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Free Download PCA Relevant Exam Dumps & High-quality PCA Valid Exam Discount

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Free Download PCA Relevant Exam Dumps & High-quality PCA Valid Exam Discount

Posted at yesterday 17:46      View：13 | Replies：0        Print      Only Author   [Copy Link] 1#
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Linux Foundation PCA Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 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
  • 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 4
  • 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 5
  • 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.

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Linux Foundation Prometheus Certified Associate Exam Sample Questions (Q37-Q42):NEW QUESTION # 37
Which of the following signals belongs to symptom-based alerting?
  • A. Disk space
  • B. API latency
  • C. CPU usage
  • D. Database availability
Answer: B
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 # 38
Given the metric prometheus_tsdb_lowest_timestamp_seconds, how do you know in which month the lowest timestamp of your Prometheus TSDB belongs?
  • A. prometheus_tsdb_lowest_timestamp_seconds % month
  • B. month(prometheus_tsdb_lowest_timestamp_seconds)
  • C. (time() - prometheus_tsdb_lowest_timestamp_seconds) / 86400
  • D. format_date(prometheus_tsdb_lowest_timestamp_seconds,"%M")
Answer: C
Explanation:
The metric prometheus_tsdb_lowest_timestamp_seconds provides the oldest stored sample timestamp in Prometheus's local TSDB (in Unix epoch seconds). To determine the age or approximate date of this timestamp, you compare it with the current time (using time() in PromQL).
The expression:
(time() - prometheus_tsdb_lowest_timestamp_seconds) / 86400
converts the difference between the current time and the oldest timestamp from seconds into days (1 day = 86,400 seconds). This gives the number of days since the earliest sample was stored, allowing you to infer the time range and approximate month manually.
The other options are invalid because PromQL does not support direct date formatting (format_date) or month() extraction functions.
Reference:
Extracted and verified from Prometheus documentation - TSDB Internal Metrics, Time Functions in PromQL, and Using time() for Relative Calculations.

NEW QUESTION # 39
You'd like to monitor a short-lived batch job. What Prometheus component would you use?
  • A. PullGateway
  • B. PushGateway
  • C. PullProxy
  • D. PushProxy
Answer: B
Explanation:
Prometheus normally operates on a pull-based model, where it scrapes metrics from long-running targets. However, short-lived batch jobs (such as cron jobs or data processing tasks) often finish before Prometheus can scrape them. To handle this scenario, Prometheus provides the Pushgateway component.
The Pushgateway allows ephemeral jobs to push their metrics to an intermediary gateway. Prometheus then scrapes these metrics from the Pushgateway like any other target. This ensures short-lived jobs have their metrics preserved even after completion.
The Pushgateway should not be used for continuously running applications because it breaks Prometheus's usual target lifecycle semantics. Instead, it is intended solely for transient job metrics, like backups or CI/CD tasks.
Reference:
Verified from Prometheus documentation - Pushing Metrics - The Pushgateway and Use Cases for Short-Lived Jobs sections.

NEW QUESTION # 40
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 # 41
How many metric types does Prometheus text format support?
  • A. 0
  • B. 1
  • C. 2
  • D. 3
Answer: A
Explanation:
Prometheus defines four core metric types in its official exposition format, which are: Counter, Gauge, Histogram, and Summary. These types represent the fundamental building blocks for expressing quantitative measurements of system performance, behavior, and state.
A Counter is a cumulative metric that only increases (e.g., number of requests served).
A Gauge represents a value that can go up and down, such as memory usage or temperature.
A Histogram samples observations (e.g., request durations) and counts them in configurable buckets, providing both counts and sum of observed values.
A Summary is similar to a histogram but provides quantile estimation over a sliding time window along with count and sum metrics.
These four types are the only officially supported metric types in the Prometheus text exposition format as defined by the Prometheus data model. Any additional metrics or custom naming conventions are built on top of these core types but do not constitute new types.
Reference:
Extracted and verified from Prometheus official documentation sections on Metric Types and Exposition Formats in the Prometheus study materials.

NEW QUESTION # 42
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