Option 4 is correct because controlling the cache key and variation restores a high hit rate without breaking personalization. Ignoring noise parameters avoids fragmenting the cache. Immutable asset URLs allow long TTLs with safe invalidation via versioned paths. Separating dynamic fragments into their own includes with short TTLs preserves freshness while keeping the shell fast. Option 1 breaks auth/session and is not viable. Option 2 is magical thinking; CDNs won’t rewrite keys for you without rules. Option 3 shifts work to the browser, worsening LCP and increasing third-party variability. The recommended changes are standard edge-caching hygiene and directly lift TTFB at peak. They’re easy to validate by monitoring hit rate and origin RPS before/after.

Option 2 is correct because structured logs with correlation IDs enable end-to-end tracing without exposing PII, and severity/field contracts let tools reliably parse events. Default redaction prevents accidental leakage. Dashboards and SLOs translate logs into actionable signals. Option 1/3 are insufficiently structured for reliable operations, making incident response slow. Option 4 is noisy, expensive, and risks sensitive data exposure. The recommended plan also supports modularity by making logging a cross-cutting concern implemented via middleware/utilities rather than scattered print statements.

Option 2 is correct because it prioritizes customer impact and rapid containment through canary/feature-flag rollback, while collecting evidence (correlation IDs) to identify the failing slice. It uses measured error budgets to decide whether a rollback or forward-fix is safer, which is standard incident command guidance. This path reduces noise by isolating deltas instead of turning on “verbose everything.” Option 1 restarts and log-spams the cluster without narrowing variables, which often amplifies the incident. Option 3 diffuses focus and delays containment; coordination beats parallel guessing under pressure. Option 4 treats the problem as capacity rather than correctness and risks data corruption by purging caches indiscriminately. The guided steps also produce artifacts (timeline, metrics) for the later postmortem. They enable a clean handoff between responders and owners by limiting blast radius first. Finally, they align with zero-downtime principles where toggles precede redeploys.

Option 1 is correct because it isolates the new variable (dynamic facets) with an A/B toggle, profiles the expensive path, and adds bounded fallbacks to protect customers while debugging. Using the Services framework traces identifies whether latency is vendor, network, or code. A temporary timeout/fallback ensures the store stays shoppable even if live facet counts are slow. Option 2 removes an essential performance control and will worsen TTFB. Option 3 is unsubstantiated blame-shifting and delays evidence gathering. Option 4 floods logs, hides signals, and adds CPU overhead during peak. The guided steps produce actionable metrics (P95 deltas) that inform a precise fix. They also let business decide whether “static facets for now” is acceptable. This approach turns a vague regression into a controlled experiment.

Option 3 is correct because it targets the concurrency/race conditions common in flash sales by using idempotent, ordered events and short-lived reservations to bridge PDP and checkout. Measuring event lag versus cache TTL explains PDP/checkout divergence objectively. The outbox pattern prevents lost updates and supports replay if consumers fall behind. Option 1 masks the symptom and increases oversell risk. Option 2 treats an ordering bug as a capacity problem without evidence. Option 4 reduces capability and creates operational risk without guaranteeing correctness. The recommended steps produce dashboards and alerts so future spikes are visible early. They also keep the customer journey smooth by failing gracefully (e.g., reserve expiry messaging). Finally, they are reversible once confidence returns.

Option 2 is correct because it eliminates the double-charge class of errors by design (idempotency) and stabilizes UX with circuit breakers and bounded timeouts. Signed webhooks ensure state transitions are authentic, and compensation jobs safely retry only known-safe cases. Correlation-based reconciliation gives finance exact traceability. Option 1 is risky vendor flailing without evidence or migration planning. Option 3 trades one failure mode for another and still leaves correctness unproven. Option 4 creates toil, prolongs harm, and teaches the system to accept financial risk. The guided path reduces blast radius first, then restores correctness. It also yields durable runbooks for future provider issues. And it supports postmortem learning by leaving precise telemetry.

Option 3 is correct because it follows the actual control plane for synonyms and validates each step with instrumentation and alerts. A canary site confirms end-to-end propagation without risking all traffic. Partial indexing and precise cache busts avoid performance cliffs and thundering herds. Option 1 is heavy-handed and can take the site down during peaks. Option 2 rejects the business requirement for near-real-time changes. Option 4 hides the symptom but degrades relevance for longer. The guided steps also surface who owns each failure (search vs CDN vs app). They create clear SLOs for propagation times. And they result in actionable dashboards for merch operations.

Option 2 is correct because a safe, representative reproduction eliminates configuration drift as the hidden variable. Diffing site preferences often reveals rounding/precision flags that differ from lower envs. Adding unit tests for edge cases prevents regressions after the fix. Option 1 is risky hot-patching and bypasses review and tests. Option 3 harms revenue and SEO and is unnecessary with the right process. Option 4 normalizes incorrect prices and risks compliance. The recommended approach also improves your data migration playbook for future locale launches. It gives QA a repeatable script for currency correctness. And it keeps incident risk low while delivering a verified fix.

Option 3 is correct because replication issues are usually scope, mapping, or permission problems, and logs will tell you where the chain broke. Validating schema compatibility between realms prevents silent skips. A targeted re-run fixes the impacted scope without disturbing unrelated content. Option 1 treats content operations as code deployment and wastes time. Option 2 is heavy and error-prone, and it ignores the pipeline. Option 4 risks a traffic spike and doesn’t fix missing origins. The guided steps also produce a runbook that content ops can follow next time. They reduce reliance on engineering for content moves. And they provide a clear owner for each stage in the replication chain.

Option 1 is correct because it provides immediate containment (WAF), a code fix (encoding), automated prevention (tests), and a safe deployment method (canary). This balances urgency with control, protecting customers while eliminating the root cause. Option 2 hurts discoverability and reputation without removing the vulnerability. Option 3 delays a fix and increases risk by experimenting in production. Option 4 abdicates responsibility; the bug is server-side. The guided path also yields artifacts for compliance audits. It improves future resilience via CI checks. And it minimizes revenue impact by keeping PLP live with protection in place.

Option 2 is correct because it reduces call volume through caching/coalescing, uses batching where possible, and adds polite client behavior with backoff, which together respects platform limits. Staggering jobs cuts artificial spikes. Monitoring headroom turns an invisible limit into an operational SLO. Option 1 may be impossible or slow and doesn’t fix waste. Option 3 surrenders business goals rather than solving the problem. Option 4 confuses queuing with success and worsens tail latency. The guidance protects customer flows quickly while enabling continuous improvement. It also creates concrete acceptance criteria for future features. And it ensures peak events are predictable and observable.

Option 2 is correct because chunking and checkpoints localize failures, DLQ isolates bad data, and structured logs expose exactly where failures occur. Back-pressure prevents cascading failures when downstream systems slow down. Replay of failed chunks minimizes recovery time and risk. Option 1 accepts ongoing data drift and operational pain. Option 3 treats reliability as a hardware problem and may still miss the window. Option 4 increases staleness and risk of large-blast failures. The guided approach yields clear runbooks and reliable on-call alerts. It also lets business proceed with confidence during nightly ops. And it builds a foundation for future incremental imports.

Option 3 is correct because it follows the data through the pipeline with correlation and validates regional rules and mappings in a controlled environment. A minimal repro lets the team iterate quickly and confirm fixes without risking production. Contract tests ensure future changes cannot silently drop required fields. Option 1 invites compliance and audit issues. Option 2 is passive and unreliable; intermittent issues need deterministic tracing. Option 4 is guesswork and rarely addresses mapping/schema defects. The guided steps also produce documentation of the export contract. They improve monitoring by making omissions visible as failed tests, not stale reports. And they keep stakeholders informed with evidence, not speculation.

Option 3 is correct because KPI validation depends on a representative workload, not just peak numbers. Analytics (paths, device mix, geos, cache behavior) inform realistic concurrency and think times that drive server work. Ramp and spike phases expose auto-scaling and cache stampedes you won’t see in a flat test. A soak reveals leaks and scheduler drift that short tests miss. Pre-defining pass/fail per KPI avoids goalpost-moving when numbers are borderline. Production-like data avoids artificial cache hit rates and skewed search results. Feature flags let you canary risky code and isolate regressions. Options 1 and 2 are blunt and ignore behavior variability and cache dynamics. Option 4 assumes API equals UX, which hides CDN/template/JS execution costs. A disciplined, analytics-backed plan turns results into decisions the business can trust.

Option 1 is correct because it reduces demand on constrained backends while improving effective throughput. Caching and coalescing collapse identical requests, lifting the plateau without inflating tail latency. Tuning pools and keep-alive addresses head-of-line blocking and connection churn that wastes sockets. Deduping in-request calls (e.g., facet counts, recommendations) removes needless load amplification. Option 2 spreads pressure but doesn’t change total downstream capacity, so the bottleneck persists. Option 3 extends wait times, inflating P95/P99 and failing UX KPIs. Option 4 removes a critical performance control and will further stress origins. The recommended steps align with capacity planning: reduce demand first, then right-size supply. They also create measurable deltas you can verify on the next test cycle.

Option 4 is correct because secure preferences keep secrets out of source, and feature flags in site preferences enable controlled rollouts and fast toggles. Validating configuration in CI catches misconfigurations before deployment. Loading non-secret config at startup avoids per-request overhead while keeping behavior predictable. Option 1 leaves secrets in code and requires deploys for simple toggles. Option 2 puts secrets in client-side storage, which is insecure by design. Option 3 adds latency and failure modes by introducing a runtime dependency for every request. The recommended approach is auditable, safer, and supports modular deployments.

Option 2 is correct because respecting limits with backoff and coalescing reduces waste while preserving UX. Batching and caching idempotent reads (prices, inventory snapshots) shrink call volume immediately. Jitter prevents synchronized retries that cause cascades. Staggering promo jobs removes artificial spikes that steal capacity from shoppers. Headroom dashboards turn a hidden quota into an operational SLO you can manage. Option 1 is slow/uncertain and does not address inefficiency. Option 3 risks correctness and fraud, violating business rules. Option 4 inflates tail latency and fails the KPI by design. The chosen plan is testable and sustainable, and it usually passes KPIs on the next cycle.

Option 3 is correct because it targets allocation pressure, which drives gradual degradation in long runs. Streaming reduces peak memory and avoids large temporary objects. Field trimming reduces CPU spent serializing unused data. Edge compression offloads work from origins and helps TTFB/LCP. Lower log volume and PII avoid I/O stalls and legal risk while preserving necessary telemetry. Pools and memoization cap per-request allocations, stabilizing the curve. Option 1 ignores the real user impact hidden in P99. Option 2 increases noise and overhead. Option 4 is costly, and won’t fix the slope caused by allocations. The proposed changes are verifiable via heap profiles and P99 trend flattening in the next soak.

Option 1 is correct because RUM KPIs measure actual user experience, including third-party impact. Consent gating reduces tag load when users decline, and async/defer prevents render blocking. SRI/CSP protects integrity while keeping measurement. Server-side tagging reduces client overhead and variability. Option 2 sacrifices marketing insights unnecessarily. Option 3 rejects the KPI source of truth. Option 4 confuses origin speed with client-side scripting cost; tags run in the browser regardless. The recommended approach is directly traceable to improved RUM P95/P99. It also hardens security and privacy posture. And it preserves business value by keeping essential measurement.

Option 2 is correct because long-tail outliers harm real users and often indicate fragility. Cache-miss penalties can be reduced with coalescing to prevent stampedes. Prepared queries and prefetching shrink cold-path cost. Adding P99 and error budgets to acceptance criteria prevents future debates and bakes reliability into sign-off. Option 1 meets the letter but not the spirit of user experience. Option 3 hides the issue by changing the test, not the system. Option 4 cherry-picks a metric that masks pain. Addressing P99 raises confidence that promotions and spikes won’t degrade UX for a meaningful minority. It also drives better architectural hygiene around caching and fallbacks.

Option 4 is correct because shadow traffic exposes the new stack to real mixes without risking users. Golden signals let you compare apples-to-apples and decide with confidence. Thresholds translate KPIs into automated gates for promotion. Options 1 and 2 either risk a full blast or rely on incomplete coverage. Option 3 changes the architecture under test and distorts results. Shadowing also reveals cache/key mismatches and third-party behavior differences. It builds a repeatable release practice the business can trust. And it pairs well with feature flags for selective rollout.

The correct answer is the comprehensive migration spec + rehearsal + rollback, because migration risk is largely process and dependency risk. Formal mappings and transformations prevent data loss; load order and dedupe ensure referential integrity. UUID and external ID strategies guarantee idempotency. Validation criteria define “done” objectively, and rehearsals surface performance bottlenecks and missing rules. A rollback plan protects the business if acceptance fails. Option 1 is shallow and untested. Option 3 punts orders and relies on ad-hoc scripts—high risk. Option 4 pushes manual re-entry and drops historic fidelity, harming service and analytics. The chosen artifacts support predictable cutover with auditability and repeatability.

The specification clearly states SCAPI for carts, which implies using shopper JWT flows, idempotent order creation, and PCI-safe boundaries. Replacing bespoke endpoints enforces the platform’s security posture and reduces maintenance risk. While moving content to Page Designer is also necessary, cart security and transactional integrity take precedence for compliance. Keeping custom endpoints with tests still violates the architecture decision and leaves PCI scope larger. Increasing CDN cache on PLPs does not address transactional correctness or content governance. By enforcing SCAPI first, you align with standards, restore token-based auth, and unlock rate-limiting and observability. It also ensures downstream integrations follow supported contracts. After carts are corrected, Page Designer migration can proceed with lower risk. This sequence best ensures the build meets the stated business and compliance requirements.

Blue-green requires that the new code path is complete and correctly ordered before switching traffic. Validating cartridge order and running automated smoke tests (health, PDP, cart, checkout) before slot activation is essential. Repeated retries without fixing order will not stabilize the deployment. Disabling path checks invites unpredictable runtime resolution and violates standards. Keeping changes in staging longer does not guarantee correctness when promoted. By fixing cartridge ordering, you ensure controller and model resolution follow SFRA extension patterns. Smoke tests detect missing dependencies and template lookups immediately. This approach restores zero-downtime principles by making the activation a formality. It also preserves rollback confidence if a check fails. Therefore, pre-activation validation and tests are the required corrective step.

The specification expects hourly deltas and off-peak replication, so the fix is to enforce those behaviors. Adjusting schedules prevents overlap, while delta logic ensures only changed prices move, reducing load and latency. Increasing threads without deltas can harm performance and still miss SLAs. Continuous replication breaks the stated off-peak constraint and can impact shoppers during cache invalidations. Running jobs in business hours contradicts the performance and availability requirement. Enforcing blackout windows aligns with the retailer’s need to avoid checkout slowdowns. Proper delta detection reduces job duration and error surface. Monitoring should confirm job SLA adherence and replication durations. This outcome directly maps to the spec and restores business expectations.

Reservations must be durable and server-validated; session flags are not reliable across devices or failures. Implementing platform inventory reservation endpoints ensures atomic holds with accurate ATS decrement. A timed release job is required to return inventory when holds expire, meeting the 15-minute promise. Disclaimers do not satisfy functional requirements and can erode trust. Manual emails break SLA and remove auditability. Moving BOPIS later in the flow harms UX and still fails the reservation requirement. By enforcing reservations plus release scheduling, you match the specification and protect store operations. Monitoring should verify hold creation, expiry, and release success rates. This change aligns with service levels and reduces oversell risk.

Headless standards on B2C Commerce prescribe SCAPI for shopper interactions, including carts and orders, with shopper JWT for identity. Using OCAPI Shop for carts violates the spec and complicates token management. Logging more does not fix security or contract misalignment. Proxying with static keys broadens risk and still lacks per-shopper identity. iFraming SFRA breaks headless UX and introduces session fragmentation. Migrating to SCAPI aligns with supported flows, rate limiting, and idempotency. It also reduces PCI scope by keeping sensitive operations in vetted endpoints. Enforcing JWT ensures per-user authorization and auditing. This decision corrects both security and maintainability gaps to meet business requirements.

The business requirement includes learning recommendations and merchandiser pins, plus experimentation. Implementing Einstein placements honors the learning system, while rules allow pins and exclusions. Experience-level A/B or Einstein A/B enables controlled comparison with proper attribution. Random selection is not a valid test design and produces noise. Static assets defeat personalization and reduce conversion potential. Disabling experiments removes a key requirement and weakens optimization. By aligning placements and tests to the spec, you enable measurable value and governance. The build should also capture click events and consent flags. Proper monitoring of CTR and revenue per session confirms success criteria.

Sensitive attributes must never be exposed to the client. The fix is on the server: strip fields from serializers and verify FLS before any exposure. Client masking leaves data in transit and view-source accessible. CSS or DOM hiding provides no protection and violates the requirement. Training alone does not enforce policy, and mistakes will leak data. Enforcing server-side filters ensures compliance for OCAPI/SCAPI and SFRA controllers. Automated tests should assert absence of sensitive keys. Logs must be reviewed to ensure no serialization paths reintroduce fields. This remediation aligns with security standards and the explicit spec.

Low hit ratio on anonymous PLPs often indicates cache key mismatch, such as missing locale or pricebook in the key, causing fragmentation. Fixing keys and purging on catalog or price updates restores high hit rates without violating correctness. Scaling origin does not solve misuse of CDN and increases cost. Caching cart responses breaks the “no-cache” requirement and risks stale totals. Unifying PLP and cart policies contradicts the specification and hurts accuracy. By aligning keys and purge events, you retain fast anonymous browse and correct transactional behavior. Monitoring hit ratio and origin latency should improve. This action satisfies both performance targets and business correctness.

Leveraging provider endpoints that natively compute facets and expose synonym mutations is the only way to meet both latency and “no deploy” requirements. Edge caching for stable query fragments reduces tail latency and is measurable in performance tests that mirror target QPS. A webhook-triggered cache bust makes synonym edits visible within minutes without manual restarts. Option 1’s nightly cache rebuilds and release coupling fail the five-minute operational goal. Option 3 moves heavy compute into controllers and turns search into a bottleneck while complicating updates. Option 4 sacrifices required functionality during peak, contradicting the spec and degrading CX. The evaluated plan clearly ties each spec line to a design choice and acceptance evidence.

Option 3 is correct because it matches high write volume with asynchronous export, protects once-only processing through idempotency, and uses webhooks to minimize polling latency. An event feed and edge cache support the 2-minute inventory freshness requirement without overloading PDP calls. Bulk, wave-based migration reduces cutover risk while preserving returns eligibility by mapping history into standard order entities. The recommended diagrams (context, data-flow, sequence) clearly show system boundaries, contracts, and error paths. Option 1 couples UX to OMS availability and fails the 2-minute inventory SLA. Option 2’s CSV and hourly polling cannot meet real-time expectations and removes operability. Option 4 moves critical integration into the client, creating security and failure-handling gaps, and ignores business need for self-service returns. The defended design also supports observability via correlation IDs and retry/DLQ strategies.

Option 1 is correct because SLAS and a central identity provide consistent tokens and scopes for Commerce while separating authentication concerns from downstream clouds. Treating consent as a first-class replicated object meets regulatory timelines and removes drift. Phased migration by hash keys scales to 60M profiles and avoids long maintenance windows, while backfills keep deltas small. The C4 context diagram clarifies system boundaries; data-flow shows replication and CDC; the sequence diagram documents propagation on create/update/delete. Option 2’s nightly files and manual consent reconciliation guarantee inconsistency and SLA misses. Option 3 centralizes PII in the storefront, expanding data exposure and creating blocked service workflows. Option 4 delegates identity to a single cloud and tolerates week-long delays, which breaks real-time personalization and DSAR responsiveness. The proposed plan also sets up monitoring (lag, failure rates) and correlation IDs for cross-system traceability.

Option 3 is correct because it blends platform price books and promo rules with a delta pipeline, minimizing churn on large catalogs. Surrogate-key invalidation prevents global cache purges, while variation keys keep correctness per segment and locale. A read-optimized store (e.g., edge cache/remote include) offloads PLP rendering, sustaining performance at scale. Migrating historical promo data separately preserves analytics without slowing the PLP path. Option 1 would overload the pricing engine, increase tail latency, and miss caching benefits. Option 2’s full refreshes are wasteful and error-prone, and template logic for promos is brittle. Option 4’s alternative (single global key) would thrash caches and harm availability. The diagrams clarify module responsibilities and timing of invalidations, making the solution operable and auditable.

Option 2 is correct because it satisfies the 60-second SLA using streaming with signed callbacks and replay, which addresses burst handling and correctness. Defined event schemas reduce ambiguity and enable contract testing. Backfill via bulk partitions is the right match for 90-day history without affecting real-time streams. The data-flow and sequence diagrams make failure paths, retries, and replays explicit for operations. Option 1 cannot meet sub-minute latency by design. Option 3 removes server-side validation and creates security and reconciliation risks, especially with ad blockers. Option 4 misrepresents event computation and adds heavy replication; five-minute batches still miss the target and burden the CDP. The chosen approach also anticipates rate limits and includes metrics for event lag and error rates.

Option 3 is correct because it provides robust, real-time RMA creation and status propagation through secure, verifiable webhooks and idempotent endpoints. Correlation IDs enable tracing across systems, and size limits ensure attachments don’t break flows. Migrating open RMAs keeps customer experiences continuous and avoids duplicate returns. The diagrams clarify both runtime interactions and how order history composes RMA fragments. Option 1 and 2 degrade UX and create operational toil, and deferring migration harms trust. Option 4 is fragile, unsecure, and unmonitorable. The evaluated plan also includes retry/DLQ behavior, rate limits, and privacy considerations for labels and addresses.

Option 2 is correct because it leverages CDN and signed URLs to keep P95 latency low worldwide while maintaining security. Responsive renditions cut payload size and speed PDP. Batch migration with manifests and checksums ensures integrity and steady throughput for 2TB. URL rewrites avoid code churn and maintain authoring simplicity. The deployment diagram clarifies CDN/DAM/B2C roles; the data-flow shows publish triggers and cache behaviors. Option 1 overloads app servers and makes global performance unpredictable. Option 3 removes edge caching and increases cost/latency. Option 4 creates inconsistency and cache fragmentation. The recommended plan also includes TTL strategies, purge hooks, and fallback images for failures.

Option 2 is correct because hosted fields keep PAN out of Salesforce scope, and server-side tax with idempotency supports reliable retries under peak. Persisting tax decisions on the order ensures an audit trail and protects against re-computation drift. Referencing historic documents by vendor IDs keeps Commerce light while enabling retrieval. The sequence and context diagrams communicate PCI boundaries and call flows clearly. Option 1 mixes presentation with business logic and increases PCI exposure. Option 3 exposes client keys and PII and breaks compliance. Option 4 defers tax correctness and creates customer friction. The evaluated plan also factors in rate limits, circuit breakers, and observability for payment/tax calls.

Option 3 is correct because an event-hydrated ATS cache supports 2,000 stores with minute-level freshness and predictable PDP latency. Server-side eligibility checks prevent promising items that are no longer available, and idempotent reservations avoid double-holds. Bulk migration of store masters preserves IDs and avoids customer friction. The diagrams make pickup logic, cache invalidation, and cancellation clear to ops and QA. Option 1 would DDOS the POS and kill page performance. Option 2 fails freshness and reservation requirements. Option 4 relies on guesses and brittle scraping. The chosen plan also documents rate limits, retries, and alerting on lag between POS and Commerce.

Option 2 is correct because it makes the vendor system the SoR while keeping Commerce lean, and it uses idempotency and signed callbacks to ensure consistency under retries. Storing snapshots on orders supports supportability and audit. A phased migration with effective dates prevents double-billing and maintains service continuity, while token mapping preserves payment methods. The diagrams explain timing-sensitive behaviors like swap and proration. Option 1 creates shadow SoR and weekly drift. Option 3 is insecure and brittle. Option 4 sacrifices customer trust and revenue. The plan also accounts for rate limits, failures, and observability on renewal/capture flows.

Option 3 is correct because it provides both breadth (context/container) and depth (sequence, catalog, classifications) along with a pragmatic migration plan and responsibilities. Explicit rate limits and versions de-risk integration choices; a data classification matrix informs security controls and residency. The migration runbook with waves and rollback aligns delivery with risk tolerance and business windows. Stakeholder review ensures shared understanding before build. Option 1 lacks enforceable precision and alignment. Option 2’s logo map obscures contracts and volumes. Option 4 punts critical planning to later, causing rework and governance gaps. The evaluated output also facilitates test planning and SLO definitions per integration.

Option 2 is correct because hosted fields prevent PAN exposure, adapters isolate PSP specifics, and idempotency with signed webhooks ensures reliable financial state across retries. Regional routing satisfies legal and performance constraints. Token migration via vetted detokenization/retokenization preserves customer payment methods and reduces churn, and documented proof supports compliance. The diagrams reveal verification and retry paths critical to operations. Option 1 expands attack surface and logs PII. Option 3 ignores routing requirements and weakens security. Option 4 incorrectly makes Commerce a PAN store, increasing PCI scope. The evaluated plan also includes alerting on webhook failures and correlation IDs for charge tracing.

The outbox and broker pattern with idempotency directly enforces once-only effects when transient failures occur, which meets the “no duplicate orders” requirement. Correlation IDs across Commerce and OMS satisfy the traceability need for support and audit. Contract tests ensure field-level compatibility never regresses as cartridges evolve. Performance testing against the broker and OMS proves the P95 export time under realistic concurrency and payload sizes, rather than hoping production will meet it. Observability and alerting close the loop by detecting lag before it breaches SLAs. Option 1 couples the user experience to OMS availability and creates duplicate risk with blind retries. Option 2 acknowledges retries but offloads deduplication to manual steps that don’t scale. Option 4 violates security and reliability by moving critical writes into the client and trusting OMS to “figure it out.” The chosen build plan is therefore the only one that systematically proves and monitors the business requirements.

Event-driven per-key invalidation keeps PDP/PLP fast while meeting the 120-second freshness requirement, because only the affected store/SKU entries are refreshed. A deliberate cache key strategy avoids global flushes that would cause thundering herds and missed SLAs. Adding a lag monitor turns the requirement into an SLO with visible error budgets, making the behavior testable in pre-prod and watchable in prod. Option 2 destroys PDP performance and resilience by coupling the UI to POS availability. Option 3 directly contradicts the “no global flush” constraint and would create outages under bursty updates. Option 4 simply fails the 120-second requirement by design and replaces accuracy with a disclaimer. The chosen approach maps cleanly from the written spec to build steps and operational controls.

Hosted fields/HPF ensure PAN never touches your servers, which is the only certain way to comply with “no PAN in logs.” Signed webhooks and idempotent operations are critical to maintain a consistent financial state under retries and network issues. Persisting 3DS2 outcomes (including ECI/CAVV or equivalent) as structured data meets auditability requirements and makes downstream reporting deterministic. CI log scanners prevent accidental leakage before deployment, providing objective evidence that the constraint is met. Option 1 keeps sensitive flows in the browser and cannot guarantee logging discipline. Option 2’s CSS masking offers no protection, and free-form notes are not auditable. Option 4 massively increases PCI scope and relies on email for critical evidence. The correct plan operationalizes the spec through architecture, code, and automated controls.

Option 2 is correct because it preserves your supported runtime and satisfies security with signed callbacks, while leveraging a documented contract for maintainability. Contract tests keep integration honest across releases. Option 1 increases technical debt and security risk. Option 3 in the table sacrifices a key feature and undermines UX. Option 4 creates a permanent maintenance burden and diverges from vendor updates. Your defense should cite vendor support matrices, security sections, and test evidence for callback verification, throughput, and error handling under load.

Integrating automated accessibility checks and explicit manual test scripts into CI/CD is the only approach that repeatedly enforces WCAG 2.2 AA during development, not just at the end. Component guidelines ensure developers have a shared definition of done for ARIA and semantics. Acceptance tests in Testing Center make the requirement visible to product and QA, converting it into pass/fail criteria. Option 1 treats a11y as a one-off scan and will miss stateful issues like modal traps. Option 3 postpones compliance and increases remediation cost. Option 4 wrongly assumes mobile doesn’t need accessibility and introduces inconsistent experiences. The selected plan ties the business requirement (inclusive CX, legal risk reduction) to verifiable build artifacts and gates.

Blue-green gives true zero-downtime by swapping traffic between identically provisioned environments with health verification. Practiced rollback playbooks make “under 5 minutes” a demonstrated capability rather than a promise. Feature flags decouple code deploy from feature release, allowing rapid disablement of faulty paths without redeploy. Option 1 relies on manual steps and stale snapshots, which can corrupt data during rollback. Option 3 misuses “canary” and still risks full blast radius without staged percentage rollout. Option 4 increases integration drift and slows response during incidents. The evaluated plan is the only one that directly maps each requirement to a concrete build mechanism and evidence.

Parallel, chunked processing with validation and idempotent upserts is essential to meet the 5M/2-hour throughput while keeping data correct. DLQs separate malformed data without halting the whole job, meeting the “actionable errors” requirement. Telemetry provides proof of performance and lets ops detect regressions. An admin-triggered on-demand run satisfies the operational need without violating rate limits because the importer is consciously back-pressure aware. Option 1 cannot achieve the performance requirement and hides details in vague emails. Option 3 risks memory pressure and lacks proper idempotency semantics. Option 4 ignores the on-demand requirement and increases MTTR when reprocesses are needed. This build plan therefore best satisfies the spec with measurable controls.

Circuit breakers and fallbacks are the only way to honor “do not block checkout” while still providing a consistent user experience. Structured telemetry and explicit flags make degraded paths visible in analytics and support, ensuring the business can quantify impact and follow up on manual reviews. Rehearsals convert the idea into a practiced response rather than a theoretical plan. Option 1 violates the requirement by blocking checkout outright. Option 3 delays orders indefinitely and creates downstream reconciliation work. Option 4 removes critical risk controls in an ad-hoc manner, exposing the business to chargebacks and compliance issues. The evaluated approach ties each requirement to a testable behavior and ops visibility.

Encoding canonical and hreflang in templates makes them deterministic and reviewable in code, which is essential for multi-locale accuracy. Per-site sitemaps ensure each domain’s index is correct and timely, while pattern-based exclusions prevent infinite crawl spaces from facets. Automated checks and pre-prod crawls convert the requirement into verifiable evidence. Option 1’s edge injection is brittle and can desync from the app’s routing logic; a single global sitemap will be inaccurate for multi-site. Option 3 kicks the can and risks search regressions right after launch. Option 4 sacrifices required merchandising capabilities and still misses locale signals. The selected plan maps each SEO requirement to a concrete, testable build task.

Versioned routes and a compatibility policy are the only reliable way to protect consumers while evolving APIs. Schema validation that tolerates unknown fields enables producers to add non-breaking data without crashing consumers, meeting the “fail safely” requirement. Contract tests make compatibility measurable and stop breaking changes at PR time. A deprecation policy and notifications align consumer expectations and reduce incident risk. Option 1 leaves every change risky and undocumented for automation. Option 3 increases coupling and rejects the “fail safely” principle. Option 4 relies on manual coordination that doesn’t scale and lacks enforcement. The evaluated implementation converts governance requirements into enforceable CI/CD controls.

Option 2 is correct because it addresses security (no PII in logs), performance (separate cacheable PDP shell and short-TTL fragment), and modularity (service abstraction + remote include) in one design. The Services framework supplies retries, timeouts, and circuit breakers to protect storefront responsiveness. Remote include isolates the loyalty dependency and prevents a single slow service from breaking page rendering. Correlation via non-PII IDs preserves traceability without legal risk. Option 1 retains sync coupling and still logs PII, which violates secure logging practices. Option 3 pushes secrets and service calls to the client, increasing attack surface and risking ad-blocker failures. Option 4 throws away page caching, harming TTFB and scalability, and fails to demonstrate prudent engineering. The chosen approach also supports observability with per-fragment metrics and can be validated by targeted load tests.

Option 3 is correct because it systematically removes root causes: server-side validation and output encoding neutralize injection, CSRF tokens stop cross-site request forgery, and removing secrets from source prevents key leakage. Encoding must be default-on, enforced by lint/CI rules, not a best-effort in reviews. Storing secrets in secure preferences or SLAS config reduces attack surface and eases rotation. Option 1 trusts the browser and keeps keys in code, both anti-patterns. Option 2 is partial and defers critical controls until late, when fixes are costlier. Option 4 treats a security flaw as a routing problem; CDNs won’t fix injection or CSRF. The chosen plan can be verified with automated tests, static scans, and a red/green CI pipeline that blocks unsafe merges.

Option 1 is correct because SFRA’s extension pattern and hooks provide the intended seams for customization, preserving upgradeability and test isolation. Keeping partner logic in a separate cartridge maintains clear ownership and reduces merge conflicts. Middleware enables cross-cutting concerns (auth, tracking) without tangling controller code. Option 2 freezes technical debt and blocks security/feature upgrades. Option 3 forks the codebase, increasing drift and duplicating defects. Option 4 violates separation of concerns by pushing business logic into templates, harming testability and performance. The recommended approach also enables feature flags and A/B testing without rewriting core flow, and can be proven by upgrading SFRA in a branch without partner diffs.

Option 4 is correct because CDN delivery, responsive images, and proper caching are the standard path to improving LCP and scalability. Lazy-loading preserves bandwidth while width descriptors ensure the browser selects an appropriately sized asset. Versioned URLs allow long TTLs without serving stale content. Deferring and splitting non-critical scripts reduces main-thread blocking. Option 1 bloats HTML and prevents effective caching. Option 2 creates per-request work, defeats caching with random query strings, and adds latency. Option 3 wastes bandwidth, moves heavy compute to the client, and still harms LCP. The recommended plan is measurable via synthetic and RUM metrics and aligns with modern storefront best practices.

Option 2 is correct because hosted fields eliminate PAN exposure, which is the strongest control; removing PAN/BIN from logs prevents leakage. Idempotent payment operations with signed webhooks ensure financial correctness under retries and network faults. Token-only storage keeps PCI scope narrow while allowing legitimate operations. Option 1 accepts ongoing risk and lacks determinism. Option 3 turns Commerce into a PAN store and expands compliance burden. Option 4 pushes secrets to the client and still logs sensitive data. The recommended approach can be validated by log scans in CI, negative tests for idempotency, and webhook signature verification tests.

Option 4 is correct because it restores cacheability by separating stable shells from dynamic fragments and applying precise invalidation. Surrogate keys and variation keys give fine-grained control, preventing global flushes that cause stampedes. Short-TTL fragments provide “fresh enough” personalization without destroying the cache. Option 1 sacrifices business value to hit performance, which is rarely acceptable. Option 2 treats a design problem as a capacity problem and will not scale cost-effectively. Option 3 ignores that many logged-in views are still cacheable at the shell layer. The recommended approach can be proven with cache hit-rate dashboards and change-driven invalidation tests.

Option 3 is correct because the Services framework provides the right primitives (timeouts, retries, circuit breaker) and a single client eliminates duplication. A service module keeps responsibilities clear and supports mocking in tests. Category-specific fallbacks (e.g., alternate rates or “calculate later”) protect UX while collecting telemetry. Option 1/2 add thin wrappers but dodge resilience, leaving timeouts unbounded and failures unclassified. Option 4 mixes presentation with business logic and cannot properly handle asynchronous failures. The recommended refactor improves security (consistent auth), performance (bounded latency), and modularity (testable abstraction).

Option 1 is correct because gating tags via consent aligns with privacy laws while still enabling measurement. Hashing emails only after consent reduces exposure of PII. Strong CSP (nonces/SRI) limits script injection risks and raises the bar on third-party scripts. Server-side tagging where feasible reduces client bloat and increases reliability. Option 2 throws away valuable analytics and is unnecessary with proper controls. Option 3 keeps insecure data handling and merely rate-limits harm. Option 4 shifts trust to a vendor without implementing technical protections and leaves CSP dangerously open. The recommended plan is verifiable through consent test cases, CSP violation reports, and performance/RUM checks.