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| Certification Vendor: | Splunk |
|---|---|
| Exam Name: | Splunk O11y Cloud Certified Metrics User Exam |
| Exam Number: | SPLK-4001 |
| Exam Duration: | 60 minutes |
| Available Languages: | English |
| Exam Format: | Multiple Choice |
| Exam Price: | $130 USD |
| Real Exam Qty: | 54 |
| Recommended Training: | OpenTelemetry Fundamentals Splunk Observability Cloud Training |
| Exam Registration: | Pearson VUE Registration (via Splunk portal) Splunk Certification Track (Official) |
| Sample Questions: | Splunk SPLK-4001 Sample Questions |
| Exam Way: | Online or onsite proctored exam via Pearson VUE |
| Pre Condition: | None |
| Official Syllabus URL: | https://www.splunk.com/en_us/training/certification-track/splunk-o11y-cloud-certified-metrics-user.html |
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The SPLK-4001 Exam Tests your ability to customize dashboards for visualization, configure alerts, and understand metrics data. Moreover, it evaluates your capacity to optimize data pipelines and design solutions that meet the needs of your organization. Successful completion of the exam, therefore, demonstrates the capability to navigate and exploit the full potentials of Splunk's cloud-based metric platform for best visibility and observability results.
NEW QUESTION # 80
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: A
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 # 81
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 document, 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 # 82
A customer has a dashboard and alerts created for host performance metrics for their service desk to easily track issues. The service desk manager wants to visualize the events from alerts directly on their existing dashboard. What is the best way to achieve this? (Choose all that apply.)
Answer: C,D
Explanation:
An event feed chart can display alert events from a detector directly on the dashboard, and an event overlay can show those alert events on existing dashboard charts so the service desk can correlate alerts with host performance metrics.
NEW QUESTION # 83
A customer is sending data from a machine that is over-utilized. Because of a lack of system resources, datapoints from this machine are often delayed by up to 10 minutes. Which setting can be modified in a detector to prevent alerts from firing before the datapoints arrive?
Answer: A
Explanation:
The correct answer is A. Max Delay.
Max Delay is a parameter that specifies the maximum amount of time that the analytics engine can wait for data to arrive for a specific detector. For example, if Max Delay is set to 10 minutes, the detector will wait for only a maximum of 10 minutes even if some data points have not arrived. By default, Max Delay is set to Auto, allowing the analytics engine to determine the appropriate amount of time to wait for data points1 In this case, since the customer knows that the data from the over-utilized machine can be delayed by up to 10 minutes, they can modify the Max Delay setting for the detector to 10 minutes. This will prevent the detector from firing alerts before the data points arrive, and avoid false positives or missing data1 To learn more about how to use Max Delay in Splunk Observability Cloud, you can refer to this documentation1.
1: https://docs.splunk.com/observability/alerts-detectors-notifications/detector-options.html#Max-Delay
NEW QUESTION # 84
Which component of the OpenTelemetry Collector allows for the modification of metadata?
Answer: A
Explanation:
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 information.
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 kind.
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 type.
NEW QUESTION # 85
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