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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: ITOA Administration | 10% | - Configuring ITOA - ITOA dashboard overview |
| Topic 2: Service Discovery and Installation | 8% | - Service Discovery overview - Creating adaptive threshold templates - Service Discovery installation |
| Topic 3: ITSI Administration | 14% | - Managing services using deep dives - KPI base searches and aggregation - Backup and restore ITSI configuration - ITSI searches and scheduled searches |
| Topic 4: ITSI Analytics | 12% | - Notable Events and aggregation policies - Glass Table thresholds - Configuring aggregation policies |
| Topic 5: ITSI Overview and Concepts | 8% | - Entities and services - ITSI key concepts and terminology - ITSI product overview |
| Topic 6: Glass Tables | 14% | - Creating Glass Tables - Entity Drilldown - Configuring Glass Table elements |
| Topic 7: Advanced Topics | 16% | - Custom KPIs and calculations - Service-level availability reporting - ITSI and Splunk Enterprise Security integration - ITSI content management |
| Topic 8: ITSI Service Design | 18% | - Service Health Score calculation - Service dependencies and dependencies view - Entity templates and filters - Creating and configuring services |
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NEW QUESTION # 75
Which of the following describes default deep dives?
Answer: C
Explanation:
In Splunk IT Service Intelligence (ITSI), default deep dives are auto-generated and can be accessed via the Service Analyzer. Deep dives are an essential feature of ITSI that provide an in-depth, granular view into the health and performance of services and their associated KPIs. These default deep dives are automatically created for each service, allowing users to quickly drill down into the detailed operational metrics and performance data of their services. By accessing these deep dives through the Service Analyzer, ITSI users can efficiently investigate issues, understand service dependencies, and make informed decisions to maintain optimal service health. The auto-generated nature of these default deep dives simplifies the monitoring and analysis process, providing immediate insights into service performance without the need for manual setup or configuration.
NEW QUESTION # 76
Besides creating notable events, what are the default alert actions a correlation search can execute? (Choose all that apply.)
Answer: A,B,D
Explanation:
Throttling applies to any correlation search alert type, including notable events and actions (RSS feed, email, run script, and ticketing).
Reference:
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. Reference: Configure correlation search settings in ITSI
NEW QUESTION # 77
Which anomaly detection algorithm fulfills the paired monitoring requirement?
Answer: A
Explanation:
Splunk ITSI offers two built#in anomaly detection algorithms:TrendingandEntity Cohesion. TheTrending algorithm works on theaggregate KPI series, comparing recent KPI behavior with its historical pattern to detect unusual trending patterns over time. It doesnotevaluate behavior across separate entities within the KPI split - it simply looks at deviations from historical trends in the combined KPI values. On the other hand, the Entity Cohesionalgorithm is specifically designed to detect when entities that are expected to behave similarly begin to diverge in behavior. When a KPI is split by entity (for example, multiple servers, locations, or service tiers), Entity Cohesion normalizes each entity's time series and compares them against each other.
If one entity's pattern differs significantly from the group's patterns, it is flagged as an anomaly. This matches the "paired monitoring requirement" of producing an alert whenone entity in the KPI is not behaving similarly to the other entities. The option describing entity cohesion paired with that requirement reflects the correct use case for this algorithm in ITSI. Neither trending anomaly detection nor entity cohesion anomaly detection is intended to detect multiple KPIs deviating at the service level - such cross#KPI alerts are handled by other alerting constructs like multi#KPI alerts or correlation searches, not these specific anomaly algorithms.
NEW QUESTION # 78
Which of the following applies when configuring time policies for KPI thresholds?
Answer: C
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 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.
NEW QUESTION # 79
After a notable event has been closed, how long will the meta data for that event remain in the KV Store by default?
Answer: A
Explanation:
By default, notable event metadata is archived after six months to keep the KV store from growing too large.
NEW QUESTION # 80
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