Updated Dumps for SPLK-3002 Exam

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SPLK-3002
Exam Name: Splunk IT Service Intelligence Certified
Admin Exam
Full version: 90 Q&As
Full version of SPLK-3002 Dumps
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1. What is the range for a normal Service Health score category?
A. 20-40
B. 40-60
C. 60-80
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D. 80-100
Answer: D
Explanation:
In Splunk IT Service Intelligence (ITSI), the Service Health Score is a metric that provides a
quantifiable measure of the overall health and performance of a service. The score ranges from
0 to 100, with higher scores indicating better health. The range for a normal Service Health
score category is typically from 80 to 100. Scores within this range suggest that the service is
performing well, with no significant issues affecting its health. This categorization helps IT and
business stakeholders quickly assess the operational status of their services, enabling them to
focus on services that may require attention or intervention due to lower health scores.
2. Which index is used to store KPI values?
A. itsi_summary_metrics
B. itsi_metrics
C. itsi_service_health
D. itsi_summary
Answer: A
Explanation:
The IT Service Intelligence (ITSI) metrics summary index, itsi_summary_metrics, is a metrics-
based summary index that stores KPI data.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/Configure/MetricsIndexRef
A is the correct answer because the itsi_summary_metrics index is used to store KPI values in
ITSI. This index improves the performance of the searches dispatched by ITSI, particularly for
very large environments. Every KPI is summarized in both the itsi_summary events index and
the itsi_summary_metrics metrics index.
Reference: Overview of ITSI indexes
3. Which of the following services often has KPIs but no entities?
A. Security Service.
B. Network Service.
C. Business Service.
D. Technical Service.
Answer: C
Explanation:
In the context of Splunk IT Service Intelligence (ITSI), a Business Service often has Key
Performance Indicators (KPIs) but might not have directly associated entities. Business
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Services represent high-level aggregations of organizational functions or processes and are
typically measured by KPIs that reflect the performance of underlying technical services or
components rather than direct infrastructure entities .
For example, a Business Service might monitor overall transaction completion times or
customer satisfaction scores, which are abstracted from the specific technical entities that
underlie these metrics. This abstraction allows Business Services to provide a business-centric
view of IT health and performance, focusing on outcomes rather than specific technical
components.
4. Which of the following applies when configuring time policies for KPI thresholds?
A. A person can only configure 24 policies, one for each hour of the day.
B. They are great if you expect normal behavior at 1:00 to be different than normal behavior at
5:00
C. If a person expects a KPI to change significantly through a cycle on a daily basis, don’t use
it.
D. It is possible for multiple time policies to overlap.
Answer: B
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 threshold values 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
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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.
Reference: Create time-based static KPI thresholds in ITSI
5. Which of the following describes a realistic troubleshooting workflow in ITSI?
A. Correlation Search C> Deep Dive C> Notable Event
B. Service Analyzer C> Notable Event Review C> Deep Dive
C. Service Analyzer C> Aggregation Policy C> Deep Dive
D. Correlation search C> KPI C> Aggregation Policy
Answer: B
Explanation:
A realistic troubleshooting workflow in ITSI is:
B) Service Analyzer C> Notable Event Review C> Deep Dive
This workflow involves using the Service Analyzer dashboard to monitor the health and
performance of your services and KPIs, using the Notable Event Review dashboard to
investigate and manage the notable events generated by ITSI, and using the Deep Dive
dashboard to analyze the historical trends and anomalies of your KPIs and metrics.
The other workflows are not realistic because they involve components that are not part of the
troubleshooting process, such as correlation search, aggregation policy, and KPI. These
components are used to create and configure the alerts and episodes that ITSI generates, not
to investigate and resolve them.
Reference: [Service Analyzer dashboard in ITSI], Overview of Episode Review in ITSI,
[Overview of deep dives in ITSI]
6. Which capabilities are enabled through “teams”?
A. Teams allow searches against the itsi_summary index.
B. Teams restrict notable event alert actions.
C. Teams restrict searches against the itsi_notable_audit index.
D. Teams allow restrictions to service content in UI views.
Answer: D
Explanation:
D is the correct answer because teams allow you to restrict access to service content in UI
views such as service analyzers, glass tables, deep dives, and episode review. Teams also
control access to services and KPIs for editing and viewing purposes. Teams do not affect the
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ability to search against the itsi_summary index, restrict notable event alert actions, or restrict
searches against the itsi_notable_audit index.
Reference: Overview of teams in ITSI
7. Which index contains ITSI Episodes?
A. itsi_tracked_alerts
B. itsi_grouped_alerts
C. itsi_notable_archive
D. itsi_summary
Answer: B
Explanation:
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/Configure/IndexOverview
B is the correct answer because ITSI episodes are stored in the itsi_grouped_alerts index. This
index contains notable events that have been grouped together based on predefined
aggregation policies. Episodes help you reduce alert noise and focus on resolving incidents
faster.
Reference: [Overview of episodes in ITSI]
8. When troubleshooting KPI search performance, which search names in job activity identify
base searches?
A. Indicator - XXXX - Base Search
B. Indicator - Shared - xxxx - ITSI Search
C. Indicator - Base - xxxx - ITSI Search
D. Indicator - Base - XXXX - Shared Search
Answer: B
Explanation:In the context of troubleshooting KPI search performance in Splunk IT Service Intelligence
(ITSI), the search names in the job activity that identify base searches typically follow the
pattern "Indicator - Shared - xxxx - ITSI Search." These base searches are fundamental
components of the KPI calculation process, aggregating and preparing data for further analysis
by KPIs. Identifying these base searches in the job activity is crucial for diagnosing performance
issues, as these searches can be resource-intensive and impact overall system performance.
Understanding the naming convention helps administrators and analysts quickly pinpoint the
base searches related to specific KPIs, facilitating more effective troubleshooting and
optimization of search performance within the ITSI environment.
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9. Which of the following is a characteristic of custom deep dives?
A. Allows itoa_analyst roles to add comments.
B. Requires at least 7 days' data to show anomalies.
C. Combines metric, event, KPI, and service health score lanes.
D. Uses drilldown to generate notable events via anomaly detection.
Answer: C
Explanation:
Custom deep dives in Splunk IT Service Intelligence (ITSI) are versatile and highly customizable
dashboards that allow users to analyze various types of data in a unified view. One of the key
characteristics of custom deep dives is their ability to combine lanes of different data types,
such as metrics, events, Key Performance Indicators (KPIs), and service health scores. This
multifaceted approach provides a comprehensive and layered view of the IT environment,
enabling analysts and operators to correlate different data types and gain deeper insights into
the health and performance of services. By incorporating these diverse data lanes, custom deep
dives facilitate a more holistic understanding of the operational landscape, aiding in more
effective troubleshooting and decision-making.
10. Which index will contain useful error messages when troubleshooting ITSI issues?
A. _introspection
B. _internal
C. itsi_summary
D. itsi_notable_audit
Answer: B
Explanation:
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/EA/TroubleshootRE
The index that will contain useful error messages when troubleshooting ITSI issues is:
B) _internal. This is true because the _internal index contains logs and metrics generated by
Splunk processes, such as splunkd and metrics.log. These logs can help you diagnose
problems with your Splunk environment, including ITSI components and features.
The other indexes will not contain useful error messages because:
A) _introspection. This is not true because the _introspection index contains data about Splunk
resource usage, such as CPU, memory, disk space, and so on. These data can help you
monitor the performance and health of your Splunk environment, but not the error messages.
C) itsi_summary. This is not true because the itsi_summary index contains summarized data for
your KPIs and services, such as health scores, severity levels, threshold values, and so on.
These data can help you analyze the trends and anomalies of your IT services, but not the error
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messages.
D) itsi_notable_audit. This is not true because the itsi_notable_audit index contains audit data
for your notable events and episodes, such as creation time, owner
11. Which of the following is an advantage of using adaptive time thresholds?
A. Automatically update thresholds daily to manage dynamic changes to KPI values.
B. Automatically adjust KPI calculation to manage dynamic event data.
C. Automatically adjust aggregation policy grouping to manage escalating severity.
D. Automatically adjust correlation search thresholds to adjust sensitivity over time.
Answer: A
Explanation:
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/TimePolicies
Adaptive thresholds are thresholds calculated by machine learning algorithms that dynamically
adapt and change based on the KPI’s observed behavior. Adaptive thresholds are useful for
monitoring KPIs that have unpredictable or seasonal patterns that are difficult to capture with
static thresholds .
For example, you might use adaptive thresholds for a KPI that measures web traffic volume,
which can vary depending on factors such as holidays, promotions, events, and so on. The
advantage of using adaptive thresholds is:
A) Automatically update thresholds daily to manage dynamic changes to KPI values. This is true
because adaptive thresholds use historical data from a training window to generate threshold
values for each time block in a threshold template. Each night at midnight, ITSI recalculates
adaptive threshold values for a KPI by organizing the data from the training window into distinct
buckets and then analyzing each bucket separately. This way, the thresholds reflect the most
recent changes in the KPI data and account for any anomalies or trends.
The other options are not advantages of using adaptive thresholds because:
B) Automatically adjust KPI calculation to manage dynamic event data. This is not true because
adaptive thresholds do not affect the KPI calculation, which is based on the base search and
the aggregation method. Adaptive thresholds only affect the threshold values that are used to
determine the KPI severity level.
C) Automatically adjust aggregation policy grouping to manage escalating severity. This is not
true
because adaptive thresholds do not affect the aggregation policy, which is a set of rules that
determines how to group notable events into episodes. Adaptive thresholds only affect the
threshold values that are used to generate notable events based on KPI severity level.
D) Automatically adjust correlation search thresholds to adjust sensitivity over time. This is not
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true because adaptive thresholds do not affect the correlation search, which is a search that
looks for relationships between data points and generates notable events. Adaptive thresholds
only affect the threshold values that are used by KPIs, which can be used as inputs for
correlation searches.
Reference: Create adaptive KPI thresholds in ITSI
12. Which of the following is a best practice when configuring maintenance windows?
A. Disable any glass tables that reference a KPI that is part of an open maintenance window.
B. Develop a strategy for configuring a service’s notable event generation when the service’s
maintenance window is open.
C. Give the maintenance window a buffer, for example, 15 minutes before and after actual
maintenance work.
D. Change the color of services and entities that are part of an open maintenance window in the
service analyzer.
Answer: C
Explanation:
It's a best practice to schedule maintenance windows with a 15- to 30-minute time buffer before
and after you start and stop your maintenance work.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/Configure/AboutMW
A maintenance window is a period of time when a service or entity is undergoing maintenance
operations or does not require active monitoring. It is a best practice to schedule maintenance
windows with a 15- to 30-minute time buffer before and after you start and stop your
maintenance work. This gives the system an opportunity to catch up with the maintenance state
and reduces the chances of ITSI generating false positives during maintenance operations .
For example, if a server will be shut down for maintenance at 1:00PM and restarted at 5:00PM,
the ideal maintenance window is 12:30PM to 5:30PM. The 15- to 30-minute time buffer is a
rough estimate based on 15 minutes being the time period over which most KPIs are configured
to search data and identify alert triggers.
Reference: Overview of maintenance windows in ITSI
13. Which of the following is a characteristic of notable event groups?
A. Notable event groups combine independent notable events.B. Notable event groups are created in the itsi_tracked_alerts index.
C. Notable event groups allow users to adjust threshold settings.
D. All of the above.
Answer: A
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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.
14. Which of the following items apply to anomaly detection? (Choose all that apply.)
A. Use AD on KPIs that have an unestablished baseline of data points. This allows the ML
pattern to perform it’s magic.
B. A minimum of 24 hours of data is needed for anomaly detection, and a minimum of 4 entities
for cohesive analysis.
C. Anomaly detection automatically generates notable events when KPI data diverges from the
pattern.
D. There are 3 types of anomaly detection supported in ITSI: adhoc, trending, and cohesive.
Answer: B, C
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.
Reference: [Anomaly Detection]
15. What is the default importance value for dependent services’ health scores?
A. 11
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B. 1
C. Unassigned
D. 10
Answer: D
Explanation:
By default, impacting service health scores have an importance value of 11.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/Dependencies
A service template is a predefined set of KPIs and entity rules that you can apply to a service or
a group of services. A service template helps you standardize the configuration and monitoring
of similar services across your IT environment. A service template can also include dependent
services, which are services that are required for another service to function properly .
For example, a web server service might depend on a database service and a network service.
The default importance value for dependent services’ health scores is:
D) 10. This is true because the importance value indicates how much a dependent service
contributes to the health score of the parent service. The default value is 10, which means that
the dependent service has the highest impact on the parent service’s health score. You can
change the importance value of a dependent service in the service template settings.
The other options are not correct because:
A) 11. This is not true because 11 is an invalid value for importance. The valid range is from 1
(lowest) to 10 (highest).
B) 1. This is not true because 1 is the lowest value for importance, not the default value. A value
of 1 means that the dependent service has the lowest impact on the parent service’s health
score.
C) Unassigned. This is not true because every dependent service has an assigned importance
value, which defaults to 10.
Reference: Create and manage service templates in ITSI, Set KPI importance values in ITSI
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