Pass the PeopleCert DevOps DevOps-SRE Questions and answers with CertsForce

Comprehensive and Detailed Explanation From Exact Extract:

A blameless post-mortem is a foundational SRE practice that encourages truthful, detailed reporting after an incident. The purpose is to learn, not punish. Google SRE emphasizes that engineers must feel psychologically safe to report what they did, what they assumed, and why they made those decisions.

From the Site Reliability Engineering Book, Chapter “Postmortem Culture”:

“Blameless postmortems encourage engineers to share the full details of their actions and assumptions without fear of punishment, enabling learning and preventing repeated failures.”

The book further states:

“Understanding the assumptions made during an incident is critical to uncovering systemic issues.”

Thus:

Engineers must report without fear of retribution

They must report assumptions and decisions made during the incident

Therefore, the correct answer is C. Both A and B.

Why the other options are insufficient:

A Only partially correct

B Only partially correct

D Testing data may be included, but it is not the defining feature of blameless postmortems

Comprehensive and Detailed Explanation From Exact Extract:

The SRE Book defines the Four Golden Signals of monitoring as Latency, Traffic, Errors, and Saturation. Specifically, it describes “Errors” as: “the rate of requests that fail, whether explicitly, implicitly, or by policy.” (SRE Book – Chapter: Monitoring Distributed Systems). This includes HTTP 5xx responses, timeouts, and requests served but not meeting success criteria.

This definition matches option B exactly.

Option A describes latency, not errors.

Option C describes traffic.

Option D describes saturation (resource usage).

Therefore, B is the correct and SRE-accurate description of the golden signal for errors.

Comprehensive and Detailed Explanation From Exact Extract:

SRE applies economic principles, including diminishing marginal returns, to reliability engineering. As per the SRE Book: “Improving reliability becomes more expensive as the target approaches 100%, moving from increasing returns, to diminishing returns, and eventually negative returns.” (SRE Book – SLO Economics). This framework helps explain why striving for 100% availability is impractical and cost-ineffective.

The three recognized stages are:

Increasing returns – early improvements are inexpensive and highly impactful

Diminishing returns – costs rise while benefits shrink

Negative returns – achieving additional “nines” may reduce value due to slowed innovation

“Possible returns” is not part of this model.

Thus, D is the correct answer.

Comprehensive and Detailed Explanation From Exact Extract:

In the Full SRE model, reliability becomes a first-class citizen because SREs own the complete operational responsibility for the service and apply SRE principles end-to-end. The Google SRE Book describes several adoption models (Consulting, Embedded, and Full), and only the Full SRE model has SREs fully accountable for reliability outcomes.

From the Site Reliability Engineering Book, Chapter “SRE Engagement Models”:

“In the Full SRE model, the SRE team is responsible for end-to-end reliability. Reliability becomes a first-class objective, supported through SLOs, error budgets, and systematic reduction of toil.”

The Full model includes:

Full ownership of reliability

Enforcement of SLOs

Error budget policies

Engineering-driven improvement

Other models:

Consulting → SRE gives guidance but doesn’t own reliability

Embedded → temporary embedding to train teams, not full ownership

Platform → focuses on shared tooling, not service ownership

Thus, D. Full is correct.

Comprehensive and Detailed Explanation From Exact Extract:

Simplicity is a core SRE design principle. Google states: “Small, frequent, automated changes reduce risk and improve system stability.” (SRE Book – Release Engineering). Automating continuous, small deployments creates a simple and repeatable pipeline that increases agility while maintaining reliability. This approach aligns with both DevOps and SRE practices: reducing deployment complexity, lowering blast radius, and supporting rapid iteration.

Option B best reflects this philosophy: automated, continuous small deployments provide simplicity, stability, and agility simultaneously.

Option A improves process clarity but does not directly affect agility.

Option C is beneficial but broader and not specific to simplicity.

Option D focuses on control rather than agility.

Thus, B is correct.

Comprehensive and Detailed Explanation From Exact Extract:

Traditional monitoring works well when systems are static and predictable. However, cloud-native, distributed, and microservice-based architectures create highly dynamic environments. In these cases, observability becomes more effective because it provides visibility across entire systems, rather than focusing on individual components.

From Google’s Observability guidance:

“Traditional monitoring relies on predefined dashboards and known failure modes. In modern cloud systems, component-level monitoring becomes insufficient because failures occur in ways that cannot always be predicted.”

Further, in the SRE Workbook:

“Monitoring individual components does not provide adequate visibility into complex distributed systems. Observability enables teams to understand system-wide behavior and user impact.”

Why options are incorrect:

A Observability is not inherently cheaper.

C While true, it is not the best reason; observability's benefit is broader than scale alone.

D Traditional monitoring can support containers but often becomes noisy and ineffective.

Thus, the best answer is B.

Comprehensive and Detailed Explanation From Exact Extract:

When a team exceeds its error budget, SRE practice requires applying error budget policies that restrict feature releases and shift focus toward reliability improvement. The idea is to prevent further degradation of user experience and ensure the service meets the agreed reliability targets.

The Site Reliability Engineering Book, Chapter “Service Level Objectives,” states:

“If the service exceeds its error budget, all new feature launches or risky changes are halted until reliability returns to acceptable levels. Engineering work should be directed toward addressing the causes of the budget overrun.”

This aligns with option A, which describes a reliability-focused response during sprint planning. Limiting sprint planning to post-mortem action items and reliability improvements is a direct application of error budget policies.

Additional guidance from the SRE Workbook:

“Error budget burn should directly influence decision-making. When the budget is exhausted, the team must focus on remediation work rather than new features.”

Why the other options are incorrect:

B Reviewing the error budget’s realism can be done periodically, but it is not the immediate consequence of a breach.

C Extending the error budget invalidates its purpose and is discouraged.

D Ignoring the error budget contradicts the entire SRE model and Google’s official guidance.

Therefore, A is the only correct answer.

Comprehensive and Detailed Explanation From Exact Extract:

In SRE practice, “full-service ownership” means the same team is responsible for the service across its entire lifecycle: design, development, deployment, operation, and continuous improvement. Google’s Site Reliability Engineering: How Google Runs Production Systems describes SRE as a software-engineering-focused approach to operations, where engineers are responsible not only for writing the code but also for running it in production and improving its reliability over time. This aligns with the “you build it, you run it” model, which removes the traditional wall between development and operations and makes one team accountable for the whole service.

Option D — “The team is accountable for coding, shipping, and improving the application” — best matches this concept because it explicitly includes building (coding), releasing (shipping), and iterating (improving). Options A and B do not clearly include operational responsibility, and option C narrows the focus only to performance and reliability, omitting ownership of development and releases. Therefore, D most accurately reflects full-service ownership as described in SRE literature.

References (conceptual alignment):

Site Reliability Engineering: How Google Runs Production Systems, Betsy Beyer et al., O’Reilly, particularly:

Introduction & “What Is Site Reliability Engineering?”

“Introduction to SRE at Google” and discussions of dev/ops ownership boundaries.

Comprehensive and Detailed Explanation From Exact Extract:

SRE supports microservices architecture because it improves reliability by reducing blast radius, allowing independent deployments, and enabling scalable autonomous teams. The SRE Book notes: “Microservices enable teams to independently iterate and improve reliability without the constraints of large monolithic systems.” (SRE Book – Distributed Systems). One of the strongest reasons to adopt microservices is modernizing and refactoring large legacy monoliths, allowing them to be broken into independently deployable, maintainable components.

Option A is therefore the best justification.

Options B, C, and D may involve architectural choices, but they do not explain why microservices are the preferred architecture for reliability and scalability.

Thus, A is correct.

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.