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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Topic 2: Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Topic 3: Tools and MCPs | 10.6% | - Tool Use and Tool Schemas - Building Custom Tools and MCP Servers - Model Context Protocol |
| Topic 4: Agents and Workflows | 14.7% | - Agent Patterns and Frameworks - Agent Construction with Claude - Agent Architecture |
| Topic 5: Security and Safety | 8.1% | - Secure Tool Use and Guardrails - Safety and Responsible Development - Application Security - Prompt Injection and Untrusted Content |
| Topic 6: Applications and Integration | 33.1% | - Software Engineering Fundamentals - Multimodal and Structured Outputs - Streaming, Error Handling and Reliability - Message Batches and Prompt Caching - API Integration and Application Development - Claude API and Client SDKs |
| Topic 7: Prompt and Context Engineering | 11% | - Context Engineering - Prompt Engineering - Context Management and Long-Context Techniques |
| Topic 8: Model Selection and Optimization | 16.8% | - Model Selection - Model Capabilities and Trade-offs - Cost and Latency Optimization - Performance Optimization |
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NEW QUESTION # 50
You are establishing the guardrail strategy for a Claude application. The team wants to ensure guardrail failure does not expose the application to unsafe behavior.
The guardrail strategy would...
Answer: B
Explanation:
Option A is correct because the safest guardrail architecture is defense in depth, not dependence on one control. Anthropic's guardrail guidance explicitly recommends combining multiple safeguards: input screening and validation, hardened system instructions, safe handling of untrusted tool content, least-privilege permissions, output screening, monitoring, and red-team testing. The important engineering property is independence: if one layer misses an attack or unsafe request, another layer can still prevent harmful behavior or block delivery.
Option B places control only at the output boundary. Human review can be useful for high-risk cases, but it does not protect tool execution, data access, prompt injection, or other failures that can occur before final output. Option C relies on a single system-prompt control; system instructions are probabilistic and cannot provide complete enforcement against adversarial or malformed inputs. Option D is weaker still because model-level safety alone does not enforce application-specific policies.
Therefore, A best matches Claude Developer security guidance: layer preventive, detective, and enforcement controls so there is no single guardrail whose failure exposes the application. Relevant Study Guide topics:
guardrails, prompt injection, input validation, output screening, least privilege, defense in depth, and production monitoring.
NEW QUESTION # 51
Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?
Answer: B
Explanation:
Option A correctly separates sampling configuration from context management. Temperature historically controlled the randomness of token selection; it did not increase the number of tokens Claude could accept within a request. Anthropic's current Messages API documentation continues to describe temperature in terms of randomness and, for newer model generations, marks manual temperature control as deprecated. Therefore, changing temperature cannot solve a context-capacity problem.
Long transcripts instead require context-engineering techniques. Appropriate approaches include chunking documents, summarizing earlier material, retrieving only relevant sections, or using context editing
/compaction so high-value information remains visible while unnecessary material is removed. Anthropic's context-editing guidance explicitly supports summarization and replacement of growing conversation history to keep long-running workloads within usable context limits.
B incorrectly conflates generation parameters with context capacity. C may save some tokens but removes persistent application instructions and is therefore architecturally unsound. D modifies an unrelated parameter without addressing the root cause. Relevant Study Guide topics: context windows, token budgets, sampling parameters, summarization, chunking, and context engineering.
NEW QUESTION # 52
Your Claude application's outputs are inconsistent in format. The team's instructions are scattered across the system prompt and user messages, with some instructions stated only once and others repeated.
How would you fix the inconsistency?
Answer: D
Explanation:
Option B creates a clear separation between persistent application instructions and per-request user content.
Stable behavioral rules-role, output conventions, formatting constraints, policies, and other instructions intended to apply across requests-belong in the system-level instruction layer. User messages should primarily carry task-specific input and requirements that vary from interaction to interaction.
Anthropic's Messages API defines the system prompt specifically as the mechanism for providing overarching context and instructions to Claude. Current guidance likewise recommends the top-level system field for instructions that should apply from the beginning of the conversation, while more transient information belongs in conversation messages.
A mixes persistent application behavior with user-controlled content and weakens separation of concerns. C preserves the inconsistent architecture responsible for the problem. D duplicates instructions unnecessarily, wastes context tokens, and increases the possibility that future edits create conflicting versions of the same rule.
Consolidating stable instructions also makes prompts easier to version, evaluate, review, and maintain.
Therefore, B is the correct design. Relevant Study Guide topics: system prompts, instruction hierarchy, prompt organization, formatting consistency, separation of concerns, and maintainable prompt construction.
NEW QUESTION # 53
Your Claude application processes 50-page legal contracts and produces summaries with citation references back to the source. The team is debating whether to send each contract whole or split it into smaller pieces.
The contracts fit within Claude's context window. Initial testing shows that whole-document processing produces summaries with stronger cross-section reasoning but occasionally drifts on citation accuracy in later sections. Chunked processing produces stronger citation accuracy per chunk but loses cross-section reasoning.
The team has not decided which property matters more.
How would you guide the team's decision?
Answer: C
Explanation:
B is correct because the architecture cannot be chosen intelligently until the team defines which quality attribute is more important to the actual business use case . The examination source explicitly selects B.
Whole-document and chunked processing each perform better on different dimensions, so there is no universally superior option.
Anthropic's evaluation guidance emphasizes defining concrete success criteria first and validating candidate approaches against representative examples. The correct sequence is therefore to determine whether cross- section reasoning or citation precision is the more critical requirement, establish measurable acceptance thresholds, and evaluate both strategies on representative legal contracts.
Anthropic's legal summarization guidance also confirms that chunking or meta-summarization is useful for long documents, while acknowledging that processing an entire document can be appropriate when it fits within Claude's context window. For citations specifically, Anthropic provides native citation support that chunks document text into citation-addressable units and improves source-grounded citation behavior.
A and C each privilege one quality dimension before establishing business priority. D focuses on cost and latency even though the unresolved problem is quality tradeoff.
Relevant Claude Developer topics: Understanding Reqs, success criteria, requirement prioritization, long- context processing, chunking, citation accuracy, cross-document reasoning, representative evaluations, and architecture tradeoffs .
NEW QUESTION # 54
Your team uses several plugins across multiple Claude applications, and a recent plugin update introduced a regression. The team had not been tracking plugin versions, so the team cannot easily identify which version was previously working. How would you address this?
Answer: D
Explanation:
Option B is correct because plugin dependencies and releases must be versioned deliberately if the team wants reproducible behavior and a reliable rollback path. Claude Code's plugin documentation supports explicit semantic versions in plugin manifests and marketplace entries, and the plugin system uses the resolved version as part of update detection and caching. Version constraints can also keep dependent plugins on a tested compatible range until the team intentionally upgrades.
The incident happened because the team could not determine which plugin version had previously worked.
Explicit tracking solves that directly: record the version in project configuration, pin known-good releases where stability matters, test upgrades, and change versions through reviewed configuration updates. This creates traceability and makes regression isolation much faster.
Option A removes useful extension functionality instead of managing it. Option C treats third-party change as uncontrollable even though the platform provides version-management mechanisms. Option D automatically moving everything to the latest release increases change frequency and can reproduce the same regression problem.
Therefore, B is the correct configuration-management response. Relevant Study Guide topics: Claude Code plugins, semantic versioning, dependency constraints, reproducible configuration, controlled upgrades, rollback, and configuration management.
NEW QUESTION # 55
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