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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developer Productivity & Operational Enablement | 7% | - Improve developer workflows with AI-assisted tooling - Configure Claude tools and environments for teams - Support debugging, monitoring, and operational resolution |
| Topic 2: Solution Design & Architecture | 17% | - Design multi-agent systems and orchestration strategies - Align solutions to business value pillars - Select architectural patterns: workflow, agentic, augmented LLM - Translate business problems into Claude-based AI solutions - Design end-to-end architectures and feedback loops |
| Topic 3: Integration | 19% | - Implement Model Context Protocol (MCP) integrations - Integrate with data pipelines and RAG systems - Integrate Claude with enterprise systems, APIs, and tools - Design authentication, authorization, and observability |
| Topic 4: Governance, Safety & Risk Management | 14% | - Implement guardrails and safety controls - Manage data privacy and security compliance - Address ethical AI considerations and bias mitigation - Ensure regulatory compliance (GDPR, HIPAA, etc.) |
| Topic 5: Stakeholder Communication & Lifecycle Management | 14% | - Manage stakeholder feedback and expectation alignment - Conduct structured discovery and requirement gathering - Communicate architectural decisions and trade-offs - Document architectures and support full lifecycle phases |
| Topic 6: Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Select appropriate Claude models based on trade-offs - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques |
| Topic 7: Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Test accuracy, reliability, latency, and cost - Define evaluation metrics and success criteria - Optimize performance, prompting, and model selection |
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NEW QUESTION # 71
A senior architect is managing stakeholder expectations for a Claude-based reporting assistant midway through development. Stakeholders have escalating concerns about response latency.
Which two actions most directly address stakeholder expectation alignment in this situation? (Select two.)
Answer: B,D
Explanation:
Expectation alignment begins with measured evidence. Option A gives stakeholders the median experience through p50 latency and the slower-tail experience through p95, then compares both with the agreed SLA.
The measurements should reflect representative input sizes, concurrency, retrieval activity, tool calls, geographic routing, and cache conditions.
If the target remains infeasible after reasonable optimization, Option E establishes the correct governance response. The architect should collaboratively revise the SLA, scope, cost envelope, model choice, or user- experience design rather than silently accepting a breach or making unilateral changes. Any revision should document the business impact and accepted trade-offs.
Option B commits all resources before the problem's severity, causes, and business priority have been established. Option C wrongly treats latency as uncontrollable; model selection, prompt length, retrieval depth, caching, streaming, and architecture can materially affect it. Option D introduces an unvalidated provider and could create quality, security, compliance, and integration regressions.
Expectation management is not merely communicating bad news. It requires transparent measurements, credible optimization options, explicit trade-offs, and jointly approved commitments that the production architecture can actually satisfy.
Study Guide references/topics: SLA alignment; p50 and p95 latency; stakeholder negotiation; evidence-based communication; architectural trade-offs; production constraints.
NEW QUESTION # 72
You are comparing patterns for a batch document-classification job that follows fixed steps: extract metadata, classify, summarize, and persist.
Which pattern is the best fit and why?
Answer: D
Explanation:
A workflow is appropriate when processing stages and transitions are known in advance. Metadata extraction, classification, summarization, and persistence can be represented as deterministic nodes with explicit schemas, validation rules, retry policies, and failure handling. This provides predictable execution, cost, observability, and testability without paying for repeated model-driven planning. Anthropic's Building Effective AI Agents distinguishes workflows, where predefined code paths control execution, from agents, where the model dynamically determines its process. Option A reaches the correct pattern for an incorrect reason: workflows and agents can both invoke tools. Options B and C introduce unnecessary autonomy and make unsupported claims about consistency or universal accuracy.
Study Guide references/topics: Workflow versus agentic patterns; deterministic orchestration; batch processing; structured outputs; predictable cost; error handling.
NEW QUESTION # 73
You are auditing a procurement-assistant agent whose defined responsibility is to draft purchase requests for review.
For each tool currently configured on the agent, select yes if the tool should remain after a least-privilege audit. Otherwise, select no if it should be removed.
Answer:
Explanation:
Explanation:
Yes, No, Yes, No, Yes
Least privilege requires each retained tool to be necessary for the agent's assigned responsibility and restricted to the minimum action level needed. Catalog lookup is appropriate because vendor and SKU information is needed to prepare an accurate request. The policy-document tool is similarly justified because the agent must ground the draft in applicable procurement rules. The draft-write tool is also aligned because it writes only to a review queue, preserving the required human approval boundary.
The inbox-send tool exceeds the stated responsibility. Sending a request directly to a vendor converts a drafting assistant into an externally acting purchasing agent and bypasses the review stage. The production- database administrator tool is unrelated to procurement and introduces unnecessary destructive capability, privilege-escalation exposure, and potential data-access risk.
Anthropic's prompt-injection guidance recommends limiting access to sensitive data and actions and applying least privilege so that a compromised agent can cause minimal damage. Claude Code also supports tool- specific allow, ask, and deny rules that are enforced outside the model. Prompt-Injection Mitigation , Claude Code Permissions Study Guide references/topics: Capability-bloat auditing; least privilege; tool scoping; human review gates; separation of drafting and execution authority.
NEW QUESTION # 74
A Claude architect needs to ensure that a security-hardening flag cannot be disabled by any individual engineer after it is set.
Which configuration scope correctly enforces this requirement?
Answer: C
Explanation:
Managed configuration is designed for organization-wide security and compliance controls that individual users and repositories must not override. Applying the hardening flag centrally establishes an administrative policy boundary and prevents engineers from disabling the control through user, project, or local settings.
Anthropic identifies managed scope as the appropriate location for security policies, non-overridable compliance requirements, and standardized configurations deployed by IT or DevOps. Managed values ordinarily take precedence over command-line arguments and every user-controlled settings scope. Claude Code Settings Option A depends on every engineer maintaining the setting and allows the user to edit or delete it. Option B affects only pipeline execution and does not protect local, interactive, IDE, or other execution paths. Option D standardizes the setting within the repository but does not make it tamper-resistant; contributors with repository write access could alter the file, use higher-precedence local settings where permitted, or operate outside the repository configuration.
The implementation should also verify active policy delivery, monitor configuration-change events, and test that startup or execution fails safely if the managed setting is absent or invalid. Central definition without enforcement and verification would not fully satisfy the requirement.
Study Guide references/topics: Managed settings; non-overridable controls; enterprise hardening; policy enforcement; configuration governance; defense against local override.
NEW QUESTION # 75
You are sequencing decomposed components in an invoice-processing pipeline.
For each of the decomposed components, select the execution layer it belongs to: "Pre-Processing," "Model Stage," or "Post-Processing."
Answer:
Explanation:
Explanation:
Model Stage, Post-Processing, Post-Processing, Model Stage, Pre-Processing, Pre-Processing Pre-processing prepares source material before Claude receives it. Optical character recognition converts scanned invoice images into machine-readable text. Because PII redaction is explicitly performed before model invocation, it also belongs in pre-processing. Additional activities at this layer can include file validation, malware scanning, normalization, page separation, and metadata extraction.
The model stage contains tasks requiring Claude's language-understanding capability. Classifying the invoice type involves interpreting textual and contextual features, while extracting structured invoice fields requires mapping unstructured content into defined business attributes.
Post-processing verifies and commits the model's result. Schema validation confirms that required fields exist, data types are correct, enumerated values are permitted, and structural constraints are satisfied.
Persistence must occur only after validation and any required human review because the system of record should not receive malformed or unapproved model output.
This decomposition separates probabilistic inference from deterministic processing. OCR, redaction, validation, and persistence do not need to be delegated to Claude when conventional components can execute them more predictably. The resulting architecture reduces model workload, strengthens privacy controls, improves auditability, and prevents unvalidated output from directly changing authoritative records.
Study Guide references/topics: Task decomposition; pre-processing; model inference; post-processing; deterministic validation; privacy-by-design; system-of-record protection.
NEW QUESTION # 76
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