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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Context Management & Responsible AI	15%	- Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles - Context window optimization 1. Context retention and summarization 2. Token management and truncation strategies
Topic 2: Claude Code Configuration & Workflows	20%	- Configuration files and structure 1. CLAUDE.md and rules system 2. Custom commands and workflows - CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution
Topic 3: 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
Topic 4: 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
Topic 5: Agentic Architecture & Orchestration	27%	- Agent design patterns 1. Hub-and-spoke multi-agent systems 2. Agent loops and control flow - Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q31-Q36):

NEW QUESTION # 31
Your CI pipeline script runs claude "Analyze this PR" but the job hangs indefinitely. What is the correct approach to run Claude Code in an automated pipeline?

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

Answer: D

Explanation:
The -p (or -print) flag is the documented way to run Claude Code in non-interactive mode. It processes the prompt, outputs the result to stdout, and exits without waiting for user input, which is exactly what CI/CD pipelines require to prevent hanging.

NEW QUESTION # 32
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. Use a PreToolUse hook to block the API response.
D. Trim the tool outputs programmatically to include only the return-relevant fields before they accumulate in the context history.

Answer: D

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 # 33
Which of the following represent architecturally sound strategies for managing context effectively across long documents or multi-agent handoffs? Choose 2 correct answers.

A. Replacing specific numeric constraints in long contexts with generalized natural language summaries.
B. Sharing the full coordinator conversation history with every subagent to guarantee maximum context.
C. Requiring subagents to output structured claim-source mappings to support accurate downstream synthesis.
D. Placing key findings summaries at the beginning of aggregated inputs to mitigate position effects.

Answer: C,D

Explanation:
Placing key findings at the beginning of inputs mitigates the lost in the middle effect, and requiring subagents to output structured claim-source mappings ensures accurate downstream synthesis. Sharing full coordinator history and using generalized summaries for numbers are heavily tested anti-patterns.

NEW QUESTION # 34
After the web search agent and document analysis agent complete their tasks, the coordinator invokes the synthesis agent. However, the synthesis agent responds that it cannot complete the task because no research findings were provided. What is the most likely cause of this issue?

A. The synthesis agent needs tools that can fetch results directly from the other agents' conversation histories.
B. The synthesis agent's context window is not large enough to hold the combined outputs from both previous agents.
C. The subagents need to share a single API connection to enable automatic context sharing between invocations.
D. The coordinator did not include the outputs from the previous agents in the synthesis agent's prompt.

Answer: D

Explanation:
The synthesis agent relies on the coordinator to provide relevant findings from prior subagents. If the coordinator fails to include these outputs in the synthesis prompt, the agent receives no actionable context and cannot produce a meaningful summary or analysis.

NEW QUESTION # 35
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?

A. Switch to a higher-capacity model tier to provide more context window space for accumulated exploration data.
B. Have the agent maintain a scratchpad file that records key findings, referencing it for subsequent questions.
C. Implement automatic context clearing every 15 minutes to ensure the agent starts with fresh, uncontaminated context.
D. Create summaries of all source files before exploration begins, loading only these compressed representations into context.

Answer: B

Explanation:
Long exploration sessions fill the context window and degrade recall. A persistent scratchpad externalizes important architectural findings so the agent can re-read them instead of relying solely on increasingly crowded conversation context.

NEW QUESTION # 36
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