The PromQL query language uses aggregation operators to combine the values of multiple time series into a single result. The question asks for the "total" number of in-flight requests across all instances. The sum() operator is specifically designed for this purpose; it calculates the sum of the values for all time series that match the provided metric name. In this case, sum(apiservercurrentinflightrequests) will add the value from instance "1" (5) and instance "2" (7), resulting in a single value of 12, which represents the total.

A. min(apiservercurrentinflightrequests) returns the minimum value (5) among the time series, not the total.

C. max(apiservercurrentinflightrequests) returns the maximum value (7) among the time series, not the total.

D. sumovertime() is a range vector function that sums values for each series over a time period, not an aggregation across different series.

1. Prometheus Official Documentation. "Querying Prometheus - Aggregation operators". The documentation explicitly states that sum() calculates the sum over dimensions. This directly supports its use for calculating the total from multiple series. (Available at: https://prometheus.io/docs/prometheus/latest/querying/operators/#aggregation-operators, Section: sum (calculate sum over dimensions))

2. Prometheus Official Documentation. "Querying Prometheus - Functions". This section details sumovertime(), clarifying that it operates on a range vector for a single series to calculate the sum of its values over time, which is different from aggregating across multiple series. (Available at: https://prometheus.io/docs/prometheus/latest/querying/functions/#sumovertime)

3. UC Berkeley, CS 162 Operating Systems and System Programming, Lecture 22. "Monitoring (Prometheus)". The lecture notes describe Prometheus metrics and the use of aggregation functions like sum() to combine data from multiple targets, such as different server instances, into a meaningful total. (Available at: https://cs162.eecs.berkeley.edu/static/lectures/22-Monitoring.pdf, Slide 48, "Example PromQL Queries")

The scrapeinterval parameter in the Prometheus configuration file (prometheus.yml) specifies the default frequency at which Prometheus will poll, or "scrape," its configured targets for metrics. This setting can be defined globally and can also be overridden for specific scrape jobs. It directly controls the time duration between consecutive metric collection requests sent from the Prometheus server to the monitored endpoints.

B. The frequency for evaluating recording and alerting rules is configured by the evaluationinterval parameter, not scrapeinterval.

C. The timing for sending alerts is managed by the Alertmanager component, using parameters like groupinterval and repeatinterval, not by the core Prometheus scrapeinterval.

D. While scraping does "refresh metrics," this term is imprecise. The official and specific function of scrapeinterval is to define the time between scraping targets.

1. Prometheus Official Documentation, Configuration: The documentation explicitly defines the global scrapeinterval setting.

Source: Prometheus Authors. (n.d.). Configuration | Prometheus. Prometheus.io.

Reference: In the section, the documentation states: "scrapeinterval: | default = 1m" and its description is "How frequently to scrape targets by default." This is also reiterated in the section for per-job overrides.

2. University of Washington, Courseware: Course materials for a class covering monitoring tools confirm the function of scrapeinterval.

Source: University of Washington. (2023). Lecture 18: Monitoring and Alerting. CSE/STAT 416: Introduction to Machine Learning.

Reference: Slide 15, under "Prometheus Configuration," defines scrapeinterval as "how often to poll a target for metrics."

In the Prometheus Query Language (PromQL), aggregation functions such as maxovertime are designed to handle special float values like NaN (Not a Number) gracefully. When calculating the maximum value over a given time range, any NaN values within the data points are ignored. The function then operates on the remaining valid numeric values. In this case, the function discards NaN and computes the maximum of the remaining values (1, 1), which is 1.

A. It errors out.

PromQL functions are built to be resilient; they handle NaN values by ignoring them rather than terminating with an error.

C. NaN

NaN is not propagated as the result unless all values in the range vector are NaN. The function's purpose is to find the maximum of the valid numbers.

D. No

This is not a valid data type or output for a PromQL function that is expected to return a numeric value or a vector.

1. Prometheus Official Documentation, Querying, Functions: The documentation for overtime functions describes their role in aggregating data over a range. The behavior with special values is consistent with standard aggregation operators.

Source: Prometheus Authors. (n.d.). Querying functions. Prometheus Documentation. Retrieved from https://prometheus.io/docs/prometheus/latest/querying/functions/#aggregationovertime

Section: maxovertime(range-vector)

2. Prometheus Official Documentation, Querying, Operators: The documentation for aggregation operators, which share the same logic as overtime functions regarding NaN handling, explicitly states that NaN values are ignored.

Source: Prometheus Authors. (n.d.). Querying operators. Prometheus Documentation. Retrieved from https://prometheus.io/docs/prometheus/latest/querying/operators/#aggregation-operators

Section: Aggregation operators (max). The text notes: "Aggregate over dimensions of a vector. ... NaN is ignored." This principle applies to all aggregation-style functions.

The question asks for the number of requests that took less than or equal to 0.1 seconds. In Prometheus, a Histogram metric type uses cumulative buckets to track observations. The time series apiserverrequestdurationsecondsbucket with the label le="" represents the count of observations that are less than or equal to that upper bound.

The line apiserverrequestdurationsecondsbucket{job="kube-apiserver", le="0.1"} 1 explicitly states that the cumulative count of requests with a duration less than or equal to 0.1 seconds is 1.

A. This is the value for the le="0.05" bucket, which counts requests taking 0.05 seconds or less, not 0.1 seconds.

B. This is the value of the sum metric, representing the total cumulative duration of all requests, not the count of requests.

D. This is the value of the count metric, which represents the total number of all requests observed, regardless of their duration.

1. Prometheus Documentation, Metric Types, Histogram: The official documentation explains that a histogram exposes cumulative counters for observation buckets as bucket{le=""}. This confirms that the value associated with le="0.1" is the count of observations less than or equal to 0.1. (Source: Prometheus.io, Metric types | Histogram, Section: "Histogram").

2. Kubernetes Documentation, System Metrics: The Kubernetes documentation lists apiserverrequestdurationseconds as a key API server metric. It is described as a "Histogram of request latency in seconds, broken down by verb, group, version, resource, and scope." This confirms the metric's purpose and type. (Source: Kubernetes.io, Metrics For Kubernetes System Components, Section: "API server metrics").

The Silence feature in Alertmanager is specifically designed to mute alerts for a defined period. Users can create a silence by defining matchers (label-value pairs) that identify the alerts to be muted. When an alert's labels match an active silence, Alertmanager prevents any notifications from being sent for that alert. This is commonly used to temporarily stop notifications during planned maintenance or while an acknowledged issue is being resolved, without altering the underlying alerting rules in Prometheus.

B. Inhibition: This feature suppresses notifications for certain alerts if other, related alerts are already firing. It's a rule-based suppression, not a temporary manual stop.

C. Deduplication: This is an automatic process where Alertmanager consolidates identical alerts from redundant sources (like HA Prometheus pairs) into a single notification, preventing duplicates.

D. Grouping: This feature bundles multiple related alerts into a single, consolidated notification to reduce alert noise, but it does not stop the notifications.

1. Prometheus Official Documentation - Silences: "Silences are a straightforward way to simply mute alerts for a specific time. Silences are configured based on matchers, just like the routing tree. If an incoming alert matches all the equal or regular expression matchers of an active silence, no notifications will be sent out for that alert."

Source: Prometheus.io, Alertmanager Concepts, Silences section. (prometheus.io/docs/alerting/latest/alertmanager/#silences)

2. Prometheus Official Documentation - Inhibition: "Inhibition is a concept of suppressing notifications for certain alerts if certain other alerts are already firing... For example, an alert that is firing for an entire cluster being unreachable can be configured to inhibit all other alerts concerning this cluster."

Source: Prometheus.io, Alertmanager Concepts, Inhibition section. (prometheus.io/docs/alerting/latest/alertmanager/#inhibition)

3. Prometheus Official Documentation - Grouping: "Grouping categorizes alerts of similar nature into a single notification. This is especially useful during larger outages when many systems fail at once and hundreds to thousands of alerts may be firing simultaneously."

Source: Prometheus.io, Alertmanager Concepts, Grouping section. (prometheus.io/docs/alerting/latest/alertmanager/#grouping)