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| Section | Weight | Objectives |
|---|---|---|
| Developer Productivity & Operational Enablement | 7% | - Developer enablement - Debugging and operational issue resolution - Claude tooling configuration for teams - AI-assisted developer workflows |
| Governance, Safety & Risk Management | 14% | - Ethical AI considerations - Regulatory and compliance requirements - AI safety and guardrails - Human-in-the-loop validation - Security and risk management |
| Integration | 19% | - RAG pipeline design
- Authentication and authorization analysis - Claude integration mechanisms
|
| Solution Design & Architecture | 17% | - End-to-end architecture design - Decomposition techniques for complex problem solving - Alignment with business value, cost, performance, and SLAs - Multi-agent systems and orchestration - Architectural patterns
|
| Claude Models, Prompting & Context Engineering | 13% | - Claude model selection and trade-offs - Guardrails - Prompt reuse and context engineering strategies - System prompts and prompt templates - Context window optimization |
| Stakeholder Communication & Lifecycle Management | 14% | - Discovery and requirements gathering - Service-level agreements - Architecture documentation - Stakeholder management - Solution lifecycle management - Communicating architectural decisions |
| Evaluation, Testing & Optimization | 16% | - System issue diagnosis - Cost and performance optimization - Production monitoring and optimization - Evaluation metrics and datasets - A/B testing - Evaluation framework design |
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NEW QUESTION # 60
You are selecting the documentation set that should accompany a Claude-based deployment at handoff.
Which set is most complete?
Answer: B
Explanation:
A complete handoff package must help different stakeholders understand, operate, troubleshoot, and evolve the Claude-based solution. An architecture overview explains the system's structure and responsibilities, while Architecture Decision Records document important decisions, alternatives, and trade-offs. Component- level diagrams provide implementation context without forcing maintainers to reconstruct dependencies from source code. Operational runbooks and an on-call playbook enable reliable incident detection, diagnosis, escalation, and recovery. Documenting known limitations is particularly important for Claude applications because model behavior can vary with prompts, context, input distribution, and external dependencies.
Options B, C, and D each provide only a partial or temporary understanding of the system. They would create knowledge gaps, increase operational risk, and make future maintenance dependent on individuals rather than durable organizational documentation.
NEW QUESTION # 61
You are integrating Claude Code into a workflow that runs against a production database.
Which guardrail design most directly preserves safety on data-modifying operations?
Answer: C
Explanation:
Option D applies three complementary controls. First, a read-only database credential creates an authorization boundary outside the model; prompt instructions alone cannot convert that credential into write access.
Second, restricting the subagent's tool list reduces capability exposure by preventing the agent from selecting unrelated or unnecessarily privileged operations. Third, explicit human confirmation creates a deliberate approval gate before any exceptional data-changing action is executed.
This is defense in depth. If Claude misinterprets a request or processes malicious instructions from untrusted content, the restricted credential and tool configuration limit the available action surface. Human review then protects operations with potentially irreversible production consequences. Logging and audit trails should remain enabled to record the actor, request, tool call, approval, affected records, and outcome.
Anthropic documents that Claude Code begins with read-only permissions in Manual mode and requests approval for actions that modify the environment. Its permission system also supports granular allow, ask, and deny controls for MCP tools and subagents. Claude Code Security , Configure Permissions Options A and B eliminate least privilege and approval boundaries. Option C removes essential detection and forensic evidence.
Study Guide references/topics: Production database safety; least privilege; read-only defaults; MCP permissions; subagent scoping; human confirmation; auditability.
NEW QUESTION # 62
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: D
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 # 63
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: D
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 # 64
You are selecting a model for a new production workload and must complete the upstream steps before testing candidate models empirically.
Which two steps must be completed BEFORE running a representative sample on a candidate model? (Select two.) Each correct answer presents part of the solution.
Answer: B,C
NEW QUESTION # 65
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