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[General] Linux Foundation PCA Reliable Exam Materials, Exam PCA Exercise

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【General】 Linux Foundation PCA Reliable Exam Materials, Exam PCA Exercise

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Linux Foundation PCA Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 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
  • 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.

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Linux Foundation Prometheus Certified Associate Exam Sample Questions (Q41-Q46):NEW QUESTION # 41
What is a difference between a counter and a gauge?
  • A. Counters change value on each scrape and gauges remain static.
  • B. Counters and gauges are different names for the same thing.
  • C. Counters have no labels while gauges can have many labels.
  • D. Counters are only incremented, while gauges can go up and down.
Answer: D
Explanation:
The key difference between a counter and a gauge in Prometheus lies in how their values change over time. A counter is a cumulative metric that only increases-it resets to zero only when the process restarts. Counters are typically used for metrics like total requests served, bytes processed, or errors encountered. You can derive rates of change from counters using functions like rate() or increase() in PromQL.
A gauge, on the other hand, represents a metric that can go up and down. It measures values that fluctuate, such as CPU usage, memory consumption, temperature, or active session counts. Gauges provide a snapshot of current state rather than a cumulative total.
This distinction ensures proper interpretation of time-series trends and prevents misrepresentation of one-time or fluctuating values as cumulative metrics.
Reference:
Extracted and verified from Prometheus official documentation - Metric Types section explaining Counters and Gauges definitions and usage examples.

NEW QUESTION # 42
Which of the following PromQL queries is invalid?
  • A. max without (instance) up
  • B. max by (instance) up
  • C. max without (instance, job) up
  • D. max on (instance) (up)
Answer: D
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 # 43
What is the role of the Pushgateway in Prometheus?
  • A. To receive metrics pushed by short-lived batch jobs for later scraping by Prometheus.
  • B. To store metrics long-term for historical analysis.
  • C. To scrape short-lived targets directly.
  • D. To visualize metrics in Grafana.
Answer: A
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 # 44
What is the difference between client libraries and exporters?
  • A. Exporters expose metrics for scraping. Client libraries push metrics via Remote Write.
  • B. Exporters and client libraries mean the same thing.
  • C. Exporters are written in Go. Client libraries are written in many languages.
  • D. Exporters run next to the services to monitor, and use client libraries internally.
Answer: D
Explanation:
The fundamental difference between Prometheus client libraries and exporters lies in how and where they are used.
Client libraries are integrated directly into the application's codebase. They allow developers to instrument their own code to define and expose custom metrics. Prometheus provides official client libraries for multiple languages, including Go, Java, Python, and Ruby.
Exporters, on the other hand, are standalone processes that run alongside the applications or systems they monitor. They use client libraries internally to collect and expose metrics from software that cannot be instrumented directly (e.g., operating systems, databases, or third-party services). Examples include the Node Exporter (for system metrics) and MySQL Exporter (for database metrics).
Thus, exporters are typically used for external systems, while client libraries are used for self-instrumented applications.
Reference:
Verified from Prometheus documentation - Writing Exporters, Client Libraries Overview, and Best Practices for Exporters and Instrumentation.

NEW QUESTION # 45
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=regexp(fe-.*)}
  • D. up{instance~"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 # 46
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