Anthropic CCAR-F : Claude Certified Architect - Foundations

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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

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.
After deploying automated code review, developers report that approximately 35% of findings are false positives following consistent patterns: style suggestions that contradict team conventions, security warnings for patterns that are safe in the deployment environment, and performance suggestions that would degrade this particular use case.
You want to reduce false positives while enabling the model to generalize its judgment to novel code patterns it has not seen before.
Which approach is most effective?

A. Include few-shot examples containing annotated code snippets that distinguish acceptable project patterns from genuine issues in each category.
B. Use keyword-based post-processing to remove findings containing terms such as "convention,"
"context-dependent," or "trade-off."
C. Create a comprehensive specification of every pattern that must not be flagged and include the complete document in the system prompt.
D. Add general instructions telling Claude to be conservative and report only definite issues.

Question 2

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.
Your extraction pipeline processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30- day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?

A. Implement post-extraction validation using pattern matching to detect amendments and flag those extractions for manual review.
B. Redesign the schema so amended fields capture multiple values, each with source location and effective date.
C. Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step.
D. Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms.

Question 3

After deploying the automated review, you notice high precision but low recall--real bugs are slipping through undetected. Investigation reveals that your review prompt instructs Claude to
"only report high-confidence issues you are certain about" and "err on the side of not commenting." Developers appreciate the low noise, but a race condition that caused a production outage was visible in a reviewed pull request and went unreported. You need to substantially improve bug detection while keeping false-positive rates manageable. What is the most effective approach?

A. Expand the context to include related tests, recent Git history, and the module's dependency graph so Claude has richer evidence for judging severity.
B. Add detailed few-shot examples demonstrating bug categories Claude should flag--race conditions, null dereferences, and error-handling gaps--while retaining the high-confidence filtering instruction.
C. Remove the conservative instructions and have Claude report every potential issue, then apply a programmatic filter that deduplicates findings and suppresses historically noisy categories.
D. Split the review into a finding stage whose objective is comprehensive coverage--reporting every potential issue with confidence and severity metadata--and a separate stage that verifies and thresholds those findings.

Question 4

Your automated review CI jobs take 18 seconds to initialize before Claude begins analyzing code.
Profiling reveals that the delay comes from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUDE.md files throughout your monorepo. You need to reduce startup time while ensuring that reviews still enforce your team's coding standards, which are documented in the root-level CLAUDE.md file. What is the most effective approach?

A. Run in --bare mode and pass --append-system-prompt-file ./CLAUDE.md to explicitly load your project standards while skipping all automatic discovery.
B. Run in --bare mode and specify all review criteria directly in the -p prompt argument for every CI invocation, without referencing external files.
C. Replace the default prompt entirely by using --system-prompt-file ./CLAUDE.md, which bypasses default prompt assembly and loads only your project rules.
D. Keep the default initialization and add --exclude-dynamic-system-prompt-sections to reduce per- machine prompt variability and improve prompt-cache hit rates across runners.

Question 5

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.
You've asked Claude Code to build a PDF report generation feature. The initial implementation queries the database correctly, but the output has formatting issues: table columns are too narrow causing content truncation, dates display without proper formatting, and page break handling is incorrect. You've noticed these issues interact--changing column widths affects how dates render, and page breaks depend on content height.
What's the most effective approach for iterating toward a working solution?

A. Show Claude an example of a correctly formatted report and ask it to match that output, rather than listing the specific technical issues.
B. Start fresh with a detailed prompt specifying all formatting requirements upfront.
C. Address the column width issue first with specific measurements, verify it works, then fix date formatting within the corrected columns, then adjust page breaks--testing after each change.
D. Provide all three issues in a single detailed message with exact specifications for each, allowing Claude to address them together in one update.

Solutions: