Free Practice Questions for PeopleCert DevOps-SRE Exam

Comprehensive and Detailed Explanation From Exact Extract:

SRE defines effective monitoring as comprehensive observability across all layers of a system, including latency, traffic, errors, saturation, dependencies, and infrastructure. The SRE Book states: “Monitoring must offer insight across all system components, enabling teams to rapidly detect and diagnose issues.” (SRE Book – Monitoring Distributed Systems). Observability instrumentation (logs, metrics, traces) provides the necessary depth for reliable digital experience monitoring.

Option B captures this exactly: broad observability across all components and layers.

Option A rejects modern observability practices—contradicting SRE guidance.

Option C is too narrow (network-only).

Option D focuses only on advanced technologies, not comprehensive coverage.

Thus, B is the best answer.

Comprehensive and Detailed Explanation From Exact Extract:

SRE defines toil as “manual, repetitive, automatable, tactical work tied to running a service” (SRE Book – Eliminating Toil). Repetitive outages are specifically noted as a form of operational toil. The SRE Book and SRE Workbook emphasize adopting automation, intelligent tooling, and machine-learning–assisted systems to reduce toil and decrease Mean Time to Repair (MTTR) and Mean Time to Restore Service (MTRS). The books state: “Reducing MTTR directly increases system reliability more effectively than attempting to eliminate all failures.” (SRE Book – Chapter: Managing Incidents).

AI and advanced automation help detect issues faster, classify patterns, trigger automated remediation, and reduce human intervention—delivering reliability gains through faster repair rather than perfect uptime.

Option A is the only option aligned with SRE’s reliability philosophy.

Options B and C incorrectly suggest increasing MTTR/MTRS.

Option D refers to “perfect MTRS,” which is impossible and contradicts SRE’s acceptance of failure.

Thus, A is correct.

Comprehensive and Detailed Explanation From Exact Extract:

Kubernetes is described in SRE-aligned literature as an open-source container orchestration platform that automates deployment, scaling, failover, and lifecycle management of containerized applications. The Site Reliability Workbook references Kubernetes as: “a container management system that automatically handles service discovery, scaling, rollout management, and self-healing.” (SRE Workbook – Production Environment chapters). Kubernetes does not replace an OS, nor is it a CI/CD platform; it sits on top of an OS and orchestrates containers across clusters.

Option C is the most accurate: it captures container management, cloud deployment context, automated scaling, and failover—key capabilities of Kubernetes.

Options A and B incorrectly describe CI/CD platforms.

Option D incorrectly labels Kubernetes as an “operating system.”

Thus, C is correct.

Comprehensive and Detailed Explanation From Exact Extract:

The SRE Book provides a precise definition: “SLIs are the carefully defined quantitative measures of some aspect of the level of service provided. SLOs are the target values or ranges for these indicators.” (SRE Book – Chapter: Service Level Objectives). This establishes a clear hierarchical relationship: SLIs are the measurements, while SLOs define the acceptable target levels for those measurements.

Therefore, option A is correct: SLIs measure things like latency, availability, throughput, and error rate.

SLOs then define the goal such as “99.9% availability over 30 days.”

Option B reverses the relationship.

Option C incorrectly says SLOs measure SLIs, which is backwards.

Option D confuses metrics and targets.

Thus, A is the only choice that aligns with Google’s official SRE definitions.

Comprehensive and Detailed Explanation From Exact Extract:

Google defines SRE as “what happens when you ask a software engineer to design an operations function.” (SRE Book – Introduction). The core engineering approach in SRE focuses on:

Eliminating toil

Building scalable systems

Applying software engineering to operational challenges

Learning from previous solutions and patterns

The SRE Book emphasizes: “SREs focus on designing and engineering solutions that reduce manual operations and scale service delivery.” (Chapter: Eliminating Toil). This aligns directly with Option B: designing a solution to eliminate toil and scale service delivery, informed by prior successful engineering patterns.

Option A focuses only on automating a single tuning activity—not holistic engineering.

Option C describes deployment, not engineering approach to operations.

Option D is about incident response, not engineering strategy.

Thus, B is the best representation of SRE’s engineering approach.

Comprehensive and Detailed Explanation From Exact Extract:

Anti-fragility focuses on an organization’s ability to respond, adapt, learn, and recover from incidents. The most useful metrics relate to incident detection, response, reliability, and recovery. Deployment frequency, while important in DevOps and DORA metrics, does not directly measure anti-fragility.

From the SRE Workbook, Incident Response section:

“Improving antifragility requires better detection, better recovery mechanisms, and clear reliability goals.”

Key metrics relevant to anti-fragility:

MTTD (Mean Time To Detect) — quicker detection improves resilience

MTTR/RPO — recoverability measures

SLOs — define acceptable reliability thresholds and guide learning

Deployment frequency primarily measures delivery velocity, not resilience.

The Site Reliability Engineering Book emphasizes:

“Antifragility is improved by learning from incidents and strengthening recovery mechanisms rather than by increasing release cadence.”

Why other options are correct for anti-fragility:

A. Mean Time To Detect — critical for detecting failures quickly

B. SLOs — define boundaries for reliability and failure tolerance

D. Recovery Point Objective — measures potential loss during failures

Thus, C is the least useful metric for improving antifragility.

Comprehensive and Detailed Explanation From Exact Extract:

A Service Level Indicator (SLI) is a measurement of some aspect of reliability (e.g., latency, availability, quality). One of its defining characteristics is that it must be measured over a specific time window. Without a time horizon, the SLI has no actionable meaning.

From the Site Reliability Engineering Book, Chapter “Service Level Indicators”:

“An SLI is a quantitative measure of some aspect of the level of service that is provided. SLIs are evaluated over a specific period of time in order to understand reliability as experienced by the user.”

The SRE Workbook further states:

“Every SLI must define a measurement window. Without a time horizon, the indicator cannot be used to calculate SLO compliance.”

Why the other options are incorrect:

A SLIs do not need to appear in an SLA; SLAs are external contracts, SLOs/SLIs are internal engineering tools.

B SLIs may include client-side, server-side, or network metrics depending on what reflects user experience.

D SLI agreement is not defined by SRE vs. Agile teams; it is defined by business and user need.

Thus, the correct answer is C.

Comprehensive and Detailed Explanation From Exact Extract:

SRE emphasizes decision-making based on measured data, not intuition. The SRE Book explains: “Monitoring and SLOs provide an objective basis for decision-making, replacing guesswork with quantifiable data.” (SRE Book – SLOs & Monitoring). Data enables SRE teams to understand system behavior, validate assumptions, detect anomalies, and prioritize engineering work. The primary benefit is not merely collecting data, but analyzing and interpreting it to support decisions grounded in facts rather than opinion.

Option A accurately reflects this principle: data analysis and interpretation enable fact-based decisions, which is the core justification for SRE’s reliance on SLIs and observability signals.

Option B overstates by claiming data alone is always sufficient.

Option C refers to data mining, which is not a core SRE concept.

Option D is partially true but narrower than the SRE philosophy of data-driven operations.

Thus, A is the most accurate SRE-aligned answer.

Comprehensive and Detailed Explanation From Exact Extract:

All the listed reasons correctly describe why observability becomes essential in modern, user-focused, dynamically scaling architectures.

The SRE Workbook and Google Observability guidance both emphasize that traditional monitoring is insufficient in environments where:

Services are distributed

Traffic is unpredictable

Customer experience is a priority

Cloud-native, containerized, or microservice architectures are used

Key excerpts:

From Google’s Observability guidance:

“Monitoring relies on known failure modes; observability enables teams to explore unknown-unknowns and understand complex, dynamic systems.”

From the SRE Workbook:

“As systems scale and architectures shift toward microservices or containers, component-level monitoring provides an incomplete picture. Observability enables teams to understand user impact and system behavior holistically.”

Thus:

A Observability is critical for containerized and dynamic environments.

B Component monitoring alone cannot show customer experience or end-to-end reliability.

C Observability helps teams diagnose issues that could not be predicted in advance ("unknown unknowns").

All statements are correct, making D the correct answer.