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QUESTION: 1

How many metric types does Prometheus text format support?

Answer(s): B

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.

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QUESTION: 2

What does scrape_interval configure in Prometheus?

It defines how frequently to scrape targets.
It defines how frequently to evaluate rules.
It defines how often to send alerts.
It defines how often to refresh metrics.

Answer(s): A

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 scrape_interval and Scraping Fundamentals sections.

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QUESTION: 3

What is a difference between a counter and a gauge?

Counters change value on each scrape and gauges remain static.
Counters and gauges are different names for the same thing.
Counters have no labels while gauges can have many labels.
Counters are only incremented, while gauges can go up and down.

Answer(s): D

Explanation:

The key difference between a counter and a gauge in Prometheus lies in how their values change over time. A counter is a cumulative metric that only increases--it resets to zero only when the process restarts. Counters are typically used for metrics like total requests served, bytes processed, or errors encountered. You can derive rates of change from counters using functions like rate() or increase() in PromQL.

A gauge, on the other hand, represents a metric that can go up and down. It measures values that fluctuate, such as CPU usage, memory consumption, temperature, or active session counts. Gauges provide a snapshot of current state rather than a cumulative total.

This distinction ensures proper interpretation of time-series trends and prevents misrepresentation of one-time or fluctuating values as cumulative metrics.

Reference:

Extracted and verified from Prometheus official documentation Metric Types section explaining Counters and Gauges definitions and usage examples.

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QUESTION: 4

What's "wrong" with the myapp_filG_uploads_total{userid=,,5123",status="failed"} metric?

The userid should not be exposed as a label.
The status should not be exposed as a label.
The _total suffix should be omitted.
The metric name should consist of dashes instead of underscores.

Answer(s): A

Explanation:

In Prometheus best practices, high-cardinality labels--especially those containing unique or user- specific identifiers--should be avoided. The metric myapp_filG_uploads_total{userid="5123",status="failed"} exposes the userid as a label, which is problematic. Each distinct value of a label generates a new time series in Prometheus. If there are thousands or millions of unique users, this would exponentially increase the number of time series, leading to cardinality explosion, degraded performance, and high memory usage.

The _total suffix is actually correct and required for counters, as per the Prometheus naming convention. The use of underscores in metric names is also correct, as Prometheus does not support dashes in metric identifiers. The status label, however, is perfectly valid because it typically has a low number of possible values (e.g., "success", "failed").

Reference:

Verified from Prometheus official documentation sections Instrumentation Metric and Label Naming Best Practices and Writing Exporters.

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QUESTION: 5

Which exporter would be best suited for basic HTTP probing?

JMX exporter
Blackbox exporter
Apache exporter
SNMP exporter

Answer(s): B

Explanation:

The Blackbox Exporter is the Prometheus component designed specifically for probing endpoints over various network protocols, including HTTP, HTTPS, TCP, ICMP, and DNS. It acts as a generic probe service, allowing Prometheus to test endpoints' availability, latency, and correctness without requiring instrumentation in the target application itself.

For basic HTTP probing, the Blackbox Exporter performs HTTP GET or POST requests to defined URLs and exposes metrics like probe success, latency, response code, and SSL certificate validity. This makes it ideal for uptime and availability monitoring.

By contrast, the JMX exporter is used for collecting metrics from Java applications, the Apache exporter for Apache HTTP Server metrics, and the SNMP exporter for network devices. Thus, only the Blackbox Exporter serves the purpose of HTTP probing.

Reference:

Verified from Prometheus documentation Blackbox Exporter Overview and Exporter Usage Guidelines.

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QUESTION: 6

How can you send metrics from your Prometheus setup to a remote system, e.g., for long-term storage?

With "scraping"
With "remote write"
With S3 Buckets
With "federation"

Answer(s): B

Explanation:

Prometheus provides a feature called Remote Write to transmit scraped and processed metrics to an external system for long-term storage, aggregation, or advanced analytics.
When configured, Prometheus continuously pushes time series data to the remote endpoint defined in the remote_write section of the configuration file.

This mechanism is often used to integrate with long-term data storage backends such as Cortex, Thanos, Mimir, or InfluxDB, enabling durable retention and global query capabilities beyond Prometheus's local time series database limits.

In contrast, "scraping" refers to data collection from targets, while "federation" allows hierarchical Prometheus setups (pulling metrics from other Prometheus instances) but does not serve as long- term storage. Using "S3 Buckets" directly is also unsupported in native Prometheus configurations.

Reference:

Extracted and verified from Prometheus documentation Remote Write/Read APIs and Long-Term Storage Integrations sections.

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QUESTION: 7

What is api_http_requests_total in the following metric?

api_http_requests_total{method="POST", handler="/messages"}

"api_http_requests_total" is a metric label name.
"api_http_requests_total" is a metric type.
"api_http_requests_total" is a metric name.
"api_http_requests_total" is a metric field.

Answer(s): C

Explanation:

In Prometheus, the part before the curly braces {} represents the metric name. Therefore, in the metric api_http_requests_total{method="POST", handler="/messages"}, the term api_http_requests_total is the metric name. Metric names describe the specific quantity being measured -- in this example, the total number of HTTP requests received by an API.

The portion within the braces defines labels, which provide additional dimensions to the metric. Here, method="POST" and handler="/messages" are labels describing request attributes. The metric name should follow Prometheus conventions: lowercase letters, numbers, and underscores only, and ending in _total for counters.

This naming scheme ensures clarity and standardization across instrumented applications. The metric type (e.g., counter, gauge) is declared separately in the exposition format, not within the metric name itself.

Reference:

Verified from Prometheus documentation Metric and Label Naming, Data Model, and Instrumentation Best Practices sections.

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QUESTION: 8

Which of the following is a valid metric name?

go routines
go.goroutines
go_goroutines
99_goroutines

Answer(s): C

Explanation:

According to Prometheus naming rules, metric names must match the regex [a-zA-Z_:][a-zA-Z0-9_:]*. This means metric names must begin with a letter, underscore, or colon, and can only contain letters, digits, and underscores thereafter.

The valid metric name among the options is go_goroutines, which follows all these rules. It starts with a letter (g), uses underscores to separate words, and contains only allowed characters.

By contrast:

go routines is invalid because it contains a space.

go.goroutines is invalid because it contains a dot (.), which is reserved for recording rule naming hierarchies, not metric identifiers.

99_goroutines is invalid because metric names cannot start with a number.

Following these conventions ensures compatibility with PromQL syntax and Prometheus' internal data model.

Reference:

Extracted from Prometheus documentation Metric Naming Conventions and Data Model Rules sections.

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Linux Foundation Prometheus Certified Associate PCA Dumps in PDF

Free Linux Foundation PCA Real Questions (page: 1)

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