Answer C Reference : https://cloud.google.com/monitoring/api/v3/kinds-and-types?hl=en GAUGE Metric : In which value measures a specific instant in time DELTA Metric : In which the value measures the change since it was last recorded CUMULATIVE metric : In which the value constantly increases over time Question asks, "Latency of HTTP responses" - This needs to be specific instant in time , which is GAUGE, hence C

C is correct

GAUGE metrics record a value at a particular point in time and DISTRIBUTION captures distribution statistics. A Heatmap is a good way to visualize latencies across all users. https://cloud.google.com/monitoring/api/v3/kinds-and-types?hl=en#metric-kinds

option c

C is partially correct, GAUGE with DISTRIBUTUION, but "use a Heatmap graph to visualize the metric" will not help to visualiaze the Latencies of all users, will help to identify regions, but it;s not a question

C is right as per GCP documentation. A gauge metric, in which the value measures a specific instant in time. For example, metrics measuring CPU utilization are gauge metrics; each point records the CPU utilization at the time of measurement. Another example of a gauge metric is the current temperature.

Submitted C

C - Gauge to see latency distribution

It's guage metric as it is asking latency of http responses.

Ans C: A: Incorrect: Stacked bar could be but latency its used with distributions B: Incorrect: If we used a cumulative the latency would be increasing in the graph. C: Correct: Latency is commonly measured as a distribution. D: Incorrect: METRIC_KIND_UNSPECIFIED is not the answer that gcp wanted. Latency is commonly measured as a distribution. Given a distribution, you can measure various percentiles. For example, you might measure the number of requests that are slower than the historical 99th percentile. Latency histogram_quantile(0.9, rate(http_request_duration_seconds_bucket[7d]) histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[7d])) https://sre.google/workbook/implementing-slos/ https://cloud.google.com/architecture/adopting-slos/

Option B

Option C

I think C