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

1. 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.
Your agent has analyzed a complex service module -reading 23 source files, tracing request flows, and identifying error handling patterns. A developer wants to compare two testing strategies before committing to one: end-to-end tests with mocked external services vs. snapshot tests capturing expected outputs. They need to independently develop both approaches to evaluate trade-offs. How should you manage the sessions?

A) Export the analysis session's key findings to a file, then create two new sessions that reference this file.
B) Continue in the original session, developing end-to-end tests first, then snapshot tests sequentially.
C) Start two fresh sessions, having each re-read the relevant source files before beginning.
D) Resume the analysis session with fork_session enabled, creating a separate branch for each testing strategy.

2. Why is evaluation important after deploying a Claude application?

A) It permanently changes model parameters.
B) It increases context size.
C) It reduces network latency.
D) It measures response quality against defined objectives.

3. You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
When researching "renewable energy adoption," the web search agent returns recent statistics (2024: 35% adoption) while the document analysis agent extracts data from internal reports (2021: 18% adoption). The synthesis agent incorrectly flags these as contradictory sources rather than recognizing the data shows growth over time. What change would best enable the synthesis agent to correctly interpret such temporal differences?

A) Add a conflict resolution agent that automatically discards older data when newer data exists for the same metric.
B) Instruct the synthesis agent to always treat the most recent data as authoritative and place older findings in a separate historical appendix.
C) Require subagents to include publication or data collection dates in their structured outputs.
D) Configure the web search agent to only return results from the past 6 months.

4. A team wants to reduce hallucinations without significantly changing application logic. Which improvement should be implemented FIRST?

A) Raise temperature.
B) Remove role instructions.
C) Shorten all outputs.
D) Add reliable retrieval sources.

5. 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.

Solutions: