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According to the Splunk Observability Cloud documentation1, 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.

The correct answer is D. Metric type.A metric type is an optional, but highly recommended field that specifies the kind of measurement that a datapoint represents. For example, a metric type can be gauge, counter, cumulative counter, or histogram. A metric type helps Splunk Observability Cloud to interpret and display the data correctly1To learn more about how to send metrics to Splunk Observability Cloud, you can refer to this documentation2.1: https://docs.splunk.com/Observability/gdi/metrics/metrics.html#Metric-types2: https://docs.splunk.com/Observability/gdi/metrics/metrics.html

According to the Splunk Observability Cloud documentation1, the maximum function is an analytic function that returns the highest value of a metric or a dimension over a specified time interval. The maximum function can be used as a transformation or an aggregation. A transformation applies the function to each metric time series (MTS) individually, while an aggregation applies the function to all MTS and returns a single value. For example, to discover the peak page visits for a site over the last day, you can use the following SignalFlow code:maximum(24h, counters(''page.visits''))This will return the highest value of the page.visits counter metric for each MTS over the last 24 hours. You can then use a chart to visualize the results and identify the peak page visits for each MTS.

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. Therefore, option A is correct.

The correct answer is C. On the chart for plot A, select Add Analytics, then select Mean:Aggregation. In the window that appears, select 'version' from the Group By field.This will create a new plot B that shows the average memory utilization for each version of the application. The engineer can then compare the values of plot B for the 'canary' and 'stable' versions to see if there is a significant difference.To learn more about how to use analytics functions in Splunk Observability Cloud, you can refer to this documentation1.1: https://docs.splunk.com/Observability/gdi/metrics/analytics.html

According to the Splunk O11y Cloud Certified Metrics User Track document1, a single-instance dashboard is a type of dashboard that displays charts and information for a single instance of a service or host. You can use a single-instance dashboard to monitor the performance and health of a specific server, such as the one that is restarting due to power supply issues. You can also create detectors for the metrics that are relevant to the server, such as CPU usage, memory usage, disk usage, and uptime. Therefore, option A is correct.

The correct answer is A. Only metrics with a dimension of host and a value beginning with test-.This filter returns the metrics that have a host dimension that matches the pattern test-. For example, test-01, test-abc, test-xyz, etc. The asterisk () is a wildcard character that can match any string of characters1To learn more about how to filter metrics in Splunk Observability Cloud, you can refer to this documentation2.1: https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics2: https://docs.splunk.com/Observability/gdi/metrics/search.html 

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''))

The correct answer is D. gRPC (4317), SignalFx (9080), Fluentd (8006).According to the web search results, these are the default ports for the corresponding components in the OpenTelemetry Collector. You can verify this by looking at the table of exposed ports and endpoints in the first result1.You can also see the agent and gateway configuration files in the same result for more details.1: https://docs.splunk.com/observability/gdi/opentelemetry/exposed-endpoints.html

The correct answer is B. Add a filter to narrow the scope of the measurement.A filter is a way to reduce the number of metric time series (MTS) that are displayed on a chart or used in a detector. A filter specifies one or more dimensions and values that the MTS must have in order to be included. For example, if you want to monitor the memory.free metric only for hosts that belong to a certain cluster, you can add a filter like cluster:my-cluster to the plot or detector. This will exclude any MTS that do not have the cluster dimension or have a different value for it1Adding a filter can help you avoid exceeding the plot cap, which is the maximum number of MTS that can be contained in a single plot. The plot cap is 100,000 by default, but it can be changed by contacting Splunk Support2 To learn more about how to use filters in Splunk Observability Cloud, you can refer to this documentation3.1: https://docs.splunk.com/Observability/gdi/metrics/search.html#Filter-metrics2: https://docs.splunk.com/Observability/gdi/metrics/detectors.html#Plot-cap3: https://docs.splunk.com/Observability/gdi/metrics/search.html