Online Access Free CCA-F Exam Questions

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've documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What's the most efficient first diagnostic step?

• A.Run /memory to check which memory files are loaded and verify your CLAUDE.md is included.
• B.Search for conflicting instructions in ~/.claude/CLauDe.md or ~/.claude/rules/ that might override your project conventions.
• C.Create path-specific rules in .claude/rules/handlers.md with YAML frontmatter scoping the error handling instructions to your API handler files.
• D.Add a more detailed code examples to your CLAUDE.md showing the exact ApiError usage pattern for different endpoint types.

Your agent uses three tools: get_property_details(property_id) returns data including street address, get_price_history(property_id) returns historical pricing, and get_neighborhood_info(address) returns area statistics. You observe that get_neighborhood_info always requires get_property_details first just to extract the address, even when users specify the property by ID. This creates unnecessary latency and failure coupling - if the first call fails, the neighborhood request also fails. What tool design change best addresses this?

• A.Add retry logic and timeout handling to get_property_details.
• B.Change get_neighborhood_info to accept property_id, resolving the address internally.
• C.Create a lookup_address(property_id) helper tool for retrieving addresses.
• D.Consolidate into a single get_property_with_neighborhood(property_id) tool returning both datasets.

Your agent has called lookup_order multiple times while investigating a customer's return requests. Each response includes 40+ fields (items, shipping details, payment info, status history). Tool outputs now represent the majority of the conversation's context. The customer mentions two more orders they want to discuss. What's the most effective approach before making additional lookups?

• A.Extract only the return-relevant fields (items, purchase date, return window, status) from each existing order response, removing verbose details
• B.Have the model generate a natural language summary of each order's key details, replacing structured responses with prose descriptions
• C.Move all tool responses to a vector database with semantic indexing, retrieving relevant portions as the conversation continues
• D.Proceed with additional lookups without modifying the existing tool output context

Your control_device tool manages smart home devices through external APIs. When a device doesn't respond within the timeout period, the tool returns an error. Production logs show that the agent simply tells users "the device is not responding" without offering helpful next steps. Which error response structure would best enable the agent to provide useful follow-up?

• A.Set is_error: true with a brief "Device offline" message and provide a separate tool the agent can call to retrieve context-specific troubleshooting suggestions.
• B.Set is_error: true with a structured technical error containing the device ID, timeout duration, and raw API response code for debugging purposes.
• C.Set is_error: true with a message explaining the likely cause and suggesting troubleshooting steps the agent can offer the user.
• D.Set is_error: false with an optimistic message indicating the command was dispatched successfully but device acknowledgment is still pending.

Which statement BEST describes Retrieval-Augmented Generation?

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