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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitor Using Built-in Content | 10% | - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Use the Cluster Analyzer to pinpoint the root of some problems - Subscribe to alerts - Interact with data using built-in content |
| Topic 2: Create Efficient Dashboards and Alerts | 10% | - Configure personal information linkages - Include instructions on interfaces - Create single-instance dashboard panels - View dashboard happenings |
| Topic 3: Introduction to Metric Visualization | 15% | - Visualize a measure in a chart - Differentiate between several chart visualization types - Look for metrics - Create widgets and showcase groups - Create charts and dashboards - Use rollups and analytical tools correctly - Analyze data in charts |
| Topic 4: Metrics Concepts | 15% | - List the components of a datapoint - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints - Data resolution, rollups - Discriminate between types of metadata |
| Topic 5: Intro to Alerting on Metrics using Detectors | 10% | - Make a solo detector - Clone an existing detector - Make a muting rule - Make the detector from a chart |
| Topic 6: Get Metrics In with OpenTelemetry | 10% | - Configure the OTel Collector - General OpenTelemetry Concepts - Troubleshooting common errors - Deploy the OTel Collector on Linux - Edit the configuration |
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NEW QUESTION # 111
A team wants to visualize the 95th percentile of request latency across all instances of a service.
Which analytic function should be applied?
Answer: B
Explanation:
The Percentile analytic function allows users to calculate a specific percentile, such as p95, across the values of a signal, which is useful for understanding latency distribution and tail performance.
NEW QUESTION # 112
For which types of charts can individual plot visualization be set?
Answer: B
Explanation:
For line, area, and column charts, you can set the individual plot visualization to change the appearance of each plot in the chart. For example, you can change the color, shape, size, or style of the lines, areas, or columns. You can also change the rollup function, data resolution, or y-axis scale for each plot.
To set the individual plot visualization for line, area, and column charts, you need to select the chart from the Metric Finder, then click on Plot Chart Options and choose Individual Plot Visualization from the list of options. You can then customize each plot according to your preferences.
NEW QUESTION # 113
To refine a search for a metric a customer types host: test-*. What does this filter return?
Answer: B
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 # 114
An SRE came across an existing detector that is a good starting point for a detector they want to create. They clone the detector, update the metric, and add multiple new signals. As a result of the cloned detector, which of the following is true?
Answer: C
Explanation:
According to the Splunk O11y Cloud Certified Metrics User Track document1, cloning a detector creates a copy of the detector that you can modify without affecting the original detector. You can change the metric, filter, and signal settings of the cloned detector. However, the new signals that you add to the cloned detector will not be reflected in the original detector, nor in the original chart that the detector was based on.
NEW QUESTION # 115
Which of the following are ways to reduce flapping of a detector? (select all that apply)
Answer: B,D
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 # 116
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