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| Section | Weight | Objectives |
|---|---|---|
| Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
| Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Agents and Workflows | 14.7% | - Workflow vs autonomous agents - Agent architecture principles - Claude Agent SDK usage - Memory and context management |
| Model Selection and Optimization | 16.8% | - Claude model family characteristics - Cost and token optimization - Latency and performance trade-offs |
| Prompt and Context Engineering | 11% | - Prompt design and structuring - Context window management - Structured output handling |
| Applications and Integration | 33.1% | - Claude Messages API - Streaming and Batch API - SDK and third-party integration - Vision capabilities |
| Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Claude Code | 3.1% | - Claude Code configuration and usage |
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NEW QUESTION # 11
A Claude application that worked well in testing is now occasionally returning outputs that mention information not present in the input. The development team initially assumed the model was hallucinating, so they asked you to troubleshoot.
What would you do first?
Answer: A
Explanation:
Option A follows disciplined production debugging: diagnose the actual failure mode before changing architecture or prompts. An output containing unsupported information might indeed be hallucination, but similar symptoms can result from stale conversation state, incorrect retrieval, unexpected tool output, prompt injection, incorrect request construction, or mismatched model/configuration versions.
A production trace should capture the user input, system instructions, relevant conversation history, retrieved content, tool calls and results, model/version, request parameters, response, and identifiers necessary to compare successful and failing cases. This establishes whether the model invented a fact or whether that fact entered context through another path.
B changes the model before establishing causality. C may eventually be useful if the confirmed problem is insufficient grounding, but implementing RAG before diagnosis can hide rather than explain the defect. D similarly changes prompting before verifying that prompt behavior is responsible.
The engineering sequence should be observe, reproduce, classify the failure, form a hypothesis, apply a targeted correction, and validate the correction with evaluations. Relevant Study Guide topics: production troubleshooting, observability, tracing, hallucination analysis, prompt injection, context failures, regression diagnosis, and lifecycle monitoring.
NEW QUESTION # 12
Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?
Answer: A
Explanation:
Option A correctly separates sampling configuration from context management. Temperature historically controlled the randomness of token selection; it did not increase the number of tokens Claude could accept within a request. Anthropic's current Messages API documentation continues to describe temperature in terms of randomness and, for newer model generations, marks manual temperature control as deprecated. Therefore, changing temperature cannot solve a context-capacity problem.
Long transcripts instead require context-engineering techniques. Appropriate approaches include chunking documents, summarizing earlier material, retrieving only relevant sections, or using context editing
/compaction so high-value information remains visible while unnecessary material is removed. Anthropic's context-editing guidance explicitly supports summarization and replacement of growing conversation history to keep long-running workloads within usable context limits.
B incorrectly conflates generation parameters with context capacity. C may save some tokens but removes persistent application instructions and is therefore architecturally unsound. D modifies an unrelated parameter without addressing the root cause. Relevant Study Guide topics: context windows, token budgets, sampling parameters, summarization, chunking, and context engineering.
NEW QUESTION # 13
Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?
Answer: A
NEW QUESTION # 14
Your Claude application's error handling currently logs every API error with the same severity level. The team wants to differentiate between errors that should page an on-call engineer and errors that should be logged for later review. How would you structure the error handling?
Answer: D
Explanation:
Option C is correct because operational error handling should distinguish failures by impact, urgency, and recoverability. Anthropic's API documentation already separates error conditions by type and documents different recovery behaviors. For example, transient connection failures, rate limits, and many 5xx errors are automatically retried by official SDKs with exponential backoff, while other failures require application correction or investigation. Treating all of those conditions as the same operational severity creates either alert fatigue or missed incidents.
A production design should classify errors using criteria such as user impact, data integrity risk, security exposure, persistence after retry, affected request volume, and whether automatic recovery succeeds. High- severity failures that threaten service availability or correctness can page the on-call engineer. Recoverable or isolated failures can be logged with structured metadata, request IDs, and metrics for later review.
Option A pages on everything and quickly makes alerts noisy. Option B destroys observability for non-paging errors. Option D preserves the original defect by assigning the same severity to every condition. Therefore, C follows sound Claude application operations. Relevant Study Guide topics: API errors, retries, observability, incident response, severity classification, structured logging, and production operations.
NEW QUESTION # 15
Your agent makes 10 to 15 tool calls per task, and you have noticed it sometimes loses track of earlier results by the time it reaches later steps. The agent's context window is large enough to hold all the messages, but the relevant information appears to get buried as the conversation grows.
How would you address this?
Answer: A
Explanation:
Option D is correct because the problem is not insufficient nominal context capacity; it is degraded signal quality as the working context grows. Anthropic's context-window guidance explicitly states that more context is not automatically better. As conversations expand, recall and accuracy can degrade through
"context rot," so long-running agents need active context management rather than simply retaining every prior tool result.
A good pattern preserves the active task state while compacting, summarizing, or pruning stale intermediate outputs. Anthropic documents server-side compaction for long-running conversations and context-editing
/pruning approaches that clear old tool results when they no longer contribute useful information. This keeps high-value constraints and conclusions visible without carrying every verbose response forward.
Option A changes frameworks without fixing the architectural cause. Option B may reduce call count but also creates larger multi-purpose tools and does not guarantee better context quality. Option C increases capacity while preserving the same low-signal accumulation, so the relevant facts can remain buried.
Therefore, D best implements context engineering for agentic workloads. Relevant Study Guide topics:
context engineering, context rot, compaction, tool-result pruning, long-running agents, state preservation, and context-window optimization.
NEW QUESTION # 16
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