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152 Questions
Exam Name: Claude Certified Architect - Foundations
Related Certification(s):
Anthropic Claude Certification
Anthropic CCAR-F Exam Topics - You’ll Be Tested in Actual Exam
Five distinct topic areas make up the Anthropic Claude Certified Architect - Foundations (CCAR-F) exam, spanning system design, configuration, and applied reasoning. The exam starts where good architecture starts — with agentic systems. Candidates must understand how autonomous agents are structured, how multi-agent pipelines are orchestrated, and how decisions flow across components. That architectural thinking connects directly to tool design, where knowing how to build reliable, well-scoped tools shapes what an agent can actually do. Model Context Protocol integration sits within this space too, governing how external tools and services communicate with Claude in structured, predictable ways. Prompt engineering then becomes the mechanism that ties instructions to outputs, and the exam tests whether candidates can craft prompts that produce consistent, structured results across varied scenarios. Structured output formats matter here. Context management builds on all of this, because even a well-designed agent with excellent prompts will fail if context windows aren't handled carefully. Reliability under real conditions — token limits, long conversations, degraded inputs — is what separates functional prototypes from production-ready systems. Claude Code configuration and workflows round out the picture, covering how development environments are set up, how automated workflows are defined, and how Claude integrates into engineering pipelines. The Anthropic Claude Certified Architect - Foundations exam treats these areas as interconnected skills, not isolated checkboxes. Agentic architecture tends to carry the most conceptual weight, as nearly every other topic either feeds into it or depends on decisions made at that level.
Anthropic CCAR-F Exam Short Quiz
Attempt this Anthropic CCAR-F exam quiz to self-assess your preparation for the actual Anthropic Claude Certified Architect - Foundations exam. CertBoosters also provides premium Anthropic CCAR-F exam questions to pass the Anthropic Claude Certified Architect - Foundations exam in the shortest possible time. Be sure to try our free practice exam software for the Anthropic CCAR-F exam.
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Anthropic CCAR-F Exam Quiz
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AnthropicCCAR-F
Q1:
The automated review consistently flags patterns your team uses intentionally---force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers are dismissing approximately 30% of all findings as project-specific false positives. Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?
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ABuild post-processing keyword filters that suppress findings containing terms such as ''force unwrap,'' ''large class,'' or ''deprecated import'' before results reach developers.
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BConfigure the review to analyze only the changed lines in the diff without surrounding file context, reducing the amount of code the model evaluates during each review.
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CHave developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.
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DDocument the team's accepted patterns and intentional conventions in the project's CLAUDE.md file so the model receives this context during every review.
AnthropicCCAR-F
Q2:
The synthesis agent receives summarized findings from the web-search and document-analysis agents, then passes a consolidated summary to the report generator. During testing, you discover that the generated reports make factual claims without proper citations---the report generator cannot attribute statements to their original sources because that metadata was lost during the summarization steps. What is the most effective approach to ensure proper source attribution in the final reports?
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AHave the report generator query the web-search agent to relocate sources for claims in the final report.
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BHave each agent output structured data that separates content summaries from source metadata, including URLs, document names, and page numbers.
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CSkip summarization and pass the complete raw outputs from the web-search and document-analysis agents directly to the report generator.
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DInstruct the synthesis agent to embed source references inline within its summary text using a consistent citation format.
AnthropicCCAR-F
Q3:
Your code-review prompts include both implementation changes and the corresponding test file, but the review comments fail to identify untested code paths. The model correctly flags functions that have no tests at all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack coverage. What is the most effective way to improve branch-level gap detection without overcomplicating the pipeline?
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AInterleave the implementation and tests in the prompt, presenting each function immediately before its test cases.
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BAdd explicit instructions requiring Claude to enumerate every conditional branch and exception path, then verify that each path has a corresponding test assertion.
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CImplement a two-pass pipeline in which one model call extracts all conditional branches and another cross-references them against test assertions.
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DInclude few-shot examples showing code with an uncovered branch and the corresponding review comment identifying the missing test case.
AnthropicCCAR-F
Q4:
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team's CLAUDE.md includes a rule: ''Use 4-space indentation and always run Prettier formatting.'' Despite this, code reviews reveal that roughly 30% of files Claude Code generates use inconsistent formatting---sometimes 2-space indentation, sometimes missing trailing commas. Adding emphasis (''IMPORTANT: You MUST use Prettier formatting'') reduces violations to about 15%, but doesn't eliminate them.
What is the most effective way to ensure all generated code is consistently formatted?
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AExtract the formatting rules into a dedicated skill that Claude loads automatically when generating code, with more detailed examples of correct formatting.
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BAdd a Stop hook with a prompt-based check that evaluates whether generated code follows formatting standards and prompts Claude to fix violations.
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CSplit the formatting rules into path-scoped .claude/rules/ files that load when Claude works on matching file types.
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DConfigure a PostToolUse hook with an Edit|Write matcher that automatically runs Prettier on each file Claude modifies.
AnthropicCCAR-F
Q5:
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.
An engineer asks the agent to find every file in a monorepo that imports the @company/auth package to understand how authentication is used across services.
Which built-in tool is most appropriate for this task?
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ARead, beginning with package.json files to trace dependency declarations.
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BGlob, to find files containing auth in their filename or path.
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CGrep, to search file contents for the import-statement pattern.
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DBash, to execute find . -type d -name '*auth*' and explore matching directories.
