PCA Instrumentation And Exporters Practice Test 2 — 15 Questions

Question 1

You are instrumenting a Go application. What is the standard way to register a metric?

Accepted Answer

prometheus.MustRegister(). The prometheus.MustRegister() function is the standard way to register custom metrics with the default registry.

Answer

prometheus.Enable()

Answer

prometheus.Set()

Answer

prometheus.Add()

Question 2

A Histogram has buckets [0.1, 0.5, 1, 5]. A request takes 0.7 seconds. Which buckets will be incremented?

Accepted Answer

1, 5. Prometheus histogram buckets are cumulative: each bucket counts all observations less than or equal to its upper bound. A 0.7-second request is ≤1 and ≤5, so the buckets with upper bounds 1 and 5 are both incremented. The 0.1 and 0.5 buckets are not incremented because 0.7 exceeds those bounds.

Answer

None

Answer

0.1, 0.5

Answer

Only 1

Question 3

What is the effect of using a 'Summary' metric with client-side quantiles?

Accepted Answer

It prevents cross-instance aggregation. Client-side quantiles are calculated per instance, making it impossible to aggregate them across multiple instances of an application.

Answer

It improves performance

Answer

It allows cross-instance aggregation

Answer

It doubles the scrape time

Question 4

When writing a custom exporter in Python, what is the most common way to expose the metrics?

Accepted Answer

Using start_http_server(). The prometheus_client library provides a start_http_server() method to expose metrics on a specified port.

Answer

Writing to a file

Answer

Using a message queue

Answer

Using a database connection

Question 5

What is the purpose of the 'le' label in a Histogram?

Accepted Answer

Less than or equal to. The 'le' label stands for 'less than or equal to' and defines the upper bound of the histogram bucket.

Answer

Latency estimate

Answer

Logarithmic exponent

Answer

Last entry

Question 6

When creating a custom exporter, which component is responsible for scraping the data?

Accepted Answer

The Prometheus server. In the Prometheus pull model, the Prometheus server is the component that initiates scrapes by sending HTTP GET requests to the /metrics endpoint exposed by exporters. The exporter's job is to expose metrics, not to scrape them. This is why custom exporters only need to serve metrics in the Prometheus text format — the server handles the scraping schedule.

Answer

The Pushgateway

Answer

The exporter itself

Answer

The client library

Question 7

When using the Pushgateway, what happens if you push a metric with the same name and labels as an existing one?

Accepted Answer

It overwrites the old value. The Pushgateway overwrites the existing metric with the new value provided in the push request.

Answer

It creates a new series

Answer

It rejects the push

Answer

It adds to the existing value

Question 8

What is the impact of naming a metric starting with an underscore?

Accepted Answer

Reserved for internal use. Metric names starting with underscores are reserved for internal use by Prometheus and should be avoided for user-defined metrics.

Answer

Automatic aggregation

Answer

Performance loss

Answer

Syntax error

Question 9

When using the Prometheus Go client, what is the difference between a Gauge and a GaugeVec?

Accepted Answer

GaugeVec supports labels. In the Prometheus Go client, a Gauge is a metric that represents a single numerical value that can go up or down, while a GaugeVec is a vector of Gauges that supports labels. The GaugeVec allows you to create multiple Gauge metrics differentiated by label values (e.g., by instance, method, or status), enabling multi-dimensional metrics. This is the key difference: GaugeVec supports labels, whereas a plain Gauge does not.

Answer

Gauge is faster

Answer

GaugeVec is for Histograms

Answer

There is no difference

Question 10

You notice your custom exporter is timing out during scraping. What is the most likely cause?

Accepted Answer

The scrape timeout is too short. If the exporter is overloaded or generating a massive amount of data, the scrape_timeout value in Prometheus might be too low.

Answer

The metric type is wrong

Answer

The port is blocked

Answer

The label names are incorrect

Question 11

What is the primary function of the 'blackbox_exporter' in a monitoring architecture?

Accepted Answer

Endpoint probing. It allows monitoring of endpoints from the outside (blackbox perspective) rather than the inside (whitebox instrumentation).

Answer

Database performance

Answer

Log aggregation

Answer

Hardware monitoring

Question 12

What is the purpose of the 'instance' label in Prometheus?

Accepted Answer

To identify the target. It is an automatically added label that identifies the target (host:port) that the metric was scraped from.

Answer

To filter alerts

Answer

To identify the user

Answer

To identify the job

Question 13

When configuring a custom exporter, how do you provide labels to a metric?

Accepted Answer

By passing a label dictionary. Labels are passed as a dictionary or map when creating the metric object in the client library.

Answer

In the scrape config

Answer

Through a config file

Answer

Via environment variables

Question 14

You are seeing 'NaN' values in your gauge metrics. What does this indicate?

Accepted Answer

An undefined gauge state. NaN (Not a Number) is a valid value for gauges in Prometheus, often indicating that a value is currently undefined or not applicable.

Answer

A scrape error

Answer

Metric type mismatch

Answer

Label collision

Question 15

Which metric type is most appropriate for tracking the current total number of active user sessions?

Accepted Answer

Gauge. Since the count of active sessions can rise and fall, a gauge is the correct metric type.

Answer

Summary

Answer

Counter

Answer

Histogram