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Anthropic CCA-F Exam Syllabus Topics:

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

1. You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs. direct execution.
You need to add a date validation check ensuring event dates are in the future. This requires adding a conditional statement to one existing function in a single file. What is the most appropriate approach?

A) Start with extended thinking mode enabled to ensure thorough reasoning about the validation logic.
B) Use direct execution to make the change.
C) Enter plan mode first to create a detailed implementation strategy before making the change.
D) Enter plan mode to analyze how the validation might impact other parts of the reservation flow.

2. Your conversational assistant frequently generates multiple clarifying questions when users make ambiguous requests. When a user asks "Can you help me with the report?", the assistant responds: "I'd be happy to help! Could you tell me: 1) Which report? 2) What kind of help - drafting, reviewing, or formatting? 3) What's your deadline?" User analytics show a 40% conversation abandonment rate after these multi-question responses.
What's the most effective way to reduce friction while appropriately handling ambiguity?

A) Add a preprocessing step using a smaller model to classify request ambiguity on a 1-5 scale, routing high-ambiguity requests to a clarification dialog and low-ambiguity requests directly to the assistant.
B) Create a lookup table of common request patterns with predefined default interpretations, having the assistant respond with those defaults without stating the assumptions made.
C) Modify the system prompt to instruct the assistant to make reasonable assumptions from available context, state those assumptions explicitly, and offer to adjust if the interpretation is wrong.
D) Limit the assistant to one clarifying question per turn, using conversation history to accumulate answers over multiple exchanges rather than requesting everything upfront.

3. You're implementing the escalation logic for when the agent should call escalate_to_human. Your team proposes four different approaches for triggering escalation. Which approach will most reliably identify cases that genuinely require human intervention?

A) Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.
B) Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.
C) Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer's stated issue, ensuring a reasonable attempt before involving a human.
D) Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and trigger escalation when the frustration score exceeds a configured threshold.

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

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

Solutions: