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| Section | Weight | Objectives |
|---|---|---|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Context Management & Reliability | 15% | - Context handling
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
>> CCAR-F Reliable Test Answers <<
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NEW QUESTION # 56
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.
Your team wants Claude to follow a detailed code review checklist (8 items covering API changes, test coverage, documentation, security, etc.) when reviewing pull requests. The team also uses Claude extensively for other tasks: writing new features, debugging production issues, and generating documentation. Currently, developers paste the checklist at the start of each review session.
Which approach best addresses this workflow need?
Answer: C
Explanation:
The checklist is a reusable, task-specific procedure that should load only when a pull-request review is being performed. A /review command provides an explicit invocation mechanism, prevents repeated copy-and- paste, and keeps the checklist out of unrelated coding, debugging, and documentation sessions.
In current Claude Code terminology, custom commands have been consolidated into skills. A .claude/skills
/review/SKILL.md file creates a /review command, while the legacy .claude/commands/review.md format remains supported. Anthropic recommends skills when users repeatedly supply the same checklist or multi- step procedure; unlike CLAUDE.md content, the skill body is loaded only when invoked or determined relevant. ( https://code.claude.com/docs/en/skills ) Option B introduces an isolated agent context when the requirement is primarily to inject a repeatable review procedure. A subagent could be useful for independent verification, but it is not necessary merely to avoid repasting the checklist. Option C bloats every session with instructions that apply only to code review. Option D controls editing permissions and planning behavior; it does not encode review criteria.
The review skill can also pre-authorize read-only tools and accept a pull-request identifier or branch as an argument.
Official references/topics: Claude Code Skills; Custom Slash Commands; On-Demand Instructions; Reusable Review Workflows.
NEW QUESTION # 57
Your agent is handling a billing dispute. After calling get_customer and lookup_order, it identifies that the dispute involves a promotional pricing error requiring manager approval - beyond the agent's authorization level. How should the workflow handle this mid-process escalation?
Answer: D
Explanation:
A structured handoff containing the customer details, order information, and the specific issue ensures the human agent has all relevant context to act immediately. This approach avoids delays or repeated clarification and preserves continuity when authority limits prevent the agent from completing the task.
NEW QUESTION # 58
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.
Monitoring shows 12% of extractions fail Pydantic validation with specific errors like "expected float for quantity, got '2 to 3'". Retrying these requests without modification produces identical failures.
What's the most effective approach to recover from these validation failures?
Answer: D
Explanation:
An unchanged retry repeats the same task specification and therefore commonly reproduces the same invalid interpretation. The validator has generated precise corrective information- quantity requires a float, but the model returned the range string 2 to 3 . Supplying that error in a follow-up turn converts a generic retry into an iterative repair operation.
Anthropic identifies iterative refinement as a method for detecting and correcting inconsistencies by feeding an earlier output back into a subsequent request with targeted instructions. ( https://docs.anthropic.com/en/docs
/test-and-evaluate/strengthen-guardrails/reduce-hallucinations ) Option A applies that pattern directly. Claude receives the invalid output, the exact Pydantic error, and an instruction to return a schema-compliant correction. The application should cap retries, retain the original source, and escalate cases that cannot be represented without information loss.
Option B does not guarantee correct formatting; lower temperature may make the same wrong output more repeatable. Option C can be valuable for systematic OCR or source-format problems, but it is unnecessarily broad when the immediate failure is already described by the validator. Option D increases cost and complexity without first using the actionable feedback available from the existing validation layer.
For supported models, native Structured Outputs should also be considered because they guarantee schema- conformant JSON and can prevent many Pydantic shape failures before they occur. ( https://platform.claude.
com/docs/en/build-with-claude/structured-outputs )
Official references/topics: Iterative Refinement; Validation-Error Feedback; Bounded Retry Loops; Structured Outputs.
NEW QUESTION # 59
The automated review consistently flags patterns your team uses intentionally-force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers are dismissing approximately 30% of all findings as project-specific false positives. Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?
Answer: C
Explanation:
Option D supplies the missing repository-specific knowledge before Claude evaluates the code. The project's CLAUDE.md should state that force-unwrapping is permitted within test paths, explain the coordinator-class architecture, and identify the internally maintained deprecated module as an approved dependency. These instructions should be concise and scoped carefully so exceptions do not become global permissions.
Anthropic's CLAUDE.md documentation defines project CLAUDE.md files as persistent context for coding standards, architecture, workflows, and team conventions. They are loaded at the beginning of applicable sessions, preventing the organization from repeatedly supplying the same corrections. Anthropic also recommends recording information that Claude should know whenever it works in the repository.
Option A filters generated findings after tokens have already been consumed and can hide genuine issues containing similar terminology. Option B removes valuable surrounding context and would likely increase false positives and missed cross-file defects. Option C distributes project knowledge across individual source lines, creates maintenance overhead, and requires custom preprocessing. Centralized, narrowly scoped CLAUDE.md instructions address the root cause and improve the initial review judgment instead of repairing its output afterward.
NEW QUESTION # 60
A team wants to reduce hallucinations without significantly changing application logic. Which improvement should be implemented FIRST?
Answer: A
Explanation:
Grounding Claude with trusted retrieved information is one of the most effective methods for reducing hallucinations. It provides current, authoritative context while requiring relatively small architectural changes compared with retraining or redesigning the application.
NEW QUESTION # 61
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