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| Section | Weight | Objectives |
|---|---|---|
| Applications and Integration | 33.1% | - Streaming, Error Handling and Reliability - Software Engineering Fundamentals - Message Batches and Prompt Caching - API Integration and Application Development - Claude API and Client SDKs - Multimodal and Structured Outputs |
| Tools and MCPs | 10.6% | - Tool Use and Tool Schemas - Model Context Protocol - Building Custom Tools and MCP Servers |
| Model Selection and Optimization | 16.8% | - Model Selection - Cost and Latency Optimization - Model Capabilities and Trade-offs - Performance Optimization |
| Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Agents and Workflows | 14.7% | - Agent Patterns and Frameworks - Agent Construction with Claude - Agent Architecture |
| Prompt and Context Engineering | 11% | - Context Engineering - Prompt Engineering - Context Management and Long-Context Techniques |
| Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Security and Safety | 8.1% | - Application Security - Secure Tool Use and Guardrails - Safety and Responsible Development - Prompt Injection and Untrusted Content |
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NEW QUESTION # 37
You are writing a system prompt for a Claude application that needs to produce output in a specific JSON shape. The downstream system will reject any output that does not match the schema.
Your prompt would need to...
Answer: C
Explanation:
The supplied question selects D . If a downstream component requires an exact machine-readable structure, the expected structure must be communicated explicitly rather than left to Claude's discretion. The prompt should define required fields, types, nesting, permissible values where relevant, and instruct Claude not to emit surrounding prose.
Anthropic's consistency guidance states that developers should precisely specify the desired output format when format consistency matters. More importantly, current Claude APIs provide Structured Outputs for cases requiring guaranteed JSON Schema conformance; Anthropic explicitly recommends Structured Outputs instead of prompt-only techniques when valid schema-compliant JSON is mandatory.
Therefore, D is the strongest prompt choice among the listed alternatives. In a contemporary production implementation, the design can be strengthened further by supplying the schema through Claude's structured- output configuration and performing downstream semantic validation where business rules exceed JSON Schema.
A permits arbitrary formatting. B intentionally creates inconsistent representations. C assumes post- processing can reliably reconstruct missing or ambiguously formatted information, which is significantly less robust than specifying the contract up front.
Relevant Claude Developer topics: system prompts, JSON formatting, structured outputs, schema constraints, output contracts, validation, and downstream integration reliability .
NEW QUESTION # 38
You are designing an agent that handles a multi-step research task. You want the agent to break the task into smaller pieces, hand each piece to a focused subagent, and consolidate the results.
The agent pattern you would apply is...
Answer: D
Explanation:
The supplied Claude Developer source explicitly marks A . The scenario contains the defining elements of an orchestrator/subagent architecture : decomposition of a larger objective, delegation of independent subtasks to specialized workers, and aggregation of their outputs by a coordinating agent.
This architecture is appropriate when subtasks can be performed with focused context or specialized tools.
Instead of forcing one agent to carry every intermediate detail, the orchestrator can formulate assignments, launch suitable subagents, receive condensed results, identify missing information, and synthesize the final research product. This also enables context isolation and potentially parallel execution.
Anthropic's published multi-agent architecture uses this orchestrator-worker approach for research workloads:
a lead agent decomposes a query, delegates work to specialized subagents, and then integrates their findings.
This is particularly useful where exploration is broad and individual subtasks benefit from independent context windows. The broader model-selection guidance also identifies orchestrator strategies as useful where work can be partitioned among worker models.
B describes storage rather than delegation. C is a context-management technique, not a decomposition pattern.
D centralizes all responsibilities in a single loop.
Relevant Claude Developer topics: Agent Patterns, orchestration, subagents, task decomposition, specialization, delegation, context isolation, and result consolidation .
NEW QUESTION # 39
Your team uses several plugins across multiple Claude applications, and a recent plugin update introduced a regression. The team had not been tracking plugin versions, so the team cannot easily identify which version was previously working. How would you address this?
Answer: C
Explanation:
Option B is correct because plugin dependencies and releases must be versioned deliberately if the team wants reproducible behavior and a reliable rollback path. Claude Code's plugin documentation supports explicit semantic versions in plugin manifests and marketplace entries, and the plugin system uses the resolved version as part of update detection and caching. Version constraints can also keep dependent plugins on a tested compatible range until the team intentionally upgrades.
The incident happened because the team could not determine which plugin version had previously worked.
Explicit tracking solves that directly: record the version in project configuration, pin known-good releases where stability matters, test upgrades, and change versions through reviewed configuration updates. This creates traceability and makes regression isolation much faster.
Option A removes useful extension functionality instead of managing it. Option C treats third-party change as uncontrollable even though the platform provides version-management mechanisms. Option D automatically moving everything to the latest release increases change frequency and can reproduce the same regression problem.
Therefore, B is the correct configuration-management response. Relevant Study Guide topics: Claude Code plugins, semantic versioning, dependency constraints, reproducible configuration, controlled upgrades, rollback, and configuration management.
NEW QUESTION # 40
Your Claude application's outputs are inconsistent in format. The team's instructions are scattered across the system prompt and user messages, with some instructions stated only once and others repeated.
How would you fix the inconsistency?
Answer: A
Explanation:
Option B creates a clear separation between persistent application instructions and per-request user content.
Stable behavioral rules-role, output conventions, formatting constraints, policies, and other instructions intended to apply across requests-belong in the system-level instruction layer. User messages should primarily carry task-specific input and requirements that vary from interaction to interaction.
Anthropic's Messages API defines the system prompt specifically as the mechanism for providing overarching context and instructions to Claude. Current guidance likewise recommends the top-level system field for instructions that should apply from the beginning of the conversation, while more transient information belongs in conversation messages.
A mixes persistent application behavior with user-controlled content and weakens separation of concerns. C preserves the inconsistent architecture responsible for the problem. D duplicates instructions unnecessarily, wastes context tokens, and increases the possibility that future edits create conflicting versions of the same rule.
Consolidating stable instructions also makes prompts easier to version, evaluate, review, and maintain.
Therefore, B is the correct design. Relevant Study Guide topics: system prompts, instruction hierarchy, prompt organization, formatting consistency, separation of concerns, and maintainable prompt construction.
NEW QUESTION # 41
Your Claude application's token costs have grown faster than expected. The team has not been tracking token usage by feature, so the team cannot identify which features are driving cost. The team is debating how to respond.
How would you respond?
Answer: C
Explanation:
Option D follows the fundamental optimization rule of measuring before changing. Without feature-level usage telemetry, the team cannot know whether cost growth comes from request volume, long prompts, excessive outputs, low cache-hit rates, expensive models, agent loops, tool results, or one particularly inefficient workflow.
Anthropic's Usage and Cost API exists specifically to provide granular historical usage and cost information.
It supports token tracking and breakdowns by dimensions including model, workspace, service tier, API key, context window, and other usage characteristics. Anthropic describes this data as useful for monitoring, cost reconciliation, optimization, and determining whether system changes improve efficiency.
B applies a model downgrade indiscriminately and may damage features whose quality requirements genuinely demand a stronger model. C similarly imposes an arbitrary token reduction without identifying where waste exists. A abandons cost management entirely.
The correct lifecycle is instrument, establish a baseline, identify high-cost features, analyze why they are expensive, apply targeted optimizations, and measure again. Relevant Study Guide topics: observability, usage telemetry, cost attribution, token accounting, production monitoring, capacity planning, and evidence- driven optimization.
NEW QUESTION # 42
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