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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agents and Workflows | 14.7% | - Agent Construction with Claude - Agent Architecture - Agent Patterns and Frameworks |
| Topic 2: Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Topic 3: Model Selection and Optimization | 16.8% | - Cost and Latency Optimization - Model Selection - Model Capabilities and Trade-offs - Performance Optimization |
| Topic 4: Applications and Integration | 33.1% | - Claude API and Client SDKs - Streaming, Error Handling and Reliability - Software Engineering Fundamentals - API Integration and Application Development - Message Batches and Prompt Caching - Multimodal and Structured Outputs |
| Topic 5: Tools and MCPs | 10.6% | - Tool Use and Tool Schemas - Model Context Protocol - Building Custom Tools and MCP Servers |
| Topic 6: Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Topic 7: Security and Safety | 8.1% | - Safety and Responsible Development - Prompt Injection and Untrusted Content - Application Security - Secure Tool Use and Guardrails |
| Topic 8: Prompt and Context Engineering | 11% | - Prompt Engineering - Context Management and Long-Context Techniques - Context Engineering |
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NEW QUESTION # 36
You are setting up a Claude application that requires API keys for several external services.
What is the best way to store the keys?
Answer: D
Explanation:
Option B follows standard secrets-management practice and Anthropic's explicit guidance for API credentials.
API keys are authentication secrets and should not be embedded in source code or committed to repositories.
Anthropic's authentication documentation explicitly recommends storing API keys in a secrets manager, rotating them periodically, and revoking credentials suspected of compromise. Anthropic SDKs can load Claude credentials from environment variables such as ANTHROPIC_API_KEY, allowing the secret to be injected at runtime instead of compiled into the application.
The same principle applies to external service credentials used by a Claude application. Development, staging, and production should normally receive independently scoped credentials through the deployment environment or secret-management infrastructure.
A creates a high-probability credential leak because repository history can retain secrets even after a later deletion. C violates isolation and least privilege by sharing credentials across services and users. D uses email as an uncontrolled secret-distribution channel and creates inconsistent manual configuration.
Therefore, B supplies credentials only at runtime while keeping them outside source control and enabling rotation and environment-specific access. Relevant Study Guide topics: API-key management, secrets managers, environment configuration, credential rotation, least privilege, and secure configuration.
NEW QUESTION # 37
Your team is integrating Claude into an existing REST API service. The service handles concurrent requests, and you are deciding how to structure the Claude API calls within the existing async codebase.
How would you structure the Claude calls?
Answer: B
Explanation:
Option C is the natural integration model for an application that already uses asynchronous I/O. Claude API calls are network-bound operations, so blocking the application's event loop while waiting for responses would reduce concurrency and impair throughput. The Anthropic Python SDK explicitly provides AsyncAnthropic, and its documented usage awaits client.messages.create() directly. It also supports an aiohttp backend when improved asynchronous concurrency is desired.
This means Claude requests can participate in the same cooperative asynchronous execution model as other database, HTTP, or service calls. While one request waits for remote I/O, the runtime can continue serving other work rather than dedicating the event loop to an idle blocking operation.
A can be used when integrating unavoidable blocking libraries into async software, but it adds thread-pool management when an official asynchronous client already exists. B needlessly redesigns a concurrent REST service around a synchronous integration. D is specifically harmful because blocking the event loop prevents normal concurrent request processing.
Therefore, C uses the abstraction provided for exactly this architecture. Relevant Study Guide topics: Claude SDK clients, asynchronous APIs, awaitable I/O, concurrency, REST-service integration, and scalable application architecture.
NEW QUESTION # 38
Your Claude application returns confident-sounding answers, but occasionally those answers contain factual errors that downstream systems treat as ground truth. The team is concerned about the application's confidence-versus-accuracy gap.
How would you address the gap?
Answer: A
Explanation:
Option B establishes the correct trust boundary. Fluent or confident language is not evidence that a generated claim is factually correct. If downstream systems treat output as authoritative data, the application must independently establish whether the output meets its correctness requirements before accepting it.
Validation can take several forms depending on the workload: compare generated facts against authoritative records, require citations or source references, constrain output to retrieved evidence, apply deterministic business rules, or use separate evaluation/classification stages. Anthropic's agent engineering guidance repeatedly emphasizes explicit evaluation criteria and validation rather than relying on apparent confidence.
A confuses sampling behavior with factual reliability. Lowering temperature does not establish factual correctness and may only make an incorrect answer more repeatable. C supplies maximum oversight but is unnecessarily expensive and removes useful automation even for low-risk, easily validated cases. D communicates uncertainty to users but does not protect downstream systems that automatically consume the response.
Therefore, B treats model output as untrusted until verified to the level required by the application. Relevant Study Guide topics: output validation, grounding, factuality, confidence calibration, source verification, trust boundaries, and downstream safety.
NEW QUESTION # 39
Your team's Claude agent has accumulated several customizations that bypass the SDK's defaults, including custom history management, retry logic, and error handling. A new team member has proposed reverting all the customizations to maintain the codebase more easily. The tech lead disagrees and says each customization was added for a reason.
How would you advise the team?
Answer: C
Explanation:
The correct approach is to evaluate each customization independently against both its original requirement and the capabilities currently supplied by the Claude SDK. Custom code has an ongoing maintenance cost, but automatically removing it is unsafe because some modifications may encode genuine application requirements that the standard SDK behavior still does not address. Conversely, automatically retaining every customization can preserve obsolete complexity after the SDK has evolved.
This is particularly relevant because Anthropic's SDKs already provide capabilities that developers might otherwise implement themselves. Official Claude API documentation states that SDKs provide built-in request formatting, error handling, retries, timeouts, and other integration functionality. For example, transient connection, rate-limit, and server errors are retried automatically with exponential backoff.
Therefore, each custom history, retry, or error-management layer should be reviewed for its actual purpose. If an SDK primitive now provides equivalent behavior, removing duplicate code improves maintainability. If the customization still satisfies a distinct requirement, it should remain.
The supplied exam source explicitly identifies D as the intended answer. Relevant topics: Agent Construction, SDK capabilities, abstraction boundaries, technical-debt management, retries, error handling, and agent state management.
NEW QUESTION # 40
Your Claude application's prompt was written months ago and has not been updated. The team has discovered through evals that the prompt produces good results on common cases but underperforms on a specific category of inputs that has grown in volume.
How would you respond?
Answer: C
Explanation:
The correct response is iterative prompt improvement backed by evaluation , making A the appropriate choice. The supplied Claude Certified Developer Foundations material explicitly selects A. When production input distribution changes, a prompt that previously met requirements can become inadequate. The correct engineering response is not to preserve the prompt merely because it once worked; prompts are application components that should evolve with observed workload behavior.
Anthropic's official evaluation guidance describes prompt development as a cycle involving test cases, an initial prompt, iterative testing and refinement, final validation, and deployment. Success criteria should be specific and measurable, and evaluations should include representative cases that expose known failure modes.
The underperforming category should therefore be incorporated into the evaluation set. The team can modify instructions, examples, context organization, or other prompt components, then compare the revised prompt against both the newly important category and existing common cases. This prevents improvement in one segment from silently producing regressions elsewhere.
B avoids the defect instead of correcting it. C risks degrading previously successful behavior. D creates unnecessary architectural fragmentation.
Relevant Claude Developer topics: prompt iteration, evaluation-driven development, regression testing, representative test sets, prompt optimization, and production feedback loops .
NEW QUESTION # 41
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