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Splunk O11y Cloud Certified Metrics User Sample Questions (Q20-Q25):

NEW QUESTION # 20
Which of the following are supported rollup functions in Splunk Observability Cloud?

A. 1min, 5min, 10min, 15min, 30min
B. average, latest, lag, min, max, sum, rate
C. sigma, epsilon, pi, omega, beta, tau
D. std_dev, mean, median, mode, min, max

Answer: B

Explanation:
Explanation
According to the Splunk O11y Cloud Certified Metrics User Track document1, Observability Cloud has the following rollup functions: Sum: (default for counter metrics): Returns the sum of all data points in the MTS reporting interval. Average (default for gauge metrics): Returns the average value of all data points in the MTS reporting interval. Min: Returns the minimum data point value seen in the MTS reporting interval. Max:
Returns the maximum data point value seen in the MTS reporting interval. Latest: Returns the most recent data point value seen in the MTS reporting interval. Lag: Returns the difference between the most recent and the previous data point values seen in the MTS reporting interval. Rate: Returns the rate of change of data points in the MTS reporting interval. Therefore, option A is correct.

NEW QUESTION # 21
What information is needed to create a detector?

A. Alert Status, Alert Criteria, Alert Settings, Alert Message, Alert Recipients
B. Alert Signal, Alert Criteria, Alert Settings, Alert Message, Alert Recipients
C. Alert Signal, Alert Condition, Alert Settings, Alert Message, Alert Recipients
D. Alert Status, Alert Condition, Alert Settings, Alert Meaning, Alert Recipients

Answer: C

Explanation:
Explanation
According to the Splunk Observability Cloud documentation1, to create a detector, you need the following information:
Alert Signal: This is the metric or dimension that you want to monitor and alert on. You can select a signal from a chart or a dashboard, or enter a SignalFlow query to define the signal.
Alert Condition: This is the criteria that determines when an alert is triggered or cleared. You can choose from various built-in alert conditions, such as static threshold, dynamic threshold, outlier, missing data, and so on. You can also specify the severity level and the trigger sensitivity for each alert condition.
Alert Settings: This is the configuration that determines how the detector behaves and interacts with other detectors. You can set the detector name, description, resolution, run lag, max delay, and detector rules. You can also enable or disable the detector, and mute or unmute the alerts.
Alert Message: This is the text that appears in the alert notification and event feed. You can customize the alert message with variables, such as signal name, value, condition, severity, and so on. You can also use markdown formatting to enhance the message appearance.
Alert Recipients: This is the list of destinations where you want to send the alert notifications. You can choose from various channels, such as email, Slack, PagerDuty, webhook, and so on. You can also specify the notification frequency and suppression settings.

NEW QUESTION # 22
When writing a detector with a large number of MTS, such as memory. free in a deployment with 30,000 hosts, it is possible to exceed the cap of MTS that can be contained in a single plot. Which of the choices below would most likely reduce the number of MTS below the plot cap?

A. Add a restricted scope adjustment to the plot.
B. Add a filter to narrow the scope of the measurement.
C. Select the Sharded option when creating the plot.
D. When creating the plot, add a discriminator.

Answer: B

Explanation:
Explanation
The correct answer is B. Add a filter to narrow the scope of the measurement.
A filter is a way to reduce the number of metric time series (MTS) that are displayed on a chart or used in a detector. A filter specifies one or more dimensions and values that the MTS must have in order to be included.
For example, if you want to monitor the memory.free metric only for hosts that belong to a certain cluster, you can add a filter like cluster:my-cluster to the plot or detector. This will exclude any MTS that do not have the cluster dimension or have a different value for it1 Adding a filter can help you avoid exceeding the plot cap, which is the maximum number of MTS that can be contained in a single plot. The plot cap is 100,000 by default, but it can be changed by contacting Splunk Support2 To learn more about how to use filters in Splunk Observability Cloud, you can refer to this documentation3.
1: https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics 2:
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Plot-cap 3:
https://docs.splunk.com/Observability/gdi/metrics/search.html

NEW 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?

A. The detector has a muting rule.
B. The detector has an incorrect signal,
C. The detector is disabled.
D. The detector has an incorrect alert rule.

Answer: A

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 normal1 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 there1 To learn more about how to use muting rules in Splunk Observability Cloud, you can refer to this documentation1.

NEW QUESTION # 24
What are the best practices for creating detectors? (select all that apply)

A. Have a consistent type of measurement.
B. View data at highest resolution.
C. View detector in a chart.
D. Have a consistent value.

Answer: A,B,C,D

Explanation:
Explanation
The best practices for creating detectors are:
View data at highest resolution. This helps to avoid missing important signals or patterns in the data that could indicate anomalies or issues1 Have a consistent value. This means that the metric or dimension used for detection should have a clear and stable meaning across different sources, contexts, and time periods. For example, avoid using metrics that are affected by changes in configuration, sampling, or aggregation2 View detector in a chart. This helps to visualize the data and the detector logic, as well as to identify any false positives or negatives. It also allows to adjust the detector parameters and thresholds based on the data distribution and behavior3 Have a consistent type of measurement. This means that the metric or dimension used for detection should have the same unit and scale across different sources, contexts, and time periods. For example, avoid mixing bytes and bits, or seconds and milliseconds.
1: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors 2:
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors 3:
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#View-detector-in-a-chart :
https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Best-practices-for-detectors

NEW QUESTION # 25
......

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