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[General] Valid Linux Foundation PCA Exam Question Free Updates For 1 year

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【General】 Valid Linux Foundation PCA Exam Question Free Updates For 1 year

Posted at yesterday 15:46      View：7 | 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
  • 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
  • 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 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 (Q54-Q59):NEW QUESTION # 54
How would you name a metric that measures gRPC response size?
  • A. grpc_response_size
  • B. grpc_response_size_total
  • C. grpc_response_size_bytes
  • D. grpc_response_size_sum
Answer: C
Explanation:
Following Prometheus's metric naming conventions, every metric should indicate:
What it measures (the quantity or event).
The unit of measurement in base SI units as a suffix.
Since the metric measures response size, the base unit is bytes. Therefore, the correct and compliant metric name is:
grpc_response_size_bytes
This clearly communicates that it measures gRPC response payload sizes expressed in bytes.
The _bytes suffix is the Prometheus-recommended unit indicator for data sizes. The other options violate naming rules:
_total is reserved for counters.
_sum is used internally by histograms or summaries.
Omitting the unit (grpc_response_size) is discouraged, as it reduces clarity.
Reference:
Extracted and verified from Prometheus documentation - Metric Naming Conventions, Instrumentation Best Practices, and Standard Units for Size and Time Measurements.

NEW QUESTION # 55
How would you name a metric that tracks HTTP request duration?
  • A. http_request_duration_seconds
  • B. http.request_latency
  • C. http_request_duration
  • D. request_duration_seconds
Answer: A
Explanation:
According to Prometheus metric naming conventions, a metric name must clearly describe what is being measured and include a unit suffix that specifies the base unit of measurement, following SI standards. For durations, the suffix _seconds is mandatory.
Therefore, the correct and standards-compliant name for a metric tracking HTTP request duration is:
http_request_duration_seconds
This name communicates:
http_request → the subject being measured (HTTP requests),
duration → the aspect being measured (the latency or time taken),
_seconds → the unit of measurement (seconds).
This metric name typically corresponds to a histogram or summary, exposing submetrics such as _count, _sum, and _bucket. These represent the number of observations, total duration, and distribution across time buckets respectively.
Options A, B, and C fail to fully comply with Prometheus naming standards - they either omit the http_ prefix, use invalid separators (dots), or lack the required unit suffix.
Reference:
Verified from Prometheus documentation - Metric and Label Naming Conventions, Instrumentation Best Practices, and Histogram and Summary Metric Naming Patterns.

NEW QUESTION # 56
How can you send metrics from your Prometheus setup to a remote system, e.g., for long-term storage?
  • A. With S3 Buckets
  • B. With "remote write"
  • C. With "scraping"
  • D. With "federation"
Answer: B
Explanation:
Prometheus provides a feature called Remote Write to transmit scraped and processed metrics to an external system for long-term storage, aggregation, or advanced analytics. When configured, Prometheus continuously pushes time series data to the remote endpoint defined in the remote_write section of the configuration file.
This mechanism is often used to integrate with long-term data storage backends such as Cortex, Thanos, Mimir, or InfluxDB, enabling durable retention and global query capabilities beyond Prometheus's local time series database limits.
In contrast, "scraping" refers to data collection from targets, while "federation" allows hierarchical Prometheus setups (pulling metrics from other Prometheus instances) but does not serve as long-term storage. Using "S3 Buckets" directly is also unsupported in native Prometheus configurations.
Reference:
Extracted and verified from Prometheus documentation - Remote Write/Read APIs and Long-Term Storage Integrations sections.

NEW QUESTION # 57
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 # 58
What is api_http_requests_total in the following metric?
api_http_requests_total{method="OST", handler="/messages"}
  • A. "api_http_requests_total" is a metric label name.
  • B. "api_http_requests_total" is a metric name.
  • C. "api_http_requests_total" is a metric type.
  • D. "api_http_requests_total" is a metric field.
Answer: B
Explanation:
In Prometheus, the part before the curly braces {} represents the metric name. Therefore, in the metric api_http_requests_total{method="OST", 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="OST" 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.

NEW QUESTION # 59
......
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