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Anthropic CCA-F Exam Syllabus Topics:

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

Question 1

The agent verifies customer identity through a multi-step process before resetting passwords.
During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened. What's the most likely cause of this behavior?

A. The prompt lacks instructions telling Claude to remember information across multiple exchanges.
B. The conversation history isn't being passed in subsequent API requests.
C. Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
D. The verification tool is clearing the agent's internal state after each successful validation step.

Question 2

Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?

A. Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.
B. Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
C. Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
D. Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.

Question 3

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 spent 25 minutes exploring a game engine's rendering subsystem -reading shader code, buffer management, and frame synchronization logic. An engineer now asks it to understand how the physics engine integrates with rendering for collision debug overlays. You notice recent responses reference "typical rendering patterns" rather than the specific VulkanPipeline and FrameGraph classes it discovered earlier.
What's the most effective approach?

A. Summarize key rendering findings, then spawn a sub-agent for physics exploration with that summary in its initial context.
B. Use /clear to reset context completely, then start fresh with physics exploration using file paths from the project's CLAUDE.md.
C. Continue in the current context with more targeted prompts referencing the specific classes by name.
D. Spawn a sub-agent to explore physics independently, then manually synthesize its findings with the rendering knowledge accumulated in the main conversation.

Question 4

After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?

A. Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.
B. Pass the full accumulated context from all prior agents.
C. Pass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.
D. Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.

Question 5

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.
An engineer submits two requests:
- Request A: "Rename the getUserData function to fetchUserProfile
everywhere it's used."
- Request B: "Improve error handling throughout the data processing
module - add try/catch blocks, meaningful error messages, and ensure
failures don't silently corrupt data."
For which request does specifying an explicit multi-phase workflow (such as analyze -> propose
-> implement with review) most improve outcome quality?

A. Both requests benefit equally
B. Request B, the error handling task
C. Neither request benefits significantly
D. Request A, the function rename task

Solutions: