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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Built-in Monitoring Content | 10% | - Using pre-built dashboards and navigators - Content packs and quick-start monitoring |
| Topic 2: Analytics & Insights | 10% | - Aggregation, filtering, and transformation functions - Correlation and pattern detection |
| Topic 3: Dashboards & Operational Efficiency | 10% | - Reducing alert fatigue and optimizing monitoring - Designing clear, performant dashboards |
| Topic 4: Visualizing Metrics | 20% | - Visualization best practices and effective communication - Using SignalFlow for custom queries - Creating and configuring charts, graphs, and time-series |
| Topic 5: Alerting with Detectors | 15% | - Notification rules and incident response - Common use cases: latency, saturation, errors - Threshold, anomaly, and multi-condition detectors |
| Topic 6: Metrics Concepts | 20% | - Data retention and resolution settings - Metric types: Gauges, Counters, Histograms, Summaries - Dimensions, time-series data, and cardinality |
| Topic 7: Data Collection & Ingestion | 15% | - Signal flow and data processing pipeline - OpenTelemetry configuration and deployment - Integration with Kubernetes and cloud environments |
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NEW QUESTION # 77
For a high-resolution metric, what is the highest possible native resolution of the metric?
Answer: C
Explanation:
According to the Splunk Test Blueprint - O11y Cloud Metrics User document1, one of the metrics concepts that is covered in the exam is data resolution and rollups. Data resolution refers to the granularity of the metric data points, and rollups are the process of aggregating data points over time to reduce the amount of data stored.
The Splunk O11y Cloud Certified Metrics User Track document, states that one of the recommended courses for preparing for the exam is Introduction to Splunk Infrastructure Monitoring, which covers the basics of metrics monitoring and visualization.
In the Introduction to Splunk Infrastructure Monitoring course, there is a section on Data Resolution and Rollups, which explains that Splunk Observability Cloud collects high-resolution metrics at 1- second intervals by default, and then applies rollups to reduce the data volume over time. The document also provides a table that shows the different rollup intervals and retention periods for different resolutions.
Therefore, based on these documents, we can conclude that for a high-resolution metric, the highest possible native resolution of the metric is 1 second.
NEW QUESTION # 78
Which of the following are supported rollup functions in Splunk Observability Cloud?
Answer: B
Explanation:
According to the Splunk O11y Cloud Certified Metrics User Track document1, Observability Cloud has the following rollup functions:
Sum: (default for counter metrics): Returns the sum of all data points in the MTS reporting interval.
Average (default for gauge metrics): Returns the average value of all data points in the MTS reporting interval.
Min: Returns the minimum data point value seen in the MTS reporting interval.
Max: Returns the maximum data point value seen in the MTS reporting interval.
Latest: Returns the most recent data point value seen in the MTS reporting interval.
Lag: Returns the difference between the most recent and the previous data point values seen in the MTS reporting interval.
Rate: Returns the rate of change of data points in the MTS reporting interval.
NEW QUESTION # 79
Which action allows you to analyze data across different dimensions and perform calculations based on a set of rows?
Answer: B
NEW QUESTION # 80
A customer deals with a holiday rush of traffic during November each year, but does not want to be flooded with alerts when this happens. The increase in traffic is expected and consistent each year. Which detector condition should be used when creating a detector for this data?
Answer: D
Explanation:
historical anomaly is a detector condition that allows you to trigger an alert when a signal deviates from its historical pattern. Historical anomaly uses machine learning to learn the normal behavior of a signal based on its past data, and then compares the current value of the signal with the expected value based on the learned pattern. You can use historical anomaly to detect unusual changes in a signal that are not explained by seasonality, trends, or cycles. Historical anomaly is suitable for creating a detector for the customer's data, because it can account for the expected and consistent increase in traffic during November each year. Historical anomaly can learn that the traffic pattern has a seasonal component that peaks in November, and then adjust the expected value of the traffic accordingly. This way, historical anomaly can avoid triggering alerts when the traffic increases in November, as this is not an anomaly, but rather a normal variation.
However, historical anomaly can still trigger alerts when the traffic deviates from the historical pattern in other ways, such as if it drops significantly or spikes unexpectedly.
NEW QUESTION # 81
Where does the Splunk distribution of the OpenTelemetry Collector store the configuration files on Linux machines by default?
Answer: B
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 # 82
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