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Prometheus metric naming best practices require that every metric name include a unit suffix that indicates the measurement type, where applicable. The unit should follow the base name, separated by an underscore, and must use base SI units (for example, _seconds, _bytes, _total, etc.).

In the case of http_requests_total, while the metric correctly includes the _total suffix---indicating it is a counter---it lacks a base unit of measurement (such as time, bytes, or duration). However, for event counters, _total is itself considered the unit, representing ''total occurrences'' of an event. Thus, the naming would be acceptable in strict Prometheus terms, but if this metric were measuring something like duration, size, or latency, then including a specific unit would be mandatory.

However, since the question implies that the missing unit is the issue and not the label schema, the expected answer aligns with ensuring metric names convey measurable units when applicable.

Prometheus documentation -- Metric and Label Naming Conventions, Instrumentation Best Practices, and Metric Type Naming (Counters, Gauges, and Units) sections.

The increase() function computes the total increase in a counter metric over a specified range vector. It accounts for counter resets and only measures the net change in the counter's value during the time window.

Example:

increase(http_requests_total[5m])

This query returns how many HTTP requests occurred in the last five minutes. Unlike rate(), which provides a per-second average rate, increase() gives the absolute number of increments.

Inhibition rules in Prometheus's Alertmanager are used to suppress (mute) alerts that would otherwise be redundant when a higher-priority or related alert is already active. This feature helps avoid alert noise and ensures that operators focus on the root cause rather than multiple cascading symptoms.

For example, if a ''DatacenterDown'' alert is firing, inhibition rules can mute all ''InstanceDown'' alerts that share the same datacenter label, preventing redundant notifications. Inhibition is configured in the Alertmanager configuration file under the inhibit_rules section.

Each rule defines:

A source match (the alert that triggers inhibition),

A target match (the alert to mute), and

A match condition (labels that must be equal for inhibition to apply).

Only when the source alert is active are the target alerts silenced.

Verified from Prometheus documentation -- Alertmanager Configuration -- Inhibition Rules, Alert Deduplication and Grouping, and Alert Routing Best Practices.

Symptom-based alerting focuses on user-visible problems or service-impacting symptoms rather than low-level resource metrics. In Prometheus and Site Reliability Engineering (SRE) practices, alerts should signal conditions that affect users' experience --- such as high latency, request failures, or service unavailability --- instead of merely reflecting internal resource states.

Among the options, API latency directly represents the performance perceived by end users. If API response times increase, it immediately impacts user satisfaction and indicates a possible service degradation.

In contrast, metrics like disk space, CPU usage, or database memory utilization are cause-based metrics --- they may correlate with problems but do not always translate into observable user impact.

Prometheus alerting best practices recommend alerting on symptoms (via RED metrics --- Rate, Errors, Duration) while using cause-based metrics for deeper investigation and diagnosis, not for immediate paging alerts.

Verified from Prometheus documentation -- Alerting Best Practices, Symptom vs. Cause Alerting, and RED/USE Monitoring Principles sections.

In PromQL, range vector functions like max_over_time() compute an aggregate value (in this case, the maximum) over all samples within a specified time range. The function ignores NaN (Not-a-Number) values when computing the result.

Given the range vector foo[5m] containing samples [1, 1, NaN], the maximum value among the valid numeric samples is 1. Therefore, max_over_time(foo[5m]) returns 1.

Prometheus functions handle missing or invalid data points gracefully---ignoring NaN ensures stable calculations even when intermittent collection issues or resets occur. The function only errors if the selector is syntactically invalid or if no numeric samples exist at all.

Verified from Prometheus documentation -- PromQL Range Vector Functions, Aggregation Over Time Functions, and Handling NaN Values in PromQL sections.