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| Section | Weight | Objectives |
|---|---|---|
| Detectors for Common Use Cases | 15% | - Advanced Detector Scenarios
|
| Get Metrics In with OpenTelemetry | 10% | - OpenTelemetry Collector Deployment
|
| Metrics Concepts | 15% | - Core Metrics Data Model
|
| Monitor Using Built-in Content | 10% | - Observability Navigation & Monitoring
|
| Alerting on Metrics with Detectors | 10% | - Detectors and Alerting
|
| Introduction to Visualizing Metrics | 15% | - Metrics Visualization
|
| Create Efficient Dashboards and Alerts | 10% | - Dashboard Optimization
|
| Finding Insights Using Analytics | 15% | - Metrics Analytics
|
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NEW QUESTION # 115
Which of the following are accurate reasons to clone a detector? (select all that apply)
Answer: A,D
Explanation:
Explanation
The correct answers are A and D.
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.
B and C are not valid reasons because:
Cloning a detector does not reduce the amount of billed TAPM for the detector. TAPM stands for Tracked Active Problem Metric, which is a metric that has been alerted on by a detector. Cloning a detector does not change the number of TAPM that are generated by the original detector or the clone.
Cloning a detector does not add an additional recipient to the detector's alerts. Cloning a detector copies the alert recipients from the original detector, but it does not add any new ones. To add an additional recipient to a detector's alerts, you need to edit the alert settings of the detector.
NEW QUESTION # 116
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 # 117
Which of the following are ways to reduce flapping of a detector? (select all that apply)
Answer: A,C
Explanation:
According to the Splunk Lantern article Resolving flapping detectors in Splunk Infrastructure Monitoring, flapping is a phenomenon where alerts fire and clear repeatedly in a short period of time, due to the signal fluctuating around the threshold value. To reduce flapping, the article suggests the following ways:
Configure a duration or percent of duration for the alert: This means that you require the signal to stay above or below the threshold for a certain amount of time or percentage of time before triggering an alert. This can help filter out noise and focus on more persistent issues. Apply a smoothing transformation (like a rolling mean) to the input data for the detector: This means that you replace the original signal with the average of its last several values, where you can specify the window length. This can reduce the impact of a single extreme observation and make the signal less fluctuating.
NEW QUESTION # 118
Which component of the OTel Collector is responsible for receiving telemetry data from instrumented applications?
Answer: B
NEW QUESTION # 119
How can analytic functions be applied to a subset of multiple time series (MTS) in a signal?
(Select all that apply)
Answer: A,D
NEW QUESTION # 120
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