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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Selection and Optimization | 16.8% | - Claude model family characteristics - Latency and performance trade-offs - Cost and token optimization |
| Topic 2: Agents and Workflows | 14.7% | - Workflow vs autonomous agents - Memory and context management - Agent architecture principles - Claude Agent SDK usage |
| Topic 3: Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Topic 4: Security and Safety | 8.1% | - AI application security - Guardrails and safety controls |
| Topic 5: Applications and Integration | 33.1% | - Vision capabilities - Streaming and Batch API - Claude Messages API - SDK and third-party integration |
| Topic 6: Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
| Topic 7: Prompt and Context Engineering | 11% | - Context window management - Structured output handling - Prompt design and structuring |
| Topic 8: Claude Code | 3.1% | - Claude Code configuration and usage |
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NEW QUESTION # 18
Your Claude application uses structured output that is consumed by downstream code. The team wants to handle malformed or unexpected output gracefully so it does not crash downstream systems.
The best choice for handling this issue would be to...
Answer: B
Explanation:
Option D applies a fundamental production engineering principle: treat externally generated data as potentially malformed and parse it defensively before use. Downstream application logic should not assume that every field exists, every type is correct, or every unexpected property can safely be ignored. Instead, the parser should validate expected structures, handle optional or missing values deliberately, reject incompatible types, and convert failures into controlled application errors rather than process crashes.
Anthropic's Structured Outputs documentation identifies exactly these failure classes for unconstrained model output: malformed JSON, missing required fields, inconsistent types, and schema violations can break downstream applications. Current Structured Outputs and strict tool-use capabilities can eliminate many schema-level failures through constrained decoding, but defensive handling remains an important software boundary when unexpected data can still arise from external services, legacy paths, or semantic validation requirements.
A creates unnecessary outages. B hides failures and discards potentially recoverable data without observability. C deliberately postpones a reliability requirement until after deployment.
The supplied question identifies D as correct. Relevant topics: Claude App Design, defensive programming, structured output, schema validation, parsing, error handling, downstream reliability, and type safety.
NEW QUESTION # 19
A teammate has asked how to extend Claude Code with a custom Skill that the team can invoke during sessions. The Skill consists of a set of instructions and a few support scripts the team wants Claude to be able to call when the Skill is loaded.
Where is the right place to define the Skill?
Answer: D
Explanation:
Option C matches Claude Code's documented Skill architecture. Agent Skills are filesystem-based extension artifacts rather than ordinary application modules or repeated prompt fragments. A Skill is represented by a directory containing a required SKILL.md file and can include optional supporting scripts, templates, examples, and reference material.
Anthropic documents project Skills under .claude/skills/ < skill-name > /SKILL.md. Project-level Skills can be shared through Git and automatically discovered when Claude Code loads project settings. Supporting scripts can reside alongside the Skill and be referenced from SKILL.md.
A incorrectly embeds reusable procedural material into every CLAUDE.md file, creating duplication and loading instructions even when they are irrelevant. B creates a conventional source-code library but does not register a Claude Code Skill. D makes the capability dependent on undocumented, developer-specific setup and undermines team reuse.
Therefore, C uses the extension mechanism specifically designed for discoverable, reusable Claude capabilities. Relevant Study Guide topics: Agent Skills, .claude/skills, SKILL.md, supporting resources, filesystem discovery, project-level configuration, and reusable Claude Code capabilities.
NEW QUESTION # 20
Your Claude application receives untrusted input from external sources. The team is establishing how the application should treat this untrusted input.
Untrusted input would be...
Answer: A
Explanation:
Option A is the appropriate trust-boundary treatment for external content. Untrusted text can contain malformed data, adversarial instructions, prompt-injection attempts, or content deliberately constructed to alter the agent's behavior. It should therefore be validated or screened before inclusion and clearly represented as untrusted data rather than authoritative application instructions.
Anthropic's prompt-injection guidance distinguishes direct attacks from indirect prompt injection, where Claude processes third-party content such as webpages, emails, documents, or tool output containing hostile instructions. Anthropic recommends input validation and screening, least-privilege access, safe treatment of untrusted tool content, and screening content before Claude acts on it. Importantly, Anthropic notes that tool- result content is treated as untrusted data rather than as a reliable place for application instructions.
B erases the trust distinction and exposes the application to instruction/data confusion. C introduces a deliberately weaker security boundary and does not sanitize the data. D prevents legitimate use cases unnecessarily; untrusted does not mean unusable-it means the data must be handled defensively.
Therefore, A correctly combines validation with explicit trust separation. Relevant Study Guide topics:
prompt injection, untrusted content, input validation, sanitization, data/instruction separation, least privilege, and defense in depth.
NEW QUESTION # 21
You are setting up a CI/CD pipeline for a new Claude application. The pipeline needs to run automated checks on every pull request before code can be merged.
The CI/CD checks would include...
Answer: B
Explanation:
Option A treats a Claude application as production software subject to the same disciplined engineering controls as other services while adding tests for its model integration. CI should provide repeatable feedback before merge, including deterministic unit/integration tests, linting, type or static checks where applicable, security checks, and organization-specific quality gates.
Claude-specific behavior should also be tested systematically rather than reserved for manual release verification. Anthropic's evaluation guidance recommends task-specific test cases reflecting realistic inputs and edge cases, measurable success criteria, and automation whenever practical. Claude Code documentation also explicitly identifies CI automation, including GitHub Actions and GitLab CI/CD, as supported development workflows.
B is excessive because every pull request should not directly deploy to production merely to obtain end-to-end coverage; testing environments and staged deployment exist for that purpose. C makes linting dependent on individual developer discipline rather than a shared merge gate. D delays Claude integration verification until pre-release and makes a repeatable automated check manual.
Therefore, A provides comprehensive and consistent pull-request validation. Relevant Study Guide topics: CI
/CD, automated testing, integration testing, linting, evaluations, regression prevention, and quality gates.
NEW QUESTION # 22
The team is debating whether to build a new capability as a custom tool or to use an existing built-in tool that nearly covers the use case but lacks one specific feature.
How would you decide?
Answer: B
Explanation:
The supplied Claude Certified Developer Foundations material identifies A as the best answer. The decision should begin with the actual capability gap rather than a predetermined preference for built-in or custom tooling. If the existing capability can satisfy the requirement with a small supported extension or composition, using it avoids unnecessary implementation and maintenance. If the missing capability is fundamental, a custom tool becomes justified.
Anthropic's tool architecture explicitly supports both categories. Claude provides Anthropic-managed or predefined tools while also allowing developers to define custom tools for operations specific to their application. With a custom client tool, the application defines the contract and executes the operation before returning its result to Claude.
A is more complete than C because it evaluates both sides of the boundary : whether the existing mechanism can realistically meet the missing requirement and, if not, whether custom implementation is warranted. B prioritizes precedent over current requirements. D only determines whether the unmodified built-in tool is sufficient and does not address what to do when it is almost-but not entirely-sufficient.
Relevant Claude Developer topics: built-in tools, custom tools, capability-gap analysis, tool contracts, maintenance cost, extensibility, and agent construction decisions .
NEW QUESTION # 23
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