PCA Promql Practice Test 3 — 15 Questions

Question 1

What is the behavior of the clamp_max() function when applied to an instant vector?

Accepted Answer

It restricts all values in the vector so that none exceed the specified maximum.. clamp_max(v, max) returns the input vector with every sample value capped at the specified maximum: values above max are replaced by max, while values at or below max pass through unchanged. It does not drop or flag series — it mutates the values in place. This is useful for capping spikes in graphs or enforcing upper bounds in derived metrics.

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It drops any time series whose value exceeds the specified maximum.

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It generates an alert if any time series exceeds the maximum.

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It sets values exceeding the maximum to NaN.

Question 2

When writing a query with arithmetic operators, how does PromQL handle vector matching when both vectors have the exact same label sets?

Accepted Answer

It performs a one-to-one match automatically based on identical label sets.. When both vectors in a PromQL arithmetic operation have identical label sets, PromQL performs a one-to-one match automatically — each series on the left is paired with the series on the right that has exactly the same labels. No 'on' or 'ignoring' modifier is needed because the default matching behavior already handles identical label sets. This is the foundation of operations like dividing a request-rate metric by a total-rate metric that share the same labels.

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It ignores labels entirely and matches strictly based on metric names.

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It throws a matching error unless the 'on' modifier is explicitly provided.

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It performs a cross-product join of all matching series.

Question 3

Which function evaluates the predictable linear extrapolation of a gauge metric over time to predict future values?

Accepted Answer

predict_linear(node_filesystem_free_bytes[1h], 4 * 3600). predict_linear() is the PromQL function designed to perform linear regression over a range vector and extrapolate the result forward by a specified number of seconds. In predict_linear(node_filesystem_free_bytes[1h], 4 * 3600), Prometheus fits a linear trend to the last hour of samples and projects where the gauge will be in 4 hours, which is the classic pattern for disk-full alerts. The second argument is the extrapolation horizon in seconds, not a duration string.

Answer

holt_winters(node_filesystem_free_bytes[1h], 5m, 1m)

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deriv(node_filesystem_free_bytes[1h])

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extrapolate_linear(node_filesystem_free_bytes[1h], 4 * 3600)

Question 4

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.

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vector_a * on(instance) group_left(job) vector_b

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vector_a * on(job) group_right(instance) vector_b

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vector_a * ignoring(instance) group_left vector_b

Question 5

An engineer executes topk(3, http_requests_total). What happens if there are fewer than 3 time series matching the selector?

Accepted Answer

It returns all available time series without padding or error.. If an instant vector has fewer elements than $k$, topk returns all available elements in the vector without error.

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It pads the remaining slots with synthetic time series valued at 0.

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It returns NaN for the missing slots.

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It returns an evaluation error indicating insufficient time series.

Question 6

How does Prometheus handle subqueries embedded within an instant query, such as rate(http_requests_total[5m])[30m:1m]?

Accepted Answer

It runs the inner instant query at multiple evaluation timestamps over the specified range at the given resolution, returning a range vector.. Prometheus evaluates subqueries by running the inner instant query at each step of the outer range, using the specified resolution (1m) and range (30m). This produces a range vector that can then be used by outer functions like rate(). The subquery syntax [30m:1m] means evaluate the inner expression over the last 30 minutes at 1-minute intervals.

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It caches the inner query results in memory for 30 minutes.

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It increases the global scrape interval to 1 minute for the next 30 minutes.

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It converts the range vector into a scalar through statistical interpolation.

Question 7

When evaluating a rule with offset, such as http_requests_total offset 1h, what exact time does Prometheus query?

Accepted Answer

It evaluates the query at the evaluation timestamp minus 1 hour.. The offset modifier shifts the evaluation time backward by the specified duration relative to the evaluation timestamp.

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It fetches data starting 1 hour in the future.

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It calculates the moving average over the past 1 hour.

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It delays rule evaluation by 1 hour to wait for late-arriving data.

Question 8

Which TWO of the following are valid metric types natively supported by Prometheus? (Choose TWO)

Accepted Answer

Gauge. Prometheus natively supports four core metric types: Counter, Gauge, Histogram, and Summary. Option B (Gauge) is correct because a gauge represents a single numerical value that can arbitrarily go up and down, such as current memory usage or temperature. Option C (Counter) is correct because a counter is a cumulative metric that only increases (or resets to zero on restart), such as the total number of HTTP requests served. The other options are not valid Prometheus metric types: Matrix, Vector, and Scalar are PromQL data types (instant vector, range vector, scalar, string) used in query expressions, not metric types exposed by instrumented targets.

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Matrix

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Vector

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Scalar

Question 9

Which TWO of the following statements about PromQL label matchers are correct? (Choose TWO)

Accepted Answer

The = operator performs an exact string match.. Option A is correct because in PromQL the '=' label matcher performs an exact string match, selecting time series whose label value is precisely equal to the provided string. Option E is correct because the '!~' operator performs a negative regular expression match, excluding series whose label value matches the given regex. Option B is incorrect because '?=' is not a valid PromQL label matcher operator. Option C is incorrect because '=~' is a positive regular expression match for label values, not a numeric comparison operator. Option D is incorrect because '==' is not a valid label selector operator in PromQL; equality matching uses '='.

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The ?= operator performs fuzzy string matching.

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The =~ operator is used exclusively for numeric comparisons.

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The == operator is valid inside label selectors for filtering.

Question 10

Which THREE of the following functions are specifically designed to be used with range vectors? (Choose THREE)

Accepted Answer

delta. delta (A) is designed for range vectors because it computes the difference between the first and last sample values in each time series over the range, which is meaningful only when a range vector is supplied. rate (C) is also range-vector specific: it calculates the per-second average rate of increase of a counter over the range window, requiring multiple samples from a range vector. increase (E) likewise operates on range vectors, returning the total increase of a counter over the given range by extrapolating from the samples within that window. In contrast, abs (B) is a simple instant-vector function that returns the absolute value of each sample and does not need a range, while sum (D) is an aggregation operator that reduces an instant vector across labels and is not tied to range-vector semantics.

Answer

abs

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sum

Question 11

Which THREE of the following modifiers or keywords are used in PromQL binary vector matching? (Choose THREE)

Accepted Answer

group_left. In PromQL binary vector matching, the on keyword (option C) restricts matching to the listed labels, while ignoring (option D) excludes the listed labels from the default matching set; both are valid matching modifiers. The group_left keyword (option A) is a valid many-to-one grouping modifier used with on/ignoring to allow multiple series on the left side to match a single series on the right. The other options are not PromQL keywords: keep_matching (B) does not exist, and group_by (E) is not a binary vector matching modifier (grouping in PromQL uses by/without in aggregations, not group_by).

Answer

keep_matching

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group_by

Question 12

Which THREE of the following are valid aggregation operators in PromQL? (Choose THREE)

Accepted Answer

stddev. In PromQL, the aggregation operators are applied with syntax like ([parameter,] ) [without|by (...)], and the valid set includes stddev, sum, and topk. Option C (stddev) is correct because stddev is a built-in aggregation operator that computes the standard deviation across the aggregated time series. Option D (sum) is correct because sum is a core aggregation operator that adds the values of all matched series. Option E (topk) is correct because topk is a parameterized aggregation operator that returns the k series with the highest values. Options A (median) and B (average) are not valid PromQL aggregation operators; the average is expressed as sum(...) / count(...), and median is not provided as an aggregation operator in PromQL.

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median

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average

Question 13

What is the purpose of the instant vector selector syntax http_requests_total{job="api-server"}?

Accepted Answer

It selects current values of time series matching the metric name and label filter.. The instant vector selector http_requests_total{job="api-server"} returns the most recent sample value for every time series whose metric name is http_requests_total and whose job label equals 'api-server'. It is evaluated at a single point in time (the query evaluation timestamp), producing one value per matching series rather than a range of samples. This is the fundamental building block for PromQL queries that feed into functions like rate(), sum(), or comparison operators.

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It aggregates all API server requests into a single sum.

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It triggers an alert if the job is down.

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It selects historical data points spanning the last hour for the matching series.

Question 14

Which function should be used to smooth out short-term fluctuations in a gauge metric by calculating the rolling average over a 10-minute window?

Accepted Answer

avg_over_time(node_load1[10m]). The avg_over_time() function calculates the average value of all data points in the specified range vector for each time series.

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rate(node_load1[10m])

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smooth(node_load1, 10m)

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avg(node_load1[10m])

Question 15

You are troubleshooting a query and notice that label_replace is being used. What is the primary function of label_replace(v, dst_label, replacement, src_label, regex)?

Accepted Answer

It matches a regex against a source label value and writes the result into a destination label.. The label_replace function matches the regex against the value of src_label, and if it matches, sets dst_label to the replacement string.

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It renames the Prometheus server instance in federated environments.

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It drops all time series that do not contain the specified source label.

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It replaces the metric name with a new string.