QUESTION: 21

Which of the following aggregate analytic functions will allow a user to see the highest or lowest n values of a metric?

Maximum / Minimum
Best/Worst
Exclude / Include
Top / Bottom

Answer(s): D

Explanation:

The correct answer is D. Top / Bottom.
Top and bottom are aggregate analytic functions that allow a user to see the highest or lowest n values of a metric. They can be used to select a subset of the time series in the plot by count or by percent. For example, top (5) will show the five time series with the highest values in each time period, while bottom (10%) will show the 10% of time series with the lowest values in each time period.
To learn more about how to use top and bottom functions in Splunk Observability Cloud, you can refer to this documentation.

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QUESTION: 22

Which of the following are ways to reduce flapping of a detector? (select all that apply)

Configure a duration or percent of duration for the alert.
Establish a reset threshold for the detector.
Enable the anti-flap setting in the detector options menu.
Apply a smoothing transformation (like a rolling mean) to the input data for the detector.

Answer(s): A,D

Explanation:

According to the Splunk Lantern article Resolving flapping detectors in Splunk Infrastructure Monitoring, flapping is a phenomenon where alerts fire and clear repeatedly in a short period of time, due to the signal fluctuating around the threshold value. To reduce flapping, the article suggests the following ways:
Configure a duration or percent of duration for the alert: This means that you require the signal to stay above or below the threshold for a certain amount of time or percentage of time before triggering an alert. This can help filter out noise and focus on more persistent issues. Apply a smoothing transformation (like a rolling mean) to the input data for the detector: This means that you replace the original signal with the average of its last several values, where you can specify the window length. This can reduce the impact of a single extreme observation and make the signal less fluctuating.

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QUESTION: 23

A customer is experiencing an issue where their detector is not sending email notifications but is generating alerts within the Splunk Observability UI.
Which of the below is the root cause?

The detector has an incorrect alert rule.
The detector has an incorrect signal,
The detector is disabled.
The detector has a muting rule.

Answer(s): D

Explanation:

The most likely root cause of the issue is D. The detector has a muting rule. A muting rule is a way to temporarily stop a detector from sending notifications for certain alerts, without disabling the detector or changing its alert conditions. A muting rule can be useful when you want to avoid alert noise during planned maintenance, testing, or other situations where you expect the metrics to deviate from normal.
When a detector has a muting rule, it will still generate alerts within the Splunk Observability UI, but it will not send email notifications or any other types of notifications that you have configured for the detector. You can see if a detector has a muting rule by looking at the Muting Rules tab on the detector page. You can also create, edit, or delete muting rules from there. To learn more about how to use muting rules in Splunk Observability Cloud, you can refer to this documentation.

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QUESTION: 24

To smooth a very spiky cpu.utilization metric, what is the correct analytic function to better see if the cpu. utilization for servers is trending up over time?

Rate/Sec
Median
Mean (by host)
Mean (Transformation)

Answer(s): D

Explanation:

The correct answer is D. Mean (Transformation).

According to the web search results, a mean transformation is an analytic function that returns the average value of a metric or a dimension over a specified time interval. A mean transformation can be used to smooth a very spiky metric, such as cpu.utilization, by reducing the impact of outliers and noise. A mean transformation can also help to see if the metric is trending up or down over time, by showing the general direction of the average value. For example, to smooth the cpu.utilization metric and see if it is trending up over time, you can use the following SignalFlow code:
mean(1h, counters("cpu.utilization"))
This will return the average value of the cpu.utilization counter metric for each metric time series (MTS) over the last hour. You can then use a chart to visualize the results and compare the mean values across different MTS.
Option A is incorrect because rate/sec is not an analytic function, but rather a rollup function that returns the rate of change of data points in the MTS reporting interval. Rate/sec can be used to convert cumulative counter metrics into counter metrics, but it does not smooth or trend a metric. Option B is incorrect because median is not an analytic function, but rather an aggregation function that returns the middle value of a metric or a dimension over the entire time range. Median can be used to find the typical value of a metric, but it does not smooth or trend a metric. Option C is incorrect because mean (by host) is not an analytic function, but rather an aggregation function that returns the average value of a metric or a dimension across all MTS with the same host dimension. Mean (by host) can be used to compare the performance of different hosts, but it does not smooth or trend a metric.
Mean (Transformation) is an analytic function that allows you to smooth a very spiky metric by applying a moving average over a specified time window. This can help you see the general trend of the metric over time, without being distracted by the short-term fluctuations. To use Mean (Transformation) on a cpu.utilization metric, you need to select the metric from the Metric Finder, then click on Add Analytics and choose Mean (Transformation) from the list of functions. You can then specify the time window for the moving average, such as 5 minutes, 15 minutes, or 1 hour. You can also group the metric by host or any other dimension to compare the smoothed values across different servers.
To learn more about how to use Mean (Transformation) and other analytic functions in Splunk Observability Cloud, you can refer to this documentation.
1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html#Mean-Transformation
2: https://docs.splunk.com/Observability/gdi/metrics/analytics.html

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Free SPLK-4001 Exam Braindumps (page: 6)

QUESTION: 21

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QUESTION: 22

Explanation:

QUESTION: 23

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QUESTION: 24

Explanation: