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PCA Dumps Deutsch & PCA Deutsch Prüfungsfragen

Die IT-Expertengruppe von It-Pruefung nutzt ihre Erfahrungen und Wissen aus, um weiterhin die Qualität der Prüfungsunterlagen zur PCA Zertifizierung zu verbessern und die Bedürfnisse der Prüflinge abzudecken. Wir versprechen, dass Sie beim ersten Versuch die Linux Foundation PCA Zertifizierungsprüfung bestehen können. Durch den Kauf von It-Pruefung Produkten können Sie immer schnell Updates und genauere Informationen über die Linux Foundation PCA Prüfung bekommen. Und die Produkte vom It-Pruefung bieten umfassende Wissensgebiete und Bequemelichkeit für die Kandidaten. Außerdem beträgt die Hit-Rate 100%. Es kann Ihnen 100% Selbstbewusstsein geben, so dass Sie sich unbesorgt an der Prüfung beteiligen.

Linux Foundation PCA Prüfungsplan:

Thema
Einzelheiten

Thema 1

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.

Thema 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.

Thema 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.

Thema 4

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.

Thema 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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PCA Deutsch Prüfungsfragen - PCA Probesfragen

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Linux Foundation Prometheus Certified Associate Exam PCA Prüfungsfragen mit Lösungen (Q21-Q26):

21. Frage
What is the minimum requirement for an application to expose Prometheus metrics?

A. It must be exposed to the Internet.
B. It must be able to serve text over HTTP.
C. It must be compiled for 64-bit architectures.
D. It must run on Linux.

Antwort: B

Begründung:
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.

22. Frage
What does the increase() function do in PromQL?

A. Calculates the derivative of a gauge over time.
B. Returns the total sum of values in a vector.
C. Returns the absolute increase in a counter over a specified range.
D. Calculates the percentage increase of a counter over time.

Antwort: C

Begründung:
The increase() function computes the total increase in a counter metric over a specified range vector. It accounts for counter resets and only measures the net change in the counter's value during the time window.
Example:
increase(http_requests_total[5m])
This query returns how many HTTP requests occurred in the last five minutes. Unlike rate(), which provides a per-second average rate, increase() gives the absolute number of increments.

23. Frage
Which PromQL expression computes how many requests in total are currently in-flight for the following time series data?
apiserver_current_inflight_requests{instance="1"} 5
apiserver_current_inflight_requests{instance="2"} 7

A. sum_over_time(apiserver_current_inflight_requests[10m])
B. sum(apiserver_current_inflight_requests)
C. min(apiserver_current_inflight_requests)
D. max(apiserver_current_inflight_requests)

Antwort: B

Begründung:
In Prometheus, when you have multiple time series that represent the same type of measurement across different instances, the sum() aggregation operator is used to compute their total value.
Here, each instance (1 and 2) exposes the metric apiserver_current_inflight_requests, indicating the number of active API requests currently being processed.
To find the total number of in-flight requests across all instances, the correct expression is:
sum(apiserver_current_inflight_requests)
This returns 5 + 7 = 12.
min() would return the lowest value (5).
max() would return the highest value (7).
sum_over_time() calculates the cumulative sum over a range vector, not the current value, so it's incorrect here.
Reference:
Verified from Prometheus documentation - Aggregation Operators and Summing Across Dimensions sections.

24. Frage
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")

Antwort: C

Begründung:
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.

25. Frage
What is a rule group?

A. It is a set of rules, split into groups by type.
B. It is the set (the group) of all the rules in a file.
C. It is a set of rules that are grouped by labels.
D. It is a set of rules that are executed sequentially.

Antwort: D

Begründung:
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.

26. Frage
......

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