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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tools and MCPs | 10.6% | - Tool Use and Tool Schemas - Building Custom Tools and MCP Servers - Model Context Protocol |
| Topic 2: Model Selection and Optimization | 16.8% | - Model Capabilities and Trade-offs - Model Selection - Performance Optimization - Cost and Latency Optimization |
| Topic 3: Prompt and Context Engineering | 11% | - Context Management and Long-Context Techniques - Context Engineering - Prompt Engineering |
| Topic 4: Agents and Workflows | 14.7% | - Agent Patterns and Frameworks - Agent Construction with Claude - Agent Architecture |
| Topic 5: Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Topic 6: Security and Safety | 8.1% | - Application Security - Prompt Injection and Untrusted Content - Safety and Responsible Development - Secure Tool Use and Guardrails |
| Topic 7: Applications and Integration | 33.1% | - Software Engineering Fundamentals - API Integration and Application Development - Multimodal and Structured Outputs - Streaming, Error Handling and Reliability - Message Batches and Prompt Caching - Claude API and Client SDKs |
| Topic 8: Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
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NEW QUESTION # 73
A teammate has asked you to explain the difference between context engineering and prompt engineering.
They have heard the terms used interchangeably and are unsure how each applies to a Claude application that processes long-running multi-step tasks.
How would you describe the distinction?
Answer: C
Explanation:
Option C accurately captures Anthropic's distinction. Prompt engineering primarily concerns how instructions are written, structured, and organized to obtain the desired behavior from a particular model invocation.
Techniques include explicit instructions, examples, roles, XML structure, output requirements, and task- specific prompt construction. Context engineering operates at a broader architectural level: it determines which information should actually be present in the model's context at each inference step.
Anthropic defines prompt engineering as methods for writing and organizing LLM instructions, whereas context engineering encompasses strategies for curating and maintaining the optimal set of tokens during inference. For long-running agents, context can contain system instructions, tools, MCP resources, retrieved documents, prior messages, tool results, summaries, and memory.
This distinction matters because multi-step agents continuously generate new state. Effective systems may prune obsolete results, retrieve information just in time, compact earlier conversation history, isolate subagent contexts, or store persistent state externally. B is incorrect because context engineering has not simply replaced prompt engineering; the two operate at different scopes. A defines context too narrowly, and D obscures an important architectural distinction.
Therefore, C correctly represents Claude Developer coverage of prompt engineering versus context engineering, context curation, agent state, long-horizon workflows, and context-window optimization.
NEW QUESTION # 74
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 # 75
Your Claude application's error handling currently logs every API error with the same severity level. The team wants to differentiate between errors that should page an on-call engineer and errors that should be logged for later review. How would you structure the error handling?
Answer: D
Explanation:
Option C is correct because operational error handling should distinguish failures by impact, urgency, and recoverability. Anthropic's API documentation already separates error conditions by type and documents different recovery behaviors. For example, transient connection failures, rate limits, and many 5xx errors are automatically retried by official SDKs with exponential backoff, while other failures require application correction or investigation. Treating all of those conditions as the same operational severity creates either alert fatigue or missed incidents.
A production design should classify errors using criteria such as user impact, data integrity risk, security exposure, persistence after retry, affected request volume, and whether automatic recovery succeeds. High- severity failures that threaten service availability or correctness can page the on-call engineer. Recoverable or isolated failures can be logged with structured metadata, request IDs, and metrics for later review.
Option A pages on everything and quickly makes alerts noisy. Option B destroys observability for non-paging errors. Option D preserves the original defect by assigning the same severity to every condition. Therefore, C follows sound Claude application operations. Relevant Study Guide topics: API errors, retries, observability, incident response, severity classification, structured logging, and production operations.
NEW QUESTION # 76
You are choosing between using STDIO-based communication and HTTP-based communication for an MCP server. The server will be invoked by a Claude Code session running locally.
Which communication pattern would you use?
Answer: C
Explanation:
Option D is directly supported by both the supplied examination material and Claude Code's MCP documentation. The examination source selects STDIO for this local-process scenario.
Claude Code's official MCP guidance distinguishes remote HTTP servers from local STDIO servers .
Anthropic states that STDIO servers run as local processes and communicate through standard input and standard output. They are particularly suitable for local tools requiring direct machine access or for custom scripts. Claude Code can launch the process itself and communicate with it without creating, exposing, securing, or maintaining a network listener.
HTTP is appropriate when the MCP server exists as a separately reachable network service, especially for cloud-hosted or shared services. That requirement is absent here: the server is being invoked locally from a Claude Code session.
A creates duplicate transport infrastructure without a stated availability requirement. B incorrectly assumes one transport is universally superior regardless of deployment topology. C describes HTTP polling rather than the normal MCP transport relationship and introduces unnecessary request overhead.
Therefore, local execution strongly favors STDIO; remote/shared deployment generally favors HTTP.
Relevant Claude Developer topics: MCP architecture, STDIO transport, HTTP transport, Claude Code integrations, local process communication, and deployment topology .
NEW QUESTION # 77
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: A
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 # 78
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