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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment |
| Context Management & Reliability | 15% | - Context window optimization and prioritization - Context pruning and summarization strategies - Idempotency, consistency and failure resilience - Token budget management and cost control |
| Tool Design & MCP Integration | 18% | - Tool schema design and interface boundaries - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Error handling and tool response formatting - Tool distribution and permission controls |
| Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Session state management and workflow enforcement |
| Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration - CLAUDE.md hierarchy, precedence and @import rules - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution |
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TrainingDump also has a Anthropic Practice Test engine that can be used to simulate the genuine Claude Certified Architect - Foundations (CCAR-F) exam. This online practice test engine allows you to answer questions in a simulated environment, giving you a better understanding of the exam's structure and format. With the help of this tool, you may better prepare for the Claude Certified Architect - Foundations (CCAR-F) test.
NEW QUESTION # 166
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?
Answer: D
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 # 167
An application frequently repeats the same background instructions in every API request. What is a potential downside?
Answer: A
Explanation:
Repeated instructions consume additional input tokens on every request, increasing cost and latency. Consolidating stable guidance into reusable prompt templates can improve efficiency while preserving consistent model behavior.
NEW QUESTION # 168
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, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth - having the agent propose specific code changes for each - before deciding which to implement. What's the most effective way to structure this exploration?
Answer: A
Explanation:
Forking preserves the accumulated authentication-module context while giving each refactoring approach an independent session, preventing one exploration from influencing the other.
Anthropic's Agent SDK supports creating a new session ID when resuming with session forking enabled.
NEW QUESTION # 169
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?
Answer: B
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. Option A creates another opaque, high-level tool whose output quality depends on each server interpreting the investigation correctly.
Option B produces tight coupling and increases operational complexity. Option D prevents the cross-system investigation required by the question. Resource catalogs improve discoverability, reduce blind exploration, and preserve tools for searches, queries, and mutations that genuinely require execution.
NEW QUESTION # 170
After investigating a billing dispute for more than 25 turns, you determine that duplicate charges resulted from a payment-gateway timeout triggering retry logic. The required refund of $847 exceeds your $500 authorization limit, so you must invoke escalate_to_human. The human agent will not have access to the conversation transcript. What context should you pass to enable effective resolution?
Answer: D
Explanation:
Option C gives the human agent the operational state required to continue without replaying a long conversation. The handoff should include verified identifiers, the duplicate transaction evidence, the diagnosed timeout-and-retry mechanism, the $847 refund requirement, the agent's $500 authorization constraint, completed verification steps, prior actions, and the recommended resolution. Any unresolved uncertainty should be labeled explicitly.
Anthropic's effective context-engineering guidance recommends preserving high-value state in structured notes while removing redundant conversational and tool-call history. Its long-running-agent guidance likewise describes structured handoffs as the mechanism for maintaining continuity across context resets or agent boundaries.
Option A maximizes raw information but forces the human to locate the relevant facts among more than 25 turns, increasing delay and error risk. Option B preserves evidence but omits the authorization constraint, completed verification, and explicit recommended action. Option D is too sparse to support validation or execution. A structured handoff balances fidelity and efficiency: it contains everything needed for the next actor to make the refund decision while excluding greetings, repeated explanations, and irrelevant intermediate tool output.
NEW QUESTION # 171
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In the PDF version, real CCAR-F exam questions are available. These Anthropic CCAR-F real questions are printable and portable. You can take this PDF document anywhere and study for the Claude Certified Architect - Foundations (CCAR-F) exam without time restrictions. TrainingDump regularly make changes in the CCAR-F PDF format when required. CCAR-F questions in this format are relevant to the actual test.
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