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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applications and Integration | 33.1% | - Claude Messages API - Vision capabilities - SDK and third-party integration - Streaming and Batch API |
| Topic 2: Agents and Workflows | 14.7% | - Claude Agent SDK usage - Agent architecture principles - Memory and context management - Workflow vs autonomous agents |
| Topic 3: Prompt and Context Engineering | 11% | - Context window management - Prompt design and structuring - Structured output handling |
| Topic 4: Claude Code | 3.1% | - Claude Code configuration and usage |
| Topic 5: Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
| Topic 6: Tools and Model Context Protocol (MCP) | 10.6% | - Tool integration and usage - MCP server development |
| Topic 7: Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Topic 8: Model Selection and Optimization | 16.8% | - Latency and performance trade-offs - Cost and token optimization - Claude model family characteristics |
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NEW QUESTION # 67
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: B
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 # 68
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: B
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 # 69
Your agent makes 10 to 15 tool calls per task, and you have noticed it sometimes loses track of earlier results by the time it reaches later steps. The agent's context window is large enough to hold all the messages, but the relevant information appears to get buried as the conversation grows.
How would you address this?
Answer: B
Explanation:
Option D is correct because the problem is not insufficient nominal context capacity; it is degraded signal quality as the working context grows. Anthropic's context-window guidance explicitly states that more context is not automatically better. As conversations expand, recall and accuracy can degrade through
"context rot," so long-running agents need active context management rather than simply retaining every prior tool result.
A good pattern preserves the active task state while compacting, summarizing, or pruning stale intermediate outputs. Anthropic documents server-side compaction for long-running conversations and context-editing
/pruning approaches that clear old tool results when they no longer contribute useful information. This keeps high-value constraints and conclusions visible without carrying every verbose response forward.
Option A changes frameworks without fixing the architectural cause. Option B may reduce call count but also creates larger multi-purpose tools and does not guarantee better context quality. Option C increases capacity while preserving the same low-signal accumulation, so the relevant facts can remain buried.
Therefore, D best implements context engineering for agentic workloads. Relevant Study Guide topics:
context engineering, context rot, compaction, tool-result pruning, long-running agents, state preservation, and context-window optimization.
NEW QUESTION # 70
Your team's Claude agent has accumulated several customizations that bypass the SDK's defaults, including custom history management, retry logic, and error handling. A new team member has proposed reverting all the customizations to maintain the codebase more easily. The tech lead disagrees and says each customization was added for a reason.
How would you advise the team?
Answer: D
Explanation:
The correct approach is to evaluate each customization independently against both its original requirement and the capabilities currently supplied by the Claude SDK. Custom code has an ongoing maintenance cost, but automatically removing it is unsafe because some modifications may encode genuine application requirements that the standard SDK behavior still does not address. Conversely, automatically retaining every customization can preserve obsolete complexity after the SDK has evolved.
This is particularly relevant because Anthropic's SDKs already provide capabilities that developers might otherwise implement themselves. Official Claude API documentation states that SDKs provide built-in request formatting, error handling, retries, timeouts, and other integration functionality. For example, transient connection, rate-limit, and server errors are retried automatically with exponential backoff.
Therefore, each custom history, retry, or error-management layer should be reviewed for its actual purpose. If an SDK primitive now provides equivalent behavior, removing duplicate code improves maintainability. If the customization still satisfies a distinct requirement, it should remain.
The supplied exam source explicitly identifies D as the intended answer. Relevant topics: Agent Construction, SDK capabilities, abstraction boundaries, technical-debt management, retries, error handling, and agent state management.
NEW QUESTION # 71
You are setting up the configuration management approach for a new Claude Code project. Your team will use CLAUDE.md files and settings.json files to control behavior, and you want to make sure changes are tracked and reviewable.
The configuration management approach would...
Answer: D
Explanation:
D applies configuration as code and is the answer explicitly selected in the supplied examination source.
Claude Code treats project configuration as part of the repository's working context. Anthropic documents project-level CLAUDE.md as the mechanism for providing project instructions and context, while shared project configuration belongs in .claude/settings.json. Crucially, Anthropic states that .claude/settings.json is intended to be checked into source control and shared with the team .
Keeping these files alongside the code establishes a single versioned relationship between application behavior and the configuration that influences Claude Code. Standard pull requests then provide diffs, peer review, ownership controls, CI validation, audit history, and straightforward rollback. A particular commit can therefore reproduce both source code and its associated Claude project configuration.
A creates competing copies and synchronization risk. B artificially separates behavior configuration from the code version it affects. C creates the same problem specifically for settings.json.
Developer-local values remain distinct: .claude/settings.local.json exists specifically for settings that should not be committed.
Relevant Claude Developer topics: Confia Management, CLAUDE.md, settings.json, configuration as code, source control, pull-request review, reproducibility, auditability, and team-shared Claude Code configuration .
NEW QUESTION # 72
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