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| Section | Weight | Objectives |
|---|---|---|
| Model Selection and Optimization | 16.8% | - Model Selection - Cost and Latency Optimization - Model Capabilities and Trade-offs - Performance Optimization |
| Applications and Integration | 33.1% | - Software Engineering Fundamentals - Message Batches and Prompt Caching - Streaming, Error Handling and Reliability - Multimodal and Structured Outputs - Claude API and Client SDKs - API Integration and Application Development |
| Agents and Workflows | 14.7% | - Agent Architecture - Agent Patterns and Frameworks - Agent Construction with Claude |
| Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Prompt and Context Engineering | 11% | - Context Management and Long-Context Techniques - Context Engineering - Prompt Engineering |
| Security and Safety | 8.1% | - Application Security - Prompt Injection and Untrusted Content - Secure Tool Use and Guardrails - Safety and Responsible Development |
| Tools and MCPs | 10.6% | - Building Custom Tools and MCP Servers - Model Context Protocol - Tool Use and Tool Schemas |
| Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
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NEW QUESTION # 67
You are starting a new Claude application and have a small set of well-labeled examples that demonstrate the desired output format. You want to use these examples to guide Claude's behavior.
How would you guide the application's behavior?
Answer: A
Explanation:
The supplied examination set identifies A as correct. A small collection of high-quality labeled examples is ideally suited to few-shot or multishot prompting . The examples demonstrate concretely what acceptable input/output behavior looks like, allowing Claude to infer formatting, structure, tone, and task-specific conventions without requiring model retraining.
Anthropic's official prompting guidance states that examples are among the most reliable ways to steer Claude's output format, tone, and structure. It recommends using relevant, diverse examples and clearly separating them from the surrounding instructions. Anthropic currently recommends approximately three to five examples where practical and suggests XML structures such as < examples > and < example > to make prompt organization explicit.
B discards useful supervision by relying exclusively on zero-shot behavior. C is unnecessary for a small fixed set and does not ensure those examples are actually visible to Claude unless additional retrieval logic is created. D introduces unnecessary training complexity for a behavior that prompting already addresses efficiently.
Therefore, A provides the lowest-complexity, highest-leverage solution.
Relevant Claude Developer topics: Agent Construction, multishot prompting, few-shot learning, labeled examples, prompt design, output formatting, behavioral steering, and prompt evaluation .
NEW QUESTION # 68
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: D
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 # 69
A Claude application is producing outputs that drift away from the expected JSON format after several conversation turns. The first few turns produce correctly formatted output, but later turns gradually lose structure.
How would you address the drift?
Answer: B
Explanation:
Option A correctly treats this as a context-dependent format drift problem rather than an arbitrary model failure. Because the application initially produces valid JSON and progressively loses structure over subsequent turns, the investigation should focus on how accumulated conversation history changes the instructions and examples visible to Claude. Earlier malformed outputs, conflicting instructions, tool results, or unnecessary historical content can gradually reduce adherence to the intended format.
The application should therefore inspect the conversation state, preserve the authoritative formatting requirement, prune irrelevant content where appropriate, and validate outputs at the application boundary.
Anthropic's Structured Outputs capability is specifically designed to prevent malformed structured responses by constraining Claude to a supplied JSON Schema. Official guidance states that Structured Outputs provide valid, parseable, schema-compliant results for downstream processing.
B destroys the response rather than correcting the failure mode. C resets state but prevents meaningful multi- turn operation and does not diagnose why context causes drift. D changes model characteristics without establishing that the model is responsible.
The supplied question on page 12 identifies A as correct. Relevant topics: Agent Architecture, context engineering, format drift, structured outputs, context evolution, schema validation, and long-running conversations.
NEW QUESTION # 70
Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?
Answer: B
Explanation:
Option A correctly separates sampling configuration from context management. Temperature historically controlled the randomness of token selection; it did not increase the number of tokens Claude could accept within a request. Anthropic's current Messages API documentation continues to describe temperature in terms of randomness and, for newer model generations, marks manual temperature control as deprecated. Therefore, changing temperature cannot solve a context-capacity problem.
Long transcripts instead require context-engineering techniques. Appropriate approaches include chunking documents, summarizing earlier material, retrieving only relevant sections, or using context editing
/compaction so high-value information remains visible while unnecessary material is removed. Anthropic's context-editing guidance explicitly supports summarization and replacement of growing conversation history to keep long-running workloads within usable context limits.
B incorrectly conflates generation parameters with context capacity. C may save some tokens but removes persistent application instructions and is therefore architecturally unsound. D modifies an unrelated parameter without addressing the root cause. Relevant Study Guide topics: context windows, token budgets, sampling parameters, summarization, chunking, and context engineering.
NEW QUESTION # 71
A teammate has asked you to explain why your Claude agent's tools include detailed descriptions in the tool definition, even when the tool name is already descriptive. The teammate suggests removing the descriptions to simplify the tool definitions.
How would you respond?
Answer: B
Explanation:
The supplied exam source identifies C . Tool descriptions are part of Claude's runtime decision context. A descriptive function name can indicate what a tool appears to do, but it may not clearly tell Claude when to use it, when not to use it, what assumptions apply, or how it differs from similar tools .
Anthropic's official tool-use documentation states that Claude determines whether to call a tool based on the user's request and the tool's description. Its tool-definition guidance defines description as detailed plaintext describing what the tool does, when it should be used, and how it behaves. Anthropic's troubleshooting guidance also identifies ambiguous descriptions as a cause of selecting the wrong tool and recommends differentiating tools by when they should be used, not only by what they do.
A can supplement descriptions with examples but should not universally replace them. B incorrectly assumes names provide sufficient semantics. D removes the information from Claude's actual decision context.
Therefore, tool descriptions are operational metadata for model routing, not merely documentation for developers.
Relevant Claude Developer topics: Agent Construction, tool definitions, tool descriptions, tool selection, schema design, disambiguation, routing, and reliable tool use .
NEW QUESTION # 72
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