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| Section | Weight | Objectives |
|---|---|---|
| Built-in Monitoring Content | 10% | - Content packs and quick-start monitoring - Using pre-built dashboards and navigators |
| Dashboards & Operational Efficiency | 10% | - Designing clear, performant dashboards - Reducing alert fatigue and optimizing monitoring |
| Metrics Concepts | 20% | - Dimensions, time-series data, and cardinality - Data retention and resolution settings - Metric types: Gauges, Counters, Histograms, Summaries |
| Alerting with Detectors | 15% | - Notification rules and incident response - Threshold, anomaly, and multi-condition detectors - Common use cases: latency, saturation, errors |
| Data Collection & Ingestion | 15% | - OpenTelemetry configuration and deployment - Integration with Kubernetes and cloud environments - Signal flow and data processing pipeline |
| Analytics & Insights | 10% | - Aggregation, filtering, and transformation functions - Correlation and pattern detection |
| Visualizing Metrics | 20% | - Creating and configuring charts, graphs, and time-series - Using SignalFlow for custom queries - Visualization best practices and effective communication |
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NEW QUESTION # 122
A customer has an over-utilized host, and they want an alert when the disk utilization of a given host goes over 80% for 5 minutes. They create a detector, but do not see an alert when expected. Which of the following could be a reason for this?
Answer: D
Explanation:
If a detector's resolution is too low, it may miss short-duration spikes in metrics. Setting the detector resolution appropriately ensures that sustained conditions, like disk utilization over 80% for 5 minutes, are accurately evaluated and trigger alerts.
NEW QUESTION # 123
Where does the Splunk distribution of the OpenTelemetry Collector store the configuration files on Linux machines by default?
Answer: C
Explanation:
According to the web search results, the Splunk distribution of the OpenTelemetry Collector stores the configuration files on Linux machines in the /etc/otel/collector/ directory by default. You can verify this by looking at the first result, which explains how to install the Collector for Linux manually. It also provides the locations of the default configuration file, the agent configuration file, and the gateway configuration file.
NEW QUESTION # 124
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: A
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 # 125
A customer wants to find the total number of transmission errors that occurred over a discrete period of time. Assuming that the metric type is Cumulative Counter, which of the following combinations of rollup and analytic function could be used to accomplish this?
Answer: B
Explanation:
For a cumulative counter, the Delta rollup calculates the change in the counter value over each interval. Applying the Sum Transformation analytic function then adds those deltas together to get the total number of transmission errors over the selected time period.
NEW QUESTION # 126
Given that the metric demo. trans. count is being sent at a 10 second native resolution, which of the following is an accurate description of the data markers displayed in the chart below?
Answer: B
Explanation:
Explanation
The correct answer is D. Each data marker represents the sum of API calls in the hour leading up to the data marker.
The metric demo.trans.count is a cumulative counter metric, which means that it represents the total number of API calls since the start of the measurement. A cumulative counter metric can be used to measure the rate of change or the sum of events over a time period1 The chart below shows the metric demo.trans.count with a one-hour rollup and a line chart type. A rollup is a way to aggregate data points over a specified time interval, such as one hour, to reduce the number of data points displayed on a chart. A line chart type connects the data points with a line to show the trend of the metric over time2 Each data marker on the chart represents the sum of API calls in the hour leading up to the data marker. This is because the rollup function for cumulative counter metrics is sum by default, which means that it adds up all the data points in each time interval. For example, the data marker at 10:00 AM shows the sum of API calls from 9:00 AM to 10:00 AM3 To learn more about how to use metrics and charts in Splunk Observability Cloud, you can refer to these documentations123.
1: https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types 2:
https://docs.splunk.com/Observability/gdi/metrics/charts.html#Data-resolution-and-rollups-in-charts 3:
https://docs.splunk.com/Observability/gdi/metrics/charts.html#Rollup-functions-for-metric-types
NEW QUESTION # 127
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