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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt and Context Engineering | 11% | - Context Management and Long-Context Techniques - Context Engineering - Prompt Engineering |
| Topic 2: Model Selection and Optimization | 16.8% | - Model Selection - Performance Optimization - Model Capabilities and Trade-offs - Cost and Latency Optimization |
| Topic 3: Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Topic 4: Tools and MCPs | 10.6% | - Tool Use and Tool Schemas - Model Context Protocol - Building Custom Tools and MCP Servers |
| Topic 5: Security and Safety | 8.1% | - Application Security - Secure Tool Use and Guardrails - Prompt Injection and Untrusted Content - Safety and Responsible Development |
| Topic 6: Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Topic 7: Agents and Workflows | 14.7% | - Agent Construction with Claude - Agent Architecture - Agent Patterns and Frameworks |
| Topic 8: Applications and Integration | 33.1% | - Streaming, Error Handling and Reliability - Message Batches and Prompt Caching - Multimodal and Structured Outputs - Software Engineering Fundamentals - Claude API and Client SDKs - API Integration and Application Development |
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NEW QUESTION # 26
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: D
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 # 27
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: A
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 # 28
Your Claude application requests structured JSON output from the model. Most of the time the JSON is well- formed, but occasionally Claude returns malformed JSON that breaks downstream processing.
How would you handle the malformed output?
Answer: C
Explanation:
Option B establishes a controlled boundary between probabilistic model output and deterministic downstream code. When structured data is machine-consumed, malformed JSON must be recognized as an explicit application error rather than allowed to propagate into parsers, databases, or other services.
Anthropic's Structured Outputs documentation identifies malformed JSON, missing fields, inconsistent types, and schema violations as exactly the kinds of failures that can break downstream systems when unconstrained output is used. Current Claude capabilities can constrain responses using JSON Schema, and SDK helpers can provide parsing and validation.
Even when an application uses an older or unconstrained generation path, it should parse against the expected schema, record validation failure, and enter a bounded recovery path such as retry, repair, fallback, or controlled rejection. A human review of every request is unscalable. C removes a useful structured interface instead of making it reliable. D performs uncontrolled blind retries and provides no schema-aware error handling or bounded fallback strategy.
Therefore, B gives the application explicit failure semantics and protects downstream systems. Relevant Study Guide topics: structured output, JSON validation, schema enforcement, retries, fallback handling, defensive parsing, and downstream reliability.
NEW QUESTION # 29
You are configuring Claude Code for a new project. The team needs to set permissions, default model selections, and environment-specific behavior at the project level so the configuration is consistent across all developers working on the repository.
The Claude Code mechanism you would use is...
Answer: A
Explanation:
D is directly aligned with Claude Code's configuration model. The supplied exam source marks D . Anthropic documents settings.json as the official mechanism for configuring Claude Code through hierarchical scopes.
Shared project configuration is stored in .claude/settings.json , which is intended to be checked into source control and shared with the team.
Project settings provide consistent repository-level behavior while still participating in Claude Code's configuration precedence. They can configure permissions, environment variables, tool behavior, and other supported project policies. Claude Code additionally distinguishes shared project settings from .claude
/settings.local.json, which is intentionally excluded from source control and is suitable for developer-specific experimentation or machine-local overrides.
A spreadsheet provides documentation but no executable configuration enforcement. B allows every developer's configuration to diverge and is appropriate only for values that genuinely belong to the local environment. C requires manual repetition and cannot reliably establish repository-wide policy.
Therefore, shared project settings.json is the appropriate configuration-as-code mechanism.
Relevant Claude Developer topics: Confia Management, Claude Code configuration, settings scopes, project settings, permissions, environment configuration, team consistency, and source-controlled configuration .
NEW QUESTION # 30
Your Claude application is deployed to development, staging, and production environments. Each environment uses a different model version, different prompt versions, and different plugin dependencies, but the configuration is currently scattered across environment variables, hardcoded values, and undocumented setup scripts.
How would you manage the configuration?
Answer: C
Explanation:
Option C provides the required configuration-management discipline. Development, staging, and production may legitimately use different models, prompts, plugins, permissions, or service endpoints, but those differences must be explicit, reproducible, and auditable rather than scattered across undocumented mechanisms.
Claude Code documentation follows the same configuration-as-code principle. Project-level configuration can live in source-controlled files such as .claude/settings.json, while project instructions are maintained in repository-level CLAUDE.md. Anthropic specifically distinguishes shared project settings from local developer configuration.
A introduces uncontrolled model changes and regression risk. B hides configuration in application logic and makes environment differences harder to review. D ignores the fact that environments often require deliberate differences-for example, production credentials or pinned release versions.
The correct strategy is therefore to define configuration centrally, pin compatibility-sensitive dependencies, record environment-specific overrides, review modifications through source control, and retain rollback history. Relevant Study Guide topics: configuration management, environment isolation, model versioning, prompt versioning, dependency management, reproducibility, and controlled deployment.
NEW QUESTION # 31
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