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| Section | Weight | Objectives |
|---|---|---|
| Intro to Alerting on Metrics using Detectors | 10% | - Make a muting rule - Make a solo detector - Clone an existing detector - Make the detector from a chart |
| Create Efficient Dashboards and Alerts | 10% | - Include instructions on interfaces - Configure personal information linkages - Create single-instance dashboard panels - View dashboard happenings |
| Introduction to Metric Visualization | 15% | - Look for metrics - Analyze data in charts - Visualize a measure in a chart - Differentiate between several chart visualization types - Create widgets and showcase groups - Use rollups and analytical tools correctly - Create charts and dashboards |
| Metrics Concepts | 15% | - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints - Data resolution, rollups - List the components of a datapoint - Discriminate between types of metadata |
| Monitor Using Built-in Content | 10% | - Interact with data using built-in content - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution - Subscribe to alerts - Use the Cluster Analyzer to pinpoint the root of some problems - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers |
| Get Metrics In with OpenTelemetry | 10% | - Deploy the OTel Collector on Linux - Troubleshooting common errors - Edit the configuration - Configure the OTel Collector - General OpenTelemetry Concepts |
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49. Frage
One server in a customer's data center is regularly restarting due to power supply issues. What type of dashboard could be used to view charts and create detectors for this server?
Antwort: D
Begründung:
Explanation
According to the Splunk O11y Cloud Certified Metrics User Track document1, a single-instance dashboard is a type of dashboard that displays charts and information for a single instance of a service or host. You can use a single-instance dashboard to monitor the performance and health of a specific server, such as the one that is restarting due to power supply issues. You can also create detectors for the metrics that are relevant to the server, such as CPU usage, memory usage, disk usage, and uptime. Therefore, option A is correct.
50. Frage
A customer is sending data from a machine that is over-utilized. Because of a lack of system resources, datapoints from this machine are often delayed by up to 10 minutes. Which setting can be modified in a detector to prevent alerts from firing before the datapoints arrive?
Antwort: B
Begründung:
Explanation
The correct answer is A. Max Delay.
Max Delay is a parameter that specifies the maximum amount of time that the analytics engine can wait for data to arrive for a specific detector. For example, if Max Delay is set to 10 minutes, the detector will wait for only a maximum of 10 minutes even if some data points have not arrived. By default, Max Delay is set to Auto, allowing the analytics engine to determine the appropriate amount of time to wait for data points1 In this case, since the customer knows that the data from the over-utilized machine can be delayed by up to 10 minutes, they can modify the Max Delay setting for the detector to 10 minutes. This will prevent the detector from firing alerts before the data points arrive, and avoid false positives or missing data1 To learn more about how to use Max Delay in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/observability/alerts-detectors-notifications/detector-options.html#Max-Delay
51. Frage
What constitutes a single metrics time series (MTS)?
Antwort: B
Begründung:
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 timestamp.
52. Frage
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?
Antwort: D
Begründung:
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/Observability/gdi/metrics/analytics.html#Mean-Transformation 2:
https://docs.splunk.com/Observability/gdi/metrics/analytics.html
53. Frage
Interpreting data in charts can be affected by which of the following? (select all that apply)
Antwort: A,C,D
54. Frage
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