PCA Exam Preparation Material | Prometheus Certified Associate Exam

Explanation:
The evaluation_interval parameter defines how frequently Prometheus evaluates its recording and alerting rules. It determines the schedule at which the rule engine runs, checking whether alert conditions are met and generating new time series for recording rules.
For example, setting:
global:
evaluation_interval: 30s
means Prometheus evaluates all configured rules every 30 seconds. This setting differs from
scrape_interval, which controls how often Prometheus collects data from targets.
Having a proper evaluation interval ensures alerting latency is balanced with system performance.
Reference: Prometheus documentation C Configuration File Reference, Rule Evaluation Cycle, Global vs Job-Specific Parameters.

Explanation:
The most widely used open-source visualization and dashboarding platform for Prometheus data is Grafana. Grafana provides native integration with Prometheus as a data source, allowing users to create real-time, interactive dashboards using PromQL queries.
Grafana supports advanced visualization panels (graphs, heatmaps, gauges, tables, etc.) and enables users to design custom dashboards to monitor infrastructure, application performance, and service-level objectives (SLOs). It also provides alerting capabilities that can complement or extend Prometheus’s own alerting system.
While Kibana is part of the Elastic Stack and focuses on log analytics, Thanos extends Prometheus for long-term storage and high availability, and Loki is a log aggregation system. None of these tools serve as the primary dashboarding solution for Prometheus metrics the way Grafana does.
Grafana’s seamless Prometheus integration and templating support make it the de facto standard visualization tool in the Prometheus ecosystem.
Reference: Verified from Prometheus documentation C Visualizing Data with Grafana, and Grafana documentation C Prometheus Data Source Integration and Dashboard Creation Guide.

Explanation:
By default, Prometheus stores its time series data in a local, embedded Time Series Database (TSDB) on disk. The data is organized in block files under the data/ directory inside Prometheus’s storage path.
Each block typically covers two hours of data, containing chunks, index, and metadata files. Older blocks are compacted and deleted based on retention settings.
Reference: Prometheus documentation C Storage, TSDB Internals, Retention and Compaction.

Explanation:
Prometheus defines four core metric types in its official exposition format, which are: Counter, Gauge, Histogram, and Summary. These types represent the fundamental building blocks for expressing quantitative measurements of system performance, behavior, and state.
A Counter is a cumulative metric that only increases (e.g., number of requests served).
A Gauge represents a value that can go up and down, such as memory usage or temperature.
A Histogram samples observations (e.g., request durations) and counts them in configurable buckets, providing both counts and sum of observed values.
A Summary is similar to a histogram but provides quantile estimation over a sliding time window along with count and sum metrics.
These four types are the only officially supported metric types in the Prometheus text exposition format as defined by the Prometheus data model. Any additional metrics or custom naming conventions are built on top of these core types but do not constitute new types.
Reference: Extracted and verified from Prometheus official documentation sections on Metric Types and Exposition Formats in the Prometheus study materials.

Explanation:
In Prometheus, the scrape_interval parameter specifies how frequently the Prometheus server should scrape metrics from its configured targets. Each target exposes an HTTP endpoint (usually /metrics) that Prometheus collects data from at a fixed cadence. By default, the scrape_interval is set to 1 minute, but it can be overridden globally or per job configuration in the Prometheus YAML configuration file.
This setting directly affects the resolution of collected time series data―a shorter interval increases data granularity but also adds network and storage overhead, while a longer interval reduces load but might miss short-lived metric variations.
It is important to distinguish scrape_interval from evaluation_interval, which defines how often Prometheus evaluates recording and alerting rules. Thus, scrape_interval pertains only to data collection frequency, not to alerting or rule evaluation.
Reference: Extracted and verified from Prometheus documentation on Configuration File C scrape_interval and Scraping Fundamentals sections.