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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agents and Workflows | 14.7% | - Agent Patterns and Frameworks - Agent Construction with Claude - Agent Architecture |
| Topic 2: Security and Safety | 8.1% | - Secure Tool Use and Guardrails - Safety and Responsible Development - Prompt Injection and Untrusted Content - Application Security |
| Topic 3: Model Selection and Optimization | 16.8% | - Cost and Latency Optimization - Model Selection - Model Capabilities and Trade-offs - Performance Optimization |
| Topic 4: Prompt and Context Engineering | 11% | - Prompt Engineering - Context Engineering - Context Management and Long-Context Techniques |
| Topic 5: Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Topic 6: Applications and Integration | 33.1% | - API Integration and Application Development - Multimodal and Structured Outputs - Software Engineering Fundamentals - Claude API and Client SDKs - Streaming, Error Handling and Reliability - Message Batches and Prompt Caching |
| Topic 7: Tools and MCPs | 10.6% | - Model Context Protocol - Building Custom Tools and MCP Servers - Tool Use and Tool Schemas |
| Topic 8: Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
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NEW QUESTION # 29
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: C
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 # 30
A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?
Answer: B
Explanation:
Option B reflects the fundamental generative behavior of large language models. Claude does not retrieve a fixed response template for a given prompt. Instead, it generates the response incrementally, selecting subsequent tokens according to the probability distribution produced by the model. Consequently, semantically equivalent runs can differ in wording, ordering, whether prompt language is quoted, and whether it is paraphrased.
Anthropic's API documentation explicitly describes sampling parameters as controlling randomness in subsequent-token selection and also states that even configurations intended to minimize randomness do not make output completely deterministic. Current Claude model generations increasingly manage sampling internally, but the underlying principle remains: generative output is not guaranteed to be textually identical across equivalent calls.
Therefore, seeing the original prompt echoed in one response and paraphrased in another does not, by itself, demonstrate malformed request construction. A real request-construction problem should instead be established through traces showing different system instructions, messages, parameters, or context.
A, C, and D incorrectly infer specific implementation faults without supporting evidence.
The supplied question identifies B as the correct response. Relevant topics: SW Eng Foundations, token generation, sampling, nondeterminism, debugging, and model behavior.
NEW QUESTION # 31
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 # 32
A Claude application is occasionally refusing to answer questions that should be in scope, including questions the application has answered correctly in the past. You want to investigate.
What is the first step of your investigation?
Answer: D
Explanation:
The supplied exam source marks B as correct. The first debugging action should be evidence collection rather than immediately modifying prompts or configuration. A trace exposes the complete conditions under which the refusal occurred: user input, system instructions, accumulated conversation state, tool results, parameters, and resulting model output.
This is important because a refusal can originate from several interacting causes. A particular input pattern may trigger it; system instructions may unintentionally conflict with the requested task; prior conversational context may alter Claude's interpretation; or contextual information may make an otherwise acceptable request appear out of scope. Without examining the failing execution path, changing instructions is speculative.
Option A jumps directly to remediation before identifying the cause. C provides useful aggregate analysis but is more appropriate after individual failing executions have been inspected. D is also valuable for comparative debugging, but determining what changed requires the detailed execution data that traces provide.
The correct troubleshooting sequence is therefore: capture or inspect representative failed traces, determine the trigger, compare against successful interactions, formulate a hypothesis, make a targeted change, and re- evaluate.
Relevant Claude Developer topics: Systems Life Cycle, observability, production debugging, tracing, failure analysis, refusals, regression investigation, and evaluation-driven remediation .
NEW QUESTION # 33
You are setting up a Claude application that will run a mix of multi-turn conversations and one-off requests.
You want to use caching techniques to reduce token costs where they apply. A teammate suggests caching the model's output as well, so the application does not have to make duplicate Claude calls when similar queries arrive.
You would apply prompt caching to...
Answer: B
Explanation:
The supplied exam source marks D . Claude prompt caching is designed for repeated prompt prefixes, not semantic caching of generated answers. High-value cache candidates include stable system prompts, long instructions, tool definitions, shared background documents, repeated examples, and the previously accumulated prefix of a multi-turn conversation.
Anthropic explains that prompt caching reuses a matching prompt prefix and can substantially reduce processing time and input-token cost on subsequent requests. The cache operates across the request structure- tools, system content, and messages up to the relevant cache boundary. It is particularly useful for prompts with many examples, large repeated context, repetitive instructions, and long multi-turn conversations.
B describes response caching , which is a separate application-level technique and is not what Anthropic's prompt caching feature does. C targets the portion that usually changes most, making it a poor general cache boundary. A is incorrect because multi-turn workloads are a major prompt-caching use case.
Therefore, maximize reusable stable prefixes and place changing request-specific content after them where practical.
Relevant Claude Developer topics: Claude API Mechanics, prompt caching, cache prefixes, token-cost optimization, static context, system prompts, multi-turn conversations, and API efficiency .
NEW QUESTION # 34
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