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Latest Released Linux Foundation Testking PCA Exam Questions - PCA Prometheus Certified Associate Exam

We provide updated and real Linux Foundation PCA exam questions that are sufficient to clear the Prometheus Certified Associate Exam (PCA) exam in one go. The product of ITExamDownload is created by seasoned professionals and is frequently updated to reflect changes in the content of the PCA Exam Questions.

Linux Foundation PCA Exam Syllabus Topics:

Topic	Details
Topic 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.
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	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 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.

Linux Foundation Prometheus Certified Associate Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
What is metamonitoring?

A. Metamonitoring is monitoring social networks for end user complaints about quality of service.
B. Metamonitoring is the monitoring of non-IT systems.
C. Metamonitoring is a monitoring that covers 100% of a service.
D. Metamonitoring is the monitoring of the monitoring infrastructure.

Answer: D

Explanation:
Metamonitoring refers to monitoring the monitoring system itself-ensuring that Prometheus, Alertmanager, exporters, and dashboards are functioning properly. In other words, it's the observability of your observability stack.
This practice helps detect issues such as:
Prometheus not scraping targets,
Alertmanager being unreachable,
Exporters not exposing data, or
Storage being full or corrupted.
Without metamonitoring, an outage in the monitoring system could go unnoticed, leaving operators blind to actual infrastructure problems. A common approach is to use a secondary Prometheus instance (or external monitoring service) to monitor the health metrics of the primary Prometheus and related components.
Reference:
Verified from Prometheus documentation - Monitoring Prometheus Itself, Operational Best Practices, and Reliability of the Monitoring Infrastructure.

NEW QUESTION # 54
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: B

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 # 55
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)

Answer: B

Explanation:
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.

NEW QUESTION # 56
What is the difference between client libraries and exporters?

A. Exporters and client libraries mean the same thing.
B. Exporters expose metrics for scraping. Client libraries push metrics via Remote Write.
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 # 57
Where does Prometheus store its time series data by default?

A. In an embedded TSDB on local disk.
B. In etcd.
C. In-memory only.
D. In an external database such as InfluxDB.

Answer: A

Explanation:
By default, Prometheus stores its time series data in a local, embedded Time Series Database (TSDB) on disk. The data is organized in block files under the data/ directory inside Prometheus's storage path.
Each block typically covers two hours of data, containing chunks, index, and metadata files. Older blocks are compacted and deleted based on retention settings.

NEW QUESTION # 58
......

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Testking PCA Exam Questions | New PCA Mock Exam

Latest Released Linux Foundation Testking PCA Exam Questions - PCA Prometheus Certified Associate Exam

Linux Foundation PCA Exam Syllabus Topics:

Linux Foundation Prometheus Certified Associate Exam Sample Questions (Q53-Q58):

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