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NEW QUESTION # 97
What is the recommended approach to monitor a large number of metric time series (MTS)?
Answer: A
Explanation:
Using dimensions to filter and break down metric time series allows you to organize and manage large numbers of MTS efficiently. Dimensions provide context and enable grouping, filtering, and targeted analysis without overwhelming dashboards or alerts.
NEW QUESTION # 98
A customer has a large population of servers. They want to identify the servers where utilization has increased the most since last week. Which analytics function is needed to achieve this?
Answer: D
Explanation:
The correct answer is C. Timeshift.
According to the Splunk Observability Cloud documentation1, timeshift is an analytic function that allows you to compare the current value of a metric with its value at a previous time interval, such as an hour ago or a week ago. You can use the timeshift function to measure the change in a metric over time and identify trends, anomalies, or patterns. For example, to identify the servers where utilization has increased the most since last week, you can use the following SignalFlow code:
timeshift(1w, counters("server.utilization"))
This will return the value of the server.utilization counter metric for each server one week ago. You can then subtract this value from the current value of the same metric to get the difference in utilization. You can also use a chart to visualize the results and sort them by the highest difference in utilization.
NEW QUESTION # 99
What happens when the limit of allowed dimensions is exceeded for an MTS?
Answer: A
Explanation:
According to the web search results, dimensions are metadata in the form of key-value pairs that monitoring software sends in along with the metrics. The set of metric time series (MTS) dimensions sent during ingest is used, along with the metric name, to uniquely identify an MTS.
Splunk Observability Cloud has a limit of 36 unique dimensions per MTS. If the limit of allowed dimensions is exceeded for an MTS, the additional dimensions are dropped and not stored or indexed by Observability Cloud. This means that the data point is still ingested, but without the extra dimensions. Therefore, option A is correct.
NEW QUESTION # 100
How can analytic functions be applied to a subset of multiple time series (MTS) in a signal?
(Select all that apply)
Answer: A,B
NEW QUESTION # 101
A customer is sending in datapoints regarding the number of API calls made to a particular service. The datapoints are being sent every 10 seconds, and represent the number of API calls seen in the last 10 seconds. What metric type is this?
Answer: B
Explanation:
A gauge metric represents a value measured at a specific point in time or over a defined interval.
In this scenario, the datapoints indicate the number of API calls observed in each 10-second interval, which aligns with the characteristics of a gauge metric.
NEW QUESTION # 102
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