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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Get Metrics In with OpenTelemetry | 10% | - OpenTelemetry Collector Deployment
|
| Topic 2: Create Efficient Dashboards and Alerts | 10% | - Dashboard Optimization
|
| Topic 3: Monitor Using Built-in Content | 10% | - Observability Navigation & Monitoring
|
| Topic 4: Metrics Concepts | 15% | - Core Metrics Data Model
|
| Topic 5: Alerting on Metrics with Detectors | 10% | - Detectors and Alerting
|
| Topic 6: Finding Insights Using Analytics | 15% | - Metrics Analytics
|
| Topic 7: Introduction to Visualizing Metrics | 15% | - Metrics Visualization
|
| Topic 8: Detectors for Common Use Cases | 15% | - Advanced Detector Scenarios
|
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NEW QUESTION # 64
With exceptions for transformations or timeshifts, at what resolution do detectors operate?
Answer: B
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 # 65
What does the "Resource Running Out" detector condition type help predict?
Answer: C
Explanation:
The Resource Running Out condition uses trend analysis to predict when a resource, such as disk space, will be exhausted based on its current rate of consumption, enabling proactive alerts before an outage occurs.
NEW QUESTION # 66
Which of the following is optional, but highly recommended to include in a datapoint?
Answer: B
Explanation:
The correct answer is D. Metric type.
A metric type is an optional, but highly recommended field that specifies the kind of measurement that a datapoint represents. For example, a metric type can be gauge, counter, cumulative counter, or histogram. A metric type helps Splunk Observability Cloud to interpret and display the data correctly1 To learn more about how to send metrics to Splunk Observability Cloud, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types 2: https://docs.splunk.com/Observability/gdi/metrics/metrics.html
NEW QUESTION # 67
What constitutes a single metrics time series (MTS)?
Answer: A
Explanation:
A metric time series (MTS) is a collection of data points that have the same metric and the same set of dimensions. For example, the following sets of data points are in three separate MTS:
MTS1: Gauge metric cpu.utilization, dimension "hostname": "host1"
MTS2: Gauge metric cpu.utilization, dimension "hostname": "host2"
MTS3: Gauge metric memory.usage, dimension "hostname": "host1"
A metric is a numerical measurement that varies over time, such as CPU utilization or memory usage. A dimension is a key-value pair that provides additional information about the metric, such as the hostname or the location. A data point is a combination of a metric, a dimension, a value, and a timestamp.
NEW QUESTION # 68
Given that the metric demo. trans. count is being sent at a 10 second native resolution, which of the following is an accurate description of the data markers displayed in the chart below?
Answer: B
Explanation:
Explanation
The correct answer is D. Each data marker represents the sum of API calls in the hour leading up to the data marker.
The metric demo.trans.count is a cumulative counter metric, which means that it represents the total number of API calls since the start of the measurement. A cumulative counter metric can be used to measure the rate of change or the sum of events over a time period1 The chart below shows the metric demo.trans.count with a one-hour rollup and a line chart type. A rollup is a way to aggregate data points over a specified time interval, such as one hour, to reduce the number of data points displayed on a chart. A line chart type connects the data points with a line to show the trend of the metric over time2 Each data marker on the chart represents the sum of API calls in the hour leading up to the data marker. This is because the rollup function for cumulative counter metrics is sum by default, which means that it adds up all the data points in each time interval. For example, the data marker at 10:00 AM shows the sum of API calls from 9:00 AM to 10:00 AM3 To learn more about how to use metrics and charts in Splunk Observability Cloud, you can refer to these documentations123.
1: https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types 2:
https://docs.splunk.com/Observability/gdi/metrics/charts.html#Data-resolution-and-rollups-in-charts 3:
https://docs.splunk.com/Observability/gdi/metrics/charts.html#Rollup-functions-for-metric-types
NEW QUESTION # 69
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