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Anthropic CCA-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q14-Q19):

NEW QUESTION # 14
Which of the following scenarios are best suited for prompt chaining (fixed sequential pipelines) rather than dynamic adaptive decomposition? Choose 2 correct answers.

A. A data processing pipeline where raw text is summarized, then translated, and finally formatted into JSON.
B. A predictable, multi-aspect security review where the sequence of code analysis steps is static and predefined.
C. An open-ended investigation into an undocumented legacy API where responses guide the next queries.
D. A monolith-to-microservices architectural migration involving dozens of interacting dependencies.

Answer: A,B

Explanation:
Prompt chaining is ideal for predictable, linear workflows with static sequences of steps, such as multi-aspect code reviews following a checklist, or deterministic data processing pipelines (summarize -> translate -> format). Open-ended investigations and complex migrations require dynamic adaptive decomposition.

NEW QUESTION # 15
You are building a code review tool and instruct the model to "be thorough and flag everything suspicious." Why is this considered a prompt engineering anti-pattern?

A. It leads to over-flagging and alert fatigue, which erodes developer trust in the system.
B. It guarantees semantic correctness but compromises schema compliance.
C. It forces the model to bypass tool usage.
D. It causes the context window to rapidly exceed its maximum token limit.

Answer: A

Explanation:
Vague instructions like "be thorough" or "flag everything suspicious" lead to over-flagging and alert fatigue. When a tool flags too many non-issues (false positives), developers stop trusting the tool and ignore all flags, including real problems.

NEW QUESTION # 16
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.

Answer: D

Explanation:
Long exploration sessions fill the context window and degrade recall. A persistent scratchpad externalizes important architectural findings so the agent can re-read them instead of relying solely on increasingly crowded conversation context.

NEW QUESTION # 17
A company is building its first production Claude application. Which principle should guide the initial deployment?

A. Start with a simple design and iteratively evaluate improvements.
B. Disable monitoring to reduce costs.
C. Maximize prompt complexity immediately.
D. Optimize only for response speed.

Answer: A

Explanation:
Successful enterprise AI projects usually begin with a straightforward architecture that can be measured and refined over time. Iterative evaluation enables teams to improve prompts, retrieval, safety controls, and user experience using real-world feedback rather than unnecessary initial complexity.

NEW QUESTION # 18
Your team wants to use the Message Batches API to reduce costs by 50%. The pipeline includes a real-time blocking pre-merge code review and a nightly technical debt audit. How should you apply the Batch API?

A. Use the Batch API for both workflows to maximize cost savings.
B. Switch both to the Batch API but implement a timeout fallback to synchronous calls if it exceeds 5 minutes.
C. Use the Batch API strictly for the nightly audit, and retain synchronous calls for the blocking pre-merge review.
D. Use synchronous API calls for both workflows since the Batch API lacks JSON schema support.

Answer: C

Explanation:
The Message Batches API processes within a 24-hour window with no latency guarantees. It is perfect for latency-tolerant workloads like overnight audits, but inappropriate for blocking workflows (like pre-merge checks) where developers need immediate feedback.

NEW QUESTION # 19
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