You are managing an application that exposes an HTTP endpoint without using a load balancer. The latency of the HTTP responses is important for the user experience. You want to understand what HTTP la

The correct answer is C. In your application, create a metric with a metricKind set to GAUGE and a valueType set to. To capture and analyze the distribution of HTTP latencies across all users, you should use a GAUGE metricKind with a DISTRIBUTION valueType. GAUGE metrics represent the value measured at a specific point in time, which is appropriate for latency snapshots per request. A DISTRIBUT

Submitted by kev92· Apr 18, 2026Implementing service monitoring strategies

Question

You are managing an application that exposes an HTTP endpoint without using a load balancer. The latency of the HTTP responses is important for the user experience. You want to understand what HTTP latencies all of your users are experiencing. You use Stackdriver Monitoring. What should you do?

Options

AIn your application, create a metric with a metricKind set to DELTA and a valueType set to
BIn your application, create a metric with a metricKind set to CUMULATIVE and a valueType set to
CIn your application, create a metric with a metricKind set to GAUGE and a valueType set to
DIn your application, create a metric with a metricKind set to METRIC_KIND_UNSPECIFIED and a

How the community answered

(52 responses)

A
2% (1)
B
2% (1)
C
92% (48)
D
4% (2)

Explanation

To capture and analyze the distribution of HTTP latencies across all users, you should use a GAUGE metricKind with a DISTRIBUTION valueType. GAUGE metrics represent the value measured at a specific point in time, which is appropriate for latency snapshots per request. A DISTRIBUTION valueType stores a statistical summary (mean, percentiles, histogram buckets) of observed values, allowing you to understand the full spread of latency across all users - including p50, p95, and p99 percentiles. DELTA tracks incremental changes, and CUMULATIVE accumulates from a start time; neither is ideal for understanding the current distribution of per-request latency.

Topics

#Google Cloud Monitoring#Custom Metrics#Latency Monitoring#Metric Types

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