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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 test generation produces unit tests for new code, but reviews show that 55% are low-value:
trivial assertions that only verify functions do not throw exceptions, tests duplicating existing coverage, or tests ignoring your team's fixture conventions.
How do you reduce the rate of low-value tests being generated in the first place?

A. Implement two-phase generation in which a second Claude call scores each test against quality criteria, filtering out low-scoring tests before presenting results to developers.
B. Document testing standards in CLAUDE.md, including valuable-test criteria, available fixtures and their intended use cases, and examples distinguishing meaningful behavioral tests from trivial assertions.
C. Add post-generation coverage analysis that automatically filters out any generated test that does not increase line coverage beyond existing tests.
D. Restrict test generation to directories where historical quality metrics show higher acceptance rates, disabling it for areas where generated tests consistently require substantial editing.

Question 2

Which practice MOST improves prompt maintainability?

A. One paragraph containing all instructions.
B. Random ordering.
C. Clearly separated sections with headings.
D. Minimal punctuation.

Question 3

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 a single prompt and eliminate the recall trade-off.
B. Provide the full repository as context instead of only the changed files and surrounding code, giving the model deeper visibility into business-logic patterns.
C. 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.
D. 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.

Question 4

After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?

A. Pass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.
B. Pass the full accumulated context from all prior agents.
C. Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.
D. Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.

Question 5

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 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's the most effective approach to ensure proper source attribution in the final reports?

A. Skip summarization and pass full raw outputs from web search and document analysis directly to the report generator.
B. Have each agent output structured data separating content summaries from source metadata (URLs, document names, page numbers).
C. Instruct the synthesis agent to embed source references inline within its summary text using a consistent citation format.
D. Have the report generator query the web search agent to re-locate sources for claims in the final report.

Solutions: