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| Section | Weight | Objectives |
|---|---|---|
| Agents and Workflows | 14.7% | - Agent Architecture - Agent Construction with Claude - Agent Patterns and Frameworks |
| Model Selection and Optimization | 16.8% | - Model Capabilities and Trade-offs - Model Selection - Cost and Latency Optimization - Performance Optimization |
| Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Tools and MCPs | 10.6% | - Building Custom Tools and MCP Servers - Tool Use and Tool Schemas - Model Context Protocol |
| Security and Safety | 8.1% | - Safety and Responsible Development - Secure Tool Use and Guardrails - Prompt Injection and Untrusted Content - Application Security |
| Prompt and Context Engineering | 11% | - Context Management and Long-Context Techniques - Prompt Engineering - Context Engineering |
| Applications and Integration | 33.1% | - Software Engineering Fundamentals - Message Batches and Prompt Caching - API Integration and Application Development - Multimodal and Structured Outputs - Claude API and Client SDKs - Streaming, Error Handling and Reliability |
| Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
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NEW QUESTION # 15
You are implementing a custom tool for your Claude agent. The tool needs to interact with an external pricing service that returns product data.
Which of the following best practices would you apply as you develop this tool?
Answer: B
Explanation:
Option D combines the three key properties of a reliable Claude tool: an explicit contract, clear tool-selection guidance, and controlled execution failure handling. Anthropic's tool documentation defines user tools using a name, detailed description, and JSON input_schema. The description should explain what the tool does, when it should and should not be used, parameter semantics, and relevant limitations. Anthropic emphasizes that precise descriptions materially improve Claude's ability to select the correct tool.
A clear schema prevents ambiguous parameter interpretation and allows validation before calling the external pricing API. Where stronger guarantees are required, Anthropic also supports strict tool use, which constrains generated tool inputs to the declared JSON Schema.
The application's execution layer must also convert pricing-service failures into explicit, handled error paths rather than uncontrolled exceptions. A deprives Claude of critical selection information. B increases malformed-call risk. C delegates infrastructure reliability to the reasoning loop instead of implementing appropriate integration error handling.
Therefore, D represents production-quality custom-tool construction. Relevant Study Guide topics: custom tools, JSON Schema, tool descriptions, validation, external API integration, and error handling.
NEW QUESTION # 16
You are building a Claude application that processes 10,000 customer emails overnight to extract structured data. The work is non-interactive, runs once daily, and has a flexible completion window of several hours.
Which Claude API would you use?
Answer: A
Explanation:
Option A is correct because the Message Batches API is designed for asynchronous, high-volume processing where results do not need to be returned interactively. Anthropic's API reference states that a Message Batch can contain many independent Messages requests and may take up to 24 hours to complete. That makes it appropriate for 10,000 overnight email-extraction jobs with a several-hour completion window.
Streaming in B solves a different requirement: it exposes partial response events while a single request is being generated, which is valuable for interactive user experiences or long-running synchronous requests, but it does not provide the workload-management advantages of a batch job. C processes items sequentially and unnecessarily sacrifices throughput. D can increase throughput with concurrent real-time calls, but it adds concurrency management and rate-limit pressure when the workload explicitly tolerates asynchronous completion.
The batch design also lets each request carry a custom identifier so results can be matched back to source emails even if completion order differs. Therefore, A is the intended Claude API choice. Relevant Study Guide topics: Message Batches API, asynchronous processing, high-volume workloads, request correlation, throughput, and non-interactive application design.
NEW QUESTION # 17
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: A
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 # 18
A teammate has asked how the Claude SDK handles transient API errors, such as a temporary network issue or a brief rate-limit response. They want to know whether the application code needs to handle every transient error or whether the SDK provides any default behavior.
How would you describe the SDK's default behavior?
Answer: B
Explanation:
Option D precisely matches Anthropic's SDK behavior. Official SDK documentation states that transient conditions are automatically retried two times by default with exponential backoff. The default retry set includes connection failures, HTTP 408 Request Timeout, 409 Conflict, 429 Rate Limit, and 5xx server errors. Developers can alter or disable this behavior through the SDK's retry configuration.
This directly eliminates A because the retry count is configurable. B is incorrect because rate-limit responses are among the conditions automatically retried by the supported SDKs; the API also provides retry-after information where applicable. C is incorrect because the SDK does not merely log these failures and continue without attempting recovery.
Application-level handling is still necessary after the configured retry policy is exhausted. Production code should distinguish permanent errors from transient failures, preserve request IDs for diagnostics, and decide whether higher-level fallback, queuing, or user-visible failure handling is appropriate.
Therefore, D correctly characterizes the SDK abstraction: useful retry defaults combined with developer control. Relevant Study Guide topics: Claude SDKs, transient errors, HTTP 429, retries, exponential backoff, configuration, and resilient API integration.
NEW QUESTION # 19
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: C
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 # 20
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