100% Pass Quiz 2026 CCA-F: Claude Certified Architect Foundations (CCA-F) Authoritative Free Practice

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Anthropic CCA-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q10-Q15):

NEW QUESTION # 10
When designing a human review workflow with limited reviewer capacity, which of the following items should be prioritized for human review? (Choose 2 correct answers)

A. Documents where the tool_choice was set to "auto".
B. Randomly selected extractions from documents categorized as having 99% historical accuracy.
C. Extractions involving ambiguous or contradictory source documents.
D. Extractions where the model reports low field-level confidence.

Answer: C,D

Explanation:
When reviewer capacity is limited, human review should be prioritized and routed toward items with low model confidence and tasks involving ambiguous, contradictory, or borderline source material where human judgment is strictly required.

NEW QUESTION # 11
When extracting financial summaries from unstructured documents, how can you design a self-correction validation flow directly within the extraction process?

A. Provide few-shot examples of flawless mathematical calculations.
B. Execute the extraction inside a PreToolUse hook.
C. Increase the max_tokens limit to give the model more space to reason about the math.
D. Extract a calculated_total alongside the stated_total to programmatically flag discrepancies.

Answer: D

Explanation:
Designing self-correction validation flows involves structuring the schema to extract cross-check values (like extracting a calculated total alongside a stated total). This allows downstream business logic to easily flag discrepancies and trigger a targeted retry loop.

NEW QUESTION # 12
An application frequently repeats the same background instructions in every API request. What is a potential downside?

A. Higher token usage and cost.
B. Lower reliability.
C. Increased hallucinations only.
D. Reduced model capability.

Answer: A

Explanation:
Repeated instructions consume additional input tokens on every request, increasing cost and latency. Consolidating stable guidance into reusable prompt templates can improve efficiency while preserving consistent model behavior.

NEW QUESTION # 13
A CI pipeline uses Claude to generate missing test cases for new pull requests. However, it frequently suggests test scenarios that are already covered elsewhere. How should this be resolved?

A. Provide existing test files in the context so the model can avoid suggesting duplicate scenarios.
B. Set the model's temperature to 0.0 to ensure deterministic test generation.
C. Use a PostToolUse hook to deduplicate the generated tests against the existing codebase.
D. Instruct the model to only generate tests for functions with zero coverage.

Answer: A

Explanation:
To prevent Claude from generating duplicate or overlapping tests, you must provide the existing test files in the context window. This ensures test generation adds net-new value and accurately supplements the current test suite.

NEW QUESTION # 14
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. Split into three separate tools (issue_refund, cancel_order, request_reshipment), each defining only the parameters required for that specific operation.
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. 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.

Answer: B

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 # 15
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