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| Section | Weight | Objectives |
|---|---|---|
| Intro to Alerting on Metrics using Detectors | 10% | - Make a solo detector - Make a muting rule - Clone an existing detector - Make the detector from a chart |
| Metrics Concepts | 15% | - Discriminate between types of metadata - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints - List the components of a datapoint - Data resolution, rollups |
| Monitor Using Built-in Content | 10% | - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution - Use the Cluster Analyzer to pinpoint the root of some problems - Interact with data using built-in content - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Subscribe to alerts |
| Create Efficient Dashboards and Alerts | 10% | - Create single-instance dashboard panels - View dashboard happenings - Include instructions on interfaces - Configure personal information linkages |
| Get Metrics In with OpenTelemetry | 10% | - General OpenTelemetry Concepts - Edit the configuration - Configure the OTel Collector - Troubleshooting common errors - Deploy the OTel Collector on Linux |
| Introduction to Metric Visualization | 15% | - Analyze data in charts - Create charts and dashboards - Differentiate between several chart visualization types - Visualize a measure in a chart - Create widgets and showcase groups - Use rollups and analytical tools correctly - Look for metrics |
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NEW QUESTION # 115
A customer wants to be alerted only when a condition is true across a majority of their hosts, rather than for each individual host. Which detector condition type should they use?
Answer: D
Explanation:
By aggregating the signal across all hosts before applying a custom threshold, the detector evaluates the overall condition rather than firing separately for each individual host, allowing alerts based on the majority state.
NEW QUESTION # 116
Which of the following aggregate analytic functions will allow a user to see the highest or lowest n values of a metric?
Answer: A
Explanation:
Explanation
The correct answer is D. Top / Bottom.
Top and bottom are aggregate analytic functions that allow a user to see the highest or lowest n values of a metric. They can be used to select a subset of the time series in the plot by count or by percent. For example, top (5) will show the five time series with the highest values in each time period, while bottom (10%) will show the 10% of time series with the lowest values in each time period1 To learn more about how to use top and bottom functions in Splunk Observability Cloud, you can refer to this documentation1.
NEW QUESTION # 117
A customer is experiencing an issue where their detector is not sending email notifications but is generating alerts within the Splunk Observability UI. Which of the below is the root cause?
Answer: C
Explanation:
The most likely root cause of the issue is D. The detector has a muting rule.
A muting rule is a way to temporarily stop a detector from sending notifications for certain alerts, without disabling the detector or changing its alert conditions. A muting rule can be useful when you want to avoid alert noise during planned maintenance, testing, or other situations where you expect the metrics to deviate from normal1 When a detector has a muting rule, it will still generate alerts within the Splunk Observability UI, but it will not send email notifications or any other types of notifications that you have configured for the detector. You can see if a detector has a muting rule by looking at the Muting Rules tab on the detector page. You can also create, edit, or delete muting rules from there1 To learn more about how to use muting rules in Splunk Observability Cloud, you can refer to this documentation1.
NEW QUESTION # 118
Which component of the OpenTelemetry Collector allows for the modification of metadata?
Answer: C
Explanation:
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 # 119
A customer operates a caching web proxy. They want to calculate the cache hit rate for their service. What is the best way to achieve this?
Answer: A
Explanation:
Explanation
According to the Splunk O11y Cloud Certified Metrics User Track document1, percentages and ratios are useful for calculating the proportion of one metric to another, such as cache hits to cache misses, or successful requests to failed requests. You can use the percentage() or ratio() functions in SignalFlow to compute these values and display them in charts. For example, to calculate the cache hit rate for a service, you can use the following SignalFlow code:
percentage(counters("cache.hits"), counters("cache.misses"))
This will return the percentage of cache hits out of the total number of cache attempts. You can also use the ratio() function to get the same result, but as a decimal value instead of a percentage.
ratio(counters("cache.hits"), counters("cache.misses"))
NEW QUESTION # 120
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