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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques - Select appropriate Claude models based on trade-offs - Design system prompts, templates, and guardrails |
| Topic 2: Evaluation, Testing & Optimization | 16% | - Test accuracy, reliability, latency, and cost - Define evaluation metrics and success criteria - Optimize performance, prompting, and model selection - Implement iterative improvement pipelines |
| Topic 3: Integration | 19% | - Implement Model Context Protocol (MCP) integrations - Integrate with data pipelines and RAG systems - Design authentication, authorization, and observability - Integrate Claude with enterprise systems, APIs, and tools |
| Topic 4: Developer Productivity & Operational Enablement | 7% | - Configure Claude tools and environments for teams - Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution |
| Topic 5: Stakeholder Communication & Lifecycle Management | 14% | - Communicate architectural decisions and trade-offs - Manage stakeholder feedback and expectation alignment - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases |
| Topic 6: Governance, Safety & Risk Management | 14% | - Manage data privacy and security compliance - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls |
| Topic 7: Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Design multi-agent systems and orchestration strategies - Align solutions to business value pillars - Design end-to-end architectures and feedback loops - Translate business problems into Claude-based AI solutions |
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NEW QUESTION # 28
You are diagnosing a Claude Code session whose subagent uses 50,000 tokens of context before the engineer types a single message.
Which root cause is most likely?
Answer: B
Explanation:
Claude Code constructs the model context before the first user message. That initial context can include the system prompt, project instructions, subagent configuration, MCP server instructions, and tool definitions expressed as JSON schemas. When numerous MCP servers expose many tools and Tool Search is disabled, those schemas must be loaded upfront. A large collection of verbose tool descriptions and parameter schemas can therefore consume tens of thousands of tokens before the engineer enters any text.
Tool Search is specifically designed to prevent this form of context inflation. With Tool Search enabled, Claude initially receives only server instructions and compact tool-identification information; full tool schemas are deferred until a relevant tool is discovered and required. Anthropic consequently describes Tool Search as the mechanism for keeping MCP context consumption low as integrations scale.
Keyboard layouts, font rendering, and display scaling operate outside the language-model context and cannot generate API input tokens. Likewise, model behavior does not silently reserve 50,000 tokens for personal preferences. The diagnostic action is to inspect configured MCP servers and tool counts, determine whether Tool Search has been disabled, and remove or defer unnecessary definitions.
Study Guide references/topics: [MCP Tool Search and context scaling](https://docs.anthropic.com/en/docs
/claude-code/mcp); MCP server configuration; subagent context budgeting; tool-schema optimization.
NEW QUESTION # 29
You are compiling continuity practices that span the deployment lifecycle.
Which two practices belong on the list? (Select two.)
Each correct answer presents a complete solution.
Answer: A,E
Explanation:
Lifecycle continuity depends on preserving validated knowledge and feeding operational learning back into subsequent phases. Carrying the evaluation framework and reference set forward, as stated in Option B, creates a stable baseline across prompt changes, model migrations, retrieval adjustments, and architectural revisions. Rebuilding the evaluation system each time would undermine longitudinal comparison because changes in the test framework could be mistaken for changes in solution performance.
Option D establishes the second essential continuity mechanism: lessons from discovery, design, implementation, deployment, and production monitoring become explicit inputs to the next phase. This closes the feedback loop and prevents recurring defects, invalid assumptions, and operational findings from being lost at organizational handoffs.
Option A is overly mechanical. Stakeholders should receive communications relevant to their responsibilities and decision rights, not indiscriminate notifications at every transition. Option C confuses comprehensive archival with lifecycle continuity; retention must follow business, regulatory, security, and records- management requirements rather than an unconditional "archive everything" policy. Option E is directly contrary to iterative architecture. Decisions should be documented and governed, but material evidence or changed requirements must be allowed to reopen them.
Study Guide references/topics: Lifecycle phases; evaluation continuity; reference datasets; feedback loops; lessons learned; decision records; iterative architecture governance.
NEW QUESTION # 30
During an architectural review, the security team identifies a risk that adversarial content injected into retrieved documents could manipulate the model's behavior.
Which mitigation most directly addresses this threat?
Answer: B
Explanation:
The scenario describes indirect prompt injection: malicious instructions are embedded in third-party content that Claude processes as retrieved evidence. Option A addresses the threat at the appropriate boundary by classifying retrieved material as untrusted, screening it before use, and validating the model's output before it can influence downstream actions.
Anthropic recommends placing untrusted third-party content in clearly identified tool-result structures, explicitly instructing Claude that retrieved content cannot override system or user instructions, screening tool output for injection attempts, and applying output validation. Mitigate Jailbreaks and Prompt Injections Citations in Option B improve factual traceability but do not prevent embedded instructions from attempting to redirect model behavior. An outbound destination allow-list in Option C limits the damage that a compromised agent can cause through tool calls, but it does not directly detect or neutralize the injected content. Option D is valuable for detecting behavioral regression after a model change, not for controlling a specific malicious document during runtime.
A production implementation should combine content provenance, structural separation, injection screening, least-privilege tools, output-schema validation, and human confirmation for consequential actions.
Study Guide references/topics: Indirect prompt injection; untrusted retrieved content; input classification; output validation; layered runtime guardrails.
NEW QUESTION # 31
You are identifying signals that a deployment should re-enter design rather than continue iterating in place.
Which signal most directly indicates the need for a new design cycle?
Answer: B
NEW QUESTION # 32
You are responding to an adversarial input pattern in which users include text claiming admin authority and instructing the model to bypass safety restrictions.
Which combination of controls most effectively mitigates this attack pattern?
Answer: A
Explanation:
Self-declared administrative authority inside a prompt is untrusted data, not authenticated identity or authorization evidence. Option B correctly combines independent controls across the model, runtime, authorization, and monitoring layers.
Prompt instructions establish that user content cannot override system policy. Runtime classifiers detect known and generalized attempts to bypass controls. Tool permissions are enforced outside the model and must derive from authenticated identity, role, and approved scope-not statements contained in the conversation. Audit logging records the actor, attempted override, classifier result, tool requests, and final disposition for investigation and control improvement.
Anthropic recommends input screening, hardened system prompts, safe handling of untrusted content, narrowly scoped permissions, red-team testing, and continuous monitoring. Mitigate Jailbreaks and Prompt Injections Option A depends entirely on model behavior and provides no containment if the model fails. Option C removes the protections the attacker is attempting to defeat. Option D commits a fundamental authorization error by accepting an unverified claim as privilege elevation.
The strongest design also rate-limits repeated attempts, escalates suspicious activity, validates outputs, and requires human confirmation for consequential actions.
Study Guide references/topics: Direct prompt injection; untrusted user content; runtime classifiers; non-model authorization; scoped tools; audit logging; defense in depth.
NEW QUESTION # 33
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