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Linux Foundation Prometheus Certified Associate Exam Sample Questions (Q38-Q43):

NEW QUESTION # 38
How can you send metrics from your Prometheus setup to a remote system, e.g., for long-term storage?

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 # 39
How do you calculate the average request duration during the last 5 minutes from a histogram or summary called http_request_duration_seconds?

Answer: D

Explanation:
In Prometheus, histograms and summaries expose metrics with _sum and _count suffixes to represent total accumulated values and sample counts, respectively. To compute the average request duration over a given time window (for example, 5 minutes), you divide the rate of increase of _sum by the rate of increase of _count:
ext{Average duration} = rac{ ext{rate(http_request_duration_seconds_sum[5m])}}{ ext{rate(http_request_duration_seconds_count[5m])}} Here,
http_request_duration_seconds_sum represents the total accumulated request time, and
http_request_duration_seconds_count represents the number of requests observed.
By dividing these rates, you obtain the average request duration per request over the specified time range.
Reference:
Extracted and verified from Prometheus documentation - Querying Histograms and Summaries, PromQL Rate Function, and Metric Naming Conventions sections.


NEW QUESTION # 40
What are Inhibition rules?

Answer: A

Explanation:
Inhibition rules in Prometheus's Alertmanager are used to suppress (mute) alerts that would otherwise be redundant when a higher-priority or related alert is already active. This feature helps avoid alert noise and ensures that operators focus on the root cause rather than multiple cascading symptoms.
For example, if a "DatacenterDown" alert is firing, inhibition rules can mute all "InstanceDown" alerts that share the same datacenter label, preventing redundant notifications. Inhibition is configured in the Alertmanager configuration file under the inhibit_rules section.
Each rule defines:
A source match (the alert that triggers inhibition),
A target match (the alert to mute), and
A match condition (labels that must be equal for inhibition to apply).
Only when the source alert is active are the target alerts silenced.
Reference:
Verified from Prometheus documentation - Alertmanager Configuration - Inhibition Rules, Alert Deduplication and Grouping, and Alert Routing Best Practices.


NEW QUESTION # 41
Which PromQL statement returns the sum of all values of the metric node_memory_MemAvailable_bytes from 10 minutes ago?

Answer: A

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 # 42
Given the following Histogram metric data, how many requests took less than or equal to 0.1 seconds?
apiserver_request_duration_seconds_bucket{job="kube-apiserver", le="+Inf"} 3 apiserver_request_duration_seconds_bucket{job="kube-apiserver", le="0.05"} 0 apiserver_request_duration_seconds_bucket{job="kube-apiserver", le="0.1"} 1 apiserver_request_duration_seconds_bucket{job="kube-apiserver", le="1"} 3 apiserver_request_duration_seconds_count{job="kube-apiserver"} 3 apiserver_request_duration_seconds_sum{job="kube-apiserver"} 0.554003785

Answer: D

Explanation:
In Prometheus, histogram metrics use cumulative buckets to record the count of observations that fall within specific duration thresholds. Each bucket has a label le ("less than or equal to"), representing the upper bound of that bucket.
In the given metric, the bucket labeled le="0.1" has a value of 1, meaning exactly one request took less than or equal to 0.1 seconds. Buckets are cumulative, so:
le="0.05" → 0 requests ≤ 0.05 seconds
le="0.1" → 1 request ≤ 0.1 seconds
le="1" → 3 requests ≤ 1 second
le="+Inf" → all 3 requests total
The _sum and _count values represent total duration and request count respectively, but the number of requests below a given threshold is read directly from the bucket's le value.
Reference:
Verified from Prometheus documentation - Understanding Histograms and Summaries, Bucket Semantics, and Histogram Query Examples sections.


NEW QUESTION # 43
......

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