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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Models, Prompting & Context Engineering | 13% | - Design system prompts, templates, and guardrails - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques - Select appropriate Claude models based on trade-offs |
| Topic 2: 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 3: Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Optimize performance, prompting, and model selection - Test accuracy, reliability, latency, and cost - Define evaluation metrics and success criteria |
| Topic 4: Governance, Safety & Risk Management | 14% | - Manage data privacy and security compliance - Address ethical AI considerations and bias mitigation - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Implement guardrails and safety controls |
| Topic 5: Solution Design & Architecture | 17% | - Translate business problems into Claude-based AI solutions - Design multi-agent systems and orchestration strategies - Select architectural patterns: workflow, agentic, augmented LLM - Design end-to-end architectures and feedback loops - Align solutions to business value pillars |
| Topic 6: Integration | 19% | - Integrate Claude with enterprise systems, APIs, and tools - Integrate with data pipelines and RAG systems - Design authentication, authorization, and observability - Implement Model Context Protocol (MCP) integrations |
| Topic 7: Stakeholder Communication & Lifecycle Management | 14% | - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases - Communicate architectural decisions and trade-offs - Manage stakeholder feedback and expectation alignment |
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NEW QUESTION # 24
An operations engineer reports that a Claude-based pipeline began returning malformed JSON responses after a scheduled maintenance window, causing downstream processing failures.
Which two investigative steps most directly isolate the root cause? (Select two.)
Answer: A,C
Explanation:
The strongest investigation combines configuration comparison with controlled replay. Comparing the current system prompt and output-schema settings against the last known good version identifies maintenance-related configuration drift. Relevant differences include removed formatting instructions, altered schema requirements, incompatible parser expectations, changed stop sequences, or a disabled structured-output mechanism.
Replaying known premaintenance requests against the current configuration creates a controlled test set.
Inspecting the raw response before parsing then establishes whether malformed JSON originates in model generation or is introduced by streaming assembly, transport handling, post-processing, or the downstream parser. If the raw response is valid but the pipeline rejects it, the fault lies after generation. If the raw response is malformed, the prompt, schema enforcement, stop reason, and request configuration become the primary investigation targets.
Clearing the prompt cache assumes cache corruption without evidence. Changing model tiers introduces another variable and weakens causal attribution. Raising `max_tokens` is appropriate only when raw responses show truncation and the returned stop reason indicates the token limit was reached; it is not the most direct initial test.
Study Guide references/topics: [Increasing output consistency](https://docs.anthropic.com/en/docs/test-and- evaluate/strengthen-guardrails/increase-consistency); [agent evaluation methodology] (https://www.anthropic.
com/engineering/demystifying-evals-for-ai-agents); regression replay; last-known-good comparison; raw- output inspection.
NEW QUESTION # 25
A team manager wants all engineers working on the same repository to share identical MCP server definitions without manual synchronization.
Which configuration approach satisfies this requirement?
Answer: A
Explanation:
Project-scoped configuration is the appropriate mechanism for repository-specific settings that must remain consistent across collaborators. Claude Code recognizes `.claude/settings.json` as the shared project settings file and `.mcp.json` as the project-level MCP server configuration. When these files are committed to source control, every engineer receives the same definitions through the repository's normal clone, pull, review, and versioning workflow.
Anthropic's scope hierarchy distinguishes project configuration from user and local configuration. Files under the user scope apply to one engineer and are not automatically shared. Operating-system environment variables are also workstation-specific and require separate configuration management. Managed settings can be distributed centrally, but they are primarily intended for organization-wide security policies and controls that must be enforced by administrators; they are less appropriate when the configuration belongs specifically to one repository.
Shared configuration should contain server definitions and environment-variable references, but not plaintext credentials. Each engineer can resolve required secrets through an approved local secret mechanism while retaining identical server names, transports, arguments, and non-sensitive settings. Project MCP servers also remain subject to workspace trust and approval controls before execution.
Study Guide references/topics: [Claude Code configuration scopes](https://docs.anthropic.com/en/docs
/claude-code/settings); project-scoped MCP servers; `.mcp.json`; repository-based operational standardization; secure secret separation.
NEW QUESTION # 26
You are supporting a team whose Claude Code sessions consistently load 60 or more MCP tools from many servers, exhausting context budget before the session begins.
Which adjustment most directly addresses this issue without removing capability?
Answer: A
Explanation:
Tool Search applies progressive discovery to a large tool catalog. Instead of inserting every MCP tool's complete JSON schema and description into the initial context, Claude receives limited discovery information and loads the full definitions only when a task requires them. This preserves the available context for instructions, repository content, conversation history, and tool results without removing the underlying capabilities.
Anthropic's Claude Code documentation states that Tool Search keeps MCP context usage low by deferring tool definitions until Claude needs them. Only the tools actually selected enter the working context. Claude Code MCP Tool Search Anthropic's advanced-tool-use guidance also describes conventional multi-server configurations consuming tens of thousands of tokens before work begins, while on-demand discovery substantially reduces that overhead. Advanced Tool Use Option B solves context pressure by destroying required functionality. Option C worsens the constraint by adding more prompt tokens. Option D expands the exact source of context bloat.
The team should also improve server and tool descriptions so discovery returns the correct tools, monitor tool- selection quality, and keep frequently required foundational tools non-deferred only when evaluations justify the additional context cost.
Study Guide references/topics: MCP scaling; Tool Search; progressive discovery; deferred schemas; context- budget management; tool-selection accuracy.
NEW QUESTION # 27
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: C
Explanation:
A new design cycle is warranted when the required change crosses an architectural boundary. Option D explicitly states that the existing architecture cannot satisfy the new requirements without redistributing component responsibilities or altering core contracts. Such changes may affect service ownership, orchestration, data flows, integration interfaces, security boundaries, failure handling, evaluation strategy, and operational accountability. They therefore require renewed discovery, impact analysis, design review, stakeholder approval, and regression planning.
Options A, B, and C are localized maintenance activities. Clarifying a runbook improves operational documentation but does not change the deployed system's structure. Adjusting an alert threshold is a controlled operational tuning activity, provided the change remains within established monitoring policy.
Editing customer-facing copy is similarly confined to the presentation or content layer. None of these changes inherently invalidates component contracts or architectural assumptions.
An architect should distinguish between iteration within an approved design and evidence that the design itself no longer supports the required outcome. Re-entering design for every minor adjustment creates unnecessary governance overhead; continuing local iteration after architectural assumptions have failed creates unmanaged technical and operational risk. Option D is the only signal that establishes a structural incompatibility.
Study Guide references/topics: Lifecycle feedback loops; design re-entry criteria; architectural significance; component responsibilities; interface contracts; controlled operational iteration.
NEW QUESTION # 28
You are assessing data-exfiltration risk in a Claude-based assistant that has tools for both internal-document retrieval and outbound HTTP calls.
Which scenario most directly indicates a data-exfiltration risk?
Answer: B
Explanation:
Option C describes indirect prompt injection combined with an outbound transmission channel. The attacker embeds instructions in content that the assistant treats as evidence. If the model follows those instructions, it can retrieve sensitive information and transmit it through the HTTP tool to an attacker-controlled destination.
Authorization to read a document does not automatically authorize external disclosure. The architecture should treat retrieved content as untrusted, restrict outbound domains, separate data access from external communication, apply least-privilege tool permissions, and require approval for sensitive transmissions.
Options A, B, and D describe normal authorized behavior without evidence of unintended disclosure.
Anthropic specifically warns that combining untrusted content, sensitive data, and network tools creates exfiltration risk. Web-fetch security guidance ; handling untrusted tool results
NEW QUESTION # 29
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