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| Section | Weight | Objectives |
|---|---|---|
| Introduction to Metric Visualization | 15% | - Analyze data in charts - Visualize a measure in a chart - Differentiate between several chart visualization types - Look for metrics - Use rollups and analytical tools correctly - Create charts and dashboards - Create widgets and showcase groups |
| Metrics Concepts | 15% | - Data resolution, rollups - Discriminate between types of metadata - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints - List the components of a datapoint |
| Intro to Alerting on Metrics using Detectors | 10% | - Make the detector from a chart - Make a muting rule - Clone an existing detector - Make a solo detector |
| Monitor Using Built-in Content | 10% | - Use the Cluster Analyzer to pinpoint the root of some problems - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution - Interact with data using built-in content - Subscribe to alerts |
| Get Metrics In with OpenTelemetry | 10% | - Deploy the OTel Collector on Linux - Edit the configuration - Troubleshooting common errors - General OpenTelemetry Concepts - Configure the OTel Collector |
| Create Efficient Dashboards and Alerts | 10% | - View dashboard happenings - Include instructions on interfaces - Configure personal information linkages - Create single-instance dashboard panels |
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NEW QUESTION # 105
Changes to which type of metadata result in a new metric time series?
Answer: D
Explanation:
Dimensions are metadata in the form of key-value pairs that are sent along with the metrics at the time of ingest. They provide additional information about the metric, such as the name of the host that sent the metric, or the location of the server. Along with the metric name, they uniquely identify a metric time series (MTS).
Changes to dimensions result in a new MTS, because they create a different combination of metric name and dimensions. For example, if you change the hostname dimension from host1 to host, you will create a new MTS for the same metric name.
Properties, sources, and tags are other types of metadata that can be applied to existing MTSes after ingest. They do not contribute to uniquely identify an MTS, and they do not create a new MTS when changed.
NEW QUESTION # 106
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?
Answer: D
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
NEW QUESTION # 107
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?
Answer: A
Explanation:
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.
NEW QUESTION # 108
What are the best practices for creating detectors? (select all that apply)
Answer: A,B,C,D
NEW QUESTION # 109
Which of the following are supported rollup functions in Splunk Observability Cloud?
Answer: C
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 # 110
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