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Anthropic Updated CCAR-F Exam Questions and Answers by alan

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Anthropic CCAR-F Exam Overview :

Exam Name: Claude Certified Architect – Foundations
Exam Code: CCAR-F Dumps
Vendor: Anthropic Certification: Claude Certified Architect
Questions: 152 Q&A's Shared By: alan
Question 36

Your automated review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline’s parser to fail. What is the most effective way to handle this?

Options:

A.

Split the review into multiple API calls that each analyze a subset of the changed files, and then merge the resulting findings arrays.

B.

Increase max_tokens to the model’s maximum and instruct Claude to keep each finding description under 50 words.

C.

Switch from tool_use to prompting Claude to return findings as a Markdown list.

D.

Add retry logic that detects truncated JSON and resends the request with instructions to report only critical- and high-severity findings.

Discussion
Question 37

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?

Options:

A.

Remove --system-prompt entirely and place the review instructions in a CLAUDE.md file, because --system-prompt is incompatible with tool use under -p.

B.

Keep --system-prompt and add --allowedTools " Read,Glob,Grep " , because non-interactive -p mode otherwise disables filesystem tools.

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.

Replace --system-prompt with --append-system-prompt and explicitly instruct Claude to inspect related repository files whenever broader context is needed.

Discussion
Question 38

A user expands the research system beyond its original web-search agent by adding specialized data sources. A financial API agent returns structured JSON containing revenue, margins, and growth rates. A news-monitoring agent returns prose summaries of recent developments. A patent-analysis agent returns structured lists of technology areas. The synthesis agent combines these results into executive briefings. Currently, it converts everything into bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose their narrative flow. What change would most improve briefing quality?

Options:

A.

Standardize all subagent outputs as prose summaries with inline citations.

B.

Standardize all subagent outputs as JSON containing fields for claim, evidence, source, and confidence.

C.

Update the synthesis agent to render each content type appropriately—financial data as tables, news as prose, and technology areas as structured lists.

D.

Add a format-conversion layer that transforms every subagent result into a common intermediate representation before synthesis.

Discussion
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Question 39

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.

A developer uses Claude Code to refactor a function during a development session. Before committing, the developer asks the same Claude session to review the code for issues. Later, a separate automated CI review catches several bugs that the same-session review missed.

What best explains this discrepancy?

Options:

A.

Claude retains the implementation context and prior decisions in the session, making it less likely to challenge assumptions underlying its own changes.

B.

The session’s context window necessarily became full, leaving insufficient capacity for meaningful review.

C.

The CI review must have used a more specific prompt, while the developer’s review request was too general.

D.

The CI environment can access the full repository, while a local Claude Code session can access only the current file.

Discussion
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