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| Section | Weight | Objectives |
|---|---|---|
| Agents and Workflows | 14.7% | - Agent Architecture - Agent Patterns and Frameworks - Agent Construction with Claude |
| Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Prompt and Context Engineering | 11% | - Prompt Engineering - Context Management and Long-Context Techniques - Context Engineering |
| Tools and MCPs | 10.6% | - Model Context Protocol - Building Custom Tools and MCP Servers - Tool Use and Tool Schemas |
| Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Model Selection and Optimization | 16.8% | - Performance Optimization - Model Capabilities and Trade-offs - Cost and Latency Optimization - Model Selection |
| Security and Safety | 8.1% | - Safety and Responsible Development - Prompt Injection and Untrusted Content - Secure Tool Use and Guardrails - Application Security |
| Applications and Integration | 33.1% | - Software Engineering Fundamentals - Streaming, Error Handling and Reliability - Claude API and Client SDKs - API Integration and Application Development - Message Batches and Prompt Caching - Multimodal and Structured Outputs |
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NEW QUESTION # 61
You are building an agent that needs to call several internal APIs and a database in a structured, repeatable way. Your team has decided to use the Claude Agent SDK rather than build a custom loop. You are setting up the agent's tool definitions and execution loop.
How would you set up the tools and execution loop?
Answer: C
Explanation:
The supplied Claude Developer examination source selects A . The purpose of choosing an agent SDK rather than implementing a custom Messages API loop is to consume the SDK's higher-level abstractions. Re- implementing dispatch, iteration, and state handling would discard much of the value provided by the SDK.
Anthropic's current documentation distinguishes low-level tool-use loops from higher-level SDK-managed abstractions. With a manually implemented Messages API workflow, application code must inspect stop_reason, execute requested client tools, append tool_result blocks, preserve conversation history, and repeat until Claude completes the turn. Anthropic's higher-level tooling can instead encapsulate this repetitive control flow. Current migration guidance also confirms that Agent SDK @tool functions are automatically dispatched by the SDK and that agents, tools, and sessions are first-class SDK concepts.
B is incorrect because structured tool use should not be replaced with informal plain-text calls. C introduces an unnecessary custom state dependency when no external persistence requirement exists. D duplicates control-loop functionality despite the explicit decision to use the SDK.
Relevant Claude Developer topics: Agent SDK, tool definitions, dispatch, agentic loops, conversation state, tool execution, and abstraction selection .
NEW QUESTION # 62
Your team is choosing how to add a capability to a Claude application. You want to apply the appropriate option, whether built-in tool, custom tool, Skill, or MCP server, based on the use case.
You would choose the option that...
Answer: C
Explanation:
Option D reflects the correct architecture-selection principle: extension mechanisms should be selected according to what the capability must accomplish rather than familiarity, novelty, or implementation convenience. Anthropic explicitly distinguishes Claude extension mechanisms by purpose. Built-in tools cover common capabilities already supported by the platform. Custom tools expose application-specific callable operations through defined schemas. Skills package reusable knowledge, instructions, and workflows.
MCP connects Claude to external systems, APIs, databases, and services through a standardized protocol.
The decision therefore depends on scope and integration boundaries. A reusable procedural workflow may belong in a Skill; access to an external enterprise system may warrant MCP; a narrowly application-specific operation can be a custom tool; and a built-in tool should generally be preferred when it already satisfies the requirement.
A makes prior team experience the architectural criterion rather than requirements. B assumes newer technology is inherently better. C optimizes implementation convenience without considering maintainability, interoperability, or reuse.
Therefore, D is the appropriate selection rule. Relevant Study Guide topics: built-in tools, custom tools, Skills, MCP, extension architecture, reuse boundaries, and capability selection.
NEW QUESTION # 63
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: D
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 # 64
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: D
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 # 65
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: C
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 # 66
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