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| Section | Weight | Objectives |
|---|
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP)
- 1. Tool schema design
- 2. MCP client/server integration patterns
|
| Agentic Architecture & Orchestration | 27% | - System orchestration patterns
- 1. Task decomposition strategies
- 2. Hub-and-spoke agent systems
- Agentic loop design and execution lifecycle
- 1. Tool use decision flow (tool_use vs end_turn)
- 2. Multi-agent coordination and delegation patterns
|
| Context Management & Reliability | 15% | - Long-context optimization
- 1. Context window management
- 2. Reliability and retry strategies
|
| Prompt Engineering & Structured Output | 20% | - Reliable structured generation
- 1. Prompt patterns for agents
- 2. Schema-guided outputs
|
| Claude Code Workflows & Configuration | 20% | - Claude Code operational patterns
- 1. Plan mode vs execution mode
- 2. CI/CD and workflow integration
|
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CCA-F Latest Exam Pattern - CCA-F 100% Correct Answers
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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q70-Q75):
NEW QUESTION # 70
When applying few-shot prompting to improve output consistency for a task with ambiguous boundaries, what is the optimal number of examples to provide?
- A. 1 example.
- B. 8 to 10 examples.
- C. 15 or more examples.
- D. 2 to 4 examples.
Answer: D
Explanation:
The golden rule of few-shot prompting is to provide 2-4 examples. Fewer than 2 does not firmly establish a pattern, while providing more than 6 examples generally bloats the prompt without adding proportional benefit.
NEW QUESTION # 71
Which of the following patterns represent best practices for implementing handoffs and enforcement in multi-step workflows? Choose 2 correct answers.
- A. Relying on the agent's self-reported confidence score to trigger mid-process escalation decisions.
- B. Compiling structured handoff summaries containing root causes and recommended actions when escalating to human agents.
- C. Using prompt-based instructions to guarantee that a lookup_order tool is always called before
- D. process _ refund.
- E. Implementing programmatic prerequisites that explicitly block downstream tool calls until required verification steps are completed.
Answer: B,D
Explanation:
Structured handoff summaries for human escalations and programmatic prerequisites (gates) to block downstream tools are recommended best practices. Relying on prompt-based enforcement for guaranteed tool sequencing and using self-reported confidence scores for escalation are unreliable anti-patterns.
NEW QUESTION # 72
Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating. The team's goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates. Which approach achieves this guarantee?
- A. Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.
- B. Implement a pre-tool-use hook that counts tool invocations and terminates the loop with an automatic escalation once the agent reaches 80% of its max_turns limit.
- C. Add orchestration-layer code that checks the agent's outcome after each loop termination - if the loop ended without a completed resolution or escalation, programmatically call escalate_to_human with the accumulated conversation context and tool results.
- D. Split the workflow into two sequential agent invocations - a first agent gathers information via get_customer and lookup_order, then a second agent receives that data and handles process_refund or escalate_to_human, each with separate turn budgets.
Answer: C
Explanation:
A system prompt cannot guarantee compliance, and turn-count hooks may escalate prematurely.
An orchestration-level fallback runs regardless of why the agent loop stopped, ensuring every interaction ends in either resolution or human escalation.
NEW QUESTION # 73
What is the specific behavior of the Claude Agent API when the tool_choice parameter is set to "any"?
- A. The agent determines whether or not to use a tool at all, sometimes opting to return only natural language text.
- B. The agent is forced to call at least one tool, but it dynamically decides which tool to use based on the context.
- C. The agent randomly selects a tool to execute to improve creative variability in testing.
- D. The agent must execute all available tools in parallel.
Answer: B
Explanation:
Setting tool_choice to "any" tells Claude that it is strictly required to use a tool to respond, but it is free to choose whichever tool from the provided array best fits the prompt's context.
NEW QUESTION # 74
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 but add JSON Schema if-then-else conditionals to enforce that parameters like amount are required only when the operation type is "refund".
- B. 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.
- C. Keep one unified tool with a nested operation_details object parameter whose internal structure varies by operation type, documented in the tool description.
- 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 # 75
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