CCA-F Prüfungsmaterialien & CCA-F Testing Engine

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Anthropic CCA-F Exam Syllabus Topics:

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

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CCA-F Testing Engine, CCA-F Vorbereitungsfragen

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Anthropic Claude Certified Architect Foundations (CCA-F) CCA-F Prüfungsfragen mit Lösungen (Q62-Q67):

62. Frage
A pull request modifies 14 files across the stock tracking module. A single-pass self-evaluation review analyzing all files together produces inconsistent results: detailed feedback for some files but superficial comments for others. How should you restructure the evaluation architecture?

A. Split into focused passes: analyze each file individually for local issues, then run a separate integration-focused pass examining cross-file data flow.
B. Require developers to manually split the PR into smaller submissions before the automated review runs.
C. Run three independent review passes on the full PR and only flag issues that appear in at least two of the three runs.
D. Switch to a higher-tier model with a larger context window to give all 14 files adequate attention in one pass.

Antwort: A

Begründung:
Splitting reviews into focused passes directly addresses the root cause: attention dilution when processing many files at once. File- by-file analysis ensures consistent depth, while a separate integration pass catches cross-file issues. Larger context windows do not solve attention quality issues, and forcing consensus suppresses real bugs.

63. Frage
A customer support agent handles high-ambiguity requests. Which of the following is a valid, architecturally sound trigger for escalating a case to a human agent?

A. The conversation exceeds 10 turns.
B. The model's self-reported confidence score drops below 0.7.
C. The agent detects a policy gap not covered by its existing rules.
D. The customer exhibits highly negative sentiment.

Antwort: C

Begründung:
Valid escalation triggers include policy gaps (situation not covered by rules), explicit customer requests for a human, capability limits, and business thresholds. Sentiment and self-reported confidence are unreliable metrics for escalation.

64. Frage
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. Specify research goals and quality criteria (coverage breadth, source diversity, recency) rather than procedural steps, letting the subagent determine its search strategy.
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. Add explicit fallback directives to the detailed instructions: "If specified searches yield fewer than N results, attempt alternative query formulations before reporting failure."
D. Remove procedural details entirely, delegating with simple goals like "research X thoroughly" and relying on the subagent's general capabilities.

Antwort: A

Begründung:
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.

65. Frage
A data extraction pipeline uses few-shot examples to teach Claude how to extract invoice line items. To ensure the model handles unexpected formats reliably, what must be included in the few-shot examples?

A. Extensive XML tags defining the model's underlying neural architecture.
B. A negative example showing the model generating conversational text instead of JSON.
C. Only examples of perfectly formatted, standard invoices to establish a strong baseline.
D. At least one example that demonstrates how to handle an ambiguous scenario or edge case.

Antwort: D

Begründung:
Few-shot examples should demonstrate ambiguous-case handling and edge case coverage. At least one example should demonstrate an ambiguous or edge case to establish the expected reasoning pattern.

66. Frage
Before automating the extraction of high-confidence fields in a quicktechie.com processing system, what validation step is crucial?

A. Checking that the model's self-reported sentiment analysis is positive.
B. Validating accuracy segmented by document type and specific field to verify consistent performance across all segments.
C. Validating the JSON schema syntax using a PreToolUse hook.
D. Confirming consensus through 3 separate monolithic review passes.

Antwort: B

Begründung:
Before reducing human review and automating high-confidence extractions, it is crucial to validate accuracy segmented by both document type and specific field. This ensures that performance is actually consistent across all segments, rather than relying on misleading aggregate averages.

67. Frage
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