Prometheus Query Language (PromQL) uses a function-and-selector syntax, not SQL. A valid query typically starts with a metric name and optionally includeslabel matchersin curly braces. In the simplified quiz syntax given, the valid PromQL-style selector is best represented byD: http_requests_total(job="apiserver"), soDis correct.

Conceptually, what this query means is “select time series for the metric http_requests_total where the job label equals apiserver.” In standard PromQL formatting you most often see this as: http_requests_total{job="apiserver"}. Many training questions abbreviate braces and focus on the idea of filtering by labels; the key is that PromQL uses label matchers rather than SQL WHERE clauses.

Options A and C are invalid because they use SQL (SELECT * FROM ...) which is not PromQL. Option B is also invalid because PromQL does not use the keyword WHERE. PromQL filtering is done by applying label matchers directly to the metric selector.

In Kubernetes observability, PromQL is central to building dashboards and alerts from cluster metrics. For example, you might compute rates from counters: rate(http_requests_total{job="apiserver"}[5m]), aggregate by labels: sum by (code) (...), or alert on error ratios. Understanding the selector and label-matcher model is foundational because Prometheus metrics are multi-dimensional—labels define the slices you can filter and aggregate on.

So, within the provided options,Dis the only one that follows PromQL’s metric+label-filter style and therefore is the verified correct answer.

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