PCA Practice Test 4 — 25 Questions

Question 1

What is the default value for the scrape interval?

Accepted Answer

1m. The default scrape interval in Prometheus is 1 minute.

Answer

10s

Answer

30s

Answer

5m

Question 2

What is the primary language used to query Prometheus data?

Accepted Answer

PromQL. PromQL (Prometheus Query Language) is the functional query language for Prometheus.

Answer

Regex

Answer

GraphQL

Answer

SQL

Question 3

You need to calculate the per-second rate of increase for a counter metric over the last 10 minutes. Which function is most appropriate?

Accepted Answer

rate(metric[10m]). rate() is the standard function for calculating the per-second rate of increase over a range vector.

Answer

increase(metric[10m])

Answer

irate(metric[10m])

Answer

delta(metric[10m])

Question 4

What is the purpose of the 'offset' modifier in a PromQL query?

Accepted Answer

To change the time range of a query to a point in the past.. The 'offset' modifier allows querying data from the past relative to the current time.

Answer

To remove specific label dimensions.

Answer

To filter out noise from the last 5 minutes.

Answer

To increase the precision of a rate calculation.

Question 5

You need to perform a binary operation between two vectors where the left vector has labels {job="api", instance="a"} and the right vector has labels {job="api"}. You want to match them using the job label while retaining the instance label from the left side. Which modifier configuration is correct?

Accepted Answer

vector_a * on(job) group_left(instance) vector_b. To match on a subset of labels while retaining extra labels on the left side during a one-to-many or many-to-one operation, group_left must be used with the on modifier.

Answer

vector_a * on(instance) group_left(job) vector_b

Answer

vector_a * on(job) group_right(instance) vector_b

Answer

vector_a * ignoring(instance) group_left vector_b

Question 6

Which operator has higher precedence in PromQL: exponential (^), multiplication (*), or addition (+)?

Accepted Answer

Exponentiation (^). Exponentiation has the highest precedence in PromQL, followed by multiplication/division, and then addition/subtraction.

Answer

Addition (+)

Answer

All arithmetic operators share equal precedence.

Answer

Multiplication (*)

Question 7

An operator needs to find the rate of change of a counter using deriv(). Why is this considered a bad practice in Prometheus?

Accepted Answer

deriv() does not account for counter resets, causing massive negative drops when counters reset.. deriv() is designed for linear regression on gauge metrics over range vectors and does not handle counter resets, leading to incorrect downward spikes when counters reset.

Answer

deriv() automatically multiplies output by an undocumented scaling factor.

Answer

deriv() requires native histograms, which are incompatible with standard counters.

Answer

deriv() only works with scalar values and rejects time series vectors.

Question 8

What is the primary operational difference between the 'rate()' and 'irate()' functions in PromQL?

Accepted Answer

rate() averages the per-second rate over the entire specified range vector, while irate() calculates the rate based on the last two data points in the range vector.. rate() calculates the average per-second rate over the whole range vector, smoothing spikes, whereas irate() calculates the instantaneous rate using only the last two points.

Answer

rate() can be used with instant vectors, whereas irate() requires range vectors.

Answer

irate() automatically accounts for counter resets, whereas rate() does not.

Answer

irate() supports gauges, while rate() only supports counters.

Question 9

You are using the 'predict_linear()' function to forecast disk space exhaustion based on the metric 'node_filesystem_free_bytes'. What type of input data does predict_linear require?

Accepted Answer

A range vector of gauge values and a time duration scalar.. predict_linear() requires a range vector of gauge values (e.g., node_filesystem_free_bytes[1h]) and a scalar representing the number of seconds into the future to predict. It uses linear regression over the range to forecast the value at the future time.

Answer

An instant vector representing current disk space.

Answer

Two instant vectors joined via an 'on' clause.

Answer

A counter range vector wrapped in a rate() function.

Question 10

Which PromQL function is specifically designed to calculate the per-second average rate of increase of a time series over a given range vector?

Accepted Answer

rate(). The rate() function in PromQL is specifically designed to calculate the per-second average rate of increase of a time series over a given range vector. It takes a range vector (e.g., http_requests_total[5m]) and returns the average per-second growth rate, handling counter resets automatically. This is the canonical function for computing rates of counters.

Answer

deriv()

Answer

irate()

Answer

increase()

Question 11

You want to find the top 3 jobs with the highest CPU usage using the metric 'process_cpu_seconds_total'. Which PromQL query structure is correct?

Accepted Answer

topk(3, rate(process_cpu_seconds_total[5m])). The correct PromQL structure is topk(3, rate(process_cpu_seconds_total[5m])). topk takes the instant-vector result of rate() over a 5-minute range vector and returns the 3 series with the highest values. The rate() function is required because process_cpu_seconds_total is a counter, and raw counter values would rank by total lifetime CPU rather than current usage.

Answer

bottomk(3, rate(process_cpu_seconds_total[5m]))

Answer

topk(3, process_cpu_seconds_total[5m])

Answer

topk(rate(process_cpu_seconds_total[5m]), 3)

Question 12

You want to notify different teams based on the 'service' label of an alert. Where should you configure this logic?

Accepted Answer

In the Alertmanager configuration. The Alertmanager configuration file contains the routing tree that maps alerts to receivers based on labels.

Answer

In the node exporter config

Answer

In the Grafana data source

Answer

In the Prometheus alerting rule

Question 13

You have a recording rule that references a metric that doesn't exist. What is the impact?

Accepted Answer

The rule evaluation results in no data. The recording rule will be evaluated, but because the expression returns no data, no new time series will be created for that rule.

Answer

Prometheus logs a critical error

Answer

The entire server crashes

Answer

The rule is automatically disabled

Question 14

You need to track the number of requests received by your application. Which metric type should you choose?

Accepted Answer

Counter. A counter is a cumulative metric that represents a single monotonically increasing counter whose value can only increase or be reset to zero on restart.

Answer

Summary

Answer

Gauge

Answer

Histogram

Question 15

A developer asks why they should use Distributed Traces instead of simply using high-cardinality metrics to debug a slow request. What is the primary advantage of traces?

Accepted Answer

Traces provide end-to-end request context across microservices.. Distributed traces capture the full causal chain of a request as it flows through multiple microservices, including parent-child span relationships, timing, and metadata. This end-to-end context lets you pinpoint which service or span in a call graph introduced latency, which high-cardinality metrics alone cannot do because metrics aggregate and lose per-request causality.

Answer

Traces are natively generated by Prometheus.

Answer

Traces have lower storage requirements.

Answer

Metrics cannot be used for alerting.

Question 16

You have a recording rule named 'job:node_cpu:avg_rate_5m'. Where is this metric stored once the rule is executed?

Accepted Answer

In the Prometheus TSDB. Recording rules in Prometheus evaluate a PromQL expression on a schedule and write the resulting samples back into the Prometheus TSDB as new time series. The metric 'job:node_cpu:avg_rate_5m' is therefore stored in the same local TSDB as scraped metrics, queryable via PromQL like any other series. This is the core purpose of recording rules: precomputing expensive queries for faster dashboards and alerts.

Answer

In the Alertmanager memory

Answer

In the Grafana database

Answer

In a local flat file

Question 17

Which TWO of the following are valid Alertmanager configuration blocks?

Accepted Answer

route. Option A, route, is a valid top-level Alertmanager configuration block that defines the routing tree, specifying how alerts are grouped, matched, and dispatched to receivers. Option B, receivers, is also a valid top-level Alertmanager block that lists named notification integrations (such as email, Slack, or PagerDuty) that routes can reference. Options C (scrape_configs), D (targets), and E (rules) are not Alertmanager configuration blocks: scrape_configs and targets belong to Prometheus's scrape configuration, and rules is a Prometheus/recording-and-alerting rule file concept, not an Alertmanager config block.

Answer

scrape_configs

Answer

targets

Answer

rules

Question 18

What is the result of applying a range selector like [5m] to a gauge metric?

Accepted Answer

A range vector containing all data points from the last 5 minutes.. Applying a range selector like [5m] to a gauge metric in PromQL returns a range vector containing all samples for that metric over the last 5 minutes. Range vectors are the input required by functions like rate(), increase(), and avg_over_time(). The selector does not aggregate or reduce the data — it simply collects the time series samples in the window.

Answer

A scalar value representing the sum.

Answer

An instant vector of the average over 5 minutes.

Answer

An error, because gauges are not time-series.

Question 19

Which function would you use to find the maximum value of a gauge metric 'node_cpu_load' over the past 30 minutes?

Accepted Answer

max_over_time(node_cpu_load[30m]). The max_over_time() function calculates the maximum value of all data points in the specified range vector for each time series.

Answer

rate(node_cpu_load[30m])

Answer

max(node_cpu_load[30m])

Answer

topk(1, node_cpu_load[30m])

Question 20

Which action should be avoided when creating labels for custom metrics?

Accepted Answer

Using high-cardinality labels. Adding high-cardinality data (like user IDs or unique request IDs) to labels causes an explosion in the number of time series, which can crash Prometheus.

Answer

Using static labels

Answer

Adding job labels

Answer

Adding instance labels