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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Finding Insights Using Analytics | 15% | - Metrics Analytics
|
| Topic 2: Introduction to Visualizing Metrics | 15% | - Metrics Visualization
|
| Topic 3: Detectors for Common Use Cases | 15% | - Advanced Detector Scenarios
|
| Topic 4: Create Efficient Dashboards and Alerts | 10% | - Dashboard Optimization
|
| Topic 5: Metrics Concepts | 15% | - Core Metrics Data Model
|
| Topic 6: Get Metrics In with OpenTelemetry | 10% | - OpenTelemetry Collector Deployment
|
| Topic 7: Alerting on Metrics with Detectors | 10% | - Detectors and Alerting
|
| Topic 8: Monitor Using Built-in Content | 10% | - Observability Navigation & Monitoring
|
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NEW QUESTION # 67
Which of the following statements about adding properties to MTS are true? (select all that apply)
Answer: A,C
Explanation:
According to the web search results, properties are key-value pairs that you can assign to dimensions of existing metric time series (MTS) in Splunk Observability Cloud. Properties provide additional context and information about the metrics, such as the environment, role, or owner of the dimension. For example, you can add the property use: QA to the host dimension of your metrics to indicate that the host that is sending the data is used for QA. To add properties to MTS, you can use either the API or the UI. The API allows you to programmatically create, update, delete, and list properties for dimensions using HTTP requests. The UI allows you to interactively create, edit, and delete properties for dimensions using the Metric Metadata page under Settings.
NEW QUESTION # 68
Which of the following statements are true about local data links? (select all that apply)
Answer: B,C
Explanation:
Explanation
The correct answers are A and D.
According to the Get started with Splunk Observability Cloud document1, one of the topics that is covered in the Getting Data into Splunk Observability Cloud course is global and local data links. Data links are shortcuts that provide convenient access to related resources, such as Splunk Observability Cloud dashboards, Splunk Cloud Platform and Splunk Enterprise, custom URLs, and Kibana logs.
The document explains that there are two types of data links: global and local. Global data links are available on all dashboards and charts, while local data links are available on only one dashboard. The document also provides the following information about local data links:
Anyone with write permission for a dashboard can add local data links that appear on that dashboard.
Local data links can have either a Splunk Observability Cloud internal destination or an external destination, such as a custom URL or a Kibana log.
Only Splunk Observability Cloud administrators can delete local data links.
Therefore, based on this document, we can conclude that A and D are true statements about local data links. B and C are false statements because:
B is false because local data links can have an external destination as well as an internal one.
C is false because anyone with write permission for a dashboard can create local data links, not just administrators.
NEW QUESTION # 69
A Software Engineer is troubleshooting an issue with memory utilization in their application. They released a new canary version to production and now want to determine if the average memory usage is lower for requests with the 'canary' version dimension. They've already opened the graph of memory utilization for their service.
How does the engineer see if the new release lowered average memory utilization?
Answer: B
Explanation:
On the chart for plot A, select Add Analytics, then select Mean: Aggregation. In the window that appears, select `version' from the Group By field. This will create a new plot B that shows the average memory utilization for each version of the application. The engineer can then compare the values of plot B for the `canary' and `stable' versions to see if there is a significant difference.
To learn more about how to use analytics functions in Splunk Observability Cloud, you can refer to this documentation.
NEW QUESTION # 70
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: A
Explanation:
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 # 71
What kind of metadata is sent with a datapoint
Answer: A
Explanation:
Dimensions are the metadata sent with a datapoint. They describe the context of the metric, such as the source, host, environment, or other identifying attributes used to filter and group metric data.
NEW QUESTION # 72
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