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Linux Foundation Prometheus Certified Associate Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
What is an example of a single-target exporter?

A. Redis Exporter
B. Node Exporter
C. SNMP Exporter
D. Blackbox Exporter

Answer: A

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 # 34
How do you calculate the average request duration during the last 5 minutes from a histogram or summary called http_request_duration_seconds?

A. rate(http_request_duration_seconds_sum[5m]) / rate(http_request_duration_seconds_count[5m])
B. rate(http_request_duration_seconds_total[5m]) / rate(http_request_duration_second$_count[5m])
C. rate(http_request_duration_seconds_total[5m]) / rate(http_request_duration_seconds_average[5m])
D. rate(http_request_duration_seconds_sum[5m]) / rate(http_request_duration_seconds_average[5m])

Answer: A

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 # 35
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: 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 # 36
The following is a list of metrics exposed by an application:
http_requests_total{code="500"} 10
http_requests_total{code="200"} 20
http_requests_total{code="400"} 30
http_requests_total{verb="POST"} 30
http_requests_total{verb="GET"} 30
What is the issue with the metric family?

A. The value represents two different things across the dimensions: code and verb.
B. Metric names are missing a prefix to indicate which application is exposing the query.

Answer: A

Explanation:
Prometheus requires that a single metric name represents one well-defined thing, and all time series in that metric share the same set of label keys so the value's meaning is consistent across dimensions. The official guidance states that metrics should not "mix different dimensions under the same name," and that a metric name should have a consistent label schema; otherwise, "the same metric name would represent different things," making queries ambiguous and aggregations error-prone. In the example, http_requests_total{code="..."} expresses per-status-code request counts, while http_requests_total{verb="..."} expresses per-HTTP-method request counts. Because some series have only code and others only verb, the value changes its meaning across label sets, violating the consistency principle for a metric family. The correct approach is to expose one metric with both labels present on every series, e.g., http_requests_total{code="200", method="GET"}, ensuring every time series has the same label keys and the value always means "count of requests," sliced by the same dimensions. A missing application prefix is optional and not the core issue here.

NEW QUESTION # 37
How would you name a metric that measures gRPC response size?

A. grpc_response_size_bytes
B. grpc_response_size_total
C. grpc_response_size
D. grpc_response_size_sum

Answer: A

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 # 38
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