SPLK-3002 Practice Questions

Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/Configure/ImplementPerms

A, B, and C are correct answers because service access control requirements for ITSI Team Access

should be considered before creating the ITSI Service, as different teams may have different

permissions and views of the service data. Entities, entity meta-data, and entity rules should also be

planned carefully to support the service design and configuration, as they determine how ITSI maps

data sources to services and KPIs. Services, entities, and saved searches are stored in the ITSI app,

while events created by KPI execution are stored in the itsi_summary index for faster retrieval and

analysis. Reference: ITSI service design best practices, Overview of ITSI indexes

In the context of Smart Mode configuration within Splunk IT Service Intelligence (ITSI), the two

settings that control how fields affect grouping are "Text similarity" and "Category similarity." Smart

Mode is a feature used in event grouping that leverages machine learning to automatically group

related events. "Text similarity" refers to how closely the textual content of event fields must match

for those events to be grouped together, taking into account commonalities in strings or narratives

within the event data. "Category similarity," on the other hand, relates to the similarity in the

categorical attributes of events, such as event types or source types, which helps in clustering events

that are similar in nature or origin. Both of these settings are crucial in determining how events are

grouped in ITSI, influencing the granularity and relevance of the event groupings based on textual

and categorical similarities.

Deep dives in Splunk IT Service Intelligence (ITSI) allow for an in-depth analysis of services and their

KPIs over time, providing a detailed view of the operational health and performance trends. One of

the powerful actions that can be performed with a deep dive is the creation of a Multi-KPI alert from

the deep dive's current state. This functionality enables users to define alerts based on the complex

conditions observed during the deep dive analysis, allowing for the early detection of similar

situations in the future. By configuring a Multi-KPI alert directly from a deep dive, ITSI users can

leverage their insights and observations to proactively monitor for patterns or conditions that may

indicate potential service degradation or failure, enhancing the overall responsiveness and

effectiveness of the IT monitoring strategy.

A KPI lane is a type of deep dive swim lane that does not require writing SPL. You can simply select a

service and a KPI from a drop-down list and ITSI will automatically populate the lane with the

corresponding data. You can also adjust the threshold settings and time range for the KPI lane.

Reference: [KPI Lanes]

Creating a custom module in Splunk IT Service Intelligence (ITSI) is particularly beneficial for the

purpose of migrating KPI base searches and related visualizations to other ITSI installations. Custom

modules can encapsulate a set of configurations, searches, and visualizations that are tailored to

specific monitoring needs or environments. By packaging these elements into a module, it becomes

easier to transfer, deploy, and maintain consistency across different ITSI instances. This modularity

supports the reuse of developed components, simplifying the process of scaling and replicating

monitoring setups in diverse operational contexts. The ability to migrate these components

seamlessly enhances operational efficiency and ensures that best practices and custom

configurations can be shared across an organization's ITSI deployments.

Thank you for your visit.

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.

B is the correct answer because adaptive thresholding is a feature of ITSI that allows you to

dynamically adjust KPI thresholds based on historical patterns and trends. Adaptive thresholding

requires a time buffer of at least 15 minutes to calculate the thresholds based on the previous data

points. The time buffer ensures that there is enough data to perform the calculations and avoid false

positives or negatives. Reference: Configure adaptive thresholding for a KPI in ITSI

Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/Visualizations#collapseDesktop8

A glass table is a visualization tool that allows you to monitor the interrelationships and

dependencies across your IT and business services. You can add metrics like KPIs, ad hoc searches,

and service health scores that update in real time against a background that you design. One of the

features of glass tables is service swapping, which enables you to toggle displaying KPI values from

more than one service on a single widget. You can use service swapping to compare metrics across

different services without creating multiple glass tables or widgets. Reference: Overview of the glass

table editor in ITSI, [Configure service swapping on glass tables]

C is the correct answer because teams are a feature of ITSI that allow you to restrict access to service

content in UI views based on user roles. To create separate teams for finance and sales analysts, you

need to create custom roles that inherit from the itoa_analyst role, which has read-only access to ITSI

content. For example, you can create itoa_finance_analyst and itoa_sales_analyst roles that inherit

from itoa_analyst. Then, you need to create custom teams that include these roles and assign them

to the relevant services. For example, you can create a finance team that includes the

itoa_finance_analyst role and assign it to the finance services. Similarly, you can create a sales team

that includes the itoa_sales_analyst role and assign it to the sales services. This way, analysts in each

department can only see their own services and not each other’s. Reference: Create teams in

ITSI, Assign teams to services in ITSI

ITSI Saved Search Scheduling is a feature that allows you to schedule searches that run periodically to

populate the data for your KPIs. You can configure various settings for your scheduled searches, such

as the search frequency, the time range, the cron expression, and so on. One of the settings is

realtime_schedule, which controls the way the scheduler computes the next execution time of a

scheduled search. The statement that is accurate about this configuration is:

B) If this value is set to 0, the scheduler bases its determination of the next scheduled search on the

last search execution time. This is called continuous scheduling. If set to 0, the scheduler never skips

scheduled execution periods. However, the execution of the saved search might fall behind

depending on the scheduler’s load. Use continuous scheduling whenever you enable the summary

index option.

The other statements are not accurate because:

A) If this value is set to 0, the scheduler bases its determination of the next scheduled search

execution time on the current time. This is not true because this is what happens when the value is

set to 1, not 0.

C) If this value is set to 0, the scheduler may skip scheduled execution periods. This is not true

because this is what happens when the value is set to 1, not 0.

D) If this value is set to 0, the scheduler might skip some execution periods to make sure that the

scheduler is executing the searches running over the most recent time range. This is not true because

this is what happens when the value is set to 1, not 0.

Reference: Create KPI base searches in ITSI, Rrealtime_schedule in SavedSearches.conf