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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Topic 2: Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Topic 3: Context Management & Reliability | 15% | - Context handling
|
| Topic 4: Tool Design & MCP Integration | 18% | - Tool integration
|
| Topic 5: Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
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NEW QUESTION # 52
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: B
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.
Option A may improve initial coverage but cannot anticipate every deficiency revealed only after sources are examined. Option B documents incompleteness rather than correcting it. Option D retrieves more information but does not ensure that the new evidence is analyzed and tested against the original gap. The loop in Option C establishes an explicit completion condition and preserves specialization: analysis identifies the gap, the coordinator orchestrates, search agents retrieve evidence, and analysis verifies whether the gap has actually been closed.
NEW QUESTION # 53
You have configured the system so that all four subagents have access to the complete set of 18 tools. During testing, agents frequently call tools outside their specialization-the synthesis agent attempts web searches, and the report generator tries to analyze documents. What is the primary cause of this poor tool-selection behavior?
Answer: D
Explanation:
Option A identifies the direct configuration contradiction. Each system prompt defines a specialized responsibility, but the corresponding tool allowlist presents unrelated actions as available capabilities. The synthesis agent may therefore interpret web searching as an acceptable way to fill evidence gaps, while the report generator may perform document analysis instead of limiting itself to report production.
Anthropic's subagent documentation defines subagents as focused workers with their own prompts and specific tool access. Its tool-definition guidance also recommends reducing selection ambiguity by presenting clear, relevant tools. The appropriate correction is least-capability configuration: expose search tools to the search agent, document tools to the analyzer, synthesis utilities to the synthesizer, and formatting or output tools to the report generator.
Option B may affect token efficiency but does not explain the role-specific misuse pattern. Option C concerns delegation, whereas the improper calls occur after successful delegation. Option D is tempting, but Anthropic does not define four or five tools as a universal reliability threshold; 18 tools alone does not prove threshold failure. The decisive defect is misalignment between declared roles and permitted capabilities.
NEW QUESTION # 54
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: A
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. ( https://docs.anthropic.com/en
/docs/build-with-claude/develop-tests ) A weekly sampling program creates a stable benchmark and allows confidence intervals, trend analysis, regression detection, and error-taxonomy updates.
Option A detects only disagreements between stochastic extractions. Two attempts may produce the same plausible but incorrect answer, so agreement is not equivalent to factual correctness. Option B addresses known patterns but will miss new failure modes and may generate excessive false positives. Option C actually lowers the review threshold in the wrong direction: documents between 70% and 85% would be treated as automated rather than reviewed if the routing rule remains "below threshold," and changing thresholds alone does not measure high-confidence error prevalence.
Official references/topics: Evaluation Design; Representative Test Distributions; Continuous Reliability Measurement; Human-in-the-Loop Sampling.
NEW QUESTION # 55
A financial services company plans to integrate Claude into an internal document analysis platform. The architects want to minimize exposure of confidential client information while maintaining high-quality responses. Which approach BEST aligns with Anthropic's recommended architecture?
Answer: B
Explanation:
Minimizing unnecessary sensitive information before sending prompts follows the principle of data minimization. Claude performs best when provided only with relevant context. Removing unrelated confidential information reduces privacy risk while preserving model performance and supporting secure enterprise AI architecture.
NEW QUESTION # 56
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: A
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.
Option A leaves the suppression policy intact, so additional examples cannot guarantee that discovered problems will be reported. Option B improves recall but relies on historical category-level suppression, which can discard genuine findings that happen to belong to noisy categories. Option D can improve analysis quality but does not remove the instruction responsible for withholding findings. A dedicated finding stage followed by an independently configurable verification or thresholding stage produces measurable recall and precision controls without forcing one model call to optimize competing objectives.
NEW QUESTION # 57
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