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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting |
| Topic 2: Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Error handling and tool response formatting |
| Topic 3: Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Agentic loop design and stop_reason handling |
| Topic 4: Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration - CLAUDE.md hierarchy, precedence and @import rules |
| Topic 5: Context Management & Reliability | 15% | - Context window optimization and prioritization - Idempotency, consistency and failure resilience - Token budget management and cost control - Context pruning and summarization strategies |
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NEW QUESTION # 31
Why is evaluation important after deploying a Claude application?
Answer: A
Explanation:
Continuous evaluation allows organizations to measure accuracy, safety, consistency, and user satisfaction. Regular testing identifies regressions and supports iterative improvement of prompts, retrieval pipelines, and application workflows.
NEW QUESTION # 32
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your pipeline uses a tool called extract_metadata with a JSON schema for paper details. You've also defined lookup_citations and verify_doi tools for enrichment. During testing, you notice that when users include requests like "extract the metadata and tell me how cited it is," Claude sometimes calls lookup_citations first, which fails because it needs the DOI that extract_metadata would provide.
What's the most effective way to ensure structured metadata extraction happens first?
Answer: B
Explanation:
The dependency must be enforced by orchestration rather than left to probabilistic tool selection. Anthropic documents that tool_choice: { " type " : " tool " , " name " : " ... " } forces Claude to invoke the specified tool.
By contrast, auto allows Claude to decide whether and which tool to call, while any requires some tool but does not force a particular one. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/define-tools ) Option A therefore establishes a deterministic two-stage workflow. The first API turn forces extract_metadata
, producing the DOI and other structured paper details. The application validates and stores that result. A subsequent turn then exposes or permits verify_doi and lookup_citations , passing the extracted DOI as explicit state. This design converts an implicit tool dependency into an application-controlled execution graph.
Option B is incorrect because array order is not a documented precedence mechanism and cannot guarantee selection. Option C forces extract_metadata on every call, including turns where enrichment should occur, potentially creating an infinite or non-progressing workflow. Option D guarantees only that one available tool is called; Claude could still select lookup_citations before the DOI exists.
For stronger input integrity, the tools can also use strict schemas so their arguments conform to the declared JSON Schema. The sequencing requirement, however, remains the responsibility of the orchestration layer.
Official references/topics: Tool Choice; Forced Tool Invocation; Multi-Turn Tool Orchestration; Tool Dependency Management.
NEW QUESTION # 33
Which prompt revision is MOST likely to improve output quality?
Answer: D
Explanation:
High-quality prompts clearly specify the objective, necessary background information, operational constraints, and desired response format. Claude performs best when expectations are explicit and well structured.
NEW QUESTION # 34
Your conversational assistant frequently generates multiple clarifying questions when users make ambiguous requests. When a user asks "Can you help me with the report?", the assistant responds: "I'd be happy to help! Could you tell me: 1) Which report? 2) What kind of help - drafting, reviewing, or formatting? 3) What's your deadline?" User analytics show a 40% conversation abandonment rate after these multi-question responses.
What's the most effective way to reduce friction while appropriately handling ambiguity?
Answer: C
Explanation:
Making reasonable assumptions based on context and clearly stating them allows the assistant to respond immediately while giving users the opportunity to correct or refine the interpretation, reducing abandonment caused by long multi-question prompts and maintaining engagement.
NEW QUESTION # 35
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline.
The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your test generation produces unit tests for new code, but reviews show that 55% are low-value: trivial assertions that only verify functions do not throw exceptions, tests duplicating existing coverage, or tests ignoring your team's fixture conventions.
How do you reduce the rate of low-value tests being generated in the first place?
Answer: B
Explanation:
Option D improves the generation conditions instead of filtering defective output afterward. Anthropic recommends using CLAUDE.md for repository-wide standards and information that Claude should apply in every session. The file can define what constitutes a useful test, require assertions on observable behavior, identify approved fixture factories, prohibit duplication of existing scenarios, and provide contrasting examples of strong and trivial tests. These instructions are available before Claude decides what tests to create.
Option A may remove some weak tests, but it doubles model work and still permits the first stage to generate low-quality material. Option B treats line coverage as a quality measure even though a trivial assertion can execute new lines without validating behavior. Option C abandons difficult areas rather than improving generation. The standards should also direct Claude to inspect nearby tests and fixtures before writing new cases, identify the behavior or regression each test protects, and run the relevant suite afterward. Because CLAUDE.md consumes context in every session, the guidance should remain concise; lengthy testing procedures are better placed in a reusable skill referenced from the project instructions.
NEW QUESTION # 36
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