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Linux Foundation PCA Exam Syllabus Topics:

TopicDetails
Topic 1
  • Prometheus Fundamentals: This domain evaluates the knowledge of DevOps Engineers and emphasizes the core architecture and components of Prometheus. It includes topics such as configuration and scraping techniques, limitations of the Prometheus system, data models and labels, and the exposition format used for data collection. The section ensures a solid grasp of how Prometheus functions as a monitoring and alerting toolkit within distributed environments.
Topic 2
  • Alerting and Dashboarding: This section of the exam assesses the competencies of Cloud Operations Engineers and focuses on monitoring visualization and alert management. It covers dashboarding basics, alerting rules configuration, and the use of Alertmanager to handle notifications. Candidates also learn the core principles of when, what, and why to trigger alerts, ensuring they can create reliable monitoring dashboards and proactive alerting systems to maintain system stability.
Topic 3
  • Observability Concepts: This section of the exam measures the skills of Site Reliability Engineers and covers the essential principles of observability used in modern systems. It focuses on understanding metrics, logs, and tracing mechanisms such as spans, as well as the difference between push and pull data collection methods. Candidates also learn about service discovery processes and the fundamentals of defining and maintaining SLOs, SLAs, and SLIs to monitor performance and reliability.
Topic 4
  • Instrumentation and Exporters: This domain evaluates the abilities of Software Engineers and addresses the methods for integrating Prometheus into applications. It includes the use of client libraries, the process of instrumenting code, and the proper structuring and naming of metrics. The section also introduces exporters that allow Prometheus to collect metrics from various systems, ensuring efficient and standardized monitoring implementation.
Topic 5
  • PromQL: This section of the exam measures the skills of Monitoring Specialists and focuses on Prometheus Query Language (PromQL) concepts. It covers data selection, calculating rates and derivatives, and performing aggregations across time and dimensions. Candidates also study the use of binary operators, histograms, and timestamp metrics to analyze monitoring data effectively, ensuring accurate interpretation of system performance and trends.

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Linux Foundation Prometheus Certified Associate Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
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?

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 # 24
Which function would you use to calculate the 95th percentile latency from histogram data?

Answer: B

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 # 25
Where does Prometheus store its time series data by default?

Answer: A

Explanation:
By default, Prometheus stores its time series data in a local, embedded Time Series Database (TSDB) on disk. The data is organized in block files under the data/ directory inside Prometheus's storage path.
Each block typically covers two hours of data, containing chunks, index, and metadata files. Older blocks are compacted and deleted based on retention settings.


NEW QUESTION # 26
Which kind of metrics are associated with the function deriv()?

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 # 27
Which Alertmanager feature allows you to temporarily stop notifications for a specific alert?

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 # 28
......

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