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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Create Efficient Dashboards and Alerts | 10% | - Configure personal information linkages - Include instructions on interfaces - View dashboard happenings - Create single-instance dashboard panels |
| Topic 2: Metrics Concepts | 15% | - List the components of a datapoint - Data resolution, rollups - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints - Discriminate between types of metadata |
| Topic 3: Introduction to Metric Visualization | 15% | - Look for metrics - Create widgets and showcase groups - Visualize a measure in a chart - Analyze data in charts - Differentiate between several chart visualization types - Use rollups and analytical tools correctly - Create charts and dashboards |
| Topic 4: Get Metrics In with OpenTelemetry | 10% | - Configure the OTel Collector - Deploy the OTel Collector on Linux - General OpenTelemetry Concepts - Troubleshooting common errors - Edit the configuration |
| Topic 5: Monitor Using Built-in Content | 10% | - Use the Cluster Analyzer to pinpoint the root of some problems - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Subscribe to alerts - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution - Interact with data using built-in content |
| Topic 6: Intro to Alerting on Metrics using Detectors | 10% | - Make a muting rule - Make the detector from a chart - Clone an existing detector - Make a solo detector |
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NEW QUESTION # 43
What is the purpose of muting rules in Splunk Observability Cloud?
Answer: D
Explanation:
Muting rules allow users to temporarily suppress notifications from a detector, which is useful during planned maintenance windows or known issues, without disabling or deleting the detector itself.
NEW QUESTION # 44
What modes can the Splunk Distribution of the OpenTelemetry Collector be deployed in?
(Choose all that apply.)
Answer: B,D
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 # 45
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:
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 interval. 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.
NEW QUESTION # 46
Where does the Splunk distribution of the OpenTelemetry Collector store the configuration files on Linux machines by default?
Answer: B
Explanation:
The correct answer is B. /etc/otel/collector/
According to the web search results, the Splunk distribution of the OpenTelemetry Collector stores the configuration files on Linux machines in the /etc/otel/collector/ directory by default. You can verify this by looking at the first result1, which explains how to install the Collector for Linux manually. It also provides the locations of the default configuration file, the agent configuration file, and the gateway configuration file.
To learn more about how to install and configure the Splunk distribution of the OpenTelemetry Collector, you can refer to this documentation2.
1: https://docs.splunk.com/Observability/gdi/opentelemetry/install-linux-manual.html 2: https://docs.splunk.com/Observability/gdi/opentelemetry.html
NEW QUESTION # 47
Which component of the OpenTelemetry Collector allows for the modification of metadata?
Answer: B
Explanation:
Processors are components that can modify the telemetry data before sending it to exporters or other components. Processors can perform various transformations on metrics, traces, and logs, such as filtering, adding, deleting, or updating attributes, labels, or resources. Processors can also enrich the telemetry data with additional metadata from various sources, such as Kubernetes, environment variables, or system information.
For example, one of the processors that can modify metadata is the attributes processor. This processor can update, insert, delete, or replace existing attributes on metrics or traces. Attributes are key-value pairs that provide additional information about the telemetry data, such as the service name, the host name, or the span kind.
Another example is the resource processor. This processor can modify resource attributes on metrics or traces. Resource attributes are key-value pairs that describe the entity that produced the telemetry data, such as the cloud provider, the region, or the instance type.
NEW QUESTION # 48
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