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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Selection and Optimization | 16.8% | - Latency and performance trade-offs - Cost and token optimization - Claude model family characteristics |
| Topic 2: Prompt and Context Engineering | 11% | - Structured output handling - Prompt design and structuring - Context window management |
| Topic 3: Applications and Integration | 33.1% | - Vision capabilities - Claude Messages API - SDK and third-party integration - Streaming and Batch API |
| Topic 4: Claude Code | 3.1% | - Claude Code configuration and usage |
| Topic 5: Security and Safety | 8.1% | - AI application security - Guardrails and safety controls |
| Topic 6: Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Topic 7: Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
| Topic 8: Agents and Workflows | 14.7% | - Workflow vs autonomous agents - Claude Agent SDK usage - Agent architecture principles - Memory and context management |
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NEW QUESTION # 26
Your Claude application has been running for several conversation turns, and you notice the model occasionally references information that was discussed many turns ago but is no longer relevant. You suspect context drift is causing the model to weight stale content too heavily.
How would you address the drift?
Answer: A
Explanation:
Option C applies the appropriate context-management technique. The issue is not simply whether the older conversation can physically fit into the context window; it is that stale details remain prominent enough to interfere with the model's current task. Effective context engineering optimizes signal quality, not merely maximum token retention.
Anthropic's context-editing documentation describes compaction as summarizing accumulated history and replacing the full history with a structured summary when context becomes large. This preserves important task state while substantially reducing low-value detail that can distract later reasoning.
A retains precisely the stale material producing the problem and therefore does not address context drift. B destroys all useful continuity between turns, including valid goals, decisions, and intermediate state. D is similarly excessive because limiting Claude to only the latest turn discards information that may still be required.
Compaction provides the correct middle ground: retain durable conclusions, current objectives, unresolved issues, and other high-signal state while compressing obsolete conversational detail. Relevant Study Guide topics: context engineering, compaction, conversation history, long-running agents, stale context, context drift, and high-signal state preservation.
NEW QUESTION # 27
Your enterprise has a contract with AWS that requires Claude API calls to flow through Amazon Bedrock rather than the direct Anthropic API. Your team is building a new Claude application and is unfamiliar with this constraint.
How would you build the application?
Answer: B
Explanation:
Option C satisfies both the enterprise routing requirement and sound application architecture. Claude is available through Amazon Bedrock, and Anthropic provides Bedrock-specific SDK integration rather than requiring applications to call api.anthropic.com directly. Current Anthropic documentation describes Claude in Amazon Bedrock as operating through AWS-managed infrastructure with AWS-native authentication, billing, and security boundaries. Newer Bedrock integrations use the Messages API shape, allowing substantial application logic to remain consistent across provider environments.
Anthropic SDKs also provide dedicated Bedrock clients-for example, Python includes AnthropicBedrockMantle for current Bedrock deployments. Keeping business logic separated from provider- specific authentication, endpoints, model identifiers, and transport configuration reduces migration and maintenance risk.
A violates architectural simplicity by duplicating every call unnecessarily. B knowingly violates the enterprise requirement until migration occurs. D directly ignores the contractual routing constraint and is therefore invalid regardless of technical feasibility.
The correct approach is to make Bedrock the configured inference provider while keeping higher-level application and agent behavior decoupled from provider-specific implementation details. Relevant Claude Developer topics are Claude API mechanics, cloud-provider integrations, Amazon Bedrock, SDK configuration, authentication boundaries, model invocation, and provider abstraction.
NEW QUESTION # 28
You are designing an agent that handles a complex claim-processing workflow. Each claim moves through fact extraction, eligibility evaluation, and a decision step. The three subtasks have distinct success criteria, and some claims require iteration between fact extraction and eligibility evaluation before a decision can be reached.
Which agent pattern would you apply?
Answer: C
Explanation:
A is correct because the workflow is state-dependent, non-linear, and iterative . The source explicitly identifies the graph-based pattern as the intended architecture for this scenario. Each stage-fact extraction, eligibility evaluation, and decision-making-has its own completion criteria, and the process may need to move backward from eligibility evaluation to fact extraction when information is incomplete. A graph representation naturally models these conditional transitions and loops.
Anthropic's current orchestration guidance supports workflows containing branching, loops, filtering, staged execution, and state-dependent control flow , rather than forcing every task through one fixed sequence.
Dynamic workflow orchestration can use explicit control logic so the next processing stage depends on current state and previous results.
B concerns progressive output delivery, not workflow-state transitions. C provides a generic agentic tool loop but does not explicitly model distinct states or transition criteria. D is unsuitable because it prohibits the required return path between extraction and eligibility evaluation.
Therefore, a graph-based architecture provides the necessary conditional routing, iteration, and stage-specific validation.
Relevant Claude Developer topics: Agent Patterns, graph workflows, state transitions, conditional branching, loops, stage-specific success criteria, and agent orchestration .
NEW QUESTION # 29
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 # 30
You are explaining to a stakeholder why running the same Claude prompt twice can produce slightly different results. The stakeholder is concerned this means the application is broken.
How would you address the stakeholder's concern?
Answer: C
Explanation:
Option C correctly explains a fundamental property of generative language models. The supplied examination material identifies C as the correct answer. Claude generates subsequent tokens probabilistically rather than retrieving a single fixed answer for each prompt. Consequently, identical or highly similar requests can produce variations in wording, ordering, explanation depth, and sometimes substantive details.
Anthropic's API documentation explicitly states that sampling parameters control randomness and, importantly, that even configurations historically using a temperature of 0.0 were not fully deterministic .
Current newer Claude models increasingly manage sampling behavior internally, so application design should not assume byte-for-byte identical responses across executions.
Production systems therefore handle variation through architecture: schema validation for machine-consumed output, deterministic business-rule checks, retries where appropriate, evaluations for acceptable behavioral ranges, and application-level safeguards. Temperature adjustment may reduce variation on models that support the parameter, but it does not fundamentally convert an LLM into a deterministic function.
A incorrectly labels normal model behavior as a defect. B confuses network latency with generation variability. D overstates model snapshot behavior; pinning a model prevents silent model-version changes but does not eliminate sampling variability.
Relevant topics: sampling, nondeterminism, validation, retries, model versions, and robust Claude API integration .
NEW QUESTION # 31
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