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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Agentic Architecture & Orchestration	27%	- Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management - Agent design patterns 1. Agent loops and control flow 2. Hub-and-spoke multi-agent systems
Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. Tools, resources, and prompts integration 2. MCP server and client setup - Tool definition and best practices 1. Tool descriptions and selection logic 2. Error handling and validation
Context Management & Responsible AI	15%	- Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization - Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles
Prompt Engineering & Structured Output	20%	- Advanced prompting techniques 1. System prompts and role framing 2. Few-shot and chain-of-thought prompting - Structured data generation 1. Output validation and reliability 2. JSON schema enforcement
Claude Code Configuration & Workflows	20%	- Configuration files and structure 1. Custom commands and workflows 2. CLAUDE.md and rules system - CI/CD and development integration 1. Plan mode vs direct execution 2. Pipeline automation and review

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q77-Q82):

NEW QUESTION # 77
When designing JSON schemas and interfaces for an agent's tools, which of the following practices represent recommended tool design principles? Choose 2 correct answers.

A. Combining all related backend API endpoints into a single monolithic tool to simplify the allowedTools array.
B. Expanding tool descriptions to explicitly document what happens with empty inputs or invalid boundary data.
C. Splitting a generic document analysis tool into distinct, purpose-built extract_data and verify _ claim tools.
D. Keeping tool descriptions under 10 words to minimize context window token consumption.

Answer: B,C

Explanation:
Recommended tool design principles include providing highly detailed descriptions (including edge cases and empty input handling) and splitting generic or monolithic tools into specialized, purpose-specific tools with clear input/output contracts. Minimal descriptions and monolithic tools severely degrade the LLM's ability to select and configure tools reliably.

NEW QUESTION # 78
Your agent achieves a low first-contact resolution rate because it attempts to autonomously handle complex policy exceptions, while simultaneously escalating straightforward tasks. What is the most effective approach to improve this escalation calibration?

A. Deploy a separate classifier model to predict which requests need escalation before the main agent begins.
B. Implement sentiment analysis to detect frustration levels and automatically escalate when they are high.
C. Require the agent to self-report a confidence score (1-10) and route to humans when confidence falls below 7.
D. Add explicit escalation criteria to the system prompt with few-shot examples demonstrating when to escalate versus resolve autonomously.

Answer: D

Explanation:
Adding explicit escalation criteria with few-shot examples directly addresses unclear decision boundaries. Self-reported confidence scores are poorly calibrated, sentiment analysis does not measure complexity, and a separate classifier is over-engineered before trying prompt optimization.

NEW QUESTION # 79
When a get_customer tool lookup returns multiple potential matches for a user, what is the required pattern for ambiguity resolution?

A. Instruct the agent to ask the user for additional identifiers for clarification.
B. Return a structured error payload and permanently terminate the loop.
C. Select the most recently active account using a heuristic.
D. Automatically escalate the session to a human supervisor.

Answer: A

Explanation:
When multiple matches are found, relying on heuristics to guess the correct user can lead to security and billing risks. The correct reliability decision is instructing the agent to ask the user for additional clarifying identifiers rather than using heuristic selection.

NEW QUESTION # 80
Your order management system requires tools for three distinct operations: issuing refunds (requires amount and reason), canceling orders (requires reason), and requesting reshipments (requires shipping address). Each operation shares an order_id parameter but has different additional requirements. You notice during testing that with your current unified tool design, the agent frequently omits required parameters or includes irrelevant ones. What design change will most effectively improve parameter accuracy?

A. Keep one unified tool with all parameters marked optional, but add detailed few-shot examples in the system prompt showing correct parameter combinations for each operation type.
B. Keep one unified tool with a nested operation_details object parameter whose internal structure varies by operation type, documented in the tool description.
C. Keep one unified tool but add JSON Schema if-then-else conditionals to enforce that parameters like amount are required only when the operation type is "refund".
D. Split into three separate tools (issue_refund, cancel_order, request_reshipment), each defining only the parameters required for that specific operation.

Answer: D

Explanation:
Separate tools with operation-specific schemas make the required parameters clear and reduce ambiguity during tool selection. This improves parameter accuracy because each tool exposes only the fields needed for that exact action, such as refund amount and reason, cancellation reason, or reshipment address.

NEW QUESTION # 81
To prevent Claude from fabricating values when a requested field is absent from the source document, how should you engineer your JSON extraction schema?

A. Reduce the temperature to 0.0 to eliminate hallucinations.
B. Set the tool parameter tool_choice: " any".
C. Add a system prompt instruction to "never fabricate data".
D. Design the field with "type" : [" string ,"null"] and mark it as optional/nullable.

Answer: D

Explanation:
If a field is strictly required but the data doesn't exist, the model may fabricate a value to satisfy the schema constraint. Making the field optional or nullable (e.g., allowing 'null') provides a valid escape hatch so the model can comply with the schema without hallucinating.

NEW QUESTION # 82
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