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| Section | Weight | Objectives |
|---|---|---|
| Security and Safety | 8.1% | - Secure Tool Use and Guardrails - Application Security - Prompt Injection and Untrusted Content - Safety and Responsible Development |
| Model Selection and Optimization | 16.8% | - Model Selection - Performance Optimization - Cost and Latency Optimization - Model Capabilities and Trade-offs |
| Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Agents and Workflows | 14.7% | - Agent Construction with Claude - Agent Patterns and Frameworks - Agent Architecture |
| Prompt and Context Engineering | 11% | - Prompt Engineering - Context Management and Long-Context Techniques - Context Engineering |
| Applications and Integration | 33.1% | - API Integration and Application Development - Message Batches and Prompt Caching - Multimodal and Structured Outputs - Software Engineering Fundamentals - Claude API and Client SDKs - Streaming, Error Handling and Reliability |
| Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Tools and MCPs | 10.6% | - Tool Use and Tool Schemas - Model Context Protocol - Building Custom Tools and MCP Servers |
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NEW QUESTION # 81
A teammate has asked you to explain why the team's Claude application is billed for output tokens at a different rate than input tokens. They had assumed the rate was the same for both.
How would you explain the difference?
Answer: B
Explanation:
The supplied Claude Certified Developer Foundations question identifies D as the correct answer. Anthropic prices input and output tokens separately, so application cost models must independently account for the number of tokens supplied to Claude and the number generated in response.
Anthropic's official pricing documentation confirms that output-token pricing is normally higher than base input-token pricing. For example, current Claude models maintain distinct columns for base input tokens and output tokens; the exact dollar amounts depend on the selected model. This distinction matters because an application with relatively small prompts but very large generated responses can incur substantial output- token expenditure even when input volume is modest.
Therefore, cost forecasting should use approximately: input tokens × input rate + output tokens × output rate , with additional adjustments where prompt caching, batch processing, server tools, or other pricing modifiers apply. Option A incorrectly assumes equivalent rates, B reverses the normal relationship, and C incorrectly excludes generated tokens from billing.
Relevant Claude Developer topics: Claude API Mechanics, token accounting, model pricing, input/output token usage, cost modeling, prompt caching, and production API economics .
NEW QUESTION # 82
Your team's Claude application has been in production for a year, and the team has decided to formalize its testing strategy. Currently, the team writes ad-hoc tests for individual features but has no overall testing approach.
What testing approach would you formalize?
Answer: D
Explanation:
Option B establishes a layered testing strategy rather than relying on a single testing granularity. Unit tests validate deterministic functions and isolated components quickly. Integration tests verify boundaries between application code and Claude-related components such as API clients, tool execution, parsing, persistence, and error handling. End-to-end tests then validate the most important user workflows across the complete application stack.
This separation is particularly useful for Claude applications because deterministic software failures and probabilistic model-quality failures should not be treated identically. Anthropic's evaluation guidance recommends defining specific, measurable success criteria and constructing representative test cases to determine whether model behavior meets those criteria. These evaluations complement conventional software tests rather than replacing them.
A focuses exclusively on test-driven development; TDD can be valuable but does not define all required test levels. C preserves the existing ad-hoc methodology rather than establishing a repeatable quality strategy. D overuses expensive and slower end-to-end tests while omitting the faster diagnostic value of unit and integration tests.
The supplied exam source marks B as correct. Relevant topics: SW Eng Foundations, test strategy, unit testing, integration testing, end-to-end testing, Claude evaluations, regression coverage, and production reliability.
NEW QUESTION # 83
Your Claude agent has too many tools, and many of them have overlapping functionality. The agent often picks an inappropriate tool when several could plausibly handle a request.
How would you address the tool selection problem?
Answer: A
Explanation:
Option D addresses the architectural cause of the failure: an ambiguous tool surface. Claude chooses tools partly from their names, descriptions, schemas, and the relationship between the request and the capability described. If multiple tools appear to perform substantially the same task, selection becomes unnecessarily difficult.
Anthropic's tool-definition guidance explicitly recommends consolidating related operations into fewer tools and making descriptions clear about both what a tool does and when it should be used. The documentation notes that fewer, more capable tools reduce selection ambiguity and make the available tool surface easier for Claude to navigate.
Option A makes the ambiguity worse by expanding an already overlapping tool set. B destroys required application capabilities. C could improve selection somewhat, because examples can clarify complex inputs, but it leaves the underlying duplication intact. Examples complement good tool design; they are not a substitute for distinct tool responsibilities.
Therefore, D is the strongest solution: remove unused tools, merge functions that represent the same conceptual operation, and write discriminative descriptions defining appropriate and inappropriate usage.
Relevant Study Guide topics: tool design, tool selection, descriptions, tool consolidation, agent construction, and reducing ambiguity.
NEW QUESTION # 84
A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?
Answer: B
NEW QUESTION # 85
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...
Answer: C
Explanation:
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.
NEW QUESTION # 86
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