Anthropic CCAR-F Exam
Claude Certified Architect - Foundations
(CCAR-F)
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1/8
, 1.When designing an automated code review system for a large repository, you notice that developers are
experiencing 'alert fatigue' because the AI flags too many non-issues and stylistic preferences.
What is the most effective prompt engineering technique to reduce these false positives?
A. Instruct the model to be thorough and flag everything suspicious to ensure no bugs are missed.
B. Define explicit, measurable review criteria, such as flag functions exceeding 50 lines of code, instead
of relying on vague instructions.
C. Set the model's temperature to 0.0 to ensure deterministic output and eliminate all false positives.
D. Provide at least 15 few-shot examples covering every possible coding style in the repository.
Answer: B
Explanation:
Explicit, measurable criteria produce consistent, actionable results that build developer trust and reduce
false positives. Vague instructions like 'be thorough' lead to over-flagging and alert fatigue. Temperature
adjustments do not fix vague criteria, and providing 15 few-shot examples bloats the prompt without
proportional benefit.
2.You are building a classifier to categorize customer reviews, but the model struggles with ambiguous
scenarios like sarcasm and mixed sentiments. You decide to use few-shot prompting.
What is the recommended best practice for this scenario?
A. Provide exactly 1 example of a standard positive review to establish the JSON format.
B. Provide 8 to 10 examples to ensure every possible sentiment category is covered comprehensively.
C. Provide 2 to 4 examples, ensuring that at least one example specifically addresses an ambiguous
edge case like sarcasm.
D. Avoid few-shot prompting and instead use a detailed system prompt explaining the exact definition of
sarcasm.
Answer: C
Explanation:
The optimal number of few-shot examples for ambiguous tasks is 2-4. Providing more than 6 examples
bloats the prompt without adding proportional value. It is critical that at least one of these examples
covers an edge case or ambiguous scenario to establish the expected reasoning pattern.
3.A pull request modifies 14 files across a stock tracking module. When analyzing all files in a single pass,
Claude provides superficial comments, misses obvious bugs, and produces contradictory feedback.
How should you redesign the multi-pass review architecture to resolve this?
A. Switch to a higher-tier model with a larger context window so all 14 files receive adequate attention in
one pass.
B. Require developers to manually split large PRs into smaller submissions of 3-4 files before triggering
the automated review.
C. Run three independent single-pass reviews on the full PR and only flag issues that achieve consensus
across at least two runs.
D. Split the review into focused passes: analyze each file individually for local issues, followed by a
separate integration-focused pass for cross-file data flow.
Answer: D
Explanation:
Splitting reviews into focused passes directly addresses attention dilution when processing many files at
2/8
Claude Certified Architect - Foundations
(CCAR-F)
https://www.passquestion.com/ccar-f.html
35% OFF on All, Including CCAR-F Questions and Answers
Pass CCAR-F Exam with PassQuestion CCAR-F questions and
answers in the first attempt.
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1/8
, 1.When designing an automated code review system for a large repository, you notice that developers are
experiencing 'alert fatigue' because the AI flags too many non-issues and stylistic preferences.
What is the most effective prompt engineering technique to reduce these false positives?
A. Instruct the model to be thorough and flag everything suspicious to ensure no bugs are missed.
B. Define explicit, measurable review criteria, such as flag functions exceeding 50 lines of code, instead
of relying on vague instructions.
C. Set the model's temperature to 0.0 to ensure deterministic output and eliminate all false positives.
D. Provide at least 15 few-shot examples covering every possible coding style in the repository.
Answer: B
Explanation:
Explicit, measurable criteria produce consistent, actionable results that build developer trust and reduce
false positives. Vague instructions like 'be thorough' lead to over-flagging and alert fatigue. Temperature
adjustments do not fix vague criteria, and providing 15 few-shot examples bloats the prompt without
proportional benefit.
2.You are building a classifier to categorize customer reviews, but the model struggles with ambiguous
scenarios like sarcasm and mixed sentiments. You decide to use few-shot prompting.
What is the recommended best practice for this scenario?
A. Provide exactly 1 example of a standard positive review to establish the JSON format.
B. Provide 8 to 10 examples to ensure every possible sentiment category is covered comprehensively.
C. Provide 2 to 4 examples, ensuring that at least one example specifically addresses an ambiguous
edge case like sarcasm.
D. Avoid few-shot prompting and instead use a detailed system prompt explaining the exact definition of
sarcasm.
Answer: C
Explanation:
The optimal number of few-shot examples for ambiguous tasks is 2-4. Providing more than 6 examples
bloats the prompt without adding proportional value. It is critical that at least one of these examples
covers an edge case or ambiguous scenario to establish the expected reasoning pattern.
3.A pull request modifies 14 files across a stock tracking module. When analyzing all files in a single pass,
Claude provides superficial comments, misses obvious bugs, and produces contradictory feedback.
How should you redesign the multi-pass review architecture to resolve this?
A. Switch to a higher-tier model with a larger context window so all 14 files receive adequate attention in
one pass.
B. Require developers to manually split large PRs into smaller submissions of 3-4 files before triggering
the automated review.
C. Run three independent single-pass reviews on the full PR and only flag issues that achieve consensus
across at least two runs.
D. Split the review into focused passes: analyze each file individually for local issues, followed by a
separate integration-focused pass for cross-file data flow.
Answer: D
Explanation:
Splitting reviews into focused passes directly addresses attention dilution when processing many files at
2/8
