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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (85%) also contain errors--cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?

A. Implement heuristic rules that flag documents containing comparison tables or appendices for review regardless of confidence score.
B. Lower the confidence threshold from 85% to 70%, routing a larger volume of extractions to human review.
C. Implement stratified random sampling reviewing a fixed percentage of high-confidence extractions weekly, enabling error rate measurement and novel pattern detection.
D. Add a verification pass that re-extracts from each high-confidence document, flagging cases where the two extraction attempts produce different results.

Question 2

You're implementing the escalation logic for when the agent should call escalate_to_human. Your team proposes four different approaches for triggering escalation. Which approach will most reliably identify cases that genuinely require human intervention?

A. Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.
B. Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer's stated issue, ensuring a reasonable attempt before involving a human.
C. Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and trigger escalation when the frustration score exceeds a configured threshold.
D. Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.

Question 3

Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document analysis agent frequently identifies gaps - for instance, noting "the retrieved sources discuss API authentication but lack details on token refresh patterns" - but under the current strict pipeline, this insight isn't actionable since search has already completed. What's the most effective architectural change?

A. Have the analysis agent report specific gaps to the coordinator, which triggers targeted searches and re-invokes analysis until sufficient.
B. Have the coordinator review analysis output for gap indicators and re-invoke search with gap- informed queries when gaps are detected.
C. Have the synthesis agent attach confidence scores to each section and flag areas with insufficient coverage for manual review.
D. Add a research planning agent before the search phase that decomposes topics into specific sub- questions.

Question 4

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

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

Question 5

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.
An engineer's exploration subagent spent 30 minutes analyzing a legacy payment system, reading 47 files and documenting data flows. The session was interrupted when the engineer's connection dropped. While away, a teammate merged a PR that renamed two utility functions.
The engineer wants to continue the same exploration.
What's the most effective approach?

A. Resume the subagent from its previous transcript and inform it about the renamed functions.
B. Resume the subagent from its previous transcript without mentioning the changes--the architecture understanding remains valid.
C. Launch a fresh subagent with a summary of prior findings.
D. Launch a fresh subagent and include the prior transcript in the initial prompt for context.

Solutions: