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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Metrics Concepts | 15% | - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints - Data resolution, rollups - Discriminate between types of metadata - List the components of a datapoint |
| Topic 2: Introduction to Metric Visualization | 15% | - Create widgets and showcase groups - Look for metrics - Visualize a measure in a chart - Create charts and dashboards - Analyze data in charts - Use rollups and analytical tools correctly - Differentiate between several chart visualization types |
| Topic 3: Monitor Using Built-in Content | 10% | - Subscribe to alerts - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution - Interact with data using built-in content - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Use the Cluster Analyzer to pinpoint the root of some problems |
| Topic 4: Intro to Alerting on Metrics using Detectors | 10% | - Make the detector from a chart - Make a muting rule - Make a solo detector - Clone an existing detector |
| Topic 5: Get Metrics In with OpenTelemetry | 10% | - Troubleshooting common errors - General OpenTelemetry Concepts - Edit the configuration - Configure the OTel Collector - Deploy the OTel Collector on Linux |
| Topic 6: Create Efficient Dashboards and Alerts | 10% | - Include instructions on interfaces - View dashboard happenings - Create single-instance dashboard panels - Configure personal information linkages |
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NEW QUESTION # 56
Which component of the OpenTelemetry Collector allows for the modification of metadata?
Answer: C
Explanation:
The component of the OpenTelemetry Collector that allows for the modification of metadata is A. Processors.
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 information1 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 kind2 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 type3 To learn more about how to use processors in the OpenTelemetry Collector, you can refer to this documentation1.
1: https://opentelemetry.io/docs/collector/configuration/#processors 2: https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/attributesprocessor 3: https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/resourceprocessor
NEW QUESTION # 57
What are the best practices for creating detectors? (select all that apply)
Answer: A,B,C,D
Explanation:
The best practices for creating detectors are:
View data at highest resolution. This helps to avoid missing important signals or patterns in the data that could indicate anomalies or issues.
Have a consistent value. This means that the metric or dimension used for detection should have a clear and stable meaning across different sources, contexts, and time periods. For example, avoid using metrics that are affected by changes in configuration, sampling, or aggregation.
View detector in a chart. This helps to visualize the data and the detector logic, as well as to identify any false positives or negatives. It also allows to adjust the detector parameters and thresholds based on the data distribution and behavior.
Have a consistent type of measurement. This means that the metric or dimension used for detection should have the same unit and scale across different sources, contexts, and time periods. For example, avoid mixing bytes and bits, or seconds and milliseconds.
NEW QUESTION # 58
Which of the following chart visualization types are unaffected by changing the time picker on a dashboard?
(select all that apply)
Answer: C,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 # 59
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:
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 period. 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 time.
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 AM.
NEW QUESTION # 60
Which of the following best describes the purpose of the "Metric Finder" in Splunk Observability Cloud?
Answer: C
Explanation:
The Metric Finder allows users to search, browse, and explore available metrics along with their dimensions, helping users discover what data is available before building charts, dashboards, or detectors.
NEW QUESTION # 61
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