that facilitates efficient analysis and response. This feature is crucial for managing the volume of events and focusing on the most critical issues by effectively organizing related events into manageable groups.