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[General] Three Easy-to-Use and Compatible PrepAwayTest Splunk SPLK-4001 Exam Questions

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【General】 Three Easy-to-Use and Compatible PrepAwayTest Splunk SPLK-4001 Exam Questions

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Splunk SPLK-4001 exam is designed for professionals who work with Splunk to collect, analyze, and visualize metrics data. Splunk O11y Cloud Certified Metrics User certification is a testament to the individual's ability to leverage Splunk to monitor and troubleshoot cloud environments. The SPLK-4001 exam is a part of the Splunk O11y (Observability) Cloud certification track, which is a comprehensive program that covers various aspects of observability such as metrics, logs, and traces.
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Splunk O11y Cloud Certified Metrics User Sample Questions (Q20-Q25):NEW QUESTION # 20
Which of the following are accurate reasons to clone a detector? (select all that apply)
  • A. To modify the rules without affecting the existing detector.
  • B. To add an additional recipient to the detector's alerts.
  • C. To explore how a detector was created without risk of changing it.
  • D. To reduce the amount of billed TAPM for the detector.
Answer: A,C
Explanation:
Explanation
The correct answers are A and D.
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the alerting concepts that is covered in the exam is detectors and alerts. Detectors are the objects that define the conditions for generating alerts, and alerts are the notifications that are sent when those conditions are met.
The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the recommended courses for preparing for the exam is Alerting with Detectors, which covers how to create, modify, and manage detectors and alerts.
In the Alerting with Detectors course, there is a section on Cloning Detectors, which explains that cloning a detector creates a copy of the detector with all its settings, rules, and alert recipients. The document also provides some reasons why you might want to clone a detector, such as:
To modify the rules without affecting the existing detector. This can be useful if you want to test different thresholds or conditions before applying them to the original detector.
To explore how a detector was created without risk of changing it. This can be helpful if you want to learn from an existing detector or use it as a template for creating a new one.
Therefore, based on these documents, we can conclude that A and D are accurate reasons to clone a detector.
B and C are not valid reasons because:
Cloning a detector does not reduce the amount of billed TAPM for the detector. TAPM stands for Tracked Active Problem Metric, which is a metric that has been alerted on by a detector. Cloning a detector does not change the number of TAPM that are generated by the original detector or the clone.
Cloning a detector does not add an additional recipient to the detector's alerts. Cloning a detector copies the alert recipients from the original detector, but it does not add any new ones. To add an additional recipient to a detector's alerts, you need to edit the alert settings of the detector.

NEW QUESTION # 21
Which of the following are ways to reduce flapping of a detector? (select all that apply)
  • A. Configure a duration or percent of duration for the alert.
  • B. Apply a smoothing transformation (like a rolling mean) to the input data for the detector.
  • C. Establish a reset threshold for the detector.
  • D. Enable the anti-flap setting in the detector options menu.
Answer: A,B
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.

NEW QUESTION # 22
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?
  • A. Mean (Transformation)
  • B. Rate/Sec
  • C. Median
  • D. Mean (by host)
Answer: A
Explanation:
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 interval1. 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 interval1. 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 range1. 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 dimension1. 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 fluctuations1 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 servers2 To learn more about how to use Mean (Transformation) and other analytic functions in Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observab ... Mean-Transformation 2:
https://docs.splunk.com/Observability/gdi/metrics/analytics.html

NEW QUESTION # 23
The Sum Aggregation option for analytic functions does which of the following?
  • A. Calculates the sum of values present in the input time series across the entire environment or per group.
  • B. Calculates 1/2 of the values present in the input time series.
  • C. Calculates the number of MTS present in the plot.
  • D. Calculates the sum of values per time series across a period of time.
Answer: A
Explanation:
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the metrics concepts that is covered in the exam is analytic functions. Analytic functions are mathematical operations that can be applied to metrics to transform, aggregate, or analyze them.
The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the recommended courses for preparing for the exam is Introduction to Splunk Infrastructure Monitoring, which covers the basics of metrics monitoring and visualization.
In the Introduction to Splunk Infrastructure Monitoring course, there is a section on Analytic Functions, which explains that analytic functions can be used to perform calculations on metrics, such as sum, average, min, max, count, etc. The document also provides examples of how to use analytic functions in charts and dashboards.
One of the analytic functions that can be used is Sum Aggregation, which calculates the sum of values present in the input time series across the entire environment or per group. The document gives an example of how to use Sum Aggregation to calculate the total CPU usage across all hosts in a group by using the following syntax:
sum(cpu.utilization) by hostgroup

NEW QUESTION # 24
To refine a search for a metric a customer types host: test-*. What does this filter return?
  • A. Only metrics with a value of test- beginning with host.
  • B. Every metric except those with a dimension of host and a value equal to test.
  • C. Only metrics with a dimension of host and a value beginning with test-.
  • D. Error
Answer: C
Explanation:
Explanation
The correct answer is A. Only metrics with a dimension of host and a value beginning with test-.
This filter returns the metrics that have a host dimension that matches the pattern test-. For example, test-01, test-abc, test-xyz, etc. The asterisk () is a wildcard character that can match any string of characters1 To learn more about how to filter metrics in Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observab ... html#Filter-metrics 2:
https://docs.splunk.com/Observability/gdi/metrics/search.html

NEW QUESTION # 25
......
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