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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Context window optimization and prioritization - Context pruning and summarization strategies - Idempotency, consistency and failure resilience - Token budget management and cost control |
| Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters |
| Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Tool distribution and permission controls - Error handling and tool response formatting |
| Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - System prompt design and persona alignment |
| Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns |
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NEW QUESTION # 150
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
You're implementing the escalation logic for when the agent should call escalate_to_human . Your team proposes four different approaches for triggering escalation.
Which approach will most reliably identify cases that genuinely require human intervention?
Answer: D
Explanation:
Option B identifies escalation through direct operational criteria rather than indirect proxies. A customer's explicit request for a person is unambiguous. A required policy exception defines an authorization boundary.
An inability to make meaningful progress identifies a case where continued autonomous execution is no longer productive.
Anthropic describes agents as systems that act, observe results, adjust, and continue until the task is completed or human input is required. Its trustworthy-agent guidance also emphasizes that agents must recognize when to pause rather than pushing through uncertainty or decisions that only a human can settle. (
https://www.anthropic.com/research/trustworthy-agents )
Option A is too rigid for high-ambiguity support cases. Maintaining exhaustive mappings for every issue, product, and customer segment recreates the brittleness that agentic reasoning is intended to avoid. Option C treats repeated tool use as a substitute for semantic progress; one definitive authorization failure may require immediate escalation, while several legitimate diagnostic calls may not. Option D confuses emotional tone with operational necessity. A calm customer may require a policy exception, while a frustrated customer may still have a straightforward automated resolution.
The escalation criteria should be encoded in the system instructions and supported by evaluations covering explicit requests, authorization limits, unresolved ambiguity, repeated non-progress, and successful self- service cases.
Official references/topics: Human-control checkpoints, escalation criteria, authorization boundaries, progress-aware agents.
NEW QUESTION # 151
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 submits two requests:
* Request A: "Rename the getUserData function to fetchUserProfile everywhere it's used."
* Request B: "Improve error handling throughout the data processing module-add try/catch blocks, meaningful error messages, and ensure failures don't silently corrupt data." For which request does specifying an explicit multi-phase workflow (such as analyze # propose # implement with review) most improve outcome quality?
Answer: A
Explanation:
Request B benefits substantially more from an explicit multi-phase workflow because it is broad, judgment- intensive, and likely to affect multiple execution paths. Improving error handling requires the agent to identify current failure modes, determine which exceptions should be caught, preserve useful diagnostic information, prevent partial state corruption, and verify that the resulting behavior remains consistent across the module.
An analyze # propose # implement # review sequence separates those concerns and creates checkpoints before consequential edits are made.
Request A is comparatively deterministic. The function and target name are already defined, and the task primarily requires locating references, applying the rename, and running tests or static analysis. Anthropic's Claude Code guidance states that planning is most useful when the approach is uncertain, the work spans multiple files, or the developer is unfamiliar with the affected code. For a small change that can be described as a straightforward diff, direct implementation is usually preferable. ( https://code.claude.com/docs/en/best- practices ) Anthropic also characterizes prompt chaining as appropriate when a task can be divided into fixed stages and additional latency is justified by increased accuracy. Request B fits that pattern because analysis and review materially reduce the risk of introducing swallowed exceptions or inconsistent recovery behavior. (
https://www.anthropic.com/research/building-effective-agents )
Official references/topics: Prompt Chaining; Plan Before Editing; Multi-Phase Coding Workflows; Verification and Review.
NEW QUESTION # 152
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.
1.5An 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 (~6,000 lines total).
What's the most effective next step for building understanding while managing context constraints?
Answer: C
Explanation:
The correct objective is to construct an architectural map before consuming the full implementation.
Identifying the base cache abstraction, its interface, and the classes that implement or invoke it gives the agent a dependency-guided path through the code. It can then inspect only the invalidation implementations and integration points relevant to the proposed trigger.
This approach protects the context window. Anthropic states that every file read occupies context and that model performance can deteriorate as the window fills. Its Claude Code guidance warns against unbounded investigation that reads large numbers of files and recommends narrowing the exploration or delegating it. (
https://code.claude.com/docs/en/best-practices )
Option A is too lexical: searching only for invalidate or expire can miss event-driven invalidation, overridden methods, cache-key mutation, and generic interface calls. Option B loads approximately 6,000 lines without first establishing relevance. Option C assumes that filename patterns and file size correlate with architectural importance; the largest files may contain incidental code while a small interface defines the entire design.
Option D follows control and type relationships rather than arbitrary file order. After reading the base class, the agent can search for subclasses, imports, construction sites, middleware hooks, and calls to the invalidation contract, progressively expanding only where evidence requires it.
Official references/topics: Context-Efficient Exploration; Dependency-Guided Reading; Architectural Interfaces; Narrowly Scoped Investigation.
NEW QUESTION # 153
Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement (losing nuance), while other times reports over-hedge with excessive qualifications (becoming unhelpful). When the web search agent returns "industry analysts estimate $50B market size (methodology varies)" and the document analysis agent returns "peer-reviewed study estimates $35B (±$7B, 95% CI)," the coordinator either picks one arbitrarily or produces vague statements like "the market may be
$35B-$50B depending on factors." What systematic approach best addresses this?
Answer: D
Explanation:
Structuring synthesis around established versus contested findings preserves uncertainty without collapsing conflicting evidence into a false single answer. It allows the report to retain source- specific context, such as analyst methodology variability and peer-reviewed confidence intervals, while still presenting a clear and useful interpretation.
NEW QUESTION # 154
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
After deployment, you find that 12% of extractions contain semantic errors that pass JSON Schema validation-for example, a duration such as "30 minutes" is incorrectly placed in an ingredient-quantity field.
Human reviewers have the capacity to check only 20% of extractions.
Which approach most effectively allocates reviewer attention?
Answer: B
Explanation:
Option A uses the limited review capacity as a risk-ranking mechanism rather than spending it uniformly.
JSON Schema validation can prove that an output has the expected types and structure, but it cannot detect a semantically misplaced value such as a duration stored as an ingredient quantity. Field-level confidence can identify the exact parts of a record that warrant inspection, provided those scores are calibrated against labeled examples rather than trusted at face value. The labeled validation set must mirror the production distribution and include edge cases; Anthropic's evaluation guidance explicitly recommends task-specific evaluations, representative distributions, measurable criteria, and automated grading where possible. Calibration then converts raw confidence into an empirically tested estimate of error risk, allowing reviewers to inspect the lowest-confidence records or fields first. Option C, random sampling, is useful for unbiased quality measurement and discovering new failure modes, but it does not maximize error detection within a fixed 20 percent review budget. Options B and D target narrow proxies-layout anomalies and missing required values-while the observed errors pass structural validation and may occur in ordinary-looking documents.
The operational design should combine calibrated risk-based review with a smaller random audit sample to detect drift and overconfidence.
NEW QUESTION # 155
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