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Splunk SPLK-4001 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Monitor Using Built-in Content: This section of the exam measures the skills of Site Reliability Engineers and focuses on utilizing Splunk built-in content for effective monitoring. It includes interpreting data in charts, subscribing to alerts, and using tools such as the Kubernetes Navigator, Cluster Analyzer, and pre-built dashboards to identify and resolve performance issues in nodes, pods, and containers.
Topic 2
  • Finding Insights Using Analytics: This section of the exam measures the skills of Data Analysts and covers analytical methods for uncovering insights from metrics. It includes finding total values across sources, combining plots, performing time-based comparisons, analyzing trends through ratios and percentages, and applying analytic functions across moving or calendar time windows.
Topic 3
  • Create Efficient Dashboards and Alerts: This section of the exam measures the skills of Performance Monitoring Engineers and focuses on optimizing dashboards and alerts for clarity and responsiveness. It includes adding instructions to dashboards, creating single-instance dashboards, configuring local data links, customizing alert messages, and troubleshooting chart and alert issues.
Topic 4
  • Introduction to Visualizing Metrics: This section of the exam measures the skills of Data Visualization Analysts and emphasizes creating, managing, and interpreting charts and dashboards. It includes searching for metrics, visualizing data with different chart types, applying rollups and analytic functions, and effectively understanding metric data within dashboards.

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Splunk O11y Cloud Certified Metrics User Sample Questions (Q125-Q130):

NEW QUESTION # 125
Which of the following statements is true about rollups?

Answer: C

Explanation:
Display rollups are applied when there is more data than can be visually represented by the available pixels on a chart. They aggregate or summarize the data to ensure it can be efficiently displayed without losing the overall trends.


NEW QUESTION # 126
A customer has a large population of servers. They want to identify the servers where utilization has increased the most since last week. Which analytics function is needed to achieve this?

Answer: D

Explanation:
The correct answer is C. Timeshift.
According to the Splunk Observability Cloud documentation1, timeshift is an analytic function that allows you to compare the current value of a metric with its value at a previous time interval, such as an hour ago or a week ago. You can use the timeshift function to measure the change in a metric over time and identify trends, anomalies, or patterns. For example, to identify the servers where utilization has increased the most since last week, you can use the following SignalFlow code:
timeshift(1w, counters("server.utilization"))
This will return the value of the server.utilization counter metric for each server one week ago. You can then subtract this value from the current value of the same metric to get the difference in utilization. You can also use a chart to visualize the results and sort them by the highest difference in utilization.


NEW QUESTION # 127
What option for the Sum analytic function could be used to calculate the total number of packets received on a host across network interfaces?

Answer: A

Explanation:
The Sum analytic function with the aggregation option combines values across multiple matching time series, such as packet counts from all network interfaces on a host, to produce a total.


NEW QUESTION # 128
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 # 129
A customer is experiencing issues getting metrics from a new receiver they have configured in the OpenTelemetry Collector. How would the customer go about troubleshooting further with the logging exporter?

Answer: D

Explanation:
The correct answer is B. Adding logging into the metrics receiver pipeline.
The logging exporter is a component that allows the OpenTelemetry Collector to send traces, metrics, and logs directly to the console. It can be used to diagnose and troubleshoot issues with telemetry received and processed by the Collector, or to obtain samples for other purposes1 To activate the logging exporter, you need to add it to the pipeline that you want to diagnose. In this case, since you are experiencing issues with a new receiver for metrics, you need to add the logging exporter to the metrics receiver pipeline. This will create a new plot that shows the metrics received by the Collector and any errors or warnings that might occur1 The image that you have sent with your question shows how to add the logging exporter to the metrics receiver pipeline. You can see that the exporters section of the metrics pipeline includes logging as one of the options. This means that the metrics received by any of the receivers listed in the receivers section will be sent to the logging exporter as well as to any other exporters listed2 To learn more about how to use the logging exporter in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/Observability/gdi/opentelemetry/components/logging-exporter.html 2: https://docs.splunk.com/Observability/gdi/opentelemetry/exposed-endpoints.html


NEW QUESTION # 130
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