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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Context Management & Reliability	15%	- Long-context optimization 1. Reliability and retry strategies 2. Context window management
Topic 2: Claude Code Workflows & Configuration	20%	- Claude Code operational patterns 1. Plan mode vs execution mode 2. CI/CD and workflow integration
Topic 3: Prompt Engineering & Structured Output	20%	- Reliable structured generation 1. Prompt patterns for agents 2. Schema-guided outputs
Topic 4: Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. MCP client/server integration patterns 2. Tool schema design
Topic 5: Agentic Architecture & Orchestration	27%	- Agentic loop design and execution lifecycle 1. Tool use decision flow (tool_use vs end_turn) 2. Multi-agent coordination and delegation patterns - System orchestration patterns 1. Hub-and-spoke agent systems 2. Task decomposition strategies

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q93-Q98):

NEW QUESTION # 93
When implementing multi-pass review architectures for automated CI/CD pipelines, why is configuring the exact same Claude SDK session to review its newly generated code considered a major anti-pattern?

A. It causes the token limit of the context window to exceed its maximum capacity.
B. The reviewer retains the reasoning context from the generation phase, creating confirmation bias and reducing the likelihood of catching subtle flaws.
C. Tools invoked in the first pass are permanently locked and cannot be reused in the second pass.
D. The Message Batches API does not support synchronous self-review workflows.

Answer: B

Explanation:
Same-session self-review is ineffective because the model retains its original reasoning context and assumptions in the conversation history, creating a blind spot or confirmation bias. Using an independent review instance with fresh context objectively evaluates the code much better.

NEW QUESTION # 94
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 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.
B. 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.
C. You manually set the tool_choice parameter to "none" after the final expected tool call to force Claude to stop requesting tools.
D. You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.

Answer: A

Explanation:
Continue the agentic loop when stop_reason is tool_use, execute the requested tool, and return its result to Claude. Exit when Claude returns a terminal reason such as end_turn; other reasons like max_tokens require separate handling.

NEW QUESTION # 95
A developer is using a multi-agent system to analyze a massive legacy codebase. The coordinator agent is running out of context window space because the search tool returns verbose file contents and exploration noise. What is the best pattern to mitigate this context degradation?

A. Delegate the verbose file-by-file exploration to a subagent, which then returns only a synthesized summary of its findings back to the coordinator's context.
B. Force the coordinator agent to use the Bash tool to run grep manually instead of reading files.
C. Share the full coordinator conversation history with all subagents to ensure maximum context propagation.
D. Switch from dynamic adaptive decomposition to static prompt chaining to force a linear review path.

Answer: A

Explanation:
Subagent delegation is a core mitigation for context degradation. By delegating verbose exploration tasks to subagents, the subagent's isolated context absorbs the exploration noise, and only the clean, synthesized results are returned to the coordinator.

NEW QUESTION # 96
When generating complex mock data setups for integration tests, natural language descriptions often produce inconsistent structural results from Claude. What is the most effective technique to fix this?

A. Ask Claude to evaluate its own test generation output before returning it.
B. Increase the max_tokens parameter to give the model more space to reason.
C. Provide 2-3 concrete input/output examples demonstrating the exact expected transformation.
D. Use the /compact command before requesting the test generation.

Answer: C

Explanation:
Concrete input/output (few-shot) examples are the most effective way to communicate expected transformations when prose descriptions are interpreted inconsistently. They demonstrate the exact structural formatting required for complex data setups.

NEW QUESTION # 97
The coordinator provides detailed step-by-step instructions to the web search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues: (1) the subagent reports "insufficient results" rather than trying alternative approaches when pre-specified searches fail, (2) research quality drops for emerging topics that don't match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources.
What's the most effective way to improve subagent adaptability?

A. Add explicit fallback directives to the detailed instructions: "If specified searches yield fewer than N results, attempt alternative query formulations before reporting failure."
B. Implement a topic classification step where the coordinator categorizes requests as "well-defined" or "exploratory" and uses different instruction styles for each category.
C. Specify research goals and quality criteria (coverage breadth, source diversity, recency) rather than procedural steps, letting the subagent determine its search strategy.
D. Remove procedural details entirely, delegating with simple goals like "research X thoroughly" and relying on the subagent's general capabilities.

Answer: C

Explanation:
Delegating goals and quality criteria gives the subagent room to adapt its strategy while still defining what good research looks like. This improves handling of failed searches, emerging topics, and useful tangential sources because the subagent can vary queries, sources, and filters to satisfy the research objective rather than rigidly following preset steps.

NEW QUESTION # 98
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