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AI-300 deep review — September 28, 2026

The AI-300 guide was read in full and mapped to 58 detailed objectives in 14 groups. It now contains six worked examples, ten labs and explanations for 48 original checks. Twenty-seven offline assertions cover feature timing, shadow load, delayed labels, PTU/cache arithmetic, evaluation failures, rank fusion and catalog totals. One local Python gate was executed and one YAML workflow fragment parsed.

This is same-context AI research and repair; independent human review is pending. No cloud, SDK, model, ML job, deployment, traffic shift, network, telemetry query, fine-tuning, purchase or paid-course content was exercised. Synthetic results do not establish deployed controls, representative quality or available capacity.

Exam and catalog evidence

The official blueprint retains its March 5 page date, no separate skills-effective date and no announced change. All 58 objectives and accepted objective/status hashes are unchanged. The credential lists an active 120-minute English exam. Its AI Skills Navigator assessment requires sign-in; only a shell was retrieved, with no questions or results read. The course lists four days and 12 course languages. Its current syllabus remained a loading shell; the old two-path/12h24 total was not reproduced and is no longer presented as current. Self-study and lab budgets are editorial estimates.

The O'Reilly bootcamp names Ammar Mohanna and two sessions. Embedded metadata places the linked event on November 3–5, 2025, with PT10H; the two public agendas total 300 + 300 = 600 minutes. This is a historical, broad MLOps/LLMOps event. A current recording or replacement event was not verified.

Direct Udemy retrieval was blocked. Indexed public outlines show Aseem Mankotia's preparation course updated July 2026, one section, 11 lectures and 3h27. Its advertised 100-minute simulation differs from Microsoft's 120-minute assessment. VARONTO Academy's practice listing is dated June 2026 and lists five 150-question sets plus one 161-question set: 911 total. Its prose still says 900. Neither paid questions nor claimed exam realism/originality were evaluated.

A Whizlabs AI-300 product now exists, correcting the previous missing-product note. Direct retrieval exposed a title shell; its indexed catalog advertises 100 videos and three quizzes (one free, two paid). Duration and question count remain unverified. Its exam language claim differs from Microsoft's English-only listing. Bounded searches did not identify dedicated Pluralsight/MeasureUp products; this is not proof of absence. Broad sample repositories and Reactor remain discovery/context sources.

Technical corrections and learning value

  • MLOps: distinguish already-ended v1 toolchain support from continuing service operation. Explain submission versus completed/evaluated jobs, exact reuse inputs and forced-rerun behavior, point-in-time feature lineage, and the narrower support of the Responsible AI dashboard.
  • Rollout and monitoring: distinguish shadow copies from routed canaries; account for duplicated execution and effects. Require inference collection, disjoint monitoring windows, mature-label coverage and subgroup evidence.
  • GenAIOps infrastructure: scope managed VNet to outbound agent traffic and document irreversible transitions/provisioning prerequisites. Separate PTU quota, capacity and billing reservations; use fictional model parameters to calculate demand and cache-loss headroom. Scope spillover by matching model/version/resource and preserve response attribution.
  • Evaluation: describe current project SDK and scenario contracts, stored interaction evaluation without replay, eligible GenAI spans and missing-content failures. Keep privacy, sample selection, evaluator coverage and pass rate as separate decisions. The local gate rejects missing, duplicate, failed and severe records; malformed input must block through caller error handling.
  • Retrieval and customization: explain RRF versus vector/reranker scores, method-specific tuning signals/access and separate training/deployment retirement. Preserve an actually available compatible rollback target.

Primary references sit beside the applied claims in the guide. Source-specific notes identify the sections read. Long model tables, code samples, complete release archives and retained broad references were not exhaustively audited.

Blog intake

Selected policy/control and observability sections of Sarah Bird's June 2 article support an original authorization/control worksheet. Linked ASSERT/ACS implementations and partner results were not executed or reproduced. A control path using a probabilistic judge does not guarantee correct classification.

Selected operations summaries in Nick Brady's May 30 roundup support a feature/API/status/permission/data-boundary worksheet. Current product documentation governs exact support and preview scope; the complete roundup, linked SDK samples and model catalog were not audited.

Maintenance and validation

The weekly/manual workflow already detects source/objective changes and dated follow-ups; it queues semantic review rather than claiming to perform it. Two October 12 follow-ups cover evaluation/network/model support and learning-catalog evidence. The guide's learning tables are synchronized into the shared catalog. Historical review dates, hashes and outcomes are retained, and accepted snapshots are unchanged.

The operation receipt is ADLC_Docs/operations/2026-09-28-ai-300-deep-review.json. It records objective mapping, source retrieval and indexed observations, findings, offline checks and the actual repository/site validation result. Reachable links alone do not establish that lessons, videos or full references were reviewed.