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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic Architecture & Orchestration | 27% | - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Agentic loop design and stop_reason handling - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns |
| Topic 2: 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 |
| Topic 3: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - MCP tool, resource and prompt implementation - Error handling and tool response formatting - Tool distribution and permission controls |
| Topic 4: Context Management & Reliability | 15% | - Context pruning and summarization strategies - Token budget management and cost control - Idempotency, consistency and failure resilience - Context window optimization and prioritization |
| Topic 5: Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution |
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NEW QUESTION # 47
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: D
Explanation:
The paths field conditionally loads each rule only when Claude works with matching files, preventing unrelated Terraform, Kubernetes, or pipeline guidance from consuming context.
Subdirectory CLAUDE.md files are directory-based, while path-scoped rules provide precise file- pattern targeting.
NEW QUESTION # 48
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: C
Explanation:
A concrete test case converts an imprecise correction into an executable success criterion. The input demonstrates the failing null-value condition, while the expected output defines exactly how the migration must handle it. Claude can modify the implementation, run the test, inspect the result, and continue iterating until the behavior passes.
Anthropic recommends giving Claude a verification mechanism such as a test suite, build result, linter, fixture comparison, or screenshot. Without such a mechanism, Claude can only judge that its output appears correct.
A pass-or-fail test closes the feedback loop by allowing Claude to perform the work, evaluate the result, and refine it using objective evidence. ( https://code.claude.com/docs/en/best-practices ) Option A provides no additional specification and may generate a different but still incorrect implementation.
Option B fixes the immediate defect manually but fails to use Claude's iterative capabilities or establish a regression check. Option C supplies more description, but regenerating the entire script increases the risk of altering already-correct behavior and still lacks objective validation.
The best workflow is to add the failing case to the migration test suite, request the smallest necessary correction, run the relevant tests, and retain that test permanently to prevent the null-handling defect from recurring.
Official references/topics: Executable verification, test-driven iteration, regression protection, tight feedback loops.
NEW QUESTION # 49
You've configured the system so that all four subagents have access to the complete set of
18 tools. During testing, agents frequently call tools outside their specialization - the synthesis agent attempts web searches, and the report generator tries to analyze documents. What is the primary cause of this poor tool selection behavior?
Answer: A
Explanation:
Providing all agents with access to a large set of tools increases the cognitive load for tool selection. When the number of options grows beyond a manageable threshold, agents are more likely to misuse tools outside their specialization, reducing efficiency and accuracy.
NEW QUESTION # 50
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 find all callers of a function before removing it. The function is defined in a core library but is also exposed through wrapper modules that rename the function for domain-specific use (e.
g., calculateTax in the library becomes computeOrderTax in the orders module).
What exploration strategy will most reliably identify all callers?
Answer: C
Explanation:
The search must first establish the function's complete naming surface. Reading the core definition and every wrapper or re-export reveals aliases such as computeOrderTax , names introduced through intermediate modules, and potentially different import paths. Once those names are known, the agent can search the entire codebase for each exposed identifier and inspect the resulting call sites.
Claude Code's Grep tool searches patterns inside file contents, whereas Read provides the surrounding implementation needed to determine whether a wrapper delegates to, renames, or conditionally invokes the original function. These tools are complementary: Read establishes semantic identity; Grep provides broad reference discovery. ( https://code.claude.com/docs/en/tools-reference?utm_source=chatgpt.com ) Option B misses every caller using a wrapper alias. Option A may find importing files, but import-level discovery is indirect and can miss re-exports, namespace imports, dependency injection, or calls made through a locally renamed symbol. It also requires unnecessary manual inspection of every consumer of the module. Option D relies on documentation, which may be incomplete or stale and cannot prove that all executable references have been found.
Before removal, the agent should also inspect tests, dynamic registrations, configuration-driven references, and generated code where relevant. The defining requirement is to map the alias chain first and then search all discovered public names.
Official references/topics: Read and Grep Tool Behavior; Symbol and Alias Discovery; Codebase Reference Tracing.
NEW QUESTION # 51
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.
Your code review assistant needs to analyze pull requests and provide feedback on three aspects: code style compliance, potential security issues, and documentation completeness. Each aspect requires reading files, running analysis tools, and generating a report section. The review process follows the same three-step workflow for every PR.
Which task decomposition pattern is most appropriate for this workflow?
Answer: A
Explanation:
Prompt chaining is appropriate because the workflow consists of predictable, fixed subtasks that apply to every pull request. The system can run a focused style-compliance analysis, then a security analysis, then a documentation review, and finally synthesize the three results into a consistent report.
Anthropic defines prompt chaining as decomposing a task into a sequence of steps in which each call handles a smaller component. It is recommended when a task can be cleanly divided into fixed subtasks, trading additional latency for better focus and accuracy. Intermediate checks can also be inserted between stages to confirm that each report section meets its requirements. ( https://www.anthropic.com/engineering/building- effective-agents ) Option A concentrates all considerations into one call, making it easier for one aspect to receive inadequate attention. Option B is unnecessary because orchestrator-workers is intended for complex work where the required subtasks cannot be predicted in advance. Here, the three review dimensions are already known and remain constant. Option D solves a different problem: routing is appropriate when inputs belong to distinct categories that require different downstream processes.
Although the three analyses could potentially be parallelized for lower latency, the option that accurately represents the stated repeatable decomposition is prompt chaining with final synthesis.
Official references/topics: Prompt Chaining, Fixed Subtasks, Intermediate Validation, Workflow Selection.
NEW QUESTION # 52
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