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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Metrics Concepts | 15% | - Core Metrics Data Model
|
| Topic 2: Finding Insights Using Analytics | 15% | - Metrics Analytics
|
| Topic 3: Detectors for Common Use Cases | 15% | - Advanced Detector Scenarios
|
| Topic 4: Introduction to Visualizing Metrics | 15% | - Metrics Visualization
|
| Topic 5: Alerting on Metrics with Detectors | 10% | - Detectors and Alerting
|
| Topic 6: Get Metrics In with OpenTelemetry | 10% | - OpenTelemetry Collector Deployment
|
| Topic 7: Create Efficient Dashboards and Alerts | 10% | - Dashboard Optimization
|
| Topic 8: Monitor Using Built-in Content | 10% | - Observability Navigation & Monitoring
|
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NEW QUESTION # 114
Which of the following chart visualization types are unaffected by changing the time picker on a dashboard? (select all that apply)
Answer: C,D
Explanation:
The chart visualization types that are unaffected by changing the time picker on a dashboard are:
Single Value: A single value chart shows the current value of a metric or an expression. It does not depend on the time range of the dashboard, but only on the data resolution and rollup function of the chart1 List: A list chart shows the values of a metric or an expression for each dimension value in a table format. It does not depend on the time range of the dashboard, but only on the data resolution and rollup function of the chart2 Therefore, the correct answer is A and D.
To learn more about how to use different chart visualization types in Splunk Observability Cloud, you can refer to this documentation3.
1: https://docs.splunk.com/Observability/gdi/metrics/charts.html#Single-value 2: https://docs.splunk.com/Observability/gdi/metrics/charts.html#List 3: https://docs.splunk.com/Observability/gdi/metrics/charts.html
NEW QUESTION # 115
A customer has put a lot of time into building a detector with multiple signals, analytics, and filters.
That customer wants to create an identical detector, but change the metric. What is the process to do so?
Answer: A
Explanation:
Cloning a detector creates an identical copy of the existing detector, preserving its signals, analytics, filters, and configuration so the customer can modify only the metric as needed.
NEW QUESTION # 116
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: B
Explanation:
historical anomaly is a detector condition that allows you to trigger an alert when a signal deviates from its historical pattern1. 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 pattern1. You can use historical anomaly to detect unusual changes in a signal that are not explained by seasonality, trends, or cycles1.
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 accordingly1. 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 unexpectedly1.
NEW QUESTION # 117
What are the best practices for creating detectors? (select all that apply)
Answer: A,B,C,D
Explanation:
The best practices for creating detectors are:
View data at highest resolution. This helps to avoid missing important signals or patterns in the data that could indicate anomalies or issues.
Have a consistent value. This means that the metric or dimension used for detection should have a clear and stable meaning across different sources, contexts, and time periods. For example, avoid using metrics that are affected by changes in configuration, sampling, or aggregation.
View detector in a chart. This helps to visualize the data and the detector logic, as well as to identify any false positives or negatives. It also allows to adjust the detector parameters and thresholds based on the data distribution and behavior.
Have a consistent type of measurement. This means that the metric or dimension used for detection should have the same unit and scale across different sources, contexts, and time periods. For example, avoid mixing bytes and bits, or seconds and milliseconds.
NEW QUESTION # 118
How can you effectively monitor metrics with cyclic patterns using detectors?
Answer: C
NEW QUESTION # 119
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