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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering & Structured Output	20%	- Tool-augmented prompting 1. Tool-use driven reasoning patterns 2. Validation and retry loops - Instruction design 1. Deterministic output formatting (e.g., JSON) 2. Few-shot prompting strategies
Topic 2: Context Management & Reliability	15%	- System reliability 1. Error propagation handling 2. Escalation strategies (fail vs retry vs human) - Context window optimization 1. "Lost in the middle" mitigation strategies 2. Managing long conversation degradation
Topic 3: Claude Code Configuration & Workflows	20%	- Claude Code configuration 1. Project-level vs user-level configuration 2. CLAUDE.md hierarchy and scoping - CI/CD and workflow integration 1. Automated pipeline integration 2. Command vs plan mode usage
Topic 4: Agentic Architecture & Orchestration	27%	- Multi-agent orchestration 1. Parallel task decomposition 2. Coordinator / sub-agent patterns - Agentic loops & lifecycle design 1. Stop reason handling and control flow 2. Tool use loop execution (request → tool_use → result → next step)
Topic 5: Tool Design & MCP Integration	18%	- Tool interface design 1. Clear tool descriptions and boundaries 2. Structured tool input/output schemas - Model Context Protocol (MCP) 1. Tool selection and routing strategies 2. MCP server integration patterns

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q30-Q35):

NEW QUESTION # 30
A quicktechie.com developer is designing a schema to extract contract terms. Source documents sometimes lack a 'termination_fee' clause. How should the schema be designed to prevent the model from fabricating a value?

A. Design the field as optional or nullable, such as ["number", "null"].
B. Add a capitalized warning in the system prompt to avoid hallucination.
C. Design the field as strictly required and let the model infer a standard default.
D. Set tool_choice. :"auto" so the model can skip the tool entirely.

Answer: A

Explanation:
Designing schema fields as optional (nullable) when source documents may not contain the information prevents the model from fabricating values to satisfy required fields. If a field is strictly required but the data does not exist, the model may hallucinate a value.

NEW QUESTION # 31
A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

A. Implement sliding window context that retains the most recent 30 turns.
B. Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.
C. Summarize earlier turns into a narrative description, preserving full message history only for the active issue.
D. Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.

Answer: D

Explanation:
Persisting structured issue data separately allows the agent to reference critical details (like order IDs, amounts, and statuses) without relying on the full conversation history. This preserves the ability to address past issues even as token limits constrain the active conversational context.

NEW QUESTION # 32
Which configuration ensures Claude will strictly invoke a specific data extraction tool rather than returning conversational text?

A. Option D
B. Option B
C. Option C
D. Option A

Answer: C

Explanation:
Forcing a specific tool with tool_choice: {"type": "tool", "name": "..."} ensures a particular tool runs, which guarantees schema compliance for strict extraction tasks. The 'any' setting forces the model to use a tool, but allows it to choose which one.

NEW QUESTION # 33
When the agent calls lookup_order and receives order details showing the item was purchased
45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?

A. The orchestration layer automatically routes to the next tool based on the order's status field.
B. The agent follows a pre-configured decision tree mapping order attributes to specific tool calls.
C. The agent executes the remaining steps in a tool sequence planned at the start of the request.
D. The order details are added to the conversation and the model reasons about which action to take.

Answer: D

Explanation:
In an agentic loop, tool results are returned to the model as context. The model then reasons over the updated information, such as the purchase age, and decides the next appropriate action based on the available tools, policies, and task objective.

NEW QUESTION # 34
A pull request modifies 14 files across a stock tracking module. When analyzing all files in a single pass, Claude provides superficial comments, misses obvious bugs, and produces contradictory feedback. How should you redesign the multi-pass review architecture to resolve this?

A. Require developers to manually split large PRs into smaller submissions of 3-4 files before triggering the automated review.
B. Switch to a higher-tier model with a larger context window so all 14 files receive adequate attention in one pass.
C. Run three independent single-pass reviews on the full PR and only flag issues that achieve consensus across at least two runs.
D. Split the review into focused passes: analyze each file individually for local issues, followed by a separate integration-focused pass for cross-file data flow.

Answer: D

Explanation:
Splitting reviews into focused passes directly addresses attention dilution when processing many files at once. Per-file analysis ensures consistent depth for local issues, while a separate integration pass catches cross-file architectural issues.

NEW QUESTION # 35
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