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| Section | Weight | Objectives |
|---|---|---|
| Model Selection and Optimization | 16.8% | - Claude model family characteristics - Cost and token optimization - Latency and performance trade-offs |
| Tools and Model Context Protocol (MCP) | 10.6% | - Tool integration and usage - MCP server development |
| Claude Code | 3.1% | - Claude Code configuration and usage |
| Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Prompt and Context Engineering | 11% | - Structured output handling - Context window management - Prompt design and structuring |
| Applications and Integration | 33.1% | - Streaming and Batch API - Claude Messages API - SDK and third-party integration - Vision capabilities |
| Agents and Workflows | 14.7% | - Agent architecture principles - Claude Agent SDK usage - Memory and context management - Workflow vs autonomous agents |
| Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
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NEW QUESTION # 23
You are designing a Claude application that will process customer support tickets in two stages: a triage stage that classifies tickets and a response stage that drafts replies. The team is debating whether to use a single Claude call that handles both stages or separate Claude calls for each stage.
How would you structure the application?
Answer: C
Explanation:
Option C follows a prompt-chaining/workflow decomposition pattern. Ticket classification and reply generation are materially different tasks. Triage may require a constrained category, priority, or routing decision, while response generation requires richer context, tone, policy compliance, and customer-facing language. Splitting the stages allows each Claude call to use a prompt, context set, model configuration, output format, and evaluation criterion appropriate to that specific responsibility.
Anthropic's guidance on effective agentic systems distinguishes predictable workflows from open-ended agents and recommends using the simplest architecture that meets requirements. Predefined workflows are particularly appropriate where work can be divided into clear processing stages.
This decomposition also creates useful engineering boundaries: the triage output can be validated before becoming input to the drafting stage; triage accuracy and response quality can be evaluated independently; and either stage can later be optimized without rewriting the entire process.
A unnecessarily restricts the second stage to rules. B introduces expensive parallel generation and a selection step without a stated requirement. D optimizes solely for call count while sacrificing task separation and observability.
The supplied exam source marks C. Relevant topics: Agent Patterns, prompt chaining, workflow decomposition, focused prompts, stage-level evaluation, and modular Claude application design.
NEW QUESTION # 24
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: C
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 # 25
Your Claude agent performs database operations. A recent incident occurred where the agent ran a destructive query that affected production data. The team wants to add deterministic controls to prevent similar incidents.
How would you prevent similar incidents?
Answer: B
Explanation:
Option B is correct because destructive production operations require deterministic enforcement outside the model's probabilistic reasoning. Claude Code hooks can intercept lifecycle events before tool execution and explicitly allow, deny, or request further handling based on concrete rules.
Anthropic's hooks documentation provides this exact security pattern. A PreToolUse hook can inspect a proposed command before execution and return a blocking decision. Anthropic's example demonstrates blocking destructive operations such as drop table, while other commands proceed normally.
That mechanism can be adapted to database controls: block DROP, destructive DELETE, unauthorized schema modifications, or production writes; require explicit approval for high-risk operations; and allow read- only or known-safe queries automatically.
A merely increases the probability that someone might notice an unsafe operation and does not prevent execution. C assumes model capability can replace access controls, which is an unacceptable safety boundary.
D is useful behavioral guidance but remains probabilistic and cannot guarantee prevention.
Therefore, B creates a deterministic control between model intent and side-effect execution. Relevant Study Guide topics: Claude hooks, PreToolUse, tool governance, deterministic enforcement, approval gates, least privilege, and destructive-operation protection.
NEW QUESTION # 26
You are deciding between deploying a Claude-powered agent on Anthropic's hosted infrastructure or self- hosting under a "bring your own cloud" model in your own AWS account. The agent processes customer data subject to your enterprise's data residency policies, but the team wants to ship quickly and avoid managing infrastructure.
Which deployment model would you recommend?
Answer: D
Explanation:
Option C follows the governing constraint in the scenario: data residency is an enterprise requirement, whereas minimizing infrastructure management is a preference. A deployment architecture cannot knowingly violate a mandatory compliance boundary merely to accelerate delivery. The question should therefore be interpreted under its stated assumption that the described BYOC environment satisfies the enterprise residency policy.
Anthropic's current Managed Agents documentation distinguishes Anthropic-managed cloud environments from self-hosted sandboxes running on infrastructure controlled by the customer. Self-hosting keeps tool execution, files, and network egress within customer infrastructure, although Anthropic documents that model- facing tool inputs and outputs can still pass through its control plane; therefore, actual regulatory approval must be based on the organization's precise data-flow requirements.
B and D explicitly defer or violate the existing policy. A unnecessarily introduces a planned migration away from the residency-compliant architecture without establishing that such a migration would be permissible.
Accordingly, C is the intended certification answer. Relevant Study Guide topics: deployment topology, data residency, self-hosting, compliance constraints, infrastructure ownership, security boundaries, and architecture tradeoffs.
NEW QUESTION # 27
Your Claude application has multi-step workflows where each step's output is needed only briefly before the agent moves on. The cumulative tool output is filling the context window with content that is no longer relevant.
How would you handle the accumulating tool output?
Answer: A
Explanation:
Option A applies the correct context-engineering strategy: remove stale tool results once they no longer contribute useful information to subsequent reasoning. Agentic workflows frequently accumulate search results, file contents, API responses, and intermediate artifacts. Keeping all of them indefinitely consumes the finite context window, raises token cost, and can reduce model focus by introducing low-value information.
Anthropic specifically documents tool result clearing for this situation. Context Editing can remove older tool results when the conversation grows, while preserving recent interactions and optionally retaining tools whose results must remain available. Anthropic describes old tool outputs such as retrieved files or search results as candidates for clearing after Claude has processed them.
Prompt caching in B solves a different problem: it can lower cost and latency for repeated static prompt prefixes, but cached tokens still constitute context and therefore do not eliminate context-window pressure. C changes model capability without solving the architectural cause. D maximizes context pollution.
The correct architecture is therefore to preserve high-signal state while pruning ephemeral intermediate outputs. This aligns with Claude Developer coverage of context engineering, long-running agents, context- window management, tool-result clearing, and efficient agent state management. Anthropic's broader context- engineering guidance likewise emphasizes curating the smallest high-signal context necessary for successful inference.
NEW QUESTION # 28
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