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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prompt Engineering & Structured Output	20%	- Prompt design strategies - Improving Claude response quality and consistency - Structured output generation and validation
Topic 2: Claude Code Configuration & Workflows	20%	- Developer productivity workflows - Integrating Claude Code into development processes - Claude Code usage and configuration
Topic 3: Context Management & Reliability	15%	- Production deployment considerations - Evaluation and reliability strategies - Managing context windows and information flow
Topic 4: Tool Design & MCP Integration	18%	- Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability
Topic 5: Agentic Architecture & Orchestration	27%	- Agent coordination and orchestration patterns - Designing agentic systems and workflows - Selecting appropriate Claude architectures

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q26-Q31):

NEW QUESTION # 26
You are building a structured data-extraction system using Claude. The system extracts information from unstructured documents, validates output against JSON schemas, and integrates the results with downstream systems.
Monitoring reveals that specifications sometimes appear inconsistently within source documents.
For example, a summary section might state "Battery: 4000 mAh," while the detailed specifications table states "Battery: 4200 mAh." Your current schema contains a single battery_capacity field.
This inconsistency occurs in approximately 15% of documents, and historical analysis confirms that the detailed specifications table is accurate 90% of the time.
What is the most effective approach?

A. Change the field to an array that captures every discovered value and its source location, leaving downstream systems to apply precedence rules.
B. Add a conflict_detected Boolean field and route every affected document for manual review.
C. Reject every extraction containing conflicting values and require the source document to be corrected before processing continues.
D. Add extraction instructions specifying that values from the detailed specifications table take precedence when conflicting values exist, while retaining the single-value schema.

Answer: D

Explanation:
Option C converts an empirically validated source hierarchy into an explicit extraction rule. The downstream contract requires one battery_capacity value, and historical analysis establishes that the detailed specifications table is substantially more reliable than the summary section. Claude should therefore be instructed to inspect all occurrences, detect conflicts, and select the detailed- table value when the two locations disagree.
Anthropic's prompting guidance emphasizes clear, direct instructions, relevant context, and explicit decision rules when order or completeness matters. Providing the reason for the precedence rule also helps the model generalize it to comparable specification conflicts.

NEW QUESTION # 27
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?

A. Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.
B. Build post-processing keyword filters that suppress findings containing terms such as "force unwrap," "large class," or "deprecated import" before results reach developers.
C. Document the team's accepted patterns and intentional conventions in the project's CLAUDE.md file so the model receives this context during every review.
D. Configure the review to analyze only the changed lines in the diff without surrounding file context, reducing the amount of code the model evaluates during each 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.

NEW QUESTION # 28
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 coordinator's allowedTools configuration doesn't include "Task", so while it can reason about delegation, it cannot invoke the tool required to spawn subagents.
B. 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.
C. Subagent context isolation means task descriptions from the coordinator don't automatically reach subagents; you need to configure explicit context forwarding in ClaudeAgentOptions.
D. 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.

Answer: A

Explanation:
Defining specialized subagents is not enough; the coordinator must also be allowed to use the Task tool that actually invokes them. If Task is missing from the coordinator's allowed tools, the model may describe delegation in text but cannot execute the subagent call, so the workflow continues without real delegated results.

NEW QUESTION # 29
You are building developer-productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools--Read, Write, Bash, Grep, and Glob--and integrates with Model Context Protocol (MCP) servers.
Your productivity agent connects to three MCP servers: an issue tracker with search_issues, get_issue, and create_comment; a documentation wiki with search_docs, get_page, and list_spaces; and a database explorer with run_query, get_schema, and list_databases. For cross- system questions such as, "Which database tables are affected by the authentication refactor in PROJ-1234?", the agent makes eight to ten sequential exploratory calls, lacks visibility into each server's available content, and exhausts context before completing complex investigations.
What architectural change best leverages MCP capabilities to address these problems?

A. Expose each server's content catalog as MCP resources, including issue summaries, documentation hierarchies, and database schemas.
B. Consolidate all three systems into one unified MCP server with cross-referencing capabilities.
C. Add an orchestrator that routes each question to one server based on keywords.
D. Add a prepare_investigation tool to each server that accepts a natural-language question and returns relevant summaries.

Answer: A

Explanation:
Option C uses MCP resources for their intended purpose: exposing contextual data that applications and agents can discover and read without treating every lookup as an action- oriented tool invocation. Anthropic's Claude Code MCP documentation states that Claude Code automatically provides mechanisms to list and read resources exposed by connected MCP servers. Resources may contain text, JSON, structured data, or other server-provided content.
The official MCP server concepts likewise explain that resources expose information from files, APIs, and databases through identifiable, discoverable URIs. Publishing issue summaries, the wiki hierarchy, and database schemas gives the agent an initial map of available evidence. It can identify relevant systems and retrieve only the necessary records before making targeted tool calls.

NEW QUESTION # 30
Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?

A. Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
B. Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
C. Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.
D. Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.

Answer: A

Explanation:
Structured error metadata gives the agent the information needed to choose the right recovery path. By distinguishing transient, validation, and permission failures, and by explicitly indicating whether retry is appropriate, the agent can avoid wasteful retries, premature escalation, and unnecessary user clarification.

NEW QUESTION # 31
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