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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Alerting with Detectors | 15% | - Threshold, anomaly, and multi-condition detectors - Notification rules and incident response - Common use cases: latency, saturation, errors |
| Topic 2: Data Collection & Ingestion | 15% | - OpenTelemetry configuration and deployment - Signal flow and data processing pipeline - Integration with Kubernetes and cloud environments |
| Topic 3: Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
| Topic 4: Built-in Monitoring Content | 10% | - Using pre-built dashboards and navigators - Content packs and quick-start monitoring |
| Topic 5: Visualizing Metrics | 20% | - Visualization best practices and effective communication - Using SignalFlow for custom queries - Creating and configuring charts, graphs, and time-series |
| Topic 6: Metrics Concepts | 20% | - Metric types: Gauges, Counters, Histograms, Summaries - Dimensions, time-series data, and cardinality - Data retention and resolution settings |
| Topic 7: Analytics & Insights | 10% | - Correlation and pattern detection - Aggregation, filtering, and transformation functions |
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問題 #50
Which chart visualization types display only the most recent value?
答案：C
解題說明：
Single Value, List, Heatmap, and Table visualizations can be configured to display only the most recent value of a metric, providing an immediate snapshot rather than a time series trend.
問題 #51
A customer has a dashboard that tracks multiple performance metrics for hosts related to a production application. They want to create an alert based on an existing chart that shows them the p90 latency for a service of their application. What is the most efficient way to achieve this?
答案：B
解題說明：
Creating a new detector from an existing chart allows the alert to leverage the same signals and analytic functions, such as p90 latency, efficiently without having to rebuild the logic manually.
問題 #52
Which of the following are ways to reduce flapping of a detector? (select all that apply)
答案：A,D
解題說明：
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.
問題 #53
Which of the following rollups will display the time delta between a datapoint being sent and a datapoint being received?
答案：D
解題說明：
According to the Splunk Observability Cloud documentation, lag is a rollup function that returns the difference between the most recent and the previous data point values seen in the metric time series reporting interval. This can be used to measure the time delta between a data point being sent and a data point being received, as long as the data points have timestamps that reflect their send and receive times. For example, if a data point is sent at 10:00:00 and received at 10:00:05, the lag value for that data point is 5 seconds.
問題 #54
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?
答案：B
解題說明：
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
問題 #55
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