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Claude Certified Architect – Foundations Single choice
You are optimizing the developer productivity agent that helps engineers explore legacy systems. During deep codebase analysis, engineers notice the agent's response time increases significantly, costs spike, and the agent sometimes forgets the original system prompt instructions. The agent frequently uses the `Grep` and `Read` tools to search through massive log files and monolithic legacy code. Which characteristic of agentic tool use best explains this issue, and what is the recommended architectural mitigation?
A Tool results continuously accumulate in the conversational context window, meaning large, raw outputs consume tokens disproportionately to their actual relevance. You should design the tools to filter, truncate, or summarize outputs before returning them to the model. expand_more
check_circle Correct Answer

Correct. In an agentic loop, the raw results of every tool call (like a massive grep output or reading a large file) are appended to the conversation history. Because models read the entire context window on every turn, large unrefined tool outputs accumulate rapidly, consuming tokens disproportionately to their usefulness, driving up costs, and diluting the model's attention from core instructions.

B The Claude Agent SDK automatically pre-fetches and injects the full contents of all files discovered by the `Glob` tool into the context window, leading to token exhaustion. You should configure the `Glob` tool to return only file paths sequentially. expand_more
cancel Incorrect

Incorrect. The `Glob` tool does not automatically pre-fetch and inject full file contents into the context window; it only returns file paths or metadata. The token exhaustion is caused by the raw accumulation of outputs from tools like Read and Grep.

C Claude reserves a fixed 50% of its maximum context window exclusively for MCP tool definitions, leaving inadequate space for the actual outputs of the `Read` tool. You should dynamically reduce the number of tools available to the agent at any given time. expand_more
cancel Incorrect

Incorrect. Claude does not reserve a fixed 50% of its context window for tool definitions. While tool definitions do consume tokens, the primary cause of token bloat during deep analysis is the accumulation of large tool return payloads in the conversation history.

D Tool results from built-in tools are duplicated into both the system prompt and the user message for persistence, effectively doubling token usage per invocation. You should replace all built-in tools with custom MCP servers. expand_more
cancel Incorrect

Incorrect. Tool results are not duplicated into the system prompt. They are appended as standard tool result blocks in the conversational message history.

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