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| Section | Weight | Objectives |
|---|---|---|
| Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters |
| Agentic Architecture & Orchestration | 27% | - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns - Session state management and workflow enforcement - Agentic loop design and stop_reason handling |
| Prompt Engineering & Structured Output | 20% | - Validation, parsing and retry loop strategies - System prompt design and persona alignment - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting |
| Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Token budget management and cost control - Context window optimization and prioritization - Context pruning and summarization strategies |
| Tool Design & MCP Integration | 18% | - Error handling and tool response formatting - Tool distribution and permission controls - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Tool schema design and interface boundaries |
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NEW QUESTION # 16
After investigating a billing dispute over 25+ turns, you've identified that duplicate charges occurred due to a payment gateway timeout triggering retry logic. The required refund ($847) exceeds your $500 authorization limit You need to call escalate_to_human, and the human agent won't have access to your conversation transcript. What context should you pass to enable effective resolution?
Answer: D
Explanation:
The human agent needs concise, complete handoff context because they cannot see the prior conversation. A structured summary with the customer ID, root cause, refund amount, and recommended action preserves the essential diagnostic and resolution details without overwhelming them with unnecessary transcript history.
NEW QUESTION # 17
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.
Your team frequently migrates React components to Vue. You've written a step-by-step workflow for Claude Code to follow during each migration, and you want every developer on the team to invoke it by typing /migrate-component. The workflow should stay in sync as the team iterates on it. Where should you place the skill file?
Answer: B
Explanation:
A project-scoped skill is available to everyone working in that repository and stays synchronized through version control. The skill directory name exposes it as /migrate-component.
NEW QUESTION # 18
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.
Your infrastructure-as-code repository includes Terraform modules ( /terraform/ ), Kubernetes manifests (
/kubernetes/ ), and CI/CD pipeline scripts ( /pipelines/ ). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?
Answer: B
Explanation:
Path-scoped rules directly satisfy the requirement that instructions load only when Claude works with matching files. Each infrastructure domain can have a separate Markdown file under .claude/rules/ , with YAML paths frontmatter targeting Terraform files, Kubernetes manifests, or pipeline configuration.
Anthropic documents that .claude/rules/ keeps project guidance modular and supports conditional loading based on glob patterns. Rules with a paths field apply only when Claude works with matching files, reducing irrelevant context and token consumption. Rules without path frontmatter load unconditionally. ( https://code.
claude.com/docs/en/memory )
Option B improves readability but leaves all 500-plus lines in every session. Option C can provide directory- specific CLAUDE.md guidance, but path-scoped rules are more precise when conventions depend on extensions or patterns spanning multiple directories. Option D still requires manual imports and does not provide the documented conditional rule-loading mechanism.
A suitable design would use rules such as terraform.md scoped to terraform/**/*.tf , kubernetes.md scoped to kubernetes/**/*.{yaml,yml} , and pipelines.md scoped to the pipeline directory. Broad project-wide commands and repository etiquette can remain in the root CLAUDE.md.
Official references/topics: .claude/rules/ ; Path-Specific Rules; YAML Frontmatter; Context-Efficient Instruction Loading.
NEW QUESTION # 19
Your pipeline includes a release-notes generation step that classifies and summarizes approximately 200 commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API call using a Sonnet-tier Claude model. The release notes are not needed until the following morning, so results have approximately 12 hours of acceptable latency. Your team needs to reduce per-token API cost for this step while keeping the same model and prompts, with no change to the model tier or output quality. Which approach satisfies all these constraints?
Answer: A
Explanation:
Option C preserves each commit's existing model, prompt, and independent processing structure while applying Anthropic's reduced batch pricing. The official Message Batches documentation states that batch processing reduces input and output token costs by 50%. Requests are processed independently and asynchronously, and each response can be correlated with its original commit through a unique custom_id.
This workload is a strong batch candidate because it contains many independent requests and does not require an immediate response. Most batches complete within one hour, although processing can continue for up to
24 hours. The pipeline must therefore tolerate asynchronous completion and retrieve the results when processing finishes.
Option A changes the request architecture, increases the risk of context or output-limit failures, and makes individual retry and result association more difficult. Reducing request count does not inherently reduce per- token pricing. Option B improves throughput but concurrency does not change token rates. Option D would reduce cost but violates the explicit requirement to retain the same model tier. The Message Batches API is the only option satisfying every stated constraint.
NEW QUESTION # 20
The document analysis agent has a single analyze_documnet tool that takes a document and a free-text instruction parameter. During evaluation, requests like "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require re-requests with clarified instructions. What's the most effective way to improve reliability?
Answer: D
Explanation:
Purpose-specific tools with clear input and output contracts remove ambiguity about what kind of analysis is expected. Separate extraction, summarization, and verification tools make the agent choose the correct operation and produce outputs in the format downstream agents can reliably use.
NEW QUESTION # 21
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