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| Certification Vendor: | Splunk |
|---|---|
| Exam Name: | Splunk O11y Cloud Certified Metrics User |
| Exam Number: | SPLK-4001 |
| Exam Duration: | 60 minutes |
| Related Certifications: | Splunk O11y Cloud Certified Metrics User |
| Real Exam Qty: | 54 |
| Available Languages: | English |
| Sample Questions: | Splunk SPLK-4001 Sample Questions |
| Pre Condition: | No prerequisite exams for this certification. |
| Official Syllabus URL: | https://www.splunk.com/en_us/training/certification-track/splunk-o11y-cloud-certified-metrics-user.html |
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Splunk SPLK-4001 Certification Exam is an excellent opportunity for IT professionals to demonstrate their knowledge and expertise in using Splunk for cloud monitoring and analysis. By preparing for and passing the exam, you can enhance your career prospects and demonstrate your commitment to staying up-to-date with the latest technologies and best practices in cloud infrastructure monitoring.
NEW QUESTION # 27
Which of the following visualization types exist within Splunk Observability Cloud? (Choose all that apply.)
Answer: A,B,D
Explanation:
Splunk Observability Cloud supports Table, Single Value, and Pie Chart visualizations to present metric data in structured, summarized, and categorical forms, enabling flexible analysis and reporting.
NEW QUESTION # 28
Which of the following are true about organization metrics? (select all that apply)
Answer: A,B,D
Explanation:
Organization metrics give insights into system usage, system limits, data ingested and token quotas. Organization metrics are included for free. A user can plot and alert on them like metrics they send to Splunk Observability Cloud. Organization metrics are a set of metrics that Splunk Observability Cloud provides to help you measure your organization's usage of the platform. They include metrics such as:
Ingest metrics: Measure the data you're sending to Infrastructure Monitoring, such as the number of data points you've sent.
App usage metrics: Measure your use of application features, such as the number of dashboards in your organization.
Integration metrics: Measure your use of cloud services integrated with your organization, such as the number of calls to the AWS CloudWatch API.
Resource metrics: Measure your use of resources that you can specify limits for, such as the number of custom metric time series (MTS) you've created.
Organization metrics are not charged and do not count against any system limits. You can view them in built-in charts on the Organization Overview page or in custom charts using the Metric Finder. You can also create alerts based on organization metrics to monitor your usage and performance.
NEW QUESTION # 29
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:
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 interval. 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.
NEW QUESTION # 30
In the Splunk distribution of the OpenTelemetry Collector, what is the difference between the agent_config.yaml and the splunk-otel-collector.conf files?
Answer: D
NEW QUESTION # 31
A user wants to reduce the cardinality of a metric before visualizing it on a chart. Which analytic function should be applied to combine multiple time series into a single time series?
Answer: B
Explanation:
Aggregation functions such as Sum combine multiple time series into a single time series by grouping across dimensions, effectively reducing cardinality for easier visualization and analysis.
NEW QUESTION # 32
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