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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Context Management & Reliability | 15% | - Context handling
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
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NEW QUESTION # 87
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.
After deployment, you find that 12% of extractions contain semantic errors that pass JSON Schema validation--for example, a duration such as "30 minutes" is incorrectly placed in an ingredient-quantity field. Human reviewers have the capacity to check only 20% of extractions.
Which approach most effectively allocates reviewer attention?
Answer: B
Explanation:
Option A uses the limited review capacity as a risk-ranking mechanism rather than spending it uniformly. JSON Schema validation can prove that an output has the expected types and structure, but it cannot detect a semantically misplaced value such as a duration stored as an ingredient quantity. Field-level confidence can identify the exact parts of a record that warrant inspection, provided those scores are calibrated against labeled examples rather than trusted at face value. The labeled validation set must mirror the production distribution and include edge cases; Anthropic's evaluation guidance explicitly recommends task-specific evaluations, representative distributions, measurable criteria, and automated grading where possible.
Calibration then converts raw confidence into an empirically tested estimate of error risk, allowing reviewers to inspect the lowest- confidence records or fields first.
NEW QUESTION # 88
Why is evaluation important after deploying a Claude application?
Answer: C
Explanation:
Continuous evaluation allows organizations to measure accuracy, safety, consistency, and user satisfaction. Regular testing identifies regressions and supports iterative improvement of prompts, retrieval pipelines, and application workflows.
NEW QUESTION # 89
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline.
The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your test generation produces unit tests for new code, but reviews show that 55% are low-value: trivial assertions that only verify functions do not throw exceptions, tests duplicating existing coverage, or tests ignoring your team's fixture conventions.
How do you reduce the rate of low-value tests being generated in the first place?
Answer: A
Explanation:
Option D improves the generation conditions instead of filtering defective output afterward. Anthropic recommends using CLAUDE.md for repository-wide standards and information that Claude should apply in every session. The file can define what constitutes a useful test, require assertions on observable behavior, identify approved fixture factories, prohibit duplication of existing scenarios, and provide contrasting examples of strong and trivial tests. These instructions are available before Claude decides what tests to create.
Option A may remove some weak tests, but it doubles model work and still permits the first stage to generate low-quality material. Option B treats line coverage as a quality measure even though a trivial assertion can execute new lines without validating behavior. Option C abandons difficult areas rather than improving generation. The standards should also direct Claude to inspect nearby tests and fixtures before writing new cases, identify the behavior or regression each test protects, and run the relevant suite afterward. Because CLAUDE.md consumes context in every session, the guidance should remain concise; lengthy testing procedures are better placed in a reusable skill referenced from the project instructions.
NEW QUESTION # 90
Your code-review prompts include both implementation changes and the corresponding test file, but the review comments fail to identify untested code paths. The model correctly flags functions that have no tests at all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack coverage. What is the most effective way to improve branch-level gap detection without overcomplicating the pipeline?
Answer: C
Explanation:
The current prompt asks for testing analysis at too high a level. Claude recognizes the obvious absence of an entire test but has not been instructed to construct a path-level inventory. Option B turns the desired behavior into an explicit verification procedure: enumerate each condition, alternative branch, early return, exception handler, and failure path, then locate a test assertion that exercises its behavior.
Anthropic's prompting best practices emphasize clear, specific instructions and explicit sequential steps when a task requires a defined analysis process. This change keeps the existing single review call while making the missing evaluation criterion unambiguous.
Option A improves proximity between implementation and tests but does not tell Claude what coverage relationship to inspect. Option C could work, but it adds orchestration, latency, cost, and another handoff before testing whether a direct instruction solves the observed failure. Option D may improve recognition of examples resembling the demonstration, but a few cases cannot enumerate every branch structure. Explicit path enumeration generalizes across unfamiliar code and creates auditable output: each reported gap can name the uncovered condition, expected behavior, and missing assertion.
NEW QUESTION # 91
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 schema includes a skills: string[] field. Production monitoring reveals three consistency issues:
(1) compound phrases like "Python and SQL" are sometimes kept as one
entry, sometimes split;
(2) implied but unstated skills occasionally appear in extractions;
(3) similar documents produce wildly different array lengths (5-10 vs
40+ entries).
Your prompt currently says "Extract all skills mentioned."
What's the most effective improvement?
Answer: C
Explanation:
The failure is caused by an underspecified extraction policy rather than an inadequate JSON structure. "Extract all skills mentioned" does not define whether coordinated phrases should be split, whether inferred capabilities qualify, or what level of specificity constitutes one skill. Few- shot examples can demonstrate all three decisions using concrete inputs and expected outputs.
Anthropic identifies examples as one of the most effective ways to improve output accuracy and consistency because they communicate behavior more precisely than abstract instructions alone.
Representative examples can show that "Python and SQL" becomes two entries, that an unstated skill must not be inferred from a job title, and that broad descriptions should not be decomposed into dozens of speculative micro-skills.
NEW QUESTION # 92
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