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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security and Safety | 8.1% | - Prompt Injection and Untrusted Content - Safety and Responsible Development - Secure Tool Use and Guardrails - Application Security |
| Topic 2: Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Topic 3: Agents and Workflows | 14.7% | - Agent Architecture - Agent Patterns and Frameworks - Agent Construction with Claude |
| Topic 4: Model Selection and Optimization | 16.8% | - Performance Optimization - Cost and Latency Optimization - Model Selection - Model Capabilities and Trade-offs |
| Topic 5: Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Topic 6: Tools and MCPs | 10.6% | - Model Context Protocol - Building Custom Tools and MCP Servers - Tool Use and Tool Schemas |
| Topic 7: Applications and Integration | 33.1% | - Multimodal and Structured Outputs - Message Batches and Prompt Caching - Software Engineering Fundamentals - API Integration and Application Development - Streaming, Error Handling and Reliability - Claude API and Client SDKs |
| Topic 8: Prompt and Context Engineering | 11% | - Context Engineering - Prompt Engineering - Context Management and Long-Context Techniques |
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NEW QUESTION # 49
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: C
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 # 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: C
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 agent has too many tools, and many of them have overlapping functionality. The agent often picks an inappropriate tool when several could plausibly handle a request.
How would you address the tool selection problem?
Answer: B
Explanation:
Option D addresses the architectural cause of the failure: an ambiguous tool surface. Claude chooses tools partly from their names, descriptions, schemas, and the relationship between the request and the capability described. If multiple tools appear to perform substantially the same task, selection becomes unnecessarily difficult.
Anthropic's tool-definition guidance explicitly recommends consolidating related operations into fewer tools and making descriptions clear about both what a tool does and when it should be used. The documentation notes that fewer, more capable tools reduce selection ambiguity and make the available tool surface easier for Claude to navigate.
Option A makes the ambiguity worse by expanding an already overlapping tool set. B destroys required application capabilities. C could improve selection somewhat, because examples can clarify complex inputs, but it leaves the underlying duplication intact. Examples complement good tool design; they are not a substitute for distinct tool responsibilities.
Therefore, D is the strongest solution: remove unused tools, merge functions that represent the same conceptual operation, and write discriminative descriptions defining appropriate and inappropriate usage.
Relevant Study Guide topics: tool design, tool selection, descriptions, tool consolidation, agent construction, and reducing ambiguity.
NEW QUESTION # 52
Your agent makes 10 to 15 tool calls per task, and you have noticed it sometimes loses track of earlier results by the time it reaches later steps. The agent's context window is large enough to hold all the messages, but the relevant information appears to get buried as the conversation grows.
How would you address this?
Answer: C
Explanation:
Option D is correct because the problem is not insufficient nominal context capacity; it is degraded signal quality as the working context grows. Anthropic's context-window guidance explicitly states that more context is not automatically better. As conversations expand, recall and accuracy can degrade through
"context rot," so long-running agents need active context management rather than simply retaining every prior tool result.
A good pattern preserves the active task state while compacting, summarizing, or pruning stale intermediate outputs. Anthropic documents server-side compaction for long-running conversations and context-editing
/pruning approaches that clear old tool results when they no longer contribute useful information. This keeps high-value constraints and conclusions visible without carrying every verbose response forward.
Option A changes frameworks without fixing the architectural cause. Option B may reduce call count but also creates larger multi-purpose tools and does not guarantee better context quality. Option C increases capacity while preserving the same low-signal accumulation, so the relevant facts can remain buried.
Therefore, D best implements context engineering for agentic workloads. Relevant Study Guide topics:
context engineering, context rot, compaction, tool-result pruning, long-running agents, state preservation, and context-window optimization.
NEW QUESTION # 53
Your Claude agent's hooks are currently triggered for every action, which slows down the agent significantly even when actions pose no risk. The team wants to scope hooks more carefully.
How would you scope the hooks?
Answer: A
Explanation:
Option A correctly applies selective enforcement. Claude Code hooks can execute automatically at lifecycle events such as PreToolUse, and matchers or conditions can narrow exactly which operations trigger a hook.
Anthropic's hook reference demonstrates this pattern by applying a PreToolUse hook specifically to destructive shell operations rather than indiscriminately processing every command. The documentation notes that if the matcher or conditional expression does not match, the handler is skipped, avoiding unnecessary process-spawn overhead.
That architecture is particularly appropriate for costly or security-sensitive checks. High-risk events- destructive file operations, privileged commands, production changes, or access to sensitive resources-can receive deterministic pre-execution enforcement while routine low-risk actions proceed without additional hook latency.
B creates an avoidable period in which safeguards disappear entirely. C reduces application availability without addressing the actual source of overhead. D is technically weaker because system-prompt instructions influence model behavior but are not equivalent to deterministic lifecycle interception capable of blocking execution.
Therefore, hooks should be scoped using event types, matchers, and conditions according to risk. Relevant Claude Developer topics are Claude Code hooks, agent construction, tool governance, deterministic controls, permission boundaries, safety/performance tradeoffs, and lifecycle interception.
NEW QUESTION # 54
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