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Exam Linux Foundation PCA Quiz, PCA Sample Questions

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Exam Linux Foundation PCA Quiz, PCA Sample Questions

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Linux Foundation PCA Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 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 (Q19-Q24):NEW QUESTION # 19
What is a rule group?
  • A. It is a set of rules that are executed sequentially.
  • B. It is a set of rules that are grouped by labels.
  • C. It is a set of rules, split into groups by type.
  • D. It is the set (the group) of all the rules in a file.
Answer: A
Explanation:
In Prometheus, a rule group is a logical collection of recording and alerting rules that are evaluated sequentially at a specified interval. Rule groups are defined in YAML files under the groups: key, with each group containing a name, an interval, and a list of rules.
For example:
groups:
- name: example
interval: 1m
rules:
- record: job:http_inprogress_requests:sum
expr: sum(http_inprogress_requests) by (job)
All rules in a group share the same evaluation schedule and are executed one after another. This ensures deterministic order, especially when one rule depends on another's result.
Reference:
Verified from Prometheus documentation - Rule Configuration, Rule Groups and Evaluation Order, and Recording & Alerting Rules Guide.

NEW QUESTION # 20
What is the minimum requirement for an application to expose Prometheus metrics?
  • A. It must be able to serve text over HTTP.
  • B. It must be exposed to the Internet.
  • C. It must run on Linux.
  • D. It must be compiled for 64-bit architectures.
Answer: A
Explanation:
Prometheus collects metrics by scraping an HTTP endpoint exposed by the target application. Therefore, the only essential requirement for an application to expose metrics to Prometheus is that it serves metrics in the Prometheus text exposition format over HTTP.
This endpoint is conventionally available at /metrics and provides metrics in plain text format (e.g., Content-Type: text/plain; version=0.0.4). The application can run on any operating system, architecture, or network - as long as Prometheus can reach its endpoint.
It does not need to be Internet-accessible (it can be internal) and is not limited to Linux or any specific bitness.
Reference:
Verified from Prometheus documentation - Exposition Formats, Instrumenting Applications, and Target Scraping Requirements sections.

NEW QUESTION # 21
What is an example of a single-target exporter?
  • A. Node Exporter
  • B. SNMP Exporter
  • C. Redis Exporter
  • D. Blackbox Exporter
Answer: C
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 # 22
Which of the following signals belongs to symptom-based alerting?
  • A. API latency
  • B. Database availability
  • C. CPU usage
  • D. Disk space
Answer: A
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 # 23
Which of the following signal belongs to symptom-based alerting?
  • A. API latency
  • B. CPU usage
  • C. Database memory utilization
  • D. Disk space
Answer: A
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 # 24
......
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