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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Metrics Concepts | 20% | - Metric types: Gauges, Counters, Histograms, Summaries - Data retention and resolution settings - Dimensions, time-series data, and cardinality |
| Topic 2: Alerting with Detectors | 15% | - Common use cases: latency, saturation, errors - Threshold, anomaly, and multi-condition detectors - Notification rules and incident response |
| Topic 3: Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
| Topic 4: Data Collection & Ingestion | 15% | - OpenTelemetry configuration and deployment - Integration with Kubernetes and cloud environments - Signal flow and data processing pipeline |
| Topic 5: Visualizing Metrics | 20% | - Creating and configuring charts, graphs, and time-series - Using SignalFlow for custom queries - Visualization best practices and effective communication |
| Topic 6: Built-in Monitoring Content | 10% | - Content packs and quick-start monitoring - Using pre-built dashboards and navigators |
| Topic 7: Analytics & Insights | 10% | - Correlation and pattern detection - Aggregation, filtering, and transformation functions |
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NEW QUESTION # 89
A customer is sending in datapoints regarding the number of API calls made to a particular service. The datapoints are being sent every 10 seconds, and represent the number of API calls seen in the last 10 seconds. What metric type is this?
Answer: D
Explanation:
A gauge metric represents a value measured at a specific point in time or over a defined interval.
In this scenario, the datapoints indicate the number of API calls observed in each 10-second interval, which aligns with the characteristics of a gauge metric.
NEW QUESTION # 90
To refine a search for a metric a customer types host: test-*. What does this filter return?
Answer: C
Explanation:
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 characters.
NEW QUESTION # 91
In a chart where signal 'A' contains a metric's current value, and signal 'B' contains a historical value, which formula will allow a user to calculate the percent change in a metric's value from a previous time period?
Answer: D
Explanation:
Percent change is calculated by subtracting the historical value from the current value, dividing that difference by the historical value, and multiplying by 100 to express the result as a percentage.
NEW QUESTION # 92
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:
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 # 93
Which of the following chart visualization types are unaffected by changing the time picker on a dashboard?
(select all that apply)
Answer: B,D
Explanation:
Explanation
The chart visualization types that are unaffected by changing the time picker on a dashboard are:
Single Value: A single value chart shows the current value of a metric or an expression. It does not depend on the time range of the dashboard, but only on the data resolution and rollup function of the chart1 List: A list chart shows the values of a metric or an expression for each dimension value in a table format. It does not depend on the time range of the dashboard, but only on the data resolution and rollup function of the chart2 Therefore, the correct answer is A and D.
To learn more about how to use different chart visualization types in Splunk Observability Cloud, you can refer to this documentation3.
1: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Single-value 2:
https://docs.splunk.com/Observability/gdi/metrics/charts.html#List 3:
https://docs.splunk.com/Observability/gdi/metrics/charts.html
NEW QUESTION # 94
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