[May 20, 2026] Genuine PCA Exam Dumps New 2026 Linux Foundation Pratice Exam
New 2026 Realistic PCA Dumps Test Engine Exam Questions in here
Linux Foundation PCA Exam Syllabus Topics:
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NEW QUESTION # 27
http_requests_total{verb="POST"} 30
http_requests_total{verb="GET"} 30
What is the issue with the metric family?
- A. verb label content should be normalized to lowercase.
- B. Unit is missing in the http_requests_total metric name.
- C. Metric names are missing a prefix to indicate which application is exposing the query.
- D. The value represents two different things across the dimensions: code and verb.
Answer: B
Explanation:
Prometheus metric naming best practices require that every metric name include a unit suffix that indicates the measurement type, where applicable. The unit should follow the base name, separated by an underscore, and must use base SI units (for example, _seconds, _bytes, _total, etc.).
In the case of http_requests_total, while the metric correctly includes the _total suffix-indicating it is a counter-it lacks a base unit of measurement (such as time, bytes, or duration). However, for event counters, _total is itself considered the unit, representing "total occurrences" of an event. Thus, the naming would be acceptable in strict Prometheus terms, but if this metric were measuring something like duration, size, or latency, then including a specific unit would be mandatory.
However, since the question implies that the missing unit is the issue and not the label schema, the expected answer aligns with ensuring metric names convey measurable units when applicable.
Reference:
Prometheus documentation - Metric and Label Naming Conventions, Instrumentation Best Practices, and Metric Type Naming (Counters, Gauges, and Units) sections.
NEW QUESTION # 28
What function calculates the tp-quantile from a histogram?
- A. avg_over_time()
- B. histogram()
- C. predict_linear()
- D. histogram_quantile()
Answer: D
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 # 29
Which Alertmanager feature prevents duplicate notifications from being sent?
- A. Deduplication
- B. Inhibition
- C. Grouping
- D. Silencing
Answer: A
Explanation:
Deduplication in Alertmanager ensures that identical alerts from multiple Prometheus servers or rule evaluations do not trigger duplicate notifications.
Alertmanager compares alerts based on their labels and fingerprints; if an alert with identical labels already exists, it merges or refreshes the existing one instead of creating a new notification.
This mechanism is essential in high-availability setups where multiple Prometheus instances monitor the same targets.
NEW QUESTION # 30
How can you send metrics from your Prometheus setup to a remote system, e.g., for long-term storage?
- A. With "remote write"
- B. With S3 Buckets
- C. With "scraping"
- D. With "federation"
Answer: A
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 # 31
Which PromQL statement returns the sum of all values of the metric node_memory_MemAvailable_bytes from 10 minutes ago?
- A. sum(node_memory_MemAvailable_bytes) offset 10m
- B. sum(node_memory_MemAvailable_bytes) setoff 10m
- C. sum(node_memory_MemAvailable_bytes offset 10m)
- D. offset sum(node_memory_MemAvailable_bytes[10m])
Answer: C
Explanation:
In PromQL, the offset modifier allows you to query metrics as they were at a past time relative to the current evaluation. To retrieve the value of node_memory_MemAvailable_bytes as it was 10 minutes ago, you place the offset keyword inside the aggregation function's argument, not after it.
The correct query is:
sum(node_memory_MemAvailable_bytes offset 10m)
This computes the total available memory across all instances, based on data from exactly 10 minutes in the past.
Placing offset after the aggregation (as in option B) is syntactically invalid because modifiers apply to instant and range vector selectors, not to complete expressions.
Reference:
Verified from Prometheus documentation - PromQL Evaluation Modifiers: offset, Aggregation Operators, and Temporal Query Examples.
NEW QUESTION # 32
What popular open-source project is commonly used to visualize Prometheus data?
- A. Grafana
- B. Kibana
- C. Thanos
- D. Loki
Answer: A
Explanation:
The most widely used open-source visualization and dashboarding platform for Prometheus data is Grafana. Grafana provides native integration with Prometheus as a data source, allowing users to create real-time, interactive dashboards using PromQL queries.
Grafana supports advanced visualization panels (graphs, heatmaps, gauges, tables, etc.) and enables users to design custom dashboards to monitor infrastructure, application performance, and service-level objectives (SLOs). It also provides alerting capabilities that can complement or extend Prometheus's own alerting system.
While Kibana is part of the Elastic Stack and focuses on log analytics, Thanos extends Prometheus for long-term storage and high availability, and Loki is a log aggregation system. None of these tools serve as the primary dashboarding solution for Prometheus metrics the way Grafana does.
Grafana's seamless Prometheus integration and templating support make it the de facto standard visualization tool in the Prometheus ecosystem.
Reference:
Verified from Prometheus documentation - Visualizing Data with Grafana, and Grafana documentation - Prometheus Data Source Integration and Dashboard Creation Guide.
NEW QUESTION # 33
What is the difference between client libraries and exporters?
- A. Exporters are written in Go. Client libraries are written in many languages.
- B. Exporters run next to the services to monitor, and use client libraries internally.
- C. Exporters and client libraries mean the same thing.
- D. Exporters expose metrics for scraping. Client libraries push metrics via Remote Write.
Answer: B
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 # 34
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 # 35
What Prometheus component would you use if targets are running behind a Firewall/NAT?
- A. Pull Proxy
- B. PushProx
- C. HA Proxy
- D. Pull Gateway
Answer: B
Explanation:
When Prometheus targets are behind firewalls or NAT and cannot be reached directly by the Prometheus server's pull mechanism, the recommended component to use is PushProx.
PushProx works by reversing the usual pull model. It consists of a PushProx Proxy (accessible by Prometheus) and PushProx Clients (running alongside the targets). The clients establish outbound connections to the proxy, which allows Prometheus to "pull" metrics indirectly. This approach bypasses network restrictions without compromising the Prometheus data model.
Unlike the Pushgateway (which is used for short-lived batch jobs, not network-isolated targets), PushProx maintains the Prometheus "pull" semantics while accommodating environments where direct scraping is impossible.
Reference:
Verified from Prometheus documentation and official PushProx design notes - Monitoring Behind NAT/Firewall, PushProx Overview, and Architecture and Usage Scenarios sections.
NEW QUESTION # 36
You'd like to monitor a short-lived batch job. What Prometheus component would you use?
- A. PushProxy
- B. PullGateway
- C. PullProxy
- D. PushGateway
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 # 37
How can you use Prometheus Node Exporter?
- A. You can use it to instrument applications with metrics.
- B. You can use it to probe endpoints over HTTP, HTTPS.
- C. You can use it to collect resource metrics from the application HTTP server.
- D. You can use it to collect metrics for hardware and OS metrics.
Answer: D
Explanation:
The Prometheus Node Exporter is a core system-level exporter that exposes hardware and operating system metrics from *nix-based hosts. It collects metrics such as CPU usage, memory, disk I/O, filesystem space, network statistics, and load averages.
It runs as a lightweight daemon on each host and exposes metrics via an HTTP endpoint (default: :9100/metrics), which Prometheus scrapes periodically.
Key clarification:
It does not instrument applications (A).
It does not collect metrics directly from application HTTP endpoints (B).
It is unrelated to HTTP probing tasks - those are handled by the Blackbox Exporter (D).
Thus, the correct use of the Node Exporter is to collect and expose hardware and OS-level metrics for Prometheus monitoring.
Reference:
Extracted and verified from Prometheus documentation - Node Exporter Overview, Host-Level Monitoring, and Exporter Usage Best Practices sections.
NEW QUESTION # 38
Which field in alerting rules files indicates the time an alert needs to go from pending to firing state?
- A. duration
- B. timeout
- C. interval
- D. for
Answer: D
Explanation:
In Prometheus alerting rules, the for field specifies how long a condition must remain true continuously before the alert transitions from the pending to the firing state. This feature prevents transient spikes or brief metric fluctuations from triggering false alerts.
Example:
alert: HighRequestLatency
expr: http_request_duration_seconds_avg > 1
for: 5m
labels:
severity: warning
annotations:
description: "Request latency is above 1s for more than 5 minutes."
In this configuration, Prometheus evaluates the expression every rule evaluation cycle. The alert only fires if the condition (http_request_duration_seconds_avg > 1) remains true for 5 consecutive minutes. If it returns to normal before that duration, the alert resets and never fires.
This mechanism adds stability and noise reduction to alerting systems by ensuring only sustained issues generate notifications.
Reference:
Verified from Prometheus documentation - Alerting Rules Configuration Syntax, Pending vs. Firing States, and Best Practices for Alert Timing and Thresholds sections.
NEW QUESTION # 39
How would you add text from the instance label to the alert's description for the following alert?
alert: InstanceDown
expr: up == 0
for: 5m
labels:
severity: page
annotations:
description: "Instance INSTANCE_NAME_HERE down"
- A. Use $labels.instance instead of INSTANCE_NAME_HERE
- B. Use $metric.instance instead of INSTANCE_NAME_HERE
- C. Use $expr.instance instead of INSTANCE_NAME_HERE
- D. Use $value.instance instead of INSTANCE_NAME_HERE
Answer: A
Explanation:
In Prometheus alerting rules, you can dynamically reference label values in annotations and labels using template variables. Each alert has access to its labels via the variable $labels, which allows direct insertion of label data into alert messages or descriptions.
To include the value of the instance label dynamically in the description, replace the placeholder INSTANCE_NAME_HERE with:
description: "Instance {{$labels.instance}} down"
or equivalently:
description: "Instance $labels.instance down"
Both forms are valid - the first follows Go templating syntax and is the recommended format.
This ensures that when the alert fires, the instance label (e.g., a hostname or IP) is automatically included in the message, producing outputs like:
Instance 192.168.1.15:9100 down
Options B, C, and D are invalid because $value, $expr, and $metric are not recognized context variables in alert templates.
Reference:
Verified from Prometheus documentation - Alerting Rules Configuration, Using Template Variables in Annotations and Labels, and Prometheus Templating Guide (Go Templates and $labels usage) sections.
NEW QUESTION # 40
Which metric type uses the delta() function?
- A. Info
- B. Gauge
- C. Histogram
- D. Counter
Answer: B
Explanation:
The delta() function in PromQL calculates the difference between the first and last samples in a range vector over a specified time window. This function is primarily used with gauge metrics, as they can move both up and down, and delta() captures that net change directly.
For example, if a gauge metric like node_memory_Active_bytes changes from 1000 to 1200 within a 5-minute window, delta(node_memory_Active_bytes[5m]) returns 200.
Unlike rate() or increase(), which are designed for monotonically increasing counters, delta() is ideal for metrics representing resource levels, capacities, or instantaneous measurements that fluctuate over time.
Reference:
Verified from Prometheus documentation - PromQL Range Functions - delta(), Gauge Semantics and Usage, and Comparing delta() and rate() sections.
NEW QUESTION # 41
What are Inhibition rules?
- A. Inhibition rules mute a set of alerts when another matching alert is firing.
- B. Inhibition rules inspect alerts when a matching set of alerts is firing.
- C. Inhibition rules repeat a set of alerts when another matching alert is firing.
- D. Inhibition rules inject a new set of alerts when a matching alert is firing.
Answer: A
Explanation:
Inhibition rules in Prometheus's Alertmanager are used to suppress (mute) alerts that would otherwise be redundant when a higher-priority or related alert is already active. This feature helps avoid alert noise and ensures that operators focus on the root cause rather than multiple cascading symptoms.
For example, if a "DatacenterDown" alert is firing, inhibition rules can mute all "InstanceDown" alerts that share the same datacenter label, preventing redundant notifications. Inhibition is configured in the Alertmanager configuration file under the inhibit_rules section.
Each rule defines:
A source match (the alert that triggers inhibition),
A target match (the alert to mute), and
A match condition (labels that must be equal for inhibition to apply).
Only when the source alert is active are the target alerts silenced.
Reference:
Verified from Prometheus documentation - Alertmanager Configuration - Inhibition Rules, Alert Deduplication and Grouping, and Alert Routing Best Practices.
NEW QUESTION # 42
Which of the following signal belongs to symptom-based alerting?
- A. API latency
- B. Disk space
- C. Database memory utilization
- D. CPU usage
Answer: A
Explanation:
Symptom-based alerting focuses on user-visible problems or service-impacting symptoms rather than low-level resource metrics. In Prometheus and Site Reliability Engineering (SRE) practices, alerts should signal conditions that affect users' experience - such as high latency, request failures, or service unavailability - instead of merely reflecting internal resource states.
Among the options, API latency directly represents the performance perceived by end users. If API response times increase, it immediately impacts user satisfaction and indicates a possible service degradation.
In contrast, metrics like disk space, CPU usage, or database memory utilization are cause-based metrics - they may correlate with problems but do not always translate into observable user impact.
Prometheus alerting best practices recommend alerting on symptoms (via RED metrics - Rate, Errors, Duration) while using cause-based metrics for deeper investigation and diagnosis, not for immediate paging alerts.
Reference:
Verified from Prometheus documentation - Alerting Best Practices, Symptom vs. Cause Alerting, and RED/USE Monitoring Principles sections.
NEW QUESTION # 43
Which exporter would be best suited for basic HTTP probing?
- A. Blackbox exporter
- B. JMX exporter
- C. SNMP exporter
- D. Apache exporter
Answer: A
Explanation:
The Blackbox Exporter is the Prometheus component designed specifically for probing endpoints over various network protocols, including HTTP, HTTPS, TCP, ICMP, and DNS. It acts as a generic probe service, allowing Prometheus to test endpoints' availability, latency, and correctness without requiring instrumentation in the target application itself.
For basic HTTP probing, the Blackbox Exporter performs HTTP GET or POST requests to defined URLs and exposes metrics like probe success, latency, response code, and SSL certificate validity. This makes it ideal for uptime and availability monitoring.
By contrast, the JMX exporter is used for collecting metrics from Java applications, the Apache exporter for Apache HTTP Server metrics, and the SNMP exporter for network devices. Thus, only the Blackbox Exporter serves the purpose of HTTP probing.
Reference:
Verified from Prometheus documentation - Blackbox Exporter Overview and Exporter Usage Guidelines.
NEW QUESTION # 44
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="fe-.*"}
- D. up{instance=regexp(fe-.*)}
Answer: A
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 # 45
Which of the following is an invalid @ modifier expression?
- A. go_goroutines @ end()
- B. sum(http_requests_total{method="GET"}) @ 1609746000
- C. go_goroutines @ start()
- D. sum(http_requests_total{method="GET"} @ 1609746000)
Answer: D
Explanation:
The @ modifier in PromQL allows querying data as it existed at a specific point in time rather than the evaluation time. It can be applied after a selector or an entire expression, but the syntax rules are strict.
✅ go_goroutines @ start() → Valid; queries value at the start of the evaluation range.
✅ sum(http_requests_total{method="GET"}) @ 1609746000 → Valid; applies the modifier after the full expression.
✅ go_goroutines @ end() → Valid; queries value at the end of the evaluation range.
❌ sum(http_requests_total{method="GET"} @ 1609746000) → Invalid, because the @ modifier cannot appear inside the selector braces; it must appear after the selector or aggregation expression.
This invalid placement violates PromQL's syntax grammar for subquery and modifier ordering.
Reference:
Verified from Prometheus documentation - PromQL @ Modifier Syntax, Evaluation Modifiers, and PromQL Expression Grammar sections.
NEW QUESTION # 46
Where does Prometheus store its time series data by default?
- A. In etcd.
- B. In an embedded TSDB on local disk.
- C. In an external database such as InfluxDB.
- D. In-memory only.
Answer: B
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 # 47
What does the rate() function in PromQL return?
- A. The average of all values in a vector.
- B. The total increase of a counter over a range.
- C. The number of samples in a range vector.
- D. The per-second rate of increase of a counter metric.
Answer: D
Explanation:
The rate() function calculates the average per-second rate of increase of a counter over the specified range. It smooths out short-term fluctuations and adjusts for counter resets.
Example:
rate(http_requests_total[5m])
returns the number of requests per second averaged over the last five minutes. This function is frequently used in dashboards and alerting expressions.
NEW QUESTION # 48
Which Prometheus component handles service discovery?
- A. Node Exporter
- B. Alertmanager
- C. Prometheus Server
- D. Pushgateway
Answer: C
Explanation:
The Prometheus Server is responsible for service discovery, which identifies the list of targets to scrape. It integrates with multiple service discovery mechanisms such as Kubernetes, Consul, EC2, and static configurations.
This allows Prometheus to automatically adapt to dynamic environments without manual reconfiguration.
NEW QUESTION # 49
Which kind of metrics are associated with the function deriv()?
- A. Summaries
- B. Gauges
- C. Counters
- D. Histograms
Answer: B
Explanation:
The deriv() function in PromQL calculates the per-second derivative of a time series using linear regression over the provided time range. It estimates the instantaneous rate of change for metrics that can both increase and decrease - which are typically gauges.
Because counters can only increase (except when reset), rate() or increase() functions are more appropriate for them. deriv() is used to identify trends in fluctuating metrics like CPU temperature, memory utilization, or queue depth, where values rise and fall continuously.
In contrast, summaries and histograms consist of multiple sub-metrics (e.g., _count, _sum, _bucket) and are not directly suited for derivative calculation without decomposition.
Reference:
Extracted and verified from Prometheus documentation - PromQL Functions - deriv(), Understanding Rates and Derivatives, and Gauge Metric Examples.
NEW QUESTION # 50
Which of the following PromQL queries is invalid?
- A. max on (instance) (up)
- B. max by (instance) up
- C. max without (instance, job) up
- D. max without (instance) up
Answer: A
Explanation:
The max operator in PromQL is an aggregation operator, not a binary vector matching operator. Therefore, the valid syntax for aggregation uses by() or without(), not on().
✅ max by (instance) up → Valid; aggregates maximum values per instance.
✅ max without (instance) up and max without (instance, job) up → Valid; aggregates over all labels except those listed.
❌ max on (instance) (up) → Invalid; the keyword on() is only valid in binary operations (e.g., +, -, and, or, unless), where two vectors are being matched on specific labels.
Hence, max on (instance) (up) is a syntax error in PromQL because on() cannot be used directly with aggregation operators.
Reference:
Verified from Prometheus documentation - Aggregation Operators, Vector Matching - on()/ignoring(), and PromQL Language Syntax Reference sections.
NEW QUESTION # 51
With the following metrics over the last 5 minutes:
up{instance="localhost"} 1 1 1 1 1
up{instance="server1"} 1 0 0 0 0
What does the following query return:
min_over_time(up[5m])
- A. {instance="localhost"} 1 {instance="server1"} 0
- B. {instance="server1"} 0
Answer: A
Explanation:
The min_over_time() function in PromQL returns the minimum sample value observed within the specified time range for each time series.
In the given data:
For up{instance="localhost"}, all samples are 1. The minimum value over 5 minutes is therefore 1.
For up{instance="server1"}, the sequence is 1 0 0 0 0. The minimum observed value is 0.
Thus, the query min_over_time(up[5m]) returns two series - one per instance:
{instance="localhost"} 1
{instance="server1"} 0
This query is commonly used to check uptime consistency. If the minimum value over the time window is 0, it indicates at least one scrape failure (target down).
Reference:
Verified from Prometheus documentation - PromQL Range Vector Functions, min_over_time() definition, and up Metric Semantics sections.
NEW QUESTION # 52
......
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