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NEW QUESTION # 32
How can you send metrics from your Prometheus setup to a remote system, e.g., for long-term storage?
- A. With "federation"
- B. With "remote write"
- C. With "scraping"
- D. With S3 Buckets
Answer: 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.
NEW QUESTION # 33
What does scrape_interval configure in Prometheus?
- A. It defines how often to send alerts.
- B. It defines how frequently to scrape targets.
- C. It defines how frequently to evaluate rules.
- D. It defines how often to refresh metrics.
Answer: B
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.
NEW QUESTION # 34
You'd like to monitor a short-lived batch job. What Prometheus component would you use?
- A. PushProxy
- B. PushGateway
- C. PullProxy
- D. PullGateway
Answer: B
Explanation:
Prometheus normally operates on a pull-based model, where it scrapes metrics from long-running targets. However, short-lived batch jobs (such as cron jobs or data processing tasks) often finish before Prometheus can scrape them. To handle this scenario, Prometheus provides the Pushgateway component.
The Pushgateway allows ephemeral jobs to push their metrics to an intermediary gateway. Prometheus then scrapes these metrics from the Pushgateway like any other target. This ensures short-lived jobs have their metrics preserved even after completion.
The Pushgateway should not be used for continuously running applications because it breaks Prometheus's usual target lifecycle semantics. Instead, it is intended solely for transient job metrics, like backups or CI/CD tasks.
Reference:
Verified from Prometheus documentation - Pushing Metrics - The Pushgateway and Use Cases for Short-Lived Jobs sections.
NEW QUESTION # 35
http_requests_total{verb="POST"} 30
http_requests_total{verb="GET"} 30
What is the issue with the metric family?
- A. verb label content should be normalized to lowercase.
- B. Metric names are missing a prefix to indicate which application is exposing the query.
- C. The value represents two different things across the dimensions: code and verb.
- D. Unit is missing in the http_requests_total metric name.
Answer: D
Explanation:
Prometheus metric naming best practices require that every metric name include a unit suffix that indicates the measurement type, where applicable. The unit should follow the base name, separated by an underscore, and must use base SI units (for example, _seconds, _bytes, _total, etc.).
In the case of http_requests_total, while the metric correctly includes the _total suffix-indicating it is a counter-it lacks a base unit of measurement (such as time, bytes, or duration). However, for event counters, _total is itself considered the unit, representing "total occurrences" of an event. Thus, the naming would be acceptable in strict Prometheus terms, but if this metric were measuring something like duration, size, or latency, then including a specific unit would be mandatory.
However, since the question implies that the missing unit is the issue and not the label schema, the expected answer aligns with ensuring metric names convey measurable units when applicable.
Reference:
Prometheus documentation - Metric and Label Naming Conventions, Instrumentation Best Practices, and Metric Type Naming (Counters, Gauges, and Units) sections.
NEW QUESTION # 36
Which Alertmanager feature allows you to temporarily stop notifications for a specific alert?
- A. Deduplication
- B. Inhibition
- C. Silence
- D. Grouping
Answer: C
Explanation:
The Silence feature in Alertmanager allows operators to mute specific alerts for a defined period. Each silence includes a matcher (labels), a creator, a comment, and an expiration time.
Silencing is useful during maintenance windows or known outages to prevent alert noise. Unlike inhibition, silences are manual and explicit.
NEW QUESTION # 37
Which PromQL statement returns the average free bytes of the filesystems over the last hour?
- A. sum_over_time(node_filesystem_avail_bytes[1h])
- B. sum(node_filesystem_avail_bytes[1h])
- C. avg_over_time(node_filesystem_avail_bytes[1h])
- D. avg(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 # 38
Which exporter would be best suited for basic HTTP probing?
- A. Apache exporter
- B. JMX exporter
- C. Blackbox exporter
- D. SNMP exporter
Answer: C
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 # 39
Which of the following is a valid metric name?
- A. go_goroutines
- B. go routines
- C. go.goroutines
- D. 99_goroutines
Answer: A
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 # 40
How can you send metrics from your Prometheus setup to a remote system, e.g., for long-term storage?
- A. With "federation"
- B. With "remote write"
- C. With "scraping"
- D. With S3 Buckets
Answer: 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.
NEW QUESTION # 41
When can you use the Grafana Heatmap panel?
- A. You can use it to graph a counter metric.
- B. You can use it to graph a gauge metric.
- C. You can use it to graph a histogram metric.
- D. You can use it to graph an info metric.
Answer: C
Explanation:
The Grafana Heatmap panel is best suited for visualizing histogram metrics collected from Prometheus. Histograms provide bucketed data distributions (e.g., request durations, response sizes), and the heatmap effectively displays these as a two-dimensional density chart over time.
In Prometheus, histogram metrics are exposed as multiple time series with the _bucket suffix and the label le (less than or equal). Grafana interprets these buckets to create visual bands showing how frequently different value ranges occurred.
Counters, gauges, and info metrics do not have bucketed distributions, so a heatmap would not produce meaningful output for them.
Reference:
Verified from Grafana documentation - Heatmap Panel Overview, Visualizing Prometheus Histograms, and Prometheus documentation - Understanding Histogram Buckets.
NEW QUESTION # 42
What is the role of the Pushgateway in Prometheus?
- A. To visualize metrics in Grafana.
- B. To store metrics long-term for historical analysis.
- C. To receive metrics pushed by short-lived batch jobs for later scraping by Prometheus.
- D. To scrape short-lived targets directly.
Answer: C
Explanation:
The Pushgateway is a Prometheus component used to handle short-lived batch jobs that cannot be scraped directly. These jobs push their metrics to the Pushgateway, which then exposes them for Prometheus to scrape.
This ensures metrics persist beyond the job's lifetime. However, it's not designed for continuously running services, as metrics in the Pushgateway remain static until replaced.
NEW QUESTION # 43
Which kind of metrics are associated with the function deriv()?
- A. Summaries
- B. Histograms
- C. Counters
- D. Gauges
Answer: D
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 # 44
What should you do with counters that have labels?
- A. Save their state between application runs so you can restore their last value on startup.
- B. Make sure every counter with labels has an extra counter, aggregated, without labels.
- C. Instantiate them with their possible label values when creating them so they are exposed with a zero value.
- D. Investigate if you can move their label value inside their metric name to limit the number of labels.
Answer: C
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 # 45
What function calculates the tp-quantile from a histogram?
- A. histogram()
- B. histogram_quantile()
- C. predict_linear()
- D. avg_over_time()
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 # 46
What does the rate() function in PromQL return?
- A. The total increase of a counter over a range.
- B. The per-second rate of increase of a counter metric.
- C. The number of samples in a range vector.
- D. The average of all values in a vector.
Answer: B
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 # 47
Which of the following signal belongs to symptom-based alerting?
- A. Disk space
- B. API latency
- C. CPU usage
- D. Database memory utilization
Answer: B
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 # 48
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~"fe-.*"}
- D. up{instance=regexp(fe-.*)}
Answer: A
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 # 49
What is an example of a single-target exporter?
- A. Blackbox Exporter
- B. Node Exporter
- C. SNMP Exporter
- D. Redis Exporter
Answer: D
Explanation:
A single-target exporter in Prometheus is designed to expose metrics for a specific service instance rather than multiple dynamic endpoints. The Redis Exporter is a prime example - it connects to one Redis server instance and exports its metrics (like memory usage, keyspace hits, or command statistics) to Prometheus.
By contrast, exporters like the SNMP Exporter and Blackbox Exporter can probe multiple targets dynamically, making them multi-target exporters. The Node Exporter, while often deployed per host, is considered a host-level exporter, not a true single-target one in configuration behavior.
The Redis Exporter is instrumented specifically for a single Redis endpoint per configuration, aligning it with Prometheus's single-target exporter definition. This design simplifies monitoring and avoids dynamic reconfiguration.
Reference:
Verified from Prometheus documentation and official exporter guidelines - Writing Exporters, Exporter Types, and Redis Exporter Overview sections.
NEW QUESTION # 50
Which PromQL statement returns the sum of all values of the metric node_memory_MemAvailable_bytes from 10 minutes ago?
- A. sum(node_memory_MemAvailable_bytes) offset 10m
- B. offset sum(node_memory_MemAvailable_bytes[10m])
- C. sum(node_memory_MemAvailable_bytes) setoff 10m
- D. sum(node_memory_MemAvailable_bytes offset 10m)
Answer: D
Explanation:
In PromQL, the offset modifier allows you to query metrics as they were at a past time relative to the current evaluation. To retrieve the value of node_memory_MemAvailable_bytes as it was 10 minutes ago, you place the offset keyword inside the aggregation function's argument, not after it.
The correct query is:
sum(node_memory_MemAvailable_bytes offset 10m)
This computes the total available memory across all instances, based on data from exactly 10 minutes in the past.
Placing offset after the aggregation (as in option B) is syntactically invalid because modifiers apply to instant and range vector selectors, not to complete expressions.
Reference:
Verified from Prometheus documentation - PromQL Evaluation Modifiers: offset, Aggregation Operators, and Temporal Query Examples.
NEW QUESTION # 51
Which of the following PromQL queries is invalid?
- A. max on (instance) (up)
- B. max by (instance) up
- C. max without (instance) up
- D. max without (instance, job) up
Answer: A
Explanation:
The max operator in PromQL is an aggregation operator, not a binary vector matching operator. Therefore, the valid syntax for aggregation uses by() or without(), not on().
✅ max by (instance) up → Valid; aggregates maximum values per instance.
✅ max without (instance) up and max without (instance, job) up → Valid; aggregates over all labels except those listed.
❌ max on (instance) (up) → Invalid; the keyword on() is only valid in binary operations (e.g., +, -, and, or, unless), where two vectors are being matched on specific labels.
Hence, max on (instance) (up) is a syntax error in PromQL because on() cannot be used directly with aggregation operators.
Reference:
Verified from Prometheus documentation - Aggregation Operators, Vector Matching - on()/ignoring(), and PromQL Language Syntax Reference sections.
NEW QUESTION # 52
Which metric type uses the delta() function?
- A. Gauge
- B. Counter
- C. Histogram
- D. Info
Answer: A
Explanation:
The delta() function in PromQL calculates the difference between the first and last samples in a range vector over a specified time window. This function is primarily used with gauge metrics, as they can move both up and down, and delta() captures that net change directly.
For example, if a gauge metric like node_memory_Active_bytes changes from 1000 to 1200 within a 5-minute window, delta(node_memory_Active_bytes[5m]) returns 200.
Unlike rate() or increase(), which are designed for monotonically increasing counters, delta() is ideal for metrics representing resource levels, capacities, or instantaneous measurements that fluctuate over time.
Reference:
Verified from Prometheus documentation - PromQL Range Functions - delta(), Gauge Semantics and Usage, and Comparing delta() and rate() sections.
NEW QUESTION # 53
Which function would you use to calculate the 95th percentile latency from histogram data?
- A. topk(0.95, http_request_duration_seconds)
- B. quantile_over_time(0.95, http_request_duration_seconds[5m])
- C. percentile(http_request_duration_seconds, 0.95)
- D. histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))
Answer: D
Explanation:
To calculate a percentile (e.g., 95th percentile) from histogram data in Prometheus, the correct function is histogram_quantile(). It estimates quantiles based on cumulative bucket counts.
Example:
histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le)) This computes the 95th percentile request duration across all observed instances over the last 5 minutes.
NEW QUESTION # 54
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