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PCA — Observability Concepts

18% of the PCA exam. Sample questions below; the full library has 22 questions tagged to this domain.

Sample questions on Observability Concepts

Observability Concepts

Q1. What is the primary distinction between black-box and white-box monitoring?

Reveal answer and explanations
  1. A Black-box monitoring uses only logs; white-box uses only metrics

    Incorrect. Signal type is orthogonal to the black-box/white-box distinction.

  2. B Black-box is done in production, white-box only in test environments

    Incorrect. Both are used in production; that is not the distinction.

  3. C Black-box probes externally; white-box inspects internal state

    Correct. Black-box monitoring treats the system as opaque and tests external behavior; white-box exposes internal counters, queue depths, and other internals.

  4. D Black-box requires an agent on every host, while white-box is agentless

    Incorrect. Agent requirements vary by tool, not by this classification.

Observability Concepts

Q2. An operator reduces the scrape interval from 60s to 5s to get finer-grained dashboards and keeps retention fixed at 15 days on the same hardware. Which outcome should they expect?

Reveal answer and explanations
  1. A Identical storage footprint, because Prometheus only persists changes

    Incorrect. Prometheus persists samples at the configured interval regardless of value changes; there is no delta-only persistence mode.

  2. B A roughly proportional increase in samples per series, raising storage and memory pressure and perhaps forcing shorter retention

    Correct. Finer scrape resolution multiplies samples per series per unit time, driving higher storage and memory use; operators must trade resolution, retention, and hardware.

  3. C Lower storage cost, because chunks compress better at higher resolution

    Incorrect. More samples per chunk improves compression ratios only modestly; higher resolution still results in substantially more bytes per series overall.

  4. D No change in resource usage, because scrape interval only affects query resolution, not ingestion because the remote write queue is assumed to have drained before the next reload cycle

    Incorrect. Scrape interval directly affects how many samples are ingested, stored, and indexed.

Observability Concepts

Q3. In a time-series database like Prometheus, what does a 'sample' represent?

Reveal answer and explanations
  1. A A full PromQL query result with multiple vectors returned to the caller

    Incorrect. A query result is typically a vector of samples, not a single sample.

  2. B A labelled dimension such as `instance` or `job` attached to a metric series

    Incorrect. That describes a label, which identifies the series, not the sample itself.

  3. C A numeric value paired with a timestamp

    Correct. A sample is the atomic datapoint in a time series — a float64 value plus a millisecond-precision timestamp.

  4. D A scrape target entry in `scrape_configs`

    Incorrect. A scrape target is a source of samples, not a sample.

Observability Concepts

Q4. Your team has an SLO on the 99th-percentile request latency. A teammate proposes computing it from the existing `request_duration_seconds_sum / request_duration_seconds_count` series. Why is that approach fundamentally inadequate?

Reveal answer and explanations
  1. A The division would panic Prometheus when the counter resets

    Incorrect. Counter division does not panic Prometheus; it simply produces misleading data.

  2. B Sum over count yields an average, which hides tail-latency; true percentiles need histogram buckets and `histogram_quantile()`

    Correct. Averages mask tail behavior; accurate quantile SLOs require either histogram buckets combined with `histogram_quantile()` or per-instance summary quantiles.

  3. C Averages violate the OpenMetrics specification and cannot be used on SLO dashboards

    Incorrect. OpenMetrics does not prohibit averages; they are simply the wrong tool for tail-latency targets.

  4. D Only summaries (not histograms) can compute percentiles, so the plan is blocked by the instrumentation choice under the default `--storage.tsdb.retention.time` and `--storage.tsdb.retention.size` flags

    Incorrect. Histograms explicitly support server-side percentile estimation via `histogram_quantile()`; both histograms and summaries can yield quantiles.

Observability Concepts

Q5. Prometheus uses which model for collecting metrics from targets?

Reveal answer and explanations
  1. A Pull over HTTP from `/metrics` endpoints

    Correct. Prometheus scrapes metrics from HTTP endpoints on targets on a configured interval; targets are passive during collection.

  2. B Push over HTTP where targets send metrics to a central Prometheus ingestion service

    Incorrect. Push is used by StatsD and Graphite models; Prometheus only supports push via the Pushgateway as an exception.

  3. C Push over UDP via StatsD with client-side aggregation before forwarding samples

    Incorrect. UDP/StatsD is a different system; Prometheus uses HTTP-based pulls.

  4. D Streaming over gRPC with bidirectional flow control and TLS mutual authentication

    Incorrect. Prometheus does not use streaming gRPC for scraping.

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How this domain is tested

Observability Concepts accounts for 18% of the PCA exam. Expect questions that test recall of terminology and the ability to read short scenarios — not deep configuration. Use the sample questions above as difficulty calibration; if any feel hard, the rest of our 22-question domain bank will close those gaps.