Anthropic CCAR-F Dumps Questions Answers

The correct objective is to construct an architectural map before consuming the full implementation. Identifying the base cache abstraction, its interface, and the classes that implement or invoke it gives the agent a dependency-guided path through the code. It can then inspect only the invalidation implementations and integration points relevant to the proposed trigger.

This approach protects the context window. Anthropic states that every file read occupies context and that model performance can deteriorate as the window fills. Its Claude Code guidance warns against unbounded investigation that reads large numbers of files and recommends narrowing the exploration or delegating it. ( https://code.claude.com/docs/en/best-practices )

Option A is too lexical: searching only for invalidate or expire can miss event-driven invalidation, overridden methods, cache-key mutation, and generic interface calls. Option B loads approximately 6,000 lines without first establishing relevance. Option C assumes that filename patterns and file size correlate with architectural importance; the largest files may contain incidental code while a small interface defines the entire design.

Option D follows control and type relationships rather than arbitrary file order. After reading the base class, the agent can search for subclasses, imports, construction sites, middleware hooks, and calls to the invalidation contract, progressively expanding only where evidence requires it.

Official references/topics: Context-Efficient Exploration; Dependency-Guided Reading; Architectural Interfaces; Narrowly Scoped Investigation.

MCP prompts are exposed as user-invoked commands rather than autonomous tools or permanently loaded system instructions. Claude Code dynamically discovers prompts from connected MCP servers and displays them in the command list using the naming convention /mcp__servername__promptname .

Arguments are supplied as space-separated values after the command. When executed, the MCP server resolves the prompt and its returned content is injected into the active conversation. Anthropic’s official documentation provides examples such as /mcp__github__list_prs and /mcp__jira__create_issue "Bug in login flow" high . ( https://code.claude.com/docs/en/mcp )

Option A would consume context continuously and incorrectly treat optional workflow templates as mandatory system instructions. Option B confuses MCP prompts with MCP tools: tools are model-callable operations, while prompts are reusable prompt templates invoked as commands. Option C describes MCP resources, which can be referenced and attached but are a distinct MCP capability.

For the stated server, the team could invoke commands such as /mcp__devops__deploy_checklist or /mcp__devops__incident_response service-name . The exact server segment is derived from the configured server name, with normalization applied where necessary.

Official references/topics: MCP Prompts; Dynamic Prompt Discovery; MCP Slash-Command Naming; Prompt Arguments.

Resuming preserves the prior architectural analysis, dependency mapping, and conversation history, avoiding the cost of repeating an hour of valid work. Anthropic states that resuming by session ID restores the agent’s full prior context, including files read, analysis performed, and decisions made. ( https://code.claude.com/docs/en/agent-sdk/sessions )

However, the session represents historical knowledge rather than a repository snapshot. Because three files changed externally, the agent must be told which files are affected and directed to re-read them before relying on earlier conclusions. Anthropic clarifies that sessions persist the conversation, not the filesystem. ( https://code.claude.com/docs/en/agent-sdk/sessions ) Targeted re-analysis updates the stale portion of the agent’s model while retaining the nine unchanged files’ established context.

Option A discards valuable work even though most of the analyzed code remains unchanged. Option C is accurate but inefficient because it reprocesses all twelve files regardless of whether they changed. Option D is unsafe: the agent may continue reasoning from obsolete function signatures, dependencies, or control flows.

The continuation prompt should identify the changed files, summarize the merge’s purpose where known, and request a focused comparison against the earlier understanding. Any architectural conclusions affected by those changes should then be revised explicitly.

Official references/topics: Session Resume; Filesystem Versus Conversation State; Targeted Re-Analysis; Repository Change Awareness.