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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Topic 2: Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Topic 3: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 4: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 5: Context Management & Reliability | 15% | - Context handling
|
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NEW QUESTION # 122
Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document-analysis agent frequently identifies evidence gaps--for example, noting that
"the retrieved sources discuss API authentication but lack details about token-refresh patterns." Under the current strict pipeline, this insight is not actionable because searching has already finished. What is the most effective architectural change?
Answer: A
Explanation:
Option C converts the linear pipeline into a controlled iterative research loop. The document- analysis agent is best positioned to identify precisely what the retrieved evidence fails to answer.
It should return structured gaps containing the missing question, evidence already examined, preferred source characteristics, and the coverage criterion that remains unsatisfied. The coordinator can then issue focused searches and resubmit the new material for analysis.
Anthropic's multi-agent research architecture uses a lead agent to decompose work, coordinate research agents, assess returned information, and continue investigation when further evidence is required. The guidance also stresses clear objectives, output formats, source requirements, and task boundaries to prevent gaps and duplicated work.
NEW QUESTION # 123
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction system uses tool use with a JSON schema containing 12 fields and detailed descriptions, totaling approximately 2,500 tokens for the complete tool definition. Processing documents under 150,000 tokens yields 98% accuracy. For documents between 175,000 and 190,000 tokens, accuracy drops to 71%, with information from the final third consistently missed. The model's context window is 200,000 tokens.
What is the most likely cause?
Answer: D
Explanation:
Option D identifies the dominant capacity problem. Anthropic states that everything sent in a request counts toward the context window : the system prompt, messages, documents, tool results, tool definitions, and the output Claude generates. A 175,000-190,000-token document is therefore not the complete input. Adding a
2,500-token tool schema, system instructions, wrappers, and the reserved output budget can push a nominally sub-200,000-token document close to or beyond the effective limit. Anthropic also warns that more context is not automatically better and that accuracy and recall can degrade as token count grows, a behavior described as context rot. That combination explains why short documents remain accurate while long documents lose information. Option A predicts a problem independent of document length, contradicting the measurements.
Option B is an unsupported generalization and does not account for tool-definition overhead. Option C recognizes long-context degradation but ignores the concrete evidence that hidden prompt components consume the same window. The correct response is to count tokens for the complete request before submission, reserve output capacity, and chunk or retrieve relevant sections when the total approaches the model's supported window.
NEW QUESTION # 124
You are building developer-productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools--Read, Write, Bash, Grep, and Glob--and integrates with Model Context Protocol (MCP) servers.
An engineer who recently joined the team asks the agent to explain the authentication and authorization architecture before making security improvements. The codebase contains more than 800 files across multiple services.
What exploration strategy will most effectively build understanding while respecting context limits?
Answer: D
Explanation:
Option D builds an evidence-based architectural map without indiscriminately loading hundreds of files. Authentication entry points may include route handlers, middleware registration, token- validation functions, session constructors, or identity-provider callbacks. After Grep identifies those anchors, targeted Read operations can establish their responsibilities, while imports, call sites, and configuration references reveal the downstream authorization flow. Anthropic's large- codebase guidance recommends scoping Claude to the portion of a repository touched by the task because unrelated instructions and file reads consume tokens and degrade performance. Its common workflows guidance similarly recommends beginning broadly and narrowing into specific components.
NEW QUESTION # 125
After deploying the automated review, you notice high precision but low recall--real bugs are slipping through undetected. Investigation reveals that your review prompt instructs Claude to
"only report high-confidence issues you are certain about" and "err on the side of not commenting." Developers appreciate the low noise, but a race condition that caused a production outage was visible in a reviewed pull request and went unreported. You need to substantially improve bug detection while keeping false-positive rates manageable. What is the most effective approach?
Answer: B
Explanation:
The prompt's conservative reporting policy is directly causing the low recall. Claude may discover a legitimate race condition during analysis but suppress it because it cannot satisfy the instruction to report only issues about which it is certain. Option C separates two objectives that should not be conflated: broad defect discovery and strict acceptance filtering.
Anthropic's current code-review prompting guidance explicitly recommends reporting every issue, including uncertain or lower-severity findings, assigning confidence and severity metadata, and allowing a separate verification stage to filter them. This maximizes recall while retaining control over developer-facing noise.
NEW QUESTION # 126
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
In production, you observe that simple fact-checking queries (e.g., "What year was the Paris Climate Agreement signed?") traverse all four subagents sequentially, consuming 40+ seconds and significant tokens per query. Complex comparative research benefits from the full pipeline.
Your query distribution is diverse and evolving as users discover new applications. What's the most effective approach to optimize for varying query complexity?
Answer: D
Explanation:
Dynamic coordination adapts the workflow to each query's actual complexity. Simple factual questions can use only the necessary agent, while complex research can invoke the full pipeline without relying on brittle, predefined routing categories.
NEW QUESTION # 127
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