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| Section | Weight | Objectives |
|---|---|---|
| Metrics Concepts | 15% | - Data resolution, rollups - Discriminate between types of metadata - List the components of a datapoint - Define components of the Splunk IM Data Model, Metrics, MTS, datapoints |
| Monitor Using Built-in Content | 10% | - Subscribe to alerts - Use the Kubernetes Navigator to investigate problems with nodes, pods, and containers - Use the Cluster Analyzer to pinpoint the root of some problems - Correctly interpret data in charts based on rollups, analytic functions, and chart resolution - Interact with data using built-in content |
| Introduction to Metric Visualization | 15% | - Look for metrics - Analyze data in charts - Visualize a measure in a chart - Create charts and dashboards - Create widgets and showcase groups - Use rollups and analytical tools correctly - Differentiate between several chart visualization types |
| Create Efficient Dashboards and Alerts | 10% | - View dashboard happenings - Create single-instance dashboard panels - Configure personal information linkages - Include instructions on interfaces |
| Get Metrics In with OpenTelemetry | 10% | - Configure the OTel Collector - General OpenTelemetry Concepts - Edit the configuration - Deploy the OTel Collector on Linux - Troubleshooting common errors |
| Intro to Alerting on Metrics using Detectors | 10% | - Make a muting rule - Make a solo detector - Clone an existing detector - Make the detector from a chart |
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NEW QUESTION # 127
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: A
Explanation:
Explanation
The correct answer is C. 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 documentation1.
1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html
NEW QUESTION # 128
Which analytic function can be used to discover peak page visits for a site over the last day?
Answer: B
Explanation:
Explanation
According to the Splunk Observability Cloud documentation1, the maximum function is an analytic function that returns the highest value of a metric or a dimension over a specified time interval. The maximum function can be used as a transformation or an aggregation. A transformation applies the function to each metric time series (MTS) individually, while an aggregation applies the function to all MTS and returns a single value. For example, to discover the peak page visits for a site over the last day, you can use the following SignalFlow code:
maximum(24h, counters("page.visits"))
This will return the highest value of the page.visits counter metric for each MTS over the last 24 hours. You can then use a chart to visualize the results and identify the peak page visits for each MTS.
NEW QUESTION # 129
A team is troubleshooting high cardinality issues with a custom metric. What is a recommended best practice to reduce cardinality?
Answer: B
Explanation:
Using high-cardinality values, such as unique request IDs or timestamps, as dimensions can cause an explosion in the number of unique time series. Avoiding this practice helps keep cardinality manageable and reduces cost and performance issues.
NEW QUESTION # 130
The Sum Aggregation option for analytic functions does which of the following?
Answer: B
Explanation:
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the metrics concepts that is covered in the exam is analytic functions. Analytic functions are mathematical operations that can be applied to metrics to transform, aggregate, or analyze them.
The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the recommended courses for preparing for the exam is Introduction to Splunk Infrastructure Monitoring, which covers the basics of metrics monitoring and visualization.
In the Introduction to Splunk Infrastructure Monitoring course, there is a section on Analytic Functions, which explains that analytic functions can be used to perform calculations on metrics, such as sum, average, min, max, count, etc. The document also provides examples of how to use analytic functions in charts and dashboards.
One of the analytic functions that can be used is Sum Aggregation, which calculates the sum of values present in the input time series across the entire environment or per group. The document gives an example of how to use Sum Aggregation to calculate the total CPU usage across all hosts in a group by using the following syntax:
sum(cpu.utilization) by hostgroup
NEW QUESTION # 131
A customer has created a chart that tracks cpu.utilization of a set of production servers. The customer wants to show which of their servers are under 5% utilization to track underutilization of resources. What is the best analytic function to achieve this?
Answer: C
Explanation:
The Bottom analytic function identifies the metrics with the lowest values, allowing the customer to highlight servers with CPU utilization under 5% and track underutilized resources effectively.
NEW QUESTION # 132
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