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Anthropic CCA-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q71-Q76):

NEW QUESTION # 71
Which of the following scenarios represent appropriate, architecturally sound use cases for Agent SDK hooks? Choose 2 correct answers.

A. Intercepting outgoing tool calls to deterministically block policy-violating actions, such as unauthorized financial transactions.
B. Guaranteeing that a model-extracted JSON object contains no semantic reasoning errors.
C. Normalizing heterogeneous numeric status codes from different MCP tools before appending them to the conversation context.
D. Enforcing the conversational tone, politeness, and persona the agent should adopt.

Answer: A,C

Explanation:
Hooks are ideal for programmatic interventions like data normalization CPostToolUse*) and policy enforcement/interception ('PreToolUse'PPostToolUse'). Conversational tone is best handled by prompts, and hooks cannot inherently guarantee the semantic accuracy of an LLM's complex reasoning.

NEW QUESTION # 72
When designing escalation patterns for an autonomous customer support agent, which of the following represent architectural anti- patterns that should be avoided? Choose 2 correct answers.

A. Escalating cases automatically based on high levels of negative customer sentiment.
B. Escalating cases when the agent detects a policy gap not covered by its instructions.
C. Relying on the model's self-reported confidence score to determine if human review is needed.
D. Escalating cases immediately when a customer explicitly requests to speak to a human.

Answer: A,C

Explanation:
Using self-reported model confidence scores and customer sentiment are heavily tested anti-patterns. Model confidence is poorly calibrated for routing, and sentiment does not correlate with task complexity. Explicit human requests and policy gaps are valid escalation triggers.

NEW QUESTION # 73
A developer at quicktechie.com is assigned a task to migrate a core utility library that is imported by over 45 different files across the repository. Which execution approach should the developer take?

A. Use Plan Mode to explore the codebase, understand dependencies, and design an architectural approach before committing to file modifications.
B. Use a custom slash command designed to bypass context limits.
C. Start with Direct Execution, and manually switch to Plan Mode only if unexpected compilation errors occur.
D. Use Direct Execution to make incremental changes and let the implementation naturally reveal dependency boundaries.

Answer: A

Explanation:
Plan mode is specifically designed for complex tasks involving large-scale changes, architectural decisions, and multi-file modifications (such as a library migration affecting 45+ files). It enables safe codebase exploration before committing to costly rework.

NEW QUESTION # 74
To ensure that a model consistently classifies the severity of identified code issues, what is the best prompt engineering technique?

A. Instruct the model to self-report a confidence score from 1-10 for each finding.
B. Allow the model to dynamically generate severity labels based on context.
C. Lower the max_tokens limit so the model focuses only on critical bugs.
D. Define explicit severity criteria with concrete code examples for each severity level.

Answer: D

Explanation:
Providing explicit severity criteria paired with concrete code examples for each level bounds the model's behavior, leading to consistent severity classification and preventing subjective miscategorization.

NEW QUESTION # 75
How should a quicktechie.com application handle verbose tool outputs (e.g., an order lookup returning 40+ fields when only 3 are relevant) to optimize the context window?

A. Require the agent to run a web search to summarize the JSON object.
B. Pass the full output and rely on the model's semantic focus to ignore irrelevant fields.
C. Trim the tool outputs programmatically to include only the return-relevant fields before they accumulate in the context history.
D. Use a PreToolUse hook to block the API response.

Answer: C

Explanation:
Tool results accumulate in context and consume tokens disproportionately to their relevance. Best practice dictates trimming verbose tool outputs to only the relevant fields before they are appended to the context history, preventing rapid context degradation.

NEW QUESTION # 76
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