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Splunk O11y Cloud Certified Metrics User Sample Questions (Q24-Q29):

NEW QUESTION # 24
To refine a search for a metric a customer types host: test-*. What does this filter return?

A. Every metric except those with a dimension of host and a value equal to test.
B. Only metrics with a value of test- beginning with host.
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/Observability/gdi/metrics/search.html#Filter-metrics 2:
https://docs.splunk.com/Observability/gdi/metrics/search.html

NEW QUESTION # 25
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?

A. On the chart for plot A, click the Compare Means button. In the window that appears, type 'version1.
B. On the chart for plot A, select Add Analytics, then select MeanrTransformation. In the window that appears, select 'version' from the Group By field.
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.
D. On the chart for plot A, scroll to the end and click Enter Function, then enter 'A/B-l'.

Answer: C

Explanation:
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 # 26
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?

A. Info, Warning, Minor, Major, and Critical.
B. Info, Warning, Minor, Severe, and Critical.
C. Info, Warning, Minor, Major, and Emergency.
D. Debug, Warning, Minor, Major, and Critical.

Answer: A

Explanation:
The correct answer is C. Info, Warning, Minor, Major, and Critical.
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 icons2 To learn more about how to create standalone detectors and use severity levels in Splunk Observability Cloud, you can refer to these documentations12.
1: https://docs.splunk.com/Observability/alerts-detectors-notifications/detectors.html#Create-a-standalone-detector 2: https://docs.splunk.com/Observability/alerts-detectors-notifications/detector-options.html#Severity-levels

NEW QUESTION # 27
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. Have a consistent value.
D. View detector in a chart.

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 # 28
What constitutes a single metrics time series (MTS)?

A. A set of metrics that are ordered in series based on timestamp.
B. A series of timestamps that all reflect the same metric.
C. A set of data points that all have the same metric name and list of dimensions.
D. A set of data points that use different dimensions but the same metric name.

Answer: C

Explanation:
Explanation
The correct answer is B. A set of data points that all have the same metric name and list of dimensions.
A metric time series (MTS) is a collection of data points that have the same metric and the same set of dimensions. For example, the following sets of data points are in three separate MTS:
MTS1: Gauge metric cpu.utilization, dimension "hostname": "host1" MTS2: Gauge metric cpu.utilization, dimension "hostname": "host2" MTS3: Gauge metric memory.usage, dimension "hostname": "host1" A metric is a numerical measurement that varies over time, such as CPU utilization or memory usage. A dimension is a key-value pair that provides additional information about the metric, such as the hostname or the location. A data point is a combination of a metric, a dimension, a value, and a timestamp1

NEW QUESTION # 29
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