AWS RAG and Agentic Systems Assessment

Demonstrate that you can make and verify production retrieval-system decisions from first principles through frontier AWS patterns.

Assessment readiness improves after completing all AWS RAG and Agentic Systems modules

Knowledge Tree

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Prerequisites

RAG Mental Models and Boundaries
Minimal Cited RAG on AWS
Corpus Engineering and Ingestion
Embeddings and ANN Mechanics
Retrieval Quality Engineering
Evaluation and Retrieval Debugging
AWS Retrieval Store Selection
Protecting RAG
Reliability, Observability, and Change Control
Cost, Latency, and Sustainability Optimization
From Static to Agentic RAG
AgentCore, MCP, and Governed Tools
Adaptive, Hierarchical, Graph, and Multimodal Retrieval

Assessment Overview

This 96-question mastery assessment progresses from Associate-style implementation decisions through Professional-style architecture trade-offs to Specialty-style high-consequence RAG and agentic scenarios. Pass with at least 77 correct answers, which is 80.21%. These are original certification-style learning questions, not an AWS Certification exam and not affiliated with or endorsed by AWS.

AWS enterprise agentic AI architecture figure showing business users and applications, governed agents, enterprise data and tools, security, observability, and operating controls. — AWS enterprise agentic architecture figure. Use it as a capstone map for relating assessment decisions across data, tools, security, evaluation, observability, and operations. It is an enterprise reference pattern, not an AWS Certification blueprint or a workload-specific implementation. Source: AWS Prescriptive Guidance (CC BY-SA 4.0).

AWS Prescriptive Guidance: Enterprise agentic AI architecture figure (CC BY-SA 4.0)Last verified: 2026-08-26

Total questions: 96
Passing threshold: 80% minimum, 77/96 correct (80.21%)
Format: scenario-based multiple choice with one defensible production decision
Estimated time: 75-95 minutes

Certification-style tier and domain distribution

Tier	Focus	Questions
Associate-style	Bounded build, protect, operate, and optimize decisions	32
Professional-style	Architecture trade-offs, change control, and organizational complexity	32
Specialty-style	Ambiguous, high-consequence security, governance, and incident scenarios	32
Every tier	All seven RAG and agentic domains, with 5/5/5/5/4/4/4 domain coverage	32

Result Snapshot

No recorded score yet. Start the assessment when ready.

Non-graded capstone readiness rubric

Use this rubric before promoting a retrieval system. It is not scored by the quiz; it is a release conversation for the accountable team.

Production readiness decisions

Decision area	Evidence to review
Workload and answer contract	Allowed sources, citations, abstention, escalation, and user decision are explicit.
Governed corpus	Ownership, provenance, ACL propagation, freshness, removal, and quarantine are tested.
Strong retrieval baseline	Lexical, dense or hybrid baseline is measured on representative queries.
Evaluation	Golden sets, retrieval and answer metrics, judge calibration, and failure slices are versioned.
Security and tenancy	Every boundary reauthorises; retrieved content and citations are untrusted.
Reliability and runbook	Trace versions, bounded retries, degraded mode, canary, and rollback are rehearsed.
Unit economics	End-to-end dated cost is compared under the required quality and security gates.
Agentic justification	Bounded agency has measured benefit over a simpler deterministic path.
Rollback	Source, index, configuration, and release state can be restored safely.

Use the AWS Well-Architected lenses as review guides

Use the Generative AI and Agentic AI lenses to review the workload evidence behind your assessment decisions. They are architecture guidance, not a certification, service guarantee, or replacement for workload-specific tests.

Open the AWS Well-Architected Agentic AI Lens

Ready to begin?

Set aside 75-95 minutes for 96 scenario-based decisions. You may exit and return before submitting.

Prerequisites

RAG Mental Models and Boundaries

Minimal Cited RAG on AWS

Corpus Engineering and Ingestion

Embeddings and ANN Mechanics

Retrieval Quality Engineering

Evaluation and Retrieval Debugging

AWS Retrieval Store Selection

Protecting RAG

Reliability, Observability, and Change Control

Cost, Latency, and Sustainability Optimization

From Static to Agentic RAG

AgentCore, MCP, and Governed Tools

Adaptive, Hierarchical, Graph, and Multimodal Retrieval

Tier

Focus

Questions

Associate-style

Bounded build, protect, operate, and optimize decisions

Professional-style

Architecture trade-offs, change control, and organizational complexity

Specialty-style

Ambiguous, high-consequence security, governance, and incident scenarios

Every tier

All seven RAG and agentic domains, with 5/5/5/5/4/4/4 domain coverage

Decision area

Evidence to review

Workload and answer contract

Allowed sources, citations, abstention, escalation, and user decision are explicit.

Governed corpus

Ownership, provenance, ACL propagation, freshness, removal, and quarantine are tested.

Strong retrieval baseline

Lexical, dense or hybrid baseline is measured on representative queries.

Evaluation

Golden sets, retrieval and answer metrics, judge calibration, and failure slices are versioned.

Security and tenancy

Every boundary reauthorises; retrieved content and citations are untrusted.

Reliability and runbook

Trace versions, bounded retries, degraded mode, canary, and rollback are rehearsed.

Unit economics

End-to-end dated cost is compared under the required quality and security gates.

Agentic justification

Bounded agency has measured benefit over a simpler deterministic path.

Rollback

Source, index, configuration, and release state can be restored safely.

AWS RAG and Agentic Systems Assessment

Knowledge Tree

Prerequisites

Assessment Overview

Result Snapshot

Non-graded capstone readiness rubric

Use the AWS Well-Architected lenses as review guides

Ready to begin?