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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Context Management & Reliability	15%	- Long-context optimization 1. Context window management 2. Reliability and retry strategies
Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. MCP client/server integration patterns 2. Tool schema design
Claude Code Workflows & Configuration	20%	- Claude Code operational patterns 1. CI/CD and workflow integration 2. Plan mode vs execution mode
Prompt Engineering & Structured Output	20%	- Reliable structured generation 1. Schema-guided outputs 2. Prompt patterns for agents
Agentic Architecture & Orchestration	27%	- System orchestration patterns 1. Task decomposition strategies 2. Hub-and-spoke agent systems - Agentic loop design and execution lifecycle 1. Multi-agent coordination and delegation patterns 2. Tool use decision flow (tool_use vs end_turn)

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q97-Q102):

NEW QUESTION # 97
How should subagents handle transient failures before propagating errors to the coordinator?

A. Silently drop the failed query and proceed with other data sources without notifying the coordinator.
B. Immediately propagate all failures to ensure the coordinator maintains full observability.
C. Implement local error recovery within the subagent and only propagate errors that cannot be resolved locally.
D. Use a PreToolUse hook to block the error from reaching the coordinator entirely.

Answer: C

Explanation:
Subagents should attempt to implement local recovery for transient failures first. If they cannot resolve the issue, they should then propagate the error to the coordinator along with what was attempted and any partial results obtained.

NEW QUESTION # 98
Why is it considered an architectural anti-pattern to configure the exact same Claude session to review its newly generated code?

A. It exceeds the maximum token limit of the context window during the generation phase.
B. Tools invoked during the first pass are permanently locked and cannot be reused in the review pass.
C. The Message Batches API does not support synchronous self-review workflows.
D. The reviewer retains the reasoning context and assumptions from the generation phase, creating confirmation bias.

Answer: D

Explanation:
Same-session self-review is an anti-pattern because the model retains the context and reasoning from the generation phase, leading to a blind spot or confirmation bias. Independent review instances with fresh contexts are much more effective at objectively evaluating code.

NEW QUESTION # 99
When processing a batch of 10,000 documents via the Message Batches API, 45 requests fail due to context window limits. What is the recommended failure handling strategy?

A. Resubmit only the failed documents (identified by their custom_id) after modifying them, such as chunking oversized documents.
B. Resubmit the entire batch of 10,000 requests to guarantee correct ordering.
C. Ignore the failures, as the API inherently guarantees a 99% success SLA.
D. Switch immediately to the synchronous API to retry the failed documents.

Answer: A

Explanation:
Batch failures should be handled by identifying the specific failed requests using their custom_id, modifying them appropriately (e.g., chunking oversized documents to fit context limits), and resubmitting only those failed items.

NEW QUESTION # 100
A well-designed structured error response for an MCP tool should include which of the following standard fields? Choose 2 correct answers.

A. system_prompt_override
B. temperature_adjustment
C. isError
D. errorCategory

Answer: C,D

Explanation:
Structured error responses in MCP rely on standard fields like isError, errorCategory, isRetryable, and context to give the agent a clear path for recovery. They do not alter underlying model parameters like temperature or the system prompt.

NEW QUESTION # 101
Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document analysis agent frequently identifies gaps - for instance, noting "the retrieved sources discuss API authentication but lack details on token refresh patterns" - but under the current strict pipeline, this insight isn't actionable since search has already completed. What's the most effective architectural change?

A. Have the analysis agent report specific gaps to the coordinator, which triggers targeted searches and re-invokes analysis until sufficient.
B. Have the synthesis agent attach confidence scores to each section and flag areas with insufficient coverage for manual review.
C. Have the coordinator review analysis output for gap indicators and re-invoke search with gap- informed queries when gaps are detected.
D. Add a research planning agent before the search phase that decomposes topics into specific sub- questions.

Answer: C

Explanation:
Making the coordinator gap-aware allows it to dynamically re-delegate targeted searches based on analysis feedback. This iterative loop ensures subtopics with insufficient coverage are addressed before synthesis, improving the depth and completeness of final reports.

NEW QUESTION # 102
......

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