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NEW QUESTION # 28
How would you add text from the instance label to the alert's description for the following alert?
alert: InstanceDown
expr: up == 0
for: 5m
labels:
severity: page
annotations:
description: "Instance INSTANCE_NAME_HERE down"
- A. Use $metric.instance instead of INSTANCE_NAME_HERE
- B. Use $expr.instance instead of INSTANCE_NAME_HERE
- C. Use $value.instance instead of INSTANCE_NAME_HERE
- D. Use $labels.instance instead of INSTANCE_NAME_HERE
Answer: D
Explanation:
In Prometheus alerting rules, you can dynamically reference label values in annotations and labels using template variables. Each alert has access to its labels via the variable $labels, which allows direct insertion of label data into alert messages or descriptions.
To include the value of the instance label dynamically in the description, replace the placeholder INSTANCE_NAME_HERE with:
description: "Instance {{$labels.instance}} down"
or equivalently:
description: "Instance $labels.instance down"
Both forms are valid - the first follows Go templating syntax and is the recommended format.
This ensures that when the alert fires, the instance label (e.g., a hostname or IP) is automatically included in the message, producing outputs like:
Instance 192.168.1.15:9100 down
Options B, C, and D are invalid because $value, $expr, and $metric are not recognized context variables in alert templates.
Reference:
Verified from Prometheus documentation - Alerting Rules Configuration, Using Template Variables in Annotations and Labels, and Prometheus Templating Guide (Go Templates and $labels usage) sections.
NEW QUESTION # 29
How would you name a metric that measures gRPC response size?
- A. grpc_response_size_sum
- B. grpc_response_size_bytes
- C. grpc_response_size
- D. grpc_response_size_total
Answer: B
Explanation:
Following Prometheus's metric naming conventions, every metric should indicate:
What it measures (the quantity or event).
The unit of measurement in base SI units as a suffix.
Since the metric measures response size, the base unit is bytes. Therefore, the correct and compliant metric name is:
grpc_response_size_bytes
This clearly communicates that it measures gRPC response payload sizes expressed in bytes.
The _bytes suffix is the Prometheus-recommended unit indicator for data sizes. The other options violate naming rules:
_total is reserved for counters.
_sum is used internally by histograms or summaries.
Omitting the unit (grpc_response_size) is discouraged, as it reduces clarity.
Reference:
Extracted and verified from Prometheus documentation - Metric Naming Conventions, Instrumentation Best Practices, and Standard Units for Size and Time Measurements.
NEW QUESTION # 30
Which kind of metrics are associated with the function deriv()?
- A. Gauges
- B. Histograms
- C. Summaries
- D. Counters
Answer: A
Explanation:
The deriv() function in PromQL calculates the per-second derivative of a time series using linear regression over the provided time range. It estimates the instantaneous rate of change for metrics that can both increase and decrease - which are typically gauges.
Because counters can only increase (except when reset), rate() or increase() functions are more appropriate for them. deriv() is used to identify trends in fluctuating metrics like CPU temperature, memory utilization, or queue depth, where values rise and fall continuously.
In contrast, summaries and histograms consist of multiple sub-metrics (e.g., _count, _sum, _bucket) and are not directly suited for derivative calculation without decomposition.
Reference:
Extracted and verified from Prometheus documentation - PromQL Functions - deriv(), Understanding Rates and Derivatives, and Gauge Metric Examples.
NEW QUESTION # 31
What is the difference between client libraries and exporters?
- A. Exporters run next to the services to monitor, and use client libraries internally.
- B. Exporters and client libraries mean the same thing.
- C. Exporters expose metrics for scraping. Client libraries push metrics via Remote Write.
- D. Exporters are written in Go. Client libraries are written in many languages.
Answer: A
Explanation:
The fundamental difference between Prometheus client libraries and exporters lies in how and where they are used.
Client libraries are integrated directly into the application's codebase. They allow developers to instrument their own code to define and expose custom metrics. Prometheus provides official client libraries for multiple languages, including Go, Java, Python, and Ruby.
Exporters, on the other hand, are standalone processes that run alongside the applications or systems they monitor. They use client libraries internally to collect and expose metrics from software that cannot be instrumented directly (e.g., operating systems, databases, or third-party services). Examples include the Node Exporter (for system metrics) and MySQL Exporter (for database metrics).
Thus, exporters are typically used for external systems, while client libraries are used for self-instrumented applications.
Reference:
Verified from Prometheus documentation - Writing Exporters, Client Libraries Overview, and Best Practices for Exporters and Instrumentation.
NEW QUESTION # 32
How can you use Prometheus Node Exporter?
- A. You can use it to probe endpoints over HTTP, HTTPS.
- B. You can use it to collect metrics for hardware and OS metrics.
- C. You can use it to instrument applications with metrics.
- D. You can use it to collect resource metrics from the application HTTP server.
Answer: B
Explanation:
The Prometheus Node Exporter is a core system-level exporter that exposes hardware and operating system metrics from *nix-based hosts. It collects metrics such as CPU usage, memory, disk I/O, filesystem space, network statistics, and load averages.
It runs as a lightweight daemon on each host and exposes metrics via an HTTP endpoint (default: :9100/metrics), which Prometheus scrapes periodically.
Key clarification:
It does not instrument applications (A).
It does not collect metrics directly from application HTTP endpoints (B).
It is unrelated to HTTP probing tasks - those are handled by the Blackbox Exporter (D).
Thus, the correct use of the Node Exporter is to collect and expose hardware and OS-level metrics for Prometheus monitoring.
Reference:
Extracted and verified from Prometheus documentation - Node Exporter Overview, Host-Level Monitoring, and Exporter Usage Best Practices sections.
NEW QUESTION # 33
Which of the following is a valid metric name?
- A. go routines
- B. 99_goroutines
- C. go.goroutines
- D. go_goroutines
Answer: D
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.
NEW QUESTION # 34
Which PromQL expression computes the rate of API Server requests across the different cloud providers from the following metrics?
apiserver_request_total{job="kube-apiserver", instance="192.168.1.220:6443", cloud="aws"} 1 apiserver_request_total{job="kube-apiserver", instance="192.168.1.121:6443", cloud="gcloud"} 5
- A. rate(sum by (cloud)(apiserver_request_total{job="kube-apiserver"})[5m])
- B. sum by (cloud)(rate(apiserver_request_total{job="kube-apiserver"}[5m]))
- C. sum by (cloud) (apiserver_request_total{job="kube-apiserver"})
- D. rate(apiserver_request_total{job="kube-apiserver"}[5m]) by (cloud)
Answer: B
Explanation:
The rate() function computes the per-second increase of a counter metric over a specified range, while sum by (label) aggregates those rates across dimensions - in this case, the cloud label.
The correct query is:
sum by (cloud)(rate(apiserver_request_total{job="kube-apiserver"}[5m])) This expression:
Calculates the rate of increase in API requests per second for each instance.
Groups and sums those rates by cloud, giving the total request rate per cloud provider.
Option A incorrectly places by (cloud) after rate(), which is not valid syntax.
Option B returns raw counter totals (not rates).
Option D incorrectly applies rate() after aggregation, which distorts the calculation since rate() must operate on individual time series before aggregation.
Reference:
Verified from Prometheus documentation - rate() Function, Aggregation Operators, and Querying Counters Across Labels sections.
NEW QUESTION # 35
Which of the following signal belongs to symptom-based alerting?
- A. CPU usage
- B. Disk space
- C. API latency
- D. Database memory utilization
Answer: C
Explanation:
Symptom-based alerting focuses on user-visible problems or service-impacting symptoms rather than low-level resource metrics. In Prometheus and Site Reliability Engineering (SRE) practices, alerts should signal conditions that affect users' experience - such as high latency, request failures, or service unavailability - instead of merely reflecting internal resource states.
Among the options, API latency directly represents the performance perceived by end users. If API response times increase, it immediately impacts user satisfaction and indicates a possible service degradation.
In contrast, metrics like disk space, CPU usage, or database memory utilization are cause-based metrics - they may correlate with problems but do not always translate into observable user impact.
Prometheus alerting best practices recommend alerting on symptoms (via RED metrics - Rate, Errors, Duration) while using cause-based metrics for deeper investigation and diagnosis, not for immediate paging alerts.
Reference:
Verified from Prometheus documentation - Alerting Best Practices, Symptom vs. Cause Alerting, and RED/USE Monitoring Principles sections.
NEW QUESTION # 36
With the following metrics over the last 5 minutes:
up{instance="localhost"} 1 1 1 1 1
up{instance="server1"} 1 0 0 0 0
What does the following query return:
min_over_time(up[5m])
- A. {instance="localhost"} 1 {instance="server1"} 0
- B. {instance="server1"} 0
Answer: A
Explanation:
The min_over_time() function in PromQL returns the minimum sample value observed within the specified time range for each time series.
In the given data:
For up{instance="localhost"}, all samples are 1. The minimum value over 5 minutes is therefore 1.
For up{instance="server1"}, the sequence is 1 0 0 0 0. The minimum observed value is 0.
Thus, the query min_over_time(up[5m]) returns two series - one per instance:
{instance="localhost"} 1
{instance="server1"} 0
This query is commonly used to check uptime consistency. If the minimum value over the time window is 0, it indicates at least one scrape failure (target down).
Reference:
Verified from Prometheus documentation - PromQL Range Vector Functions, min_over_time() definition, and up Metric Semantics sections.
NEW QUESTION # 37
What should you do with counters that have labels?
- A. Make sure every counter with labels has an extra counter, aggregated, without labels.
- B. Investigate if you can move their label value inside their metric name to limit the number of labels.
- C. Save their state between application runs so you can restore their last value on startup.
- D. Instantiate them with their possible label values when creating them so they are exposed with a zero value.
Answer: D
Explanation:
Prometheus counters with labels can cause missing time series in queries if some label combinations have not yet been observed. To ensure visibility and continuity, the recommended best practice is to instantiate counters with all expected label values at application startup, even if their initial value is zero.
This ensures that every possible labeled time series is exported consistently, which helps when dashboards or alerting rules expect the presence of those series. For example, if a counter like http_requests_total{method="POST",status="200"} has not yet received a POST request, initializing it with a zero ensures it is still exposed.
Option A is incorrect - label values should never be encoded into metric names.
Option B adds redundancy and does not solve the initialization issue.
Option D is discouraged; counters should reset naturally upon restart, reflecting Prometheus's ephemeral metric model.
Reference:
Verified from Prometheus documentation - Instrumentation Best Practices, Counters with Labels, and Avoid Missing Time Series by Initializing Metrics.
NEW QUESTION # 38
If the vector selector foo[5m] contains 1 1 NaN, what would max_over_time(foo[5m]) return?
- A. 0
- B. No answer.
- C. NaN
- D. It errors out.
Answer: A
Explanation:
In PromQL, range vector functions like max_over_time() compute an aggregate value (in this case, the maximum) over all samples within a specified time range. The function ignores NaN (Not-a-Number) values when computing the result.
Given the range vector foo[5m] containing samples [1, 1, NaN], the maximum value among the valid numeric samples is 1. Therefore, max_over_time(foo[5m]) returns 1.
Prometheus functions handle missing or invalid data points gracefully-ignoring NaN ensures stable calculations even when intermittent collection issues or resets occur. The function only errors if the selector is syntactically invalid or if no numeric samples exist at all.
Reference:
Verified from Prometheus documentation - PromQL Range Vector Functions, Aggregation Over Time Functions, and Handling NaN Values in PromQL sections.
NEW QUESTION # 39
How can you select all the up metrics whose instance label matches the regex fe-.*?
- A. up{instance="fe-.*"}
- B. up{instance=~"fe-.*"}
- C. up{instance=regexp(fe-.*)}
- D. up{instance~"fe-.*"}
Answer: B
Explanation:
PromQL supports regular expression matching for label values using the =~ operator. To select all time series whose label values match a given regex pattern, you use the syntax {label_name=~"regex"}.
In this case, to select all up metrics where the instance label begins with fe-, the correct query is:
up{instance=~"fe-.*"}
Explanation of operators:
= → exact match.
!= → not equal.
=~ → regex match.
!~ → regex not match.
Option D uses the correct =~ syntax. Options A and B use invalid PromQL syntax, and option C is almost correct but includes a misplaced extra quote style (~''), which would cause a parsing error.
Reference:
Verified from Prometheus documentation - Expression Language Data Selectors, Label Matchers, and Regular Expression Matching Rules.
NEW QUESTION # 40
Which PromQL statement returns the average free bytes of the filesystems over the last hour?
- A. sum(node_filesystem_avail_bytes[1h])
- B. avg(node_filesystem_avail_bytes[1h])
- C. avg_over_time(node_filesystem_avail_bytes[1h])
- D. sum_over_time(node_filesystem_avail_bytes[1h])
Answer: C
Explanation:
The avg_over_time() function calculates the average value of a time series over a specified range vector. It is used to measure how a gauge metric (like available filesystem bytes) behaves over time rather than at a single instant.
For example:
avg_over_time(node_filesystem_avail_bytes[1h])
This query returns the average amount of available filesystem space observed across all samples within the last hour for each time series.
By contrast:
avg() performs aggregation across different series at a single point, not over time.
sum() and sum_over_time() compute totals rather than averages.
Thus, only avg_over_time() provides the correct temporal average.
Reference:
Extracted and verified from Prometheus documentation - Range Vector Functions, avg_over_time() Definition, and Working with Gauge Metrics Over Time sections.
NEW QUESTION # 41
Which Prometheus component handles service discovery?
- A. Node Exporter
- B. Pushgateway
- C. Prometheus Server
- D. Alertmanager
Answer: C
Explanation:
The Prometheus Server is responsible for service discovery, which identifies the list of targets to scrape. It integrates with multiple service discovery mechanisms such as Kubernetes, Consul, EC2, and static configurations.
This allows Prometheus to automatically adapt to dynamic environments without manual reconfiguration.
NEW QUESTION # 42
Given the metric prometheus_tsdb_lowest_timestamp_seconds, how do you know in which month the lowest timestamp of your Prometheus TSDB belongs?
- A. month(prometheus_tsdb_lowest_timestamp_seconds)
- B. prometheus_tsdb_lowest_timestamp_seconds % month
- C. format_date(prometheus_tsdb_lowest_timestamp_seconds,"%M")
- D. (time() - prometheus_tsdb_lowest_timestamp_seconds) / 86400
Answer: D
Explanation:
The metric prometheus_tsdb_lowest_timestamp_seconds provides the oldest stored sample timestamp in Prometheus's local TSDB (in Unix epoch seconds). To determine the age or approximate date of this timestamp, you compare it with the current time (using time() in PromQL).
The expression:
(time() - prometheus_tsdb_lowest_timestamp_seconds) / 86400
converts the difference between the current time and the oldest timestamp from seconds into days (1 day = 86,400 seconds). This gives the number of days since the earliest sample was stored, allowing you to infer the time range and approximate month manually.
The other options are invalid because PromQL does not support direct date formatting (format_date) or month() extraction functions.
Reference:
Extracted and verified from Prometheus documentation - TSDB Internal Metrics, Time Functions in PromQL, and Using time() for Relative Calculations.
NEW QUESTION # 43
Which of the following signals belongs to symptom-based alerting?
- A. CPU usage
- B. Disk space
- C. Database availability
- D. API latency
Answer: D
Explanation:
Symptom-based alerting focuses on detecting user-visible or service-impacting issues rather than internal resource states. Metrics like API latency, error rates, and availability directly indicate degraded user experience and are therefore the preferred triggers for alerts.
In contrast, resource-based alerts (like CPU usage or disk space) often represent underlying causes, not symptoms. Alerting on them can produce noise and distract from actual service health problems.
For example, high API latency (http_request_duration_seconds) clearly reflects that users are experiencing delays, which is actionable and business-relevant.
This concept aligns with the RED (Rate, Errors, Duration) and USE (Utilization, Saturation, Errors) monitoring models promoted in Prometheus and SRE best practices.
Reference:
Verified from Prometheus documentation - Alerting Best Practices, Symptom vs. Cause Alerting, and RED/USE Monitoring Principles.
NEW QUESTION # 44
What does the rate() function in PromQL return?
- A. The average of all values in a vector.
- B. The number of samples in a range vector.
- C. The total increase of a counter over a range.
- D. The per-second rate of increase of a counter metric.
Answer: D
Explanation:
The rate() function calculates the average per-second rate of increase of a counter over the specified range. It smooths out short-term fluctuations and adjusts for counter resets.
Example:
rate(http_requests_total[5m])
returns the number of requests per second averaged over the last five minutes. This function is frequently used in dashboards and alerting expressions.
NEW QUESTION # 45
How do you configure the rule evaluation interval in Prometheus?
- A. You can configure the evaluation interval in the Prometheus TSDB configuration file and in the rule configuration file.
- B. You can configure the evaluation interval in the service discovery configuration and in the command-line flags.
- C. You can configure the evaluation interval in the scraping job configuration file and in the command-line flags.
- D. You can configure the evaluation interval in the global configuration file and in the rule configuration file.
Answer: D
Explanation:
Prometheus evaluates alerting and recording rules at a regular cadence determined by the evaluation_interval setting. This can be defined globally in the main Prometheus configuration file (prometheus.yml) under the global: section or overridden for specific rule groups in the rule configuration files.
The global evaluation_interval specifies how frequently Prometheus should execute all configured rules, while rule-specific intervals can fine-tune evaluation frequency for individual groups. For instance:
global:
evaluation_interval: 30s
This means Prometheus evaluates rules every 30 seconds unless a rule file specifies otherwise.
This parameter is distinct from scrape_interval, which governs metric collection frequency from targets. It has no relation to TSDB, service discovery, or command-line flags.
Reference:
Verified from Prometheus documentation - Configuration File Reference, Rule Evaluation and Recording Rules sections.
NEW QUESTION # 46
What's "wrong" with the myapp_filG_uploads_total{userid=,,5123",status="failed"} metric?
- A. The userid should not be exposed as a label.
- B. The _total suffix should be omitted.
- C. The metric name should consist of dashes instead of underscores.
- D. The status should not be exposed as a label.
Answer: 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.
NEW QUESTION # 47
Which PromQL expression computes how many requests in total are currently in-flight for the following time series data?
apiserver_current_inflight_requests{instance="1"} 5
apiserver_current_inflight_requests{instance="2"} 7
- A. max(apiserver_current_inflight_requests)
- B. sum_over_time(apiserver_current_inflight_requests[10m])
- C. min(apiserver_current_inflight_requests)
- D. sum(apiserver_current_inflight_requests)
Answer: D
Explanation:
In Prometheus, when you have multiple time series that represent the same type of measurement across different instances, the sum() aggregation operator is used to compute their total value.
Here, each instance (1 and 2) exposes the metric apiserver_current_inflight_requests, indicating the number of active API requests currently being processed.
To find the total number of in-flight requests across all instances, the correct expression is:
sum(apiserver_current_inflight_requests)
This returns 5 + 7 = 12.
min() would return the lowest value (5).
max() would return the highest value (7).
sum_over_time() calculates the cumulative sum over a range vector, not the current value, so it's incorrect here.
Reference:
Verified from Prometheus documentation - Aggregation Operators and Summing Across Dimensions sections.
NEW QUESTION # 48
Which exporter would be best suited for basic HTTP probing?
- A. Apache exporter
- B. Blackbox exporter
- C. JMX exporter
- D. SNMP exporter
Answer: 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.
NEW QUESTION # 49
Which of the following metrics is unsuitable for a Prometheus setup?
- A. promhttp_metric_handler_requests_total{code="500"}
- B. http_response_total{handler="static/*filepath"}
- C. prometheus_engine_query_log_enabled
- D. user_last_login_timestamp_seconds{email="[email protected]"}
Answer: D
Explanation:
The metric user_last_login_timestamp_seconds{email="[email protected]"} is unsuitable for Prometheus because it includes a high-cardinality label (email). Each unique email address would generate a separate time series, potentially numbering in the millions, which severely impacts Prometheus performance and memory usage.
Prometheus is optimized for low- to medium-cardinality metrics that represent system-wide behavior rather than per-user data. High-cardinality metrics cause data explosion, complicating queries and overwhelming the storage engine.
By contrast, the other metrics-prometheus_engine_query_log_enabled, promhttp_metric_handler_requests_total{code="500"}, and http_response_total{handler="static/*filepath"}-adhere to Prometheus best practices. They represent operational or service-level metrics with limited, manageable label value sets.
Reference:
Extracted and verified from Prometheus documentation - Metric and Label Naming Best Practices, Cardinality Management, and Anti-Patterns for Metric Design sections.
NEW QUESTION # 50
What function calculates the tp-quantile from a histogram?
- A. avg_over_time()
- B. histogram_quantile()
- C. predict_linear()
- D. histogram()
Answer: B
Explanation:
In Prometheus, the histogram_quantile() function is specifically designed to compute quantiles (such as tp90, tp95, or tp99) from histogram bucket data. A histogram metric records cumulative bucket counts for observed values under specific thresholds (le label).
The function works by interpolating between buckets based on the target quantile. For example, to compute the 90th percentile latency from a histogram named http_request_duration_seconds_bucket, you would use:
histogram_quantile(0.9, sum(rate(http_request_duration_seconds_bucket[5m])) by (le)) Here, 0.9 represents the tp90 quantile, and rate() converts counter increments into per-second rates.
Other options are incorrect:
histogram() is not a valid PromQL function.
predict_linear() forecasts future values of a time series.
avg_over_time() computes a simple average over a time window, not quantiles.
Reference:
Verified from Prometheus documentation - PromQL Function: histogram_quantile(), Working with Histograms, and Quantile Calculation Details.
NEW QUESTION # 51
What is the maximum number of Alertmanagers that can be added to a Prometheus instance?
- A. More than 3
- B. 0
- C. 1
- D. 2
Answer: A
Explanation:
Prometheus supports integration with multiple Alertmanager instances for redundancy and high availability. The alerting section of the Prometheus configuration file (prometheus.yml) allows specifying a list of Alertmanager targets, enabling Prometheus to send alerts to several Alertmanager nodes simultaneously.
There is no hard-coded limit on the number of Alertmanagers that can be added. The typical best practice is to run a minimum of three Alertmanagers in a clustered setup to achieve fault tolerance and ensure reliable alert delivery, but Prometheus can be configured with more than three if desired.
Each Alertmanager node in the cluster communicates state information (active, silenced, inhibited alerts) with its peers to maintain consistency.
Reference:
Verified from Prometheus documentation - Alertmanager Integration, High Availability Setup, and Prometheus Configuration - alerting Section.
NEW QUESTION # 52
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