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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Claude Code Workflows & Configuration	20%	- Claude Code operational patterns 1. CI/CD and workflow integration 2. Plan mode vs execution mode
Topic 2: Agentic Architecture & Orchestration	27%	- Agentic loop design and execution lifecycle 1. Multi-agent coordination and delegation patterns 2. Tool use decision flow (tool_use vs end_turn) - System orchestration patterns 1. Task decomposition strategies 2. Hub-and-spoke agent systems
Topic 3: Context Management & Reliability	15%	- Long-context optimization 1. Reliability and retry strategies 2. Context window management
Topic 4: Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. MCP client/server integration patterns 2. Tool schema design
Topic 5: Prompt Engineering & Structured Output	20%	- Reliable structured generation 1. Prompt patterns for agents 2. Schema-guided outputs

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q18-Q23):

NEW QUESTION # 18
When implementing a self-evaluation loop for structured data extraction, your agent reports 96% aggregate accuracy, but downstream users complain about frequent errors on complex contracts. What monitoring methodology resolves this evaluation visibility gap?

A. Switching entirely to the Message Batches API for asynchronous processing.
B. Executing the evaluation passes inside a PreToolUse hook.
C. Increasing the overall sampling size randomly to capture a broader dataset.
D. Tracking accuracy by specific document type and field segment (stratified metrics).

Answer: D

Explanation:
Aggregate accuracy metrics can mask severe per-document-type failures (e.g., contracts failing frequently while simple receipts succeed nearly 100% of the time). Tracking accuracy using stratified metrics (per document type and field) reveals these hidden failures before automating high-confidence extractions.

NEW QUESTION # 19
When implementing structured data extraction for a application, what does utilizing tool_use with a strict JSON schema explicitly guarantee?

A. Both structural compliance and semantic correctness of the extracted data.
B. Elimination of all hallucinations and inaccurate data extraction.
C. Structural compliance, ensuring valid JSON syntax and required fields, but not semantic accuracy.
D. Automatic retry loops if the extracted values fail business logic validation.

Answer: C

Explanation:
Using tool_use with JSON schemas guarantees structural compliance (all required fields present, correct types, valid enum values), which eliminates JSON syntax errors. However, it does not guarantee semantic correctness, as the model may still extract wrong values or place them in the incorrect fields.

NEW QUESTION # 20
To ensure an agent strictly outputs a specific JSON structure without generating conversational text, and guarantees that a specific tool is invoked, which tool_choice configuration is required?

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

Answer: A

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

NEW QUESTION # 21
Which of the following are valid applications of few-shot prompting in a structured data extraction workflow? Choose 2 correct answers.

A. Replacing the need for a validation-retry loop by guaranteeing 100% semantic correctness.
B. Demonstrating the specific desired output format, including location, issue, and severity, to achieve consistency.
C. Demonstrating ambiguous-case handling, such as extracting informal measurements.
D. Enforcing JSON syntax compliance and eliminating trailing comma errors in outputs.

Answer: B,C

Explanation:
Few-shot examples are ideal for demonstrating ambiguous-case handling and showing the specific desired output format to achieve consistency. They do not guarantee 100% semantic correctness or eliminate syntax errors as reliably as tool_use with JSON schemas.

NEW QUESTION # 22
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. Use a PostToolUse hook to deduplicate the generated tests against the existing codebase.
B. Set the model's temperature to 0.0 to ensure deterministic test generation.
C. Instruct the model to only generate tests for functions with zero coverage.
D. Provide existing test files in the context so the model can avoid suggesting duplicate scenarios.

Answer: D

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 # 23
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