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| Section | Weight | Objectives |
|---|---|---|
| Security and Safety | 8.1% | - Secure Tool Use and Guardrails - Prompt Injection and Untrusted Content - Safety and Responsible Development - Application Security |
| Agents and Workflows | 14.7% | - Agent Construction with Claude - Agent Patterns and Frameworks - Agent Architecture |
| Prompt and Context Engineering | 11% | - Context Engineering - Prompt Engineering - Context Management and Long-Context Techniques |
| Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Applications and Integration | 33.1% | - Streaming, Error Handling and Reliability - Multimodal and Structured Outputs - Software Engineering Fundamentals - Message Batches and Prompt Caching - Claude API and Client SDKs - API Integration and Application Development |
| Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Model Selection and Optimization | 16.8% | - Model Capabilities and Trade-offs - Model Selection - Performance Optimization - Cost and Latency Optimization |
| Tools and MCPs | 10.6% | - Building Custom Tools and MCP Servers - Model Context Protocol - Tool Use and Tool Schemas |
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NEW QUESTION # 50
Your Claude application is producing inconsistent outputs for similar inputs, even when using the same model and prompt. You want to debug the issue systematically.
Your debugging approach would...
Answer: D
Explanation:
The examination source explicitly identifies D . Non-determinism is a normal LLM property, but that fact does not justify assuming every inconsistency is unavoidable. Systematic diagnosis begins by capturing the complete execution conditions for successful and unsuccessful runs.
A useful trace should expose the actual user input, system prompt, prior messages or context, tools and tool results where applicable, model identifier, request parameters, output, stop reason, and relevant execution events. Current Anthropic agent infrastructure exposes session, span, and agent events specifically to provide observability into session state and execution progress. This makes comparison possible: the engineer can determine whether apparently "same" calls actually differ in accumulated context, tool state, model configuration, parameters, or external data.
A changes a variable before establishing the cause and, on newer Claude releases, temperature may not even be configurable. B generates additional samples but does not ensure the underlying conditions are understood.
C prematurely classifies unexplained behavior as acceptable nondeterminism.
The correct sequence is observe, compare, form a hypothesis, reproduce, modify, and evaluate .
Relevant Claude Developer topics: observability, tracing, debugging, model nondeterminism, request- state capture, failure reproduction, and production diagnostics .
NEW QUESTION # 51
You are reviewing an architectural diagram for a Claude-powered travel-booking system. The diagram shows a top-level component that interprets user requests and three subordinate components that handle flights, hotels, and ground transportation. The top-level component is responsible for routing each request, sequencing the subordinate components, and reconciling their outputs into a final itinerary. The diagram also shows that each subordinate component has its own tool list and own short conversation history that is not shared with the others.
Which architectural pattern does this diagram most closely describe?
Answer: D
Explanation:
The architecture is a manager/supervisor-or orchestrator/subagent-pattern with isolated subagent context.
The defining characteristics are a coordinating top-level agent, specialized subordinate agents, delegated responsibilities, and distinct tool/context boundaries. The supervisor determines which specialist should act, sequences work where necessary, and integrates the specialists' outputs into the final result.
Anthropic's published multi-agent research architecture uses this same structural principle. Its Research system employs an orchestrator-worker pattern in which a lead agent analyzes the task, creates specialized subagents, delegates separate research responsibilities, and consolidates their findings. Anthropic emphasizes that good delegation requires each subagent to receive a specific objective, task boundaries, expected output, and appropriate tools.
The question additionally specifies that each travel specialist has its own conversation history and tool list.
That eliminates B because context is explicitly not shared globally. C would involve fixed sequential processing rather than supervisor-directed specialization. D describes retrieval augmentation, not autonomous subordinate agents.
Therefore, A precisely matches both the hierarchy and context-isolation characteristics described.
The supplied examination set also marks A as the intended answer. Relevant topics: Agent Patterns, manager
/supervisor architecture, orchestrator-worker systems, subagents, delegation, specialized tools, and context isolation.
NEW QUESTION # 52
A teammate has asked you to explain the difference between context engineering and prompt engineering.
They have heard the terms used interchangeably and are unsure how each applies to a Claude application that processes long-running multi-step tasks.
How would you describe the distinction?
Answer: C
Explanation:
Option C accurately captures Anthropic's distinction. Prompt engineering primarily concerns how instructions are written, structured, and organized to obtain the desired behavior from a particular model invocation.
Techniques include explicit instructions, examples, roles, XML structure, output requirements, and task- specific prompt construction. Context engineering operates at a broader architectural level: it determines which information should actually be present in the model's context at each inference step.
Anthropic defines prompt engineering as methods for writing and organizing LLM instructions, whereas context engineering encompasses strategies for curating and maintaining the optimal set of tokens during inference. For long-running agents, context can contain system instructions, tools, MCP resources, retrieved documents, prior messages, tool results, summaries, and memory.
This distinction matters because multi-step agents continuously generate new state. Effective systems may prune obsolete results, retrieve information just in time, compact earlier conversation history, isolate subagent contexts, or store persistent state externally. B is incorrect because context engineering has not simply replaced prompt engineering; the two operate at different scopes. A defines context too narrowly, and D obscures an important architectural distinction.
Therefore, C correctly represents Claude Developer coverage of prompt engineering versus context engineering, context curation, agent state, long-horizon workflows, and context-window optimization.
NEW QUESTION # 53
You are building an agent that needs to call several internal APIs and a database in a structured, repeatable way. Your team has decided to use the Claude Agent SDK rather than build a custom loop. You are setting up the agent's tool definitions and execution loop.
How would you set up the tools and execution loop?
Answer: D
Explanation:
The supplied Claude Developer examination source selects A . The purpose of choosing an agent SDK rather than implementing a custom Messages API loop is to consume the SDK's higher-level abstractions. Re- implementing dispatch, iteration, and state handling would discard much of the value provided by the SDK.
Anthropic's current documentation distinguishes low-level tool-use loops from higher-level SDK-managed abstractions. With a manually implemented Messages API workflow, application code must inspect stop_reason, execute requested client tools, append tool_result blocks, preserve conversation history, and repeat until Claude completes the turn. Anthropic's higher-level tooling can instead encapsulate this repetitive control flow. Current migration guidance also confirms that Agent SDK @tool functions are automatically dispatched by the SDK and that agents, tools, and sessions are first-class SDK concepts.
B is incorrect because structured tool use should not be replaced with informal plain-text calls. C introduces an unnecessary custom state dependency when no external persistence requirement exists. D duplicates control-loop functionality despite the explicit decision to use the SDK.
Relevant Claude Developer topics: Agent SDK, tool definitions, dispatch, agentic loops, conversation state, tool execution, and abstraction selection .
NEW QUESTION # 54
The Claude application your team built has grown over six months, and the prompt-handling code has accumulated duplication and tangled control flow. The functionality is working, but new features are getting harder to add.
How would you address this?
Answer: B
Explanation:
The supplied Claude Developer item selects A . The symptoms-duplicated logic, tangled control flow, and increasing difficulty adding features-indicate accumulated technical debt in a component that has become structurally difficult to change. Because the problem is already materially reducing development velocity, deliberate refactoring should occur before additional feature pressure compounds it.
The objective is behavioral preservation with structural improvement. Duplicated prompt construction should be consolidated where the behavior is genuinely shared. Prompt preparation, configuration, API invocation, validation, error handling, and post-processing should have clear responsibilities. Control flow should be simplified so each stage is testable independently. Existing tests should first capture important current behavior so refactoring can proceed without silently changing application semantics.
B can be appropriate for minor cleanup, but the question describes broad systemic complexity. Mixing substantial structural refactoring into unrelated feature tickets makes scope, review, and regression analysis harder. C knowingly allows the debt to compound. D centralizes complexity rather than removing it and violates separation of concerns.
Relevant Claude Developer topics: SW Eng Foundations, refactoring, technical debt, DRY, separation of concerns, maintainability, modular design, regression testing, and control-flow simplification .
NEW QUESTION # 55
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