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Anthropic CCA-F Exam Syllabus Topics:

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

1. Which statement BEST describes Retrieval-Augmented Generation?

A) It changes token pricing.
B) It permanently retrains Claude.
C) It supplies relevant external knowledge during inference.
D) It compresses model weights.

2. 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 analyzing complex legal cases that cite multiple precedents, the document analysis subagent processes each sequentially. A landmark case citing 12 precedents takes over 3 minutes to analyze completely. What's the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?

A) Create a recursive agent hierarchy where analysis agents subdivide work among child agents until reaching single-precedent granularity
B) Implement a message queue where precedent analysis tasks are processed asynchronously by a pool of worker agents
C) Enable the document analysis subagent to spawn its own specialized subagents dynamically when it encounters cases with many citations
D) Have the coordinator spawn parallel document analysis subagents, each handling a subset of precedents, then aggregate results before synthesis

3. A customer writes: "I've been going back and forth on this return for days. I just want to speak to someone who can actually help me." The agent has confirmed via lookup_order that the return is straightforward - within policy and eligible for immediate processing. What should the agent do?

A) Process the refund via process_refund to resolve the underlying issue, then inform them it's complete
B) Call escalate_to_human immediately to honor the customer's request
C) Ask what specifically hasn't worked in previous attempts before deciding whether to escalate or resolve automatically
D) Acknowledge frustration, inform them this is resolvable now, and offer to complete it or escalate

4. A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API. What is the primary mechanism your application uses to determine whether to continue the loop or stop?

A) You check whether Claude's response contains a text content block - if text is present, the agent has produced its final answer and the loop should exit.
B) You manually set the tool_choice parameter to "none" after the final expected tool call to force Claude to stop requesting tools.
C) You check the stop_reason field in each API response - the loop continues while it equals
"tool_use" and exits when it changes to "end_turn" or another terminal value.
D) You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.

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

Solutions: