Anthropic Claude Certified Architect CCAR-F

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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

1. Your automated review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline's parser to fail. What is the most effective way to handle this?

A) Increase max_tokens to the model's maximum and instruct Claude to keep each finding description under 50 words.
B) Switch from tool_use to prompting Claude to return findings as a Markdown list.
C) Add retry logic that detects truncated JSON and resends the request with instructions to report only critical- and high-severity findings.
D) Split the review into multiple API calls that each analyze a subset of the changed files, and then merge the resulting findings arrays.

2. 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.
Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating.
The team's goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates.
Which approach achieves this guarantee?

A) Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.
B) Split the workflow into two sequential agent invocations-a first agent gathers information via get_customer and lookup_order, then a second agent receives that data and handles process_refund or escalate_to_human, each with separate turn budgets.
C) Add orchestration-layer code that checks the agent's outcome after each loop termination-if the loop ended without a completed resolution or escalation, programmatically call escalate_to_human with the accumulated conversation context and tool results.
D) Implement a pre-tool-use hook that counts tool invocations and terminates the loop with an automatic escalation once the agent reaches 80% of its max_turns limit.

3. 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, and Glob-and integrates with Model Context Protocol (MCP) servers.
During testing, you observe that in extended exploration sessions lasting more than 30 minutes, the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they have already explored.
What is the most effective approach to address this?

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

4. 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.
Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement, losing important nuance, while other reports use excessive qualifications and become unhelpful. The web-search agent returns, "Industry analysts estimate a $50 billion market size, although methodologies vary." The document-analysis agent returns, "A peer-reviewed study estimates $35 billion, with a ±$7 billion 95% confidence interval." The coordinator either selects one estimate arbitrarily or produces a vague $35-$50 billion range.
What systematic approach best addresses this?

A) Configure subagents to report only findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.
B) Add a verification subagent that passes claims to synthesis only when they are corroborated by at least two independent sources.
C) Instruct the synthesis agent to distinguish well-established findings from contested findings explicitly, preserving each source's original uncertainty, methodology, and supporting evidence.
D) Implement a confidence-calibration layer that normalizes subagent uncertainty expressions to probability scores between 0.0 and 1.0, and then calculate a confidence-weighted average.

5. You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline.
The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your pipeline reviews every pull request using a single API call with a static prompt containing the diff and the full text of each changed file. Unchanged files are not included. Developers report that reviews consistently miss cross-file bugs-for example, a pull request renames a function's parameters, but the review does not identify callers in unchanged files that still use the old argument order.
Evaluation shows that cross-file bugs account for 35% of production incidents originating from reviewed pull requests.
What is the most effective change to the review design?

A) Run separate review passes for each changed file with its direct dependants, and then aggregate and deduplicate the findings through a final consolidation pass.
B) Build a static dependency graph and include every file located within two dependency hops of a changed file.
C) Add instructions asking the model to list external references and reason step by step about how each change could affect unseen callers.
D) Redesign the review as a turn-limited agentic task that can read files and search the repository, following references to verify cross-file findings.

Solutions: