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| Section | Weight | Objectives |
|---|---|---|
| Metrics Concepts | 15% | - Core Metrics Data Model
|
| Introduction to Visualizing Metrics | 15% | - Metrics Visualization
|
| Alerting on Metrics with Detectors | 10% | - Detectors and Alerting
|
| Finding Insights Using Analytics | 15% | - Metrics Analytics
|
| Create Efficient Dashboards and Alerts | 10% | - Dashboard Optimization
|
| Monitor Using Built-in Content | 10% | - Observability Navigation & Monitoring
|
| Get Metrics In with OpenTelemetry | 10% | - OpenTelemetry Collector Deployment
|
| Detectors for Common Use Cases | 15% | - Advanced Detector Scenarios
|
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NEW QUESTION # 30
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: D
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 # 31
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: D
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 # 32
Which of the following are correct ports for the specified components in the OpenTelemetry Collector?
Answer: A
Explanation:
Explanation
The correct answer is D. gRPC (4317), SignalFx (9080), Fluentd (8006).
According to the web search results, these are the default ports for the corresponding components in the OpenTelemetry Collector. You can verify this by looking at the table of exposed ports and endpoints in the first result1. You can also see the agent and gateway configuration files in the same result for more details.
1: https://docs.splunk.com/observability/gdi/opentelemetry/exposed-endpoints.html
NEW QUESTION # 33
Which of the following are accurate reasons to clone a detector? (select all that apply)
Answer: A,C
Explanation:
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the alerting concepts that is covered in the exam is detectors and alerts. Detectors are the objects that define the conditions for generating alerts, and alerts are the notifications that are sent when those conditions are met.
The Splunk O11y Cloud Certified Metrics User Track document2 states that one of the recommended courses for preparing for the exam is Alerting with Detectors, which covers how to create, modify, and manage detectors and alerts.
In the Alerting with Detectors course, there is a section on Cloning Detectors, which explains that cloning a detector creates a copy of the detector with all its settings, rules, and alert recipients.
The document also provides some reasons why you might want to clone a detector, such as:
To modify the rules without affecting the existing detector. This can be useful if you want to test different thresholds or conditions before applying them to the original detector. To explore how a detector was created without risk of changing it. This can be helpful if you want to learn from an existing detector or use it as a template for creating a new one. Therefore, based on these documents, we can conclude that A and D are accurate reasons to clone a detector.
NEW QUESTION # 34
When creating a standalone detector, individual rules in it are labeled according to severity. Which of the choices below represents the possible severity levels that can be selected?
Answer: A
Explanation:
The correct answer is C. Info, Warning, Minor, Major, and Critical.
When creating a standalone detector, you can define one or more rules that specify the alert conditions and the severity level for each rule. The severity level indicates how urgent or important the alert is, and it can also affect the notification settings and the escalation policy for the alert1 Splunk Observability Cloud provides five predefined severity levels that you can choose from when creating a rule: Info, Warning, Minor, Major, and Critical. Each severity level has a different color and icon to help you identify the alert status at a glance. You can also customize the severity levels by changing their names, colors, or icons2 To learn more about how to create standalone detectors and use severity levels in Splunk Observability Cloud, you can refer to these documentations12.
1: https://docs.splunk.com/Observability/alerts-detectors-notifications/detectors.html#Create-a-standalone-detector 2: https://docs.splunk.com/Observability/alerts-detectors-notifications/detector-options.html#Severity-levels
NEW QUESTION # 35
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