Anthropic CCA-F valid study dumps : Claude Certified Architect Foundations (CCA-F)

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.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?

• A. Switch to a higher-capacity model tier to provide more context window space for accumulated exploration data.
• B. Create summaries of all source files before exploration begins, loading only these compressed representations into context.
• C. Implement automatic context clearing every 15 minutes to ensure the agent starts with fresh, uncontaminated context.
• D. Have the agent maintain a scratchpad file that records key findings, referencing it for subsequent questions.

You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer, lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
After expanding the agent's MCP tools with delivery-specific capabilities ( check_delivery_status , contact_driver , issue_credit , apply_promo_code , update_delivery_address , reschedule delivery ), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to 71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools - calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data. Which approach structurally eliminates the semantic overlap identified in the logs as the error source?

• A. Add few-shot examples to the system prompt demonstrating correct selection for each ambiguous tool pair, such as showing when issue_credit applies versus when process_refund is appropriate.
• B. Split the tools across two sub-agents - a "financial resolution" agent with process_refund , issue_credit ,and apply_promo_code , and a "delivery operations" agent with the remaining delivery tools - with a coordinator routing between them.
• C. Enable the tool search tool with defer_loading on the six new tools, keeping the original four always loaded, so the agent dynamically discovers specialized tools only when needed.
• D. Consolidate semantically overlapping tools - merge issue_credit and process_refund into a single resolve_compensation tool with an action parameter, and fold check_delivery_status into lookup_order with an optional include_tracking flag.

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

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

A healthcare company processes long clinical reports. Some exceed Claude's practical context requirements. What is the BEST architectural approach?

• A. Randomly remove paragraphs.
• B. Chunk documents before processing.
• C. Ignore the excess text.
• D. Increase temperature.