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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Context Management & Reliability | 15% | - Context handling
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
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NEW QUESTION # 154
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What's the most effective basis for selecting which extractions to route for human review?
Answer: B
Explanation:
Limited review capacity should be concentrated on records with the highest probability of semantic error.
Schema validation confirms that the response has the correct structure and data types; it does not establish that the extracted values are accurate. Ambiguous wording, contradictory passages, missing evidence, and model- reported uncertainty are direct indicators that an extraction requires human judgment.
Anthropic's reliability guidance recommends permitting Claude to express uncertainty, grounding factual outputs in source material, and validating critical information because hallucination-reduction methods do not eliminate errors completely. These principles support routing uncertain or evidentially conflicted records to reviewers rather than treating syntactically valid output as automatically trustworthy. ( https://docs.anthropic.
com/en/docs/test-and-evaluate/strengthen-guardrails/reduce-hallucinations ) Option B may waste reviewer capacity on clear, well-supported values while overlooking ambiguous errors in other fields. Option C is reactive: downstream acceptance does not guarantee semantic correctness, and silent errors may never produce processing failures. Option D provides an unbiased estimate of overall quality and should be retained as a secondary audit mechanism, but random selection is not the most efficient way to intercept risky records when only 5% can be reviewed.
In production, model confidence should not be treated as a calibrated probability by itself. It should be combined with objective signals such as contradictory source spans, missing citations, OCR quality, validation warnings, document type, and business impact.
Official references/topics: Human-in-the-Loop Review, Uncertainty Handling, Source Grounding, Risk- Based Escalation.
NEW QUESTION # 155
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, Glob) and integrates with Model Context Protocol (MCP) servers.
You've configured your Claude agent with three MCP servers: one for git operations, one for Jira ticket management, and one for documentation search.
When a user asks the agent to "create a branch for JIRA-123 and add documentation links to the ticket," how does the agent access tools across these servers?
Answer: A
Explanation:
MCP allows a Claude application to connect to multiple servers and expose their enabled tools within the same agentic interaction. Anthropic's MCP connector documentation explicitly supports connecting to multiple MCP servers in one request. Once connected, Claude can invoke a server's tool when the user's request corresponds to the capability described by that tool.
In this scenario, the git server can provide the branch-creation operation, the documentation server can locate the relevant links, and the Jira server can update ticket JIRA-123. The agent can coordinate these capabilities without requiring a separate conversational turn that restricts it to only one server. Tool descriptions and schemas tell Claude what each operation does and what inputs it requires.
NEW QUESTION # 156
A developer wants Claude to consistently answer as an experienced cybersecurity analyst regardless of the user question. Which prompt component should contain this instruction?
Answer: A
Explanation:
The system prompt establishes persistent behavioral instructions that apply throughout the conversation. Role definition, response style, and organizational policies belong there because Claude prioritizes system instructions above ordinary user requests whenever possible.
NEW QUESTION # 157
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (85%) also contain errors--cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?
Answer: D
Explanation:
Stratified random sampling provides both an ongoing quality-control mechanism and an unbiased measurement framework. By reviewing a fixed proportion of high-confidence outputs across meaningful strata--such as document type, field, source format, and business risk--the organization can estimate the residual error rate, compare performance across releases, and discover failure patterns that were not anticipated when existing rules were designed.
Anthropic recommends measurable success criteria and evaluations that mirror the real-world task distribution, including edge cases. Evaluation volume and repeatability are important because improvements must be demonstrated empirically rather than inferred from isolated examples. A weekly sampling program creates a stable benchmark and allows confidence intervals, trend analysis, regression detection, and error-taxonomy updates.
NEW QUESTION # 158
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer, lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
After expanding the agent's MCP tools with delivery-specific capabilities ( check_delivery_status , contact_driver , issue_credit , apply_promo_code , update_delivery_address , reschedule delivery ), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to 71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools - calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data. Which approach structurally eliminates the semantic overlap identified in the logs as the error source?
Answer: B
Explanation:
Merging issue_credit and process_refund into one compensation tool, and folding delivery-status lookup into lookup_order, removes competing tools with overlapping purposes. This directly eliminates the ambiguity causing incorrect tool selection, rather than merely hiding tools, routing between agents, or adding examples.
NEW QUESTION # 159
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