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Splunk SPLK-3002 exam is designed for IT professionals with experience in IT service management, data analysis, and IT operations. SPLK-3002 exam covers a range of topics, including the deployment and configuration of Splunk IT Service Intelligence, the collection and analysis of data, and the creation of dashboards and reports. SPLK-3002 Exam also covers the use of machine learning and other advanced analytics techniques to identify and resolve IT issues proactively.
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The SPLK-3002 exam covers a range of topics, including configuring ITSI, using the ITSI module framework, creating service models and KPIs, and leveraging analytics to identify and resolve issues. Candidates are also tested on their ability to use dashboards, visualizations, and alerts to monitor service health and performance. SPLK-3002 Exam consists of 60 multiple-choice questions and is administered online. Splunk IT Service Intelligence Certified Admin Sample Questions (Q96-Q101):NEW QUESTION # 96
Which ITSI functions generate notable events? (Choose all that apply.)
A. Multi-KPI alert.
B. KPI anomaly detection.
C. KPI threshold breaches.
D. Correlation search.
Answer: B,C,D
Explanation:
After you configure KPI thresholds, you can set up alerts to notify you when aggregate KPI severities change. ITSI generates notable events in Episode Review based on the alerting rules you configure.
Anomaly detection generates notable events when a KPI IT Service Intelligence (ITSI) deviates from an expected pattern.
Notable events are typically generated by a correlation search.
Reference: https://docs.splunk.com/Documentation/ITSI/4.10.1/SI/AboutSI
A, B, and D are correct answers because ITSI can generate notable events when a KPI breaches a threshold, when a KPI detects an anomaly, or when a correlation search matches a defined pattern. These are the main ways that ITSI can alert you to potential issues or incidents in your IT environment. Reference: Configure KPI thresholds in ITSI, Apply anomaly detection to a KPI in ITSI, Generate events with correlation searches in ITSI
NEW QUESTION # 97
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
Explanation:
Reference:
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]
NEW QUESTION # 98
For which ITSI function is it a best practice to use a 15-30 minute time buffer?
A. Anomaly detection.
B. Maintenance windows
C. Correlation searches.
D. Adaptive thresholding.
Answer: D
Explanation:
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
NEW QUESTION # 99
Which of the following items describe ITSI Backup and Restore functionality? (Choose all that apply.)
A. ITSI backup is inclusive of KV Store, ITSI Configurations, and index dependencies.
B. kvstore_to_json.py can be used in scripts or command line to backup ITSI for full or partial backups.
C. ITSI backups are stored as a collection of JSON formatted files.
D. A pre-configured default ITSI backup job is provided that can be modified, but not deleted.
Answer: B,C
Explanation:
Explanation
ITSI provides a kvstore_to_json.py script that lets you backup/restore ITSI configuration data, perform bulk service KPI operations, apply time zone offsets for ITSI objects, and regenerate KPI search schedules.
When you run a backup job, ITSI saves your data to a set of JSON files compressed into a single ZIP file.
NEW QUESTION # 100
Which of the following applies when configuring time policies for KPI thresholds?
A. If a person expects a KPI to change significantly through a cycle on a daily basis, don't use it.
B. It is possible for multiple time policies to overlap.
C. They are great if you expect normal behavior at 1:00 to be different than normal behavior at 5:00
D. A person can only configure 24 policies, one for each hour of the day.
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.