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Splunk SPLK-3002 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: ITSI Analytics	12%	- Configuring aggregation policies - Notable Events and aggregation policies - Glass Table thresholds
Topic 2: ITSI Overview and Concepts	8%	- ITSI product overview - ITSI key concepts and terminology - Entities and services
Topic 3: Advanced Topics	16%	- Custom KPIs and calculations - ITSI content management - Service-level availability reporting - ITSI and Splunk Enterprise Security integration
Topic 4: ITSI Administration	14%	- KPI base searches and aggregation - Backup and restore ITSI configuration - ITSI searches and scheduled searches - Managing services using deep dives
Topic 5: Glass Tables	14%	- Configuring Glass Table elements - Entity Drilldown - Creating Glass Tables
Topic 6: Service Discovery and Installation	8%	- Service Discovery installation - Service Discovery overview - Creating adaptive threshold templates
Topic 7: ITOA Administration	10%	- ITOA dashboard overview - Configuring ITOA
Topic 8: ITSI Service Design	18%	- Entity templates and filters - Creating and configuring services - Service dependencies and dependencies view - Service Health Score calculation

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Splunk IT Service Intelligence Certified Admin Sample Questions (Q50-Q55):

NEW QUESTION # 50
Which of the following is a characteristic of notable event groups?

A. All of the above.
B. Notable event groups allow users to adjust threshold settings.
C. Notable event groups are created in the itsi_tracked_alerts index.
D. Notable event groups combine independent notable events.

Answer: D

Explanation:
In Splunk IT Service Intelligence (ITSI), notable event groups are used to logically group related notable events, which enhances the manageability and analysis of events:
A).Notable event groups combine independent notable events:This characteristic allows for the aggregation of related events into a single group, making it easier for users to manage and investigate related issues. By grouping events, users can focus on the broader context of an issue rather than getting lost in the details of individual events.
While notable event groups play a critical role in organizing and managing events in ITSI, they do not inherently allow users to adjust threshold settings, which is typically handled at the KPI or service level.
Additionally, while notable event groups are utilized within the ITSI framework, the statement that they are created in the 'itsi_tracked_alerts' index might not fully capture the complexity of how event groups are managed and stored within the ITSI architecture.

NEW QUESTION # 51
How should entities be handled during the data audit phase of requirements gathering?

A. Entity meta-data for info and aliases should be identified and recorded as requirements.
B. Entities must be identified for every Service KPI defined and recorded in requirements.
C. Entities identified should be included in the entity filtering requirements, such as 'by processld' or 'by host'.
D. Entities should be noted based upon Service KPI requirements such as 'by host' or 'by product line'.

Answer: A

Explanation:
During the data audit phase of requirements gathering for Splunk IT Service Intelligence (ITSI), it's crucial to identify and record the meta-data for entities, focusing on information (info) and aliases. This step involves understanding and documenting the key attributes and identifiers that describe each entity, such as host names, IP addresses, device types, or other relevant characteristics. These attributes are used to categorize and uniquely identify entities within ITSI, enabling more effective mapping of data to services and KPIs. By meticulously recording this meta-data, organizations ensure that their ITSI implementation is aligned with their specific monitoring needs and infrastructure, facilitating accurate service modeling and event management. This practice is foundational for setting up ITSI to reflect the actual IT environment, enhancing the relevance and effectiveness of the monitoring and analysis capabilities.

NEW QUESTION # 52
Which of the following best describes a default deep dive?

A. It initially shows all the entity swim lanes.
B. It initially shows the highest importance KPIs.
C. It initially shows all of the KPIs for a selected service.
D. It initially shows the health scores for all services.

Answer: C

Explanation:
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/DeepDives C is the correct answer because a default deep dive initially shows all of the KPIs for a selected service. You can create a default deep dive by drilling down from another dashboard or by selecting a service from the deep dive lister page. A default deep dive does not show health scores, importance scores, or entity swim lanes by default. References: [Create default deep dives for services in ITSI]

NEW QUESTION # 53
Which of the following is a characteristic of base searches?

A. Search expression, entity splitting rules, and thresholds are configured at the base search level.
B. The fewer KPIs that share a common base search, the more efficiency a base search provides, and anomaly detection is more efficient.
C. The base search will execute whether or not a KPI needs it.
D. It is possible to filter to entities assigned to the service for calculating the metrics for the service's KPIs.

Answer: D

Explanation:
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/BaseSearch 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. References: Create KPI base searches in ITSI, [Filter entities for KPIs based on base searches]

NEW QUESTION # 54
Which of the following items apply to anomaly detection? (Choose all that apply.)

A. Anomaly detection automatically generates notable events when KPI data diverges from the pattern.
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. There are 3 types of anomaly detection supported in ITSI: adhoc, trending, and cohesive.

Answer: A,C

NEW QUESTION # 55
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