CCAR-F 試験問題を無料オンラインアクセス

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 asks the agent to understand how the caching layer works before adding a new cache invalidation trigger. After initial Grep searches, the agent has identified that caching logic spans 15 files including decorators, middleware, and service classes (~8,000 lines total). What's the most effective next step for building understanding while managing context constraints?

• A.Use Grep to search for "invalidate" and "expire" patterns across all files, then Read only those specific line ranges with minimal surrounding context.
• B.Analyze imports and class hierarchies to identify the base cache class, Read that file to understand the interface, then trace specific invalidation implementations.
• C.Use Glob to find files matching common caching patterns (cache.py, caching/), prioritize the largest files by reading them first, then check smaller files for gaps.
• D.Use the Read tool to sequentially load all 15 files, building complete understanding across the full caching implementation.

During initial testing of the automated review pipeline, you notice that reviews of large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8-$12 per run because of extensive agentic loops--Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort after reaching either a fixed iteration count or a fixed dollar amount. Both limits must be enforced by Claude Code itself rather than by the surrounding job runner. Which configuration change directly enforces both per-invocation limits?

• A.Set --permission-mode dontAsk to automatically deny tool-permission requests that are not in the explicitly allowed set.
• B.Add --max-turns 10 --max-budget-usd 2.00 to the claude -p invocation to cap agentic turns and expenditure.
• C.Use the --model flag to select a smaller, less expensive model so that every iteration uses fewer tokens and costs less.
• D.Set timeout-minutes: 5 on the GitHub Actions step and monitor per-run costs through the Anthropic Console usage dashboard.

Your pipeline reviews approximately 200 database-migration scripts daily using the Message Batches API. Each request includes a shared 8,000-token system prompt containing migration- review guidelines and schema documentation, followed by an individual migration script. You added cache_control breakpoints to the shared system prompt in every request, but monitoring shows cache-hit rates of only 32%, with misses concentrated among requests processed later in the batch window. Which change addresses the root cause without adding sequential-processing latency?

• A.Configure the cache breakpoints to use the extended one-hour TTL instead of the default five- minute TTL.
• B.Move the cache_control breakpoint from the shared system prompt to each migration script so similar code patterns can be reused.
• C.Add cache-prewarming requests with max_tokens: 0 at the beginning of every batch.
• D.Split the 200 requests into ten sequential batches of 20, submitting each batch only after the previous batch completes.

A retail organization observes that Claude occasionally gives different wording when summarizing identical customer reviews. The summaries remain accurate. What is the MOST likely explanation?

• A.Large language models are probabilistic.
• B.Claude is malfunctioning.
• C.Retrieval failed.
• D.The API returned cached responses.

Your content curation agent discovers articles, analyzes each for relevance, then adds selected articles to themed collections. With separate discover_articles(topic), analyze_article(id), and add_to_collection(article_id, collection_id) tools, you observe 18+ sequential tool calls per request, causing latency issues. The agent must make editorial judgments about which articles fit a collection's theme - this requires seeing all candidates with their analysis scores simultaneously to select a cohesive set. What tool composition best addresses efficiency while preserving editorial judgment?

• A.Keep all tools separate but implement response caching for analyze_article calls.
• B.Create a curate_collection(topic, collection_id) tool that handles discovery, analysis, and selection internally using configurable quality thresholds.
• C.Create a discover_and_analyze(topic) composite tool that returns all candidates with their analysis scores, keeping add_to_collection separate for selective calls.
• D.Add a preview_curation(topic, collection_id) tool that shows what would be added based on predefined rules, with an approve_curation() tool to confirm.