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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic Architecture & Orchestration	27%	- Agentic architecture patterns 1. Workflow design 2. Planning and execution strategies 3. Agent orchestration 4. Single-agent and multi-agent architectures
Topic 2: Prompt Engineering & Structured Output	20%	- Prompt design 1. Prompt engineering techniques 2. Few-shot prompting 3. Structured output and JSON schemas 4. Output validation
Topic 3: Claude Code Configuration & Workflows	20%	- Claude Code 1. Development workflows 2. Agent skills 3. Configuration and project setup 4. Code generation and automation
Topic 4: Context Management & Reliability	15%	- Context handling 1. Reliability and evaluation 2. Memory strategies 3. Cost and performance optimization 4. Context window management
Topic 5: Tool Design & MCP Integration	18%	- Tool integration 1. Model Context Protocol (MCP) 2. Tool selection and safety 3. Resource and server integration 4. Tool interface design

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Pass Guaranteed 2026 Anthropic CCAR-F: Latest Claude Certified Architect - Foundations Free Exam Questions

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q79-Q84):

NEW QUESTION # 79
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 review generates many findings per pull request, but developer feedback shows that approximately half are dismissed as "not worth addressing." Analysis reveals that these findings are often technically accurate but involve minor style preferences or patterns that are acceptable in the project.
Before adding infrastructure complexity, what prompt-design change would most effectively reduce dismissals while maintaining detection of genuine issues?

A. Define explicit reporting criteria that distinguish reportable bugs and security issues from minor style preferences and accepted local patterns.
B. Add a secondary classification model that filters findings according to predicted developer acceptance.
C. Add the instruction: "Only report findings you are highly confident are genuine problems."
D. Ask Claude to rate each finding's confidence from 1 to 10 and include only findings rated 8 or higher.

Answer: A

Explanation:
Option C defines the reviewer's actual objective instead of relying on an undefined notion of confidence. The prompt should identify reportable categories such as correctness defects, security vulnerabilities, data-loss risks, broken requirements, and meaningful performance regressions. It should also specify that cosmetic preferences, lint-enforced formatting, speculative refactoring, and intentional repository conventions must not be reported unless they cause concrete incorrect behavior.
Anthropic's Claude Code best-practices guidance recommends telling a reviewer to report gaps affecting correctness or stated requirements and to treat other observations as optional. Without such criteria, a reviewer asked to "find issues" will predictably return technically defensible but operationally low-value observations.

NEW QUESTION # 80
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.
Your system has been running for 3 weeks and human reviewers have corrected 847 extractions.
Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or
"a splash," the model either invents specific amounts or leaves fields empty--accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?

A. Update your JSON schema to add a "measurement_type" enum field (precise/informal).
B. Fine-tune the model on the 847 corrected extractions.
C. Add few-shot examples to your prompt demonstrating correct handling of informal measurements--extracting them verbatim rather than converting or omitting them.
D. Implement a post-processing layer that uses pattern matching to detect informal measurement phrases in source text and automatically populate values when the extraction is empty.

Answer: C

Explanation:
The reviewer corrections have exposed a narrow and repeatable interpretation failure. The desired policy is clear: informal measurements are valid source values and must be preserved verbatim rather than normalized into invented quantities or treated as missing. This behavior can be communicated efficiently through targeted few-shot examples.
Anthropic recommends examples for demonstrating expected behavior and improving consistency. Examples can pair source phrases such as "a handful of spinach," "a splash of vinegar," and "a pinch of salt" with outputs that retain handful, splash, and pinch exactly.
Additional counterexamples can show that Claude must not convert these phrases into grams, millilitres, or estimated serving quantities.

NEW QUESTION # 81
The coordinator agent has AgentDefinitions configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice the coordinator correctly reasons about when to delegate - it generates messages like "I'll ask the web search agent to find sources on this topic" - but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?

A. The AgentDefinitions are configured correctly, but the coordinator's system prompt doesn't explicitly list the available subagent types, preventing the model from knowing they can be invoked.
B. The coordinator's allowedTools configuration doesn't include "Task", so while it can reason about delegation, it cannot invoke the tool required to spawn subagents.
C. The coordinator's max_tokens setting is too low, causing the Task tool invocation to be truncated before the subagent type parameter can be specified.
D. Subagent context isolation means task descriptions from the coordinator don't automatically reach subagents; you need to configure explicit context forwarding in ClaudeAgentOptions.

Answer: B

NEW QUESTION # 82
When implementing your lookup_order MCP tool, the backend sometimes returns errors (e.g.,
"Order not found" or temporary database failures). What is the correct pattern for communicating these errors back to the agent?

A. Log the error server-side and return an empty result to avoid confusing the model
B. Throw an exception from the tool handler so the agent framework can catch and log it
C. Return the error message in the tool result content with the isError flag set to true
D. Return a success response with a "status" field indicating the error type

Answer: C

Explanation:
Returning structured tool results with isError set to true allows the agent to receive explicit error signals while preserving the error message. This enables the agent to reason about the failure, choose appropriate recovery actions, and provide meaningful feedback to the user.

NEW QUESTION # 83
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.
You've asked Claude to write a data migration script, but the initial output doesn't correctly handle records with null values in required fields. What's the most effective way to iterate toward a working solution?

A. Add "think harder about edge cases" to your prompt and request a complete rewrite of the migration logic.
B. Manually edit the generated code to fix the null handling, then continue working with Claude on other parts.
C. Provide a test case with example input containing null values and the expected output, then ask Claude to fix it.
D. Describe the null value problem in detail and ask Claude to regenerate the entire script with improved edge case handling.

Answer: C

Explanation:
A concrete failing test gives Claude an objective specification and immediate feedback. This supports targeted, iterative correction more effectively than vague instructions, a full rewrite, or manual editing.

NEW QUESTION # 84
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