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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Context Management & Reliability	15%	- Token budget management and cost control - Idempotency, consistency and failure resilience - Context pruning and summarization strategies - Context window optimization and prioritization
Topic 2: Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Error handling and tool response formatting - Tool distribution and permission controls - Tool schema design and interface boundaries - MCP tool, resource and prompt implementation
Topic 3: Agentic Architecture & Orchestration	27%	- Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke
Topic 4: Claude Code Configuration & Workflows	20%	- Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions
Topic 5: Prompt Engineering & Structured Output	20%	- Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies - System prompt design and persona alignment

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CCAR-F aktueller Test, Test VCE-Dumps für Claude Certified Architect - Foundations

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Anthropic Claude Certified Architect - Foundations CCAR-F Prüfungsfragen mit Lösungen (Q170-Q175):

170. Frage
Your pipeline reviews every pull request using a single API call with a static prompt containing the diff and the full text of each changed file; unchanged files are not included. Reviews are posted asynchronously and do not block pull-request creation. Developers report that reviews consistently miss bugs involving cross-file interactions-for example, a pull request renames a function's parameters, but the review does not flag callers in other files that still use the old parameter names. Post-release analysis shows that cross-file bugs account for 35% of production incidents from reviewed pull requests. What is the most effective change to your review design?

A. Run parallel review passes for each changed file with its direct dependents included, and then aggregate and deduplicate the findings through a final summarization call.
B. Use static analysis to build a dependency graph of changed code, and then expand the prompt to include every file within two dependency hops of any changed file.
C. Redesign the review as a turn-limited agentic task in which the model can read files and search the codebase through tools, following references to verify cross-file findings.
D. Add chain-of-thought instructions asking the model to list all external references in the diff and then reason step by step about how each change might affect callers in other files.

Antwort: C

Begründung:
The failure is caused by missing evidence, not insufficient reasoning over the supplied evidence. A static prompt containing only changed files cannot reliably identify callers, configuration dependencies, generated interfaces, or indirect relationships located elsewhere in the repository. Asking Claude to reason more deeply cannot recover code that it was never given.
Option A converts the review into a bounded agentic loop. Claude can search for symbol references, read relevant callers, inspect type definitions, and follow newly discovered dependencies before validating a potential defect. Anthropic describes the Claude Code agentic loop as gathering context, taking action, verifying results, and repeating based on tool feedback. A turn limit preserves predictable cost and execution time.
Option C improves coverage but imposes an arbitrary two-hop boundary and depends on the accuracy of a precomputed graph, which may omit dynamic imports, reflection, generated code, configuration references, or language-specific call relationships. Option D creates duplicated context and aggregation complexity while still limiting each reviewer to predetermined dependents. Option B changes the reasoning instructions but supplies no mechanism for checking unchanged files. Tool-enabled, just-in-time retrieval is therefore the most adaptable and reliable architecture for cross-file review.

171. Frage
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer asks the agent to find all files in the monorepo that import the @company/auth package to understand how authentication is used across services. Which built-in tool is most appropriate for this task?

A. Glob, to find files with "auth" in their filename or path
B. Read, starting with package.json files to trace dependency declarations
C. Grep, to search for the import statement pattern across file contents
D. Bash, to execute find. -type d -name "*auth*" and explore matching directories

Antwort: C

Begründung:
Grep searches file contents across the repository, making it the correct tool for locating every source file that imports @company/auth. Glob searches filenames and paths, not code content.

172. Frage
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When analyzing complex legal cases that cite multiple precedents, the document-analysis subagent processes each precedent sequentially. A landmark case citing 12 precedents takes more than three minutes to analyze completely.
What is the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?

A. Create a recursive agent hierarchy where analysis agents subdivide work among child agents until reaching single-precedent granularity.
B. Have the coordinator spawn parallel document-analysis subagents, each handling a subset of precedents, and then aggregate the results before synthesis.
C. Enable the document-analysis subagent to spawn its own specialized subagents dynamically when it encounters cases with many citations.
D. Implement a message queue where precedent-analysis tasks are processed asynchronously by a pool of worker agents.

Antwort: B

Begründung:
Option C parallelizes independent work while retaining centralized orchestration. Each precedent can be analyzed without waiting for the previous precedent, so the coordinator can divide the 12 cases into balanced subsets and invoke several document-analysis subagents concurrently. It then receives their final outputs, records which precedents completed or failed, and aggregates the results before synthesis. Anthropic's multi- agent research architecture uses an orchestrator-worker pattern in which the lead agent creates specialized subagents that operate in parallel and return findings for consolidation. Keeping spawning decisions at the coordinator also produces a clearer execution trace for monitoring and debugging. A generic asynchronous queue, option A, adds infrastructure but does not define how results remain associated with the correct research task. Options B and D create nested or recursive delegation, making execution paths, permissions, failures, and token consumption harder to observe. Anthropic also cautions that multi-agent systems consume substantially more tokens than ordinary interactions, so unbounded recursive decomposition is inefficient.
Coordinator-controlled parallel fan-out followed by deterministic aggregation provides the latency improvement without sacrificing operational visibility.

173. Frage
Your agent is handling a billing dispute. After calling get_customer and lookup_order, it identifies that the dispute involves a promotional pricing error requiring manager approval - beyond the agent's authorization level. How should the workflow handle this mid-process escalation?

A. Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human.
B. Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction.
C. Call escalate_to_human passing only the customer's original message.
D. Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID.

Antwort: A

Begründung:
A structured handoff containing the customer details, order information, and the specific issue ensures the human agent has all relevant context to act immediately. This approach avoids delays or repeated clarification and preserves continuity when authority limits prevent the agent from completing the task.

174. Frage
After the web-search and document-analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings. What is the correct approach for providing the synthesis subagent with the information it needs?

A. Spawn the subagent with only a brief task description, relying on automatic context inheritance from the coordinator.
B. Include the complete findings from both subagents directly in the synthesis subagent's prompt.
C. Pass reference identifiers and configure the subagent with read access to a shared memory store where the other subagents deposited their results.
D. Provide the subagent with tool definitions that allow it to request outputs from the other subagents through callbacks.

Antwort: B

Begründung:
Option B follows the Claude Agent SDK's default context-isolation model. A newly spawned subagent receives a fresh context window and does not automatically inherit the parent's conversation history or previous tool results. The coordinator must therefore include the information required for synthesis directly in the spawning prompt, preferably using clearly separated structured sections for web findings, document findings, source metadata, conflicts, and unresolved questions.
Anthropic's official SDK subagent documentation states that the Agent tool's prompt string is the content passed from the parent to the new subagent. It specifically advises including required file paths, errors, and decisions in that prompt because parent context is not inherited.
Option A could work only if the application had deliberately implemented and authorized such a shared- memory architecture. The question establishes no such mechanism, and reference identifiers alone provide no evidence to the subagent. Option C introduces a callback protocol that is unnecessary for a normal handoff.
Option D is incorrect because subagent isolation expressly prevents automatic inheritance. "Complete findings" means the complete evidence needed for synthesis, not every intermediate search trace or irrelevant tool result.

175. Frage
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