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| Section | Weight | Objectives |
|---|---|---|
| Agents and Workflows | 14.7% | - Agent architecture principles - Workflow vs autonomous agents - Memory and context management - Claude Agent SDK usage |
| Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Prompt and Context Engineering | 11% | - Context window management - Structured output handling - Prompt design and structuring |
| Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
| Model Selection and Optimization | 16.8% | - Cost and token optimization - Claude model family characteristics - Latency and performance trade-offs |
| Applications and Integration | 33.1% | - Vision capabilities - Claude Messages API - Streaming and Batch API - SDK and third-party integration |
| Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Claude Code | 3.1% | - Claude Code configuration and usage |
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NEW QUESTION # 70
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 # 71
You are designing an agent that processes vendor invoices. The work involves a small number of well- understood steps, but occasionally an invoice arrives in an unexpected format that requires the system to decide between rerouting, requesting clarification, or flagging for human review.
The most appropriate architecture for this system is...
Answer: A
Explanation:
Option D applies the correct hybrid pattern. Anthropic distinguishes workflows-where LLMs and tools execute along predefined code paths-from agents, where the model dynamically determines how to proceed.
Anthropic recommends choosing the simplest architecture that meets the requirement: workflows provide predictability and consistency for well-understood tasks, while agents are valuable where flexible model- driven decisions are necessary.
Standard invoice processing is explicitly described as a small number of known steps. That makes a deterministic workflow the appropriate default because the path can be tested, monitored, and reproduced.
The unexpected-format branch is different: the system must reason among rerouting, requesting clarification, or human escalation. That decision point is where agentic flexibility adds value.
A makes the entire process autonomous even though most steps do not require autonomy, increasing cost, latency, and unpredictability. B introduces multiple specialized agents despite no demonstrated need for that complexity. C collapses deterministic processing and exception handling into one monolithic model call, making validation and debugging harder.
Therefore, D combines workflow predictability with targeted agentic reasoning. Relevant Study Guide topics:
workflows versus agents, routing, exception handling, hybrid agentic architecture, escalation, and complexity minimization.
NEW QUESTION # 72
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: A
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 # 73
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: D
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 # 74
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: D
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 # 75
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