Question # 1
| Which deep dive swim lane type does not require writing SPL? | | A. Event lane. | | B. Automatic lane. | | C. Metric lane. | | D. KPI lane. |
D. KPI lane.
Explanation:
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.
Question # 2
| What are valid ITSI Glass Table editor capabilities? (Choose all that apply.) | | A. Creating glass tables.
| | B. Correlation search creation.
| | C. Service swapping configuration.
| | D. Adding KPI metric lanes to glass tables. |
A. Creating glass tables.
C. Service swapping configuration.
D. Adding KPI metric lanes to glass tables.
Explanation:
Create a glass table to visualize and monitor the interrelationships and dependencies
across your IT and business services.
The service swapping settings are saved and apply the next time you open the glass table.
You can add metrics like KPIs, ad hoc searches, and service health scores that update in
real time against a background that you design. Glass tables show real-time data
generated by KPIs and services.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.2/SI/GTOverview
The glass table editor is a tool that allows you to create and edit glass tables in ITSI. Some
of the capabilities of the glass table editor are:
Creating glass tables from scratch or from existing templates.
Configuring service swapping on widgets to toggle displaying metrics from different
services.
Adding KPI metric lanes to glass tables to show historical trends of KPI values.
The glass table editor does not support correlation search creation, which is a separate
feature in ITSI that allows you to create searches that look for relationships between data
points and generate notable events.
Question # 3
| What are valid considerations when designing an ITSI Service? (Choose all that apply.) | | A. Service access control requirements for ITSI Team Access should be considered, and
appropriate teams provisioned prior to creating the ITSI Service.
| | B. Entities, entity meta-data, and entity rules should be planned carefully to support the
service design and configuration.
| | C. Services, entities, and saved searches are stored in the ITSI app, while events created
by KPI execution are stored in the itsi_summary index.
| | D. Backfill of a KPI should always be selected so historical data points can be used
immediately and alerts based on that data can occur. |
A. Service access control requirements for ITSI Team Access should be considered, and
appropriate teams provisioned prior to creating the ITSI Service.
B. Entities, entity meta-data, and entity rules should be planned carefully to support the
service design and configuration.
C. Services, entities, and saved searches are stored in the ITSI app, while events created
by KPI execution are stored in the itsi_summary index.
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.
Question # 4
| Besides creating notable events, what are the default alert actions a correlation search can
execute? (Choose all that apply.) | | A. Ping a host.
| | B. Send email.
| | C. Include in RSS feed.
| | D. Run a script. |
B. Send email.
C. Include in RSS feed.
D. Run a script.
B, C, and D are correct answers because they are the default alert actions that a
correlation search can execute besides creating notable events. You can configure a
correlation search to send an email, include the results in an RSS feed, or run a custom
script when the search matches a defined pattern. Ping a host is not a default alert action
for correlation searches.
Question # 5
Which glass table feature can be used to toggle displaying KPI values from more than one service on a single widget?
| A. Service templates.
| B. Service dependencies.
| C. Ad-hoc search.
| D. Service swapping.
|
D. Service swapping.
Explanation:
Reference:
[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 hocsearches, 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. References: Overview of the glass table editor in ITSI, [Configure service swapping on glass tables]]
Question # 6
Which of the following are deployment recommendations for ITSI? (Choose all that apply.)
| | A. Deployments often require an increase of hardware resources above base Splunk requirements. | B. Deployments require a dedicated ITSI search head.
| C. Deployments may increase the number of required indexers based on the number of KPI searches.
| D. Deployments should use fastest possible disk arrays for indexers.
|
A. Deployments often require an increase of hardware resources above base Splunk requirements. B. Deployments require a dedicated ITSI search head.
C. Deployments may increase the number of required indexers based on the number of KPI searches.
Explanation:
You might need to increase the hardware specifications of your own Enterprise Security deployment above the minimum hardware requirements depending on your environment.
Install Splunk Enterprise Security on a dedicated search head or search head cluster.
The Splunk platform uses indexers to scale horizontally. The number of indexers required in an Enterprise Security deployment varies based on the data volume, data type, retention requirements, search type, and search concurrency.
Reference:
[Reference: https://docs.splunk.com/Documentation/ES/latest/Install/DeploymentPlanning, A, B, and C are correct answers because ITSI deployments often require more hardware resources than base Splunk requirements due to the high volume of data ingestion and processing. ITSI deployments also require a dedicated search head that runs the ITSI app and handles all ITSI-related searches and dashboards. ITSI deployments may also increase the number of required indexers based on the number and frequency of KPI searches, which can generate a large amount of summary data. References: ITSI deployment overview, ITSI deployment planning]
Question # 7
| 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. |
A. Automatically update thresholds daily to manage dynamic changes to KPI values.
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 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.
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