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| Section | Weight | Objectives |
|---|---|---|
| Data Collection & Ingestion | 15% | - Signal flow and data processing pipeline - Integration with Kubernetes and cloud environments - OpenTelemetry configuration and deployment |
| Built-in Monitoring Content | 10% | - Content packs and quick-start monitoring - Using pre-built dashboards and navigators |
| Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
| Analytics & Insights | 10% | - Correlation and pattern detection - Aggregation, filtering, and transformation functions |
| Visualizing Metrics | 20% | - Visualization best practices and effective communication - Using SignalFlow for custom queries - Creating and configuring charts, graphs, and time-series |
| Metrics Concepts | 20% | - Dimensions, time-series data, and cardinality - Metric types: Gauges, Counters, Histograms, Summaries - Data retention and resolution settings |
| Alerting with Detectors | 15% | - Common use cases: latency, saturation, errors - Notification rules and incident response - Threshold, anomaly, and multi-condition detectors |
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NEW QUESTION # 77
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: C
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 # 78
What is the finest resolution that Calendar Window Transformation datapoints can be calculated and displayed?
Answer: B
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 # 79
With exceptions for transformations or timeshifts, at what resolution do detectors operate?
Answer: C
Explanation:
Explanation
According to the Splunk Observability Cloud documentation1, detectors operate at the native resolution of the metric or dimension that they monitor, with some exceptions for transformations or timeshifts. The native resolution is the frequency at which the data points are reported by the source. For example, if a metric is reported every 10 seconds, the detector will evaluate the metric every 10 seconds. The native resolution ensures that the detector uses the most granular and accurate data available for alerting.
NEW QUESTION # 80
What is the limit on the number of properties that an MTS can have?
Answer: D
Explanation:
Explanation
The correct answer is A. 64.
According to the web search results, the limit on the number of properties that an MTS can have is 64. A property is a key-value pair that you can assign to a dimension of an existing MTS to add more context to the metrics. For example, you can add the property use: QA to the host dimension of your metrics to indicate that the host is used for QA1 Properties are different from dimensions, which are key-value pairs that are sent along with the metrics at the time of ingest. Dimensions, along with the metric name, uniquely identify an MTS. The limit on the number of dimensions per MTS is 362 To learn more about how to use properties and dimensions in Splunk Observability Cloud, you can refer to this documentation2.
1:
https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions-mts.html#Custom-properties
2: https://docs.splunk.com/Observability/metrics-and-metadata/metrics-dimensions-mts.html
NEW QUESTION # 81
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: C
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 # 82
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