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| Section | Weight | Objectives |
|---|---|---|
| Claude Code | 3.1% | - Claude Code configuration and usage |
| Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
| Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Model Selection and Optimization | 16.8% | - Claude model family characteristics - Latency and performance trade-offs - Cost and token optimization |
| Applications and Integration | 33.1% | - Vision capabilities - Claude Messages API - SDK and third-party integration - Streaming and Batch API |
| Agents and Workflows | 14.7% | - Memory and context management - Agent architecture principles - Claude Agent SDK usage - Workflow vs autonomous agents |
| Security and Safety | 8.1% | - AI application security - Guardrails and safety controls |
| Prompt and Context Engineering | 11% | - Structured output handling - Prompt design and structuring - Context window management |
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問題 #66
A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?
答案：B
解題說明：
Option B reflects the fundamental generative behavior of large language models. Claude does not retrieve a fixed response template for a given prompt. Instead, it generates the response incrementally, selecting subsequent tokens according to the probability distribution produced by the model. Consequently, semantically equivalent runs can differ in wording, ordering, whether prompt language is quoted, and whether it is paraphrased.
Anthropic's API documentation explicitly describes sampling parameters as controlling randomness in subsequent-token selection and also states that even configurations intended to minimize randomness do not make output completely deterministic. Current Claude model generations increasingly manage sampling internally, but the underlying principle remains: generative output is not guaranteed to be textually identical across equivalent calls.
Therefore, seeing the original prompt echoed in one response and paraphrased in another does not, by itself, demonstrate malformed request construction. A real request-construction problem should instead be established through traces showing different system instructions, messages, parameters, or context.
A, C, and D incorrectly infer specific implementation faults without supporting evidence.
The supplied question identifies B as the correct response. Relevant topics: SW Eng Foundations, token generation, sampling, nondeterminism, debugging, and model behavior.
問題 #67
Your Claude application uses structured output that is consumed by downstream code. The team wants to handle malformed or unexpected output gracefully so it does not crash downstream systems.
The best choice for handling this issue would be to...
答案：A
解題說明：
Option D applies a fundamental production engineering principle: treat externally generated data as potentially malformed and parse it defensively before use. Downstream application logic should not assume that every field exists, every type is correct, or every unexpected property can safely be ignored. Instead, the parser should validate expected structures, handle optional or missing values deliberately, reject incompatible types, and convert failures into controlled application errors rather than process crashes.
Anthropic's Structured Outputs documentation identifies exactly these failure classes for unconstrained model output: malformed JSON, missing required fields, inconsistent types, and schema violations can break downstream applications. Current Structured Outputs and strict tool-use capabilities can eliminate many schema-level failures through constrained decoding, but defensive handling remains an important software boundary when unexpected data can still arise from external services, legacy paths, or semantic validation requirements.
A creates unnecessary outages. B hides failures and discards potentially recoverable data without observability. C deliberately postpones a reliability requirement until after deployment.
The supplied question identifies D as correct. Relevant topics: Claude App Design, defensive programming, structured output, schema validation, parsing, error handling, downstream reliability, and type safety.
問題 #68
A teammate is debugging a Claude application whose system prompt has grown to several hundred lines and now contains overlapping, contradictory, and obsolete instructions.
How would you advise the teammate?
答案：C
解題說明：
Option B is correct. A large prompt containing overlapping, contradictory, or obsolete instructions creates instruction ambiguity and increases the likelihood that Claude will apply an unintended rule, over-weight irrelevant guidance, or produce inconsistent behavior.
Anthropic's current prompting guidance reinforces this principle: Claude performs best when instructions are clear, explicit, and direct. Prompt design should minimize ambiguity and provide the model with precisely the context and behavioral requirements needed for the task. Refactoring should therefore remove obsolete requirements, consolidate duplicated instructions, resolve contradictions, and establish one authoritative formulation for each behavioral rule. The revised prompt should then be regression-tested against representative inputs.
A makes an already overloaded prompt larger and relies on instruction ordering instead of eliminating conflicts. C addresses only one defect while retaining redundant and obsolete material. D does not inherently solve contradictions and may fragment authoritative instructions across multiple locations.
Therefore, B addresses the root cause. Relevant Study Guide topics: system prompt design, prompt maintainability, instruction clarity, conflicting instructions, prompt refactoring, regression evaluation, and context quality.
問題 #69
Your agent makes 10 to 15 tool calls per task, and you have noticed it sometimes loses track of earlier results by the time it reaches later steps. The agent's context window is large enough to hold all the messages, but the relevant information appears to get buried as the conversation grows.
How would you address this?
答案：D
解題說明：
Option D is correct because the problem is not insufficient nominal context capacity; it is degraded signal quality as the working context grows. Anthropic's context-window guidance explicitly states that more context is not automatically better. As conversations expand, recall and accuracy can degrade through
"context rot," so long-running agents need active context management rather than simply retaining every prior tool result.
A good pattern preserves the active task state while compacting, summarizing, or pruning stale intermediate outputs. Anthropic documents server-side compaction for long-running conversations and context-editing
/pruning approaches that clear old tool results when they no longer contribute useful information. This keeps high-value constraints and conclusions visible without carrying every verbose response forward.
Option A changes frameworks without fixing the architectural cause. Option B may reduce call count but also creates larger multi-purpose tools and does not guarantee better context quality. Option C increases capacity while preserving the same low-signal accumulation, so the relevant facts can remain buried.
Therefore, D best implements context engineering for agentic workloads. Relevant Study Guide topics:
context engineering, context rot, compaction, tool-result pruning, long-running agents, state preservation, and context-window optimization.
問題 #70
Your enterprise has a contract with AWS that requires Claude API calls to flow through Amazon Bedrock rather than the direct Anthropic API. Your team is building a new Claude application and is unfamiliar with this constraint.
How would you build the application?
答案：A
解題說明：
Option C satisfies both the enterprise routing requirement and sound application architecture. Claude is available through Amazon Bedrock, and Anthropic provides Bedrock-specific SDK integration rather than requiring applications to call api.anthropic.com directly. Current Anthropic documentation describes Claude in Amazon Bedrock as operating through AWS-managed infrastructure with AWS-native authentication, billing, and security boundaries. Newer Bedrock integrations use the Messages API shape, allowing substantial application logic to remain consistent across provider environments.
Anthropic SDKs also provide dedicated Bedrock clients-for example, Python includes AnthropicBedrockMantle for current Bedrock deployments. Keeping business logic separated from provider- specific authentication, endpoints, model identifiers, and transport configuration reduces migration and maintenance risk.
A violates architectural simplicity by duplicating every call unnecessarily. B knowingly violates the enterprise requirement until migration occurs. D directly ignores the contractual routing constraint and is therefore invalid regardless of technical feasibility.
The correct approach is to make Bedrock the configured inference provider while keeping higher-level application and agent behavior decoupled from provider-specific implementation details. Relevant Claude Developer topics are Claude API mechanics, cloud-provider integrations, Amazon Bedrock, SDK configuration, authentication boundaries, model invocation, and provider abstraction.
問題 #71
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