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| Section | Weight | Objectives |
|---|---|---|
| Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes |
| Agentic Architecture & Orchestration | 27% | - Selecting appropriate Claude architectures - Designing agentic systems and workflows - Agent coordination and orchestration patterns |
| Tool Design & MCP Integration | 18% | - Tool safety, reliability, and usability - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration |
| Context Management & Reliability | 15% | - Evaluation and reliability strategies - Managing context windows and information flow - Production deployment considerations |
| Prompt Engineering & Structured Output | 20% | - Prompt design strategies - Improving Claude response quality and consistency - Structured output generation and validation |
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NEW QUESTION # 146
Your test-generation process produces unit tests for new code, but reviews show that 55% are low-value: trivial assertions that verify only that functions do not throw exceptions, tests that duplicate existing coverage, or tests that ignore your team's fixture conventions. How should you reduce the rate of low-value tests being generated in the first place?
Answer: B
Explanation:
The failures reflect missing project-specific knowledge: Claude does not know which fixtures are preferred, what the existing suite already covers, or what the team considers meaningful behaviour. Option C provides this information as persistent project context before test generation begins. This changes generation quality at the source instead of filtering weak tests after spending tokens to produce them.
Anthropic's CLAUDE.md documentation recommends storing shared build and test commands, coding standards, architectural decisions, conventions, and common workflows in a project CLAUDE.md. Testing guidance can define required behavioural assertions, fixture selection rules, duplication checks, naming conventions, and representative examples of acceptable and unacceptable tests.
NEW QUESTION # 147
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: A
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 # 148
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 pipeline includes a release-notes generation step that classifies and summarizes approximately 200 commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API request using a Sonnet-tier Claude model. The release notes are not needed until the following morning, providing approximately 12 hours of acceptable latency.
Your team must reduce the per-token API cost while retaining the same model, prompts, and output quality.
Which approach satisfies all these constraints?
Answer: A
Explanation:
Option B applies Anthropic's dedicated asynchronous bulk-processing mechanism while preserving the existing model and prompt for every commit. The Message Batches API accepts independent Messages API requests, each identified by a unique custom_id, and charges both input and output usage at 50% of standard API prices. The approximately 12-hour latency allowance makes the release-notes workload well suited to batching because immediate results are unnecessary.
Option A may reduce wall-clock completion time, but concurrency does not alter the API's per-token price.
Option C changes the task structure and risks exceeding context or output limits, mixing commit-level classifications, complicating retries, and making it harder to associate errors with individual commits. A single large request also does not automatically consume fewer tokens because the model must still process all commit content. Option D violates the requirement to retain the same model tier and output-quality profile.
Batch results may arrive in an order different from submission order, so the pipeline must associate every response with its original commit through custom_id. This provides lower cost without changing the individual review prompts. Anthropic Message Batches documentation
NEW QUESTION # 149
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 # 150
After deploying automated code review, developers report that approximately 35% of flagged findings are false positives falling into consistent patterns: style suggestions contradicting team conventions, security warnings for patterns that are safe in your deployment context, and performance suggestions that would degrade your specific use case. You want to reduce false positives while maintaining the ability to catch genuine issues. Which approach best enables the model to generalize its judgment to novel code patterns it has not seen before?
Answer: C
Explanation:
Option B teaches Claude the actual decision boundaries behind the team's judgments. Contrasting examples can demonstrate why one apparent security pattern is acceptable within the organization's deployment controls while a superficially similar implementation is genuinely unsafe. The same technique can distinguish established style conventions from deviations and intentional performance trade-offs from accidental inefficiencies.
Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable ways to improve Claude's accuracy and consistency. Its official prompting guidance recommends examples that closely mirror the production task while covering important variations and edge cases. This encourages generalization based on demonstrated criteria instead of memorization of prohibited wording.
Option A can suppress legitimate findings merely because they contain a selected keyword. Option C will become lengthy, difficult to maintain, and incapable of enumerating every future code variation. Option D is too vague and may reduce recall by discouraging Claude from reporting uncertain but genuine defects. Few- shot contrasts provide precise, maintainable behavioral guidance without imposing a blanket suppression policy.
NEW QUESTION # 151
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