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| Section | Weight | Objectives |
|---|---|---|
| Dashboards & Operational Efficiency | 10% | - Designing clear, performant dashboards - Reducing alert fatigue and optimizing monitoring |
| Alerting with Detectors | 15% | - Common use cases: latency, saturation, errors - Threshold, anomaly, and multi-condition detectors - Notification rules and incident response |
| Data Collection & Ingestion | 15% | - Signal flow and data processing pipeline - Integration with Kubernetes and cloud environments - OpenTelemetry configuration and deployment |
| Metrics Concepts | 20% | - Metric types: Gauges, Counters, Histograms, Summaries - Dimensions, time-series data, and cardinality - Data retention and resolution settings |
| Visualizing Metrics | 20% | - Using SignalFlow for custom queries - Creating and configuring charts, graphs, and time-series - Visualization best practices and effective communication |
| Analytics & Insights | 10% | - Aggregation, filtering, and transformation functions - Correlation and pattern detection |
| Built-in Monitoring Content | 10% | - Content packs and quick-start monitoring - Using pre-built dashboards and navigators |
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NEW QUESTION # 104
A customer has an over-utilized host, and they want an alert when the disk utilization of a given host goes over 80% for 5 minutes. They create a detector, but do not see an alert when expected. Which of the following could be a reason for this?
Answer: C
Explanation:
If a detector's resolution is too low, it may miss short-duration spikes in metrics. Setting the detector resolution appropriately ensures that sustained conditions, like disk utilization over 80% for 5 minutes, are accurately evaluated and trigger alerts.
NEW QUESTION # 105
For which types of charts can individual plot visualization be set?
Answer: C
Explanation:
Explanation
The correct answer is C. Line, Area, Column.
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 plot1 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 preferences2 To learn more about how to use individual plot visualization in Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Individual-plot-visualization 2:
https://docs.splunk.com/Observability/gdi/metrics/charts.html#Set-individual-plot-visualization
NEW QUESTION # 106
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: B
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 # 107
Olive is creating a dashboard for her team, and she wants to include instructions on the dashboard for what to do if a metric is outside of a desired range. Which element can she use to add these details to the dashboard?
Answer: A
Explanation:
A Text note element can be added to a dashboard to provide instructions, guidance, or context for users, such as steps to follow when a metric is outside the desired range.
NEW QUESTION # 108
A team is troubleshooting high cardinality issues with a custom metric. What is a recommended best practice to reduce cardinality?
Answer: C
Explanation:
Using high-cardinality values, such as unique request IDs or timestamps, as dimensions can cause an explosion in the number of unique time series. Avoiding this practice helps keep cardinality manageable and reduces cost and performance issues.
NEW QUESTION # 109
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