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| Certification Vendor: | Splunk |
|---|---|
| Exam Name: | Splunk O11y Cloud Certified Metrics User |
| Exam Number: | SPLK-4001 |
| Available Languages: | English |
| Real Exam Qty: | 54 |
| Exam Duration: | 60 minutes |
| Related Certifications: | Splunk O11y Cloud Certified Metrics User |
| Sample Questions: | Splunk SPLK-4001 Sample Questions |
| Pre Condition: | No prerequisite exams for this certification. |
| Official Syllabus URL: | https://www.splunk.com/en_us/training/certification-track/splunk-o11y-cloud-certified-metrics-user.html |
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Splunk is a data analytics platform that helps organizations gain real-time insights into their data. One of the most important features of Splunk is its ability to provide visibility into metrics and logs, allowing organizations to monitor and troubleshoot their systems in real-time. To demonstrate expertise in this area, Splunk offers the SPLK-4001 Exam, which is designed to test an individual's knowledge of Splunk O11y Cloud Certified Metrics.
NEW QUESTION # 109
An SRE came across an existing detector that is a good starting point for a detector they want to create. They clone the detector, update the metric, and add multiple new signals. As a result of the cloned detector, which of the following is true?
Answer: A
Explanation:
According to the Splunk O11y Cloud Certified Metrics User Track document1, cloning a detector creates a copy of the detector that you can modify without affecting the original detector. You can change the metric, filter, and signal settings of the cloned detector. However, the new signals that you add to the cloned detector will not be reflected in the original detector, nor in the original chart that the detector was based on.
NEW QUESTION # 110
What Pod conditions does the Analyzer panel in Kubernetes Navigator monitor? (select all that apply)
Answer: A,B,C,D
Explanation:
The Pod conditions that the Analyzer panel in Kubernetes Navigator monitors are:
Not Scheduled: This condition indicates that the Pod has not been assigned to a Node yet. This could be due to insufficient resources, node affinity, or other scheduling constraints. Unknown:
This condition indicates that the Pod status could not be obtained or is not known by the system.
This could be due to communication errors, node failures, or other unexpected situations. Failed:
This condition indicates that the Pod has terminated in a failure state. This could be due to errors in the application code, container configuration, or external factors. Pending: This condition indicates that the Pod has been accepted by the system, but one or more of its containers has not been created or started yet. This could be due to image pulling, volume mounting, or network issues.
NEW QUESTION # 111
A user wants to create an alert that only fires if CPU usage is above 90% AND memory usage is above 85% at the same time. What should they configure?
Answer: A
Explanation:
Splunk Observability Cloud allows users to build detectors using signal expressions that combine multiple conditions (such as AND logic across two metrics), enabling alerts that trigger only when all specified conditions are met simultaneously.
NEW QUESTION # 112
A user wants to see related dashboards, runbooks, or external documentation directly from a chart. Which feature should they configure?
Answer: A
Explanation:
Data Links allow users to configure contextual links from charts or dashboards to external resources, such as runbooks, documentation, or related dashboards, streamlining the troubleshooting workflow.
NEW QUESTION # 113
A customer deals with a holiday rush of traffic during November each year, but does not want to be flooded with alerts when this happens. The increase in traffic is expected and consistent each year. Which detector condition should be used when creating a detector for this data?
Answer: A
Explanation:
Explanation
historical anomaly is a detector condition that allows you to trigger an alert when a signal deviates from its historical pattern1. Historical anomaly uses machine learning to learn the normal behavior of a signal based on its past data, and then compares the current value of the signal with the expected value based on the learned pattern1. You can use historical anomaly to detect unusual changes in a signal that are not explained by seasonality, trends, or cycles1.
Historical anomaly is suitable for creating a detector for the customer's data, because it can account for the expected and consistent increase in traffic during November each year. Historical anomaly can learn that the traffic pattern has a seasonal component that peaks in November, and then adjust the expected value of the traffic accordingly1. This way, historical anomaly can avoid triggering alerts when the traffic increases in November, as this is not an anomaly, but rather a normal variation. However, historical anomaly can still trigger alerts when the traffic deviates from the historical pattern in other ways, such as if it drops significantly or spikes unexpectedly1.
NEW QUESTION # 114
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