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| Section | Weight | Objectives |
|---|---|---|
| Alerting with Detectors | 15% | - Notification rules and incident response - Common use cases: latency, saturation, errors - Threshold, anomaly, and multi-condition detectors |
| Visualizing Metrics | 20% | - Visualization best practices and effective communication - Using SignalFlow for custom queries - Creating and configuring charts, graphs, and time-series |
| Built-in Monitoring Content | 10% | - Content packs and quick-start monitoring - Using pre-built dashboards and navigators |
| Metrics Concepts | 20% | - Dimensions, time-series data, and cardinality - Data retention and resolution settings - Metric types: Gauges, Counters, Histograms, Summaries |
| Data Collection & Ingestion | 15% | - OpenTelemetry configuration and deployment - Integration with Kubernetes and cloud environments - Signal flow and data processing pipeline |
| Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
| Analytics & Insights | 10% | - Aggregation, filtering, and transformation functions - Correlation and pattern detection |
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NEW QUESTION # 111
How is it possible to create a dashboard group that no one else can edit?
Answer: A
Explanation:
According to the web search results, dashboard groups are a feature of Splunk Observability Cloud that allows you to organize and share dashboards with other users in your organization1. You can set permissions for each dashboard group, such as who can view, edit, or manage the dashboards in the group1. To create a dashboard group that no one else can edit, you need to do the following steps:
Create a dashboard group as usual, by selecting Dashboard Group from the Create menu on the navigation bar, entering a name and description, and adding dashboards to the group1.
Select Alert settings from the Dashboard actions menu (⋯) on the top right corner of the dashboard group. This will open a dialog box where you can configure the permissions for the dashboard group1.
Under Write access, select Only me. This will restrict the write access to the dashboard group to yourself only. No one else will be able to edit or delete the dashboards in the group1.
Click Save. This will create a dashboard group that no one else can edit.
NEW QUESTION # 112
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 # 113
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: D
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.
NEW QUESTION # 114
What does the "Resource Running Out" detector condition type help predict?
Answer: B
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 # 115
What modes can the Splunk Distribution of the OpenTelemetry Collector be deployed in?
(Choose all that apply.)
Answer: B,C
Explanation:
The Splunk Distribution of the OpenTelemetry Collector can be deployed in agent mode, running on the same host as the application to collect telemetry locally, or in gateway mode, acting as a central point to receive and forward data from multiple agents.
NEW QUESTION # 116
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