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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.
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at
82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to
68%.
How should you address this trade-off to improve detection across both categories?

A. Upgrade to a more capable model tier because its stronger reasoning will handle both concern types in one prompt and eliminate the recall trade-off.
B. Replace the few-shot examples with a detailed checklist of specific logic edge cases to verify, such as division by zero in score calculations and boundary conditions in grading thresholds.
C. Provide the full repository as context instead of only the changed files and surrounding code, giving the model deeper visibility into business-logic patterns.
D. Split the review into separate focused prompts--one for security and API design and another for business logic--each with dedicated examples, and then consolidate the findings before posting.

Question 2

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 runs:
PROMPT='You are a code reviewer. Analyze the provided diff for bugs,
security issues, and style violations.'
claude -p \
--dangerously-skip-permissions \
--system-prompt "$PROMPT" \
< diff.txt
The reviews complete and return feedback, but Claude only comments on the piped diff text--it never reads surrounding files in the checked-out repository to understand broader context, even when the diff modifies a function called by many other modules.
Which change to the invocation will cause Claude to inspect related repository files while still applying your custom review instructions?

A. Replace --system-prompt with --append-system-prompt and explicitly instruct Claude to inspect related repository files whenever broader context is needed.
B. Remove --system-prompt entirely and place the review instructions in a CLAUDE.md file, because - -system-prompt is incompatible with tool use under -p.
C. Stop piping the diff through standard input and embed it inside the prompt, so Claude Code treats the invocation as an agentic session.
D. Keep --system-prompt and add --allowedTools "Read,Glob,Grep", because non-interactive -p mode otherwise disables filesystem tools.

Question 3

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. Replace the default prompt entirely by using --system-prompt-file ./CLAUDE.md, which bypasses default prompt assembly and loads only your project rules.
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. Run in --bare mode and pass --append-system-prompt-file ./CLAUDE.md to explicitly load your project standards while skipping all automatic discovery.
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 4

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. 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.
B. Remove the conservative instructions and have Claude report every potential issue, then apply a programmatic filter that deduplicates findings and suppresses historically noisy categories.
C. 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.
D. Expand the context to include related tests, recent Git history, and the module's dependency graph so Claude has richer evidence for judging severity.

Question 5

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your invoice extraction uses tool use with strict JSON schemas. JSON syntax errors never occur, but 12% of extractions fail semantic validation--for example, line-item amounts do not sum to the extracted total, or vendor IDs do not match valid formats. These failures currently route to manual review.
What is the most effective approach to reduce manual-review volume while maintaining accuracy?

A. When validation fails, make a follow-up request containing the document, extraction, and validation errors so the model can correct the result.
B. Retry the extraction up to three times when validation fails, accepting the first result that passes validation.
C. Implement post-processing logic that automatically corrects common errors, such as recalculating totals from line items when sums do not match.
D. Add stricter schema constraints with detailed field descriptions to prevent the model from initially generating invalid values.

Solutions: