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Title: How to Prepare For PCA Exam? [Print This Page]

Author: jimfish543    Time: yesterday 09:08
Title: How to Prepare For PCA Exam?
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Linux Foundation PCA Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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 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.

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Linux Foundation Prometheus Certified Associate Exam Sample Questions (Q29-Q34):NEW QUESTION # 29
How would you name a metric that measures gRPC response size?
Answer: A
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 # 30
What function calculates the tp-quantile from a histogram?
Answer: B
Explanation:
In Prometheus, the histogram_quantile() function is specifically designed to compute quantiles (such as tp90, tp95, or tp99) from histogram bucket data. A histogram metric records cumulative bucket counts for observed values under specific thresholds (le label).
The function works by interpolating between buckets based on the target quantile. For example, to compute the 90th percentile latency from a histogram named http_request_duration_seconds_bucket, you would use:
histogram_quantile(0.9, sum(rate(http_request_duration_seconds_bucket[5m])) by (le)) Here, 0.9 represents the tp90 quantile, and rate() converts counter increments into per-second rates.
Other options are incorrect:
histogram() is not a valid PromQL function.
predict_linear() forecasts future values of a time series.
avg_over_time() computes a simple average over a time window, not quantiles.
Reference:
Verified from Prometheus documentation - PromQL Function: histogram_quantile(), Working with Histograms, and Quantile Calculation Details.

NEW QUESTION # 31
You'd like to monitor a short-lived batch job. What Prometheus component would you use?
Answer: D
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 # 32
What is the minimum requirement for an application to expose Prometheus metrics?
Answer: D
Explanation:
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.

NEW QUESTION # 33
How do you calculate the average request duration during the last 5 minutes from a histogram or summary called http_request_duration_seconds?
Answer: B
Explanation:
In Prometheus, histograms and summaries expose metrics with _sum and _count suffixes to represent total accumulated values and sample counts, respectively. To compute the average request duration over a given time window (for example, 5 minutes), you divide the rate of increase of _sum by the rate of increase of _count:
        ext{Average duration} = rac{        ext{rate(http_request_duration_seconds_sum[5m])}}{        ext{rate(http_request_duration_seconds_count[5m])}} Here,
http_request_duration_seconds_sum represents the total accumulated request time, and
http_request_duration_seconds_count represents the number of requests observed.
By dividing these rates, you obtain the average request duration per request over the specified time range.
Reference:
Extracted and verified from Prometheus documentation - Querying Histograms and Summaries, PromQL Rate Function, and Metric Naming Conventions sections.

NEW QUESTION # 34
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