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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Error handling and tool response formatting - Tool schema design and interface boundaries |
| Topic 2: Claude Code Configuration & Workflows | 20% | - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration |
| Topic 3: Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement - System prompt design and persona alignment - Validation, parsing and retry loop strategies |
| Topic 4: Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection |
| Topic 5: Context Management & Reliability | 15% | - Context window optimization and prioritization - Token budget management and cost control - Idempotency, consistency and failure resilience - Context pruning and summarization strategies |
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NEW QUESTION # 102
Which practice MOST improves prompt maintainability?
Answer: A
Explanation:
Organized prompts with labeled sections improve readability for both developers and Claude.
Clear structure reduces ambiguity, simplifies maintenance, and makes future prompt modifications significantly easier.
NEW QUESTION # 103
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, and Glob--and integrates with Model Context Protocol (MCP) servers.
You are building a security-scanning workflow.
When engineers need to locate every occurrence of a dangerous function such as eval() across a large codebase, which tool should the agent use for content searching?
Answer: B
Explanation:
Option B uses the tool designed to search file contents. Anthropic's Claude Code tools reference distinguishes Grep from Glob: Grep searches lines inside files, whereas Glob matches filenames and paths. Grep is built on ripgrep and accepts regular-expression patterns, so the opening parenthesis should be escaped as eval\( when the intention is to match the literal function call.
The search can return matching files, line numbers, and surrounding context without loading every file into the model's context window.
NEW QUESTION # 104
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.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?
Answer: A
Explanation:
Long exploration sessions fill the context window and degrade recall. A persistent scratchpad externalizes important architectural findings so the agent can re-read them instead of relying solely on increasingly crowded conversation context.
NEW QUESTION # 105
Your automated code review is missing genuine bugs in pull requests. Investigation reveals that the review prompt includes this instruction: "Only flag critical issues that would definitely cause production failures.
Ignore minor concerns and anything you are uncertain about." Developers confirm that some missed findings are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with every finding tagged with metadata, and actionable. Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?
Answer: D
Explanation:
The prompt explicitly instructs Claude to suppress uncertain and lower-severity findings. Increasing reasoning depth cannot override that reporting policy: Claude may identify a real defect during analysis and still omit it from the final response. Option B separates comprehensive detection from acceptance filtering, removing the source of the false negatives while retaining metadata needed for automated decisions.
Anthropic's current code-review prompting guidance recommends reporting every issue, including uncertain or lower-severity candidates, and attaching confidence and estimated severity so a separate verification or filtering stage can rank them. A structured schema can additionally require fields such as file path, line number, category, confidence, severity, evidence, and recommended action.
Option A may improve investigation quality but leaves the suppression instruction intact. Option C removes the problematic threshold but also discards the explicit structured classification needed by downstream processing. Option D repeats the same review policy, so the second pass can suppress the same findings as the first. Option B preserves recall and machine-readable output while allowing deterministic, adjustable downstream thresholds. This is more reliable than embedding an overly restrictive acceptance decision inside the model's initial detection task.
NEW QUESTION # 106
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, and Glob-and integrates with Model Context Protocol (MCP) servers.
An engineer sees the unfamiliar error message SYNC_CONFLICT: entity version mismatch detected in production logs but does not know which of the 12 services in the codebase generates it. The engineer asks the agent to locate the responsible source code.
What exploration approach will find the responsible code most efficiently?
Answer: B
Explanation:
Option A begins with the strongest available discriminator: the exact production error text. Anthropic's tools reference specifies that Grep searches file contents and returns matching lines, enabling the agent to locate a literal error constant or message without loading the full repository. Searching both the stable code SYNC_CONFLICT and the distinctive phrase protects against cases where the identifier and displayed message are defined separately. The agent can then read only the matching files and follow the surrounding call path, error construction, and service ownership. Option B assumes that errors are stored in conventionally named directories, which may not hold in a twelve-service repository. Option C consumes substantial context before using the most direct evidence. Option D narrows the search to consumers of a known shared error module, but the message may be locally defined, generated from configuration, or wrapped by another component. Grep followed by targeted Read operations follows Anthropic's broader context-engineering principle of retrieving relevant source material just in time instead of placing an entire codebase into the active context.
NEW QUESTION # 107
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