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| Section | Weight | Objectives |
|---|---|---|
| Visualizing Metrics | 20% | - Visualization best practices and effective communication - Creating and configuring charts, graphs, and time-series - Using SignalFlow for custom queries |
| Dashboards & Operational Efficiency | 10% | - Designing clear, performant dashboards - Reducing alert fatigue and optimizing monitoring |
| Alerting with Detectors | 15% | - Threshold, anomaly, and multi-condition detectors - Common use cases: latency, saturation, errors - Notification rules and incident response |
| Built-in Monitoring Content | 10% | - Using pre-built dashboards and navigators - Content packs and quick-start monitoring |
| Data Collection & Ingestion | 15% | - Signal flow and data processing pipeline - OpenTelemetry configuration and deployment - Integration with Kubernetes and cloud environments |
| Metrics Concepts | 20% | - Data retention and resolution settings - Metric types: Gauges, Counters, Histograms, Summaries - Dimensions, time-series data, and cardinality |
| Analytics & Insights | 10% | - Correlation and pattern detection - Aggregation, filtering, and transformation functions |
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NEW QUESTION # 115
Which of the following is a valid use case for Service Level Objectives (SLOs) in Splunk Observability Cloud?
Answer: B
NEW QUESTION # 116
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:
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
NEW QUESTION # 117
What Pod conditions does the Analyzer panel in Kubernetes Navigator monitor? (select all that apply)
Answer: A,B,C,D
Explanation:
The Pod conditions that the Analyzer panel in Kubernetes Navigator monitors are:
Not Scheduled: This condition indicates that the Pod has not been assigned to a Node yet. This could be due to insufficient resources, node affinity, or other scheduling constraints1 Unknown: This condition indicates that the Pod status could not be obtained or is not known by the system. This could be due to communication errors, node failures, or other unexpected situations1 Failed: This condition indicates that the Pod has terminated in a failure state. This could be due to errors in the application code, container configuration, or external factors1 Pending: This condition indicates that the Pod has been accepted by the system, but one or more of its containers has not been created or started yet. This could be due to image pulling, volume mounting, or network issues1 Therefore, the correct answer is A, B, C, and D.
To learn more about how to use the Analyzer panel in Kubernetes Navigator, you can refer to this documentation2.
1: https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/#pod-phase 2: https://docs.splunk.com/observability/infrastructure/monitor/k8s-nav.html#Analyzer-panel
NEW QUESTION # 118
What is the finest resolution that Calendar Window Transformation datapoints can be calculated and displayed?
Answer: D
Explanation:
Calendar Window Transformation datapoints are calculated and displayed at the native resolution of the input time series, ensuring that the aggregation respects the original granularity of the metric data.
NEW QUESTION # 119
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: A
Explanation:
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. Therefore, option D is correct.
Option A is incorrect because the new signals will not be reflected in the original detector. Option B is incorrect because the new signals will not be reflected in the original chart. Option C is incorrect because you can monitor all of the new signals that you add to the cloned detector.
NEW QUESTION # 120
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