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The Splunk SPLK-4001 exam covers various topics such as the basics of metrics, the use of metrics for monitoring and analysis, and the troubleshooting of metrics data. SPLK-4001 exam also includes questions on the use of Splunk's O11y Cloud platform, including the use of dashboards, alerting, and anomaly detection. SPLK-4001 Exam is designed to test the ability of individuals to work with different data types, including logs, metrics, and traces.
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Splunk SPLK-4001 Exam is specifically designed for individuals who are interested in becoming certified in Splunk O11y Cloud Certified Metrics User. Splunk O11y Cloud Certified Metrics User certification is aimed at individuals who want to demonstrate their knowledge and skills in the areas of cloud-based metrics and monitoring.
NEW QUESTION # 26
A Software Engineer is troubleshooting an issue with memory utilization in their application. They released a new canary version to production and now want to determine if the average memory usage is lower for requests with the 'canary' version dimension. They've already opened the graph of memory utilization for their service.
How does the engineer see if the new release lowered average memory utilization?
Answer: D
Explanation:
The correct answer is C. On the chart for plot A, select Add Analytics, then select Mean:Aggregation. In the window that appears, select 'version' from the Group By field.
This will create a new plot B that shows the average memory utilization for each version of the application. The engineer can then compare the values of plot B for the 'canary' and 'stable' versions to see if there is a significant difference.
To learn more about how to use analytics functions in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html
NEW QUESTION # 27
When you add instructions to dashboards, what is their primary purpose?
Answer: D
NEW QUESTION # 28
When creating a standalone detector, individual rules in it are labeled according to severity.
Which of the choices below represents the possible severity levels that can be selected?
Answer: A
Explanation:
When creating a standalone detector, you can define one or more rules that specify the alert conditions and the severity level for each rule. The severity level indicates how urgent or important the alert is, and it can also affect the notification settings and the escalation policy for the alert1 Splunk Observability Cloud provides five predefined severity levels that you can choose from when creating a rule: Info, Warning, Minor, Major, and Critical. Each severity level has a different color and icon to help you identify the alert status at a glance. You can also customize the severity levels by changing their names, colors, or icons.
NEW QUESTION # 29
Which of the following are correct ports for the specified components in the OpenTelemetry Collector?
Answer: C
Explanation:
The correct answer is D. gRPC (4317), SignalFx (9080), Fluentd (8006).
According to the web search results, these are the default ports for the corresponding components in the OpenTelemetry Collector. You can verify this by looking at the table of exposed ports and endpoints in the first result1. You can also see the agent and gateway configuration files in the same result for more details.
1: https://docs.splunk.com/observability/gdi/opentelemetry/exposed-endpoints.html
NEW QUESTION # 30
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: D
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 # 31
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