分享最新版本的PCA題庫 -免費下載Prometheus Certified Associate Exam - PCA擬真試題

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Linux Foundation PCA 考試大綱：

主題簡介
主題 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.
主題 2
  • 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.
主題 3
  • 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.
主題 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.
主題 5
  • 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.

最新的 Cloud & Containers PCA 免費考試真題 (Q59-Q64):

問題 #59
What is api_http_requests_total in the following metric?
api_http_requests_total{method="POST", handler="/messages"}

答案：A

解題說明：
In Prometheus, the part before the curly braces {} represents the metric name. Therefore, in the metric api_http_requests_total{method="POST", handler="/messages"}, the term api_http_requests_total is the metric name. Metric names describe the specific quantity being measured - in this example, the total number of HTTP requests received by an API.
The portion within the braces defines labels, which provide additional dimensions to the metric. Here, method="POST" and handler="/messages" are labels describing request attributes. The metric name should follow Prometheus conventions: lowercase letters, numbers, and underscores only, and ending in _total for counters.
This naming scheme ensures clarity and standardization across instrumented applications. The metric type (e.g., counter, gauge) is declared separately in the exposition format, not within the metric name itself.
Reference:
Verified from Prometheus documentation - Metric and Label Naming, Data Model, and Instrumentation Best Practices sections.


問題 #60
How can you select all the up metrics whose instance label matches the regex fe-.*?

答案：A

解題說明：
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.


問題 #61
What is considered the best practice when working with alerting notifications?

答案：C

解題說明：
The Prometheus alerting philosophy emphasizes signal over noise - meaning alerts should focus only on actionable and user-impacting issues. The best practice is to alert on symptoms that indicate potential or actual user-visible problems, not on every internal metric anomaly.
This approach reduces alert fatigue, avoids desensitizing operators, and ensures high-priority alerts get the attention they deserve. For example, alerting on "service unavailable" or "latency exceeding SLO" is more effective than alerting on "CPU above 80%" or "disk usage increasing," which may not directly affect users.
Option B correctly reflects this principle: keep alerts meaningful, few, and symptom-based. The other options contradict core best practices by promoting excessive or equal-weight alerting, which can overwhelm operations teams.
Reference:
Verified from Prometheus documentation - Alerting Best Practices, Alertmanager Design Philosophy, and Prometheus Monitoring and Reliability Engineering Principles.


問題 #62
Which of the following metrics is unsuitable for a Prometheus setup?

答案：B

解題說明：
The metric user_last_login_timestamp_seconds{email="[email protected]"} is unsuitable for Prometheus because it includes a high-cardinality label (email). Each unique email address would generate a separate time series, potentially numbering in the millions, which severely impacts Prometheus performance and memory usage.
Prometheus is optimized for low- to medium-cardinality metrics that represent system-wide behavior rather than per-user data. High-cardinality metrics cause data explosion, complicating queries and overwhelming the storage engine.
By contrast, the other metrics-prometheus_engine_query_log_enabled, promhttp_metric_handler_requests_total{code="500"}, and http_response_total{handler="static/*filepath"}-adhere to Prometheus best practices. They represent operational or service-level metrics with limited, manageable label value sets.
Reference:
Extracted and verified from Prometheus documentation - Metric and Label Naming Best Practices, Cardinality Management, and Anti-Patterns for Metric Design sections.


問題 #63
What does scrape_interval configure in Prometheus?

答案：B

解題說明：
In Prometheus, the scrape_interval parameter specifies how frequently the Prometheus server should scrape metrics from its configured targets. Each target exposes an HTTP endpoint (usually /metrics) that Prometheus collects data from at a fixed cadence. By default, the scrape_interval is set to 1 minute, but it can be overridden globally or per job configuration in the Prometheus YAML configuration file.
This setting directly affects the resolution of collected time series data-a shorter interval increases data granularity but also adds network and storage overhead, while a longer interval reduces load but might miss short-lived metric variations.
It is important to distinguish scrape_interval from evaluation_interval, which defines how often Prometheus evaluates recording and alerting rules. Thus, scrape_interval pertains only to data collection frequency, not to alerting or rule evaluation.
Reference:
Extracted and verified from Prometheus documentation on Configuration File - scrape_interval and Scraping Fundamentals sections.


問題 #64
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