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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt and Context Engineering | 11% | - Prompt design and structuring - Context window management - Structured output handling |
| Topic 2: Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Topic 3: Agents and Workflows | 14.7% | - Agent architecture principles - Memory and context management - Claude Agent SDK usage - Workflow vs autonomous agents |
| Topic 4: Applications and Integration | 33.1% | - Vision capabilities - Claude Messages API - Streaming and Batch API - SDK and third-party integration |
| Topic 5: Tools and Model Context Protocol (MCP) | 10.6% | - Tool integration and usage - MCP server development |
| Topic 6: Model Selection and Optimization | 16.8% | - Latency and performance trade-offs - Cost and token optimization - Claude model family characteristics |
| Topic 7: Claude Code | 3.1% | - Claude Code configuration and usage |
| Topic 8: Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
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NEW QUESTION # 15
A teammate has asked how the Claude SDK handles transient API errors, such as a temporary network issue or a brief rate-limit response. They want to know whether the application code needs to handle every transient error or whether the SDK provides any default behavior.
How would you describe the SDK's default behavior?
Answer: D
Explanation:
Option D precisely matches Anthropic's SDK behavior. Official SDK documentation states that transient conditions are automatically retried two times by default with exponential backoff. The default retry set includes connection failures, HTTP 408 Request Timeout, 409 Conflict, 429 Rate Limit, and 5xx server errors. Developers can alter or disable this behavior through the SDK's retry configuration.
This directly eliminates A because the retry count is configurable. B is incorrect because rate-limit responses are among the conditions automatically retried by the supported SDKs; the API also provides retry-after information where applicable. C is incorrect because the SDK does not merely log these failures and continue without attempting recovery.
Application-level handling is still necessary after the configured retry policy is exhausted. Production code should distinguish permanent errors from transient failures, preserve request IDs for diagnostics, and decide whether higher-level fallback, queuing, or user-visible failure handling is appropriate.
Therefore, D correctly characterizes the SDK abstraction: useful retry defaults combined with developer control. Relevant Study Guide topics: Claude SDKs, transient errors, HTTP 429, retries, exponential backoff, configuration, and resilient API integration.
NEW QUESTION # 16
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: B
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 # 17
Your agent is processing tasks that take 30 to 60 minutes to complete. Each task has well-defined intermediate checkpoints, and the team wants the agent to be able to resume from the most recent checkpoint if a process is interrupted.
How would you implement this resumability?
Answer: A
Explanation:
Option B is the correct fault-tolerance pattern for a long-running stateful workflow. A checkpoint captures enough durable execution state-completed steps, intermediate results, pending work, identifiers, and other necessary task state-to restart from a known consistent point rather than replaying the entire workflow after interruption.
This pattern is consistent with Claude's current stateful agent architecture. Anthropic's Managed Agents documentation describes persistent sessions that preserve conversation history across interactions. When a session becomes idle, its sandbox can be checkpointed so filesystem and execution artifacts are available when work resumes. The certification concept is broader than that specific hosted implementation: long- running agent systems should externalize recoverable state at meaningful boundaries.
A longer timeout does not protect against process crashes, infrastructure restarts, network failures, or deployment interruptions. C doubles resource consumption and creates consistency problems without providing deterministic recovery. D wastes completed work and may repeat external side effects.
Therefore, B provides controlled resumability and minimizes repeated processing. Relevant Study Guide topics: checkpointing, persistent state, resumable workflows, long-running agents, fault tolerance, idempotency, and recovery architecture.
NEW QUESTION # 18
The team is debating whether to build a new capability as a custom tool or to use an existing built-in tool that nearly covers the use case but lacks one specific feature.
How would you decide?
Answer: C
Explanation:
The supplied Claude Certified Developer Foundations material identifies A as the best answer. The decision should begin with the actual capability gap rather than a predetermined preference for built-in or custom tooling. If the existing capability can satisfy the requirement with a small supported extension or composition, using it avoids unnecessary implementation and maintenance. If the missing capability is fundamental, a custom tool becomes justified.
Anthropic's tool architecture explicitly supports both categories. Claude provides Anthropic-managed or predefined tools while also allowing developers to define custom tools for operations specific to their application. With a custom client tool, the application defines the contract and executes the operation before returning its result to Claude.
A is more complete than C because it evaluates both sides of the boundary : whether the existing mechanism can realistically meet the missing requirement and, if not, whether custom implementation is warranted. B prioritizes precedent over current requirements. D only determines whether the unmodified built-in tool is sufficient and does not address what to do when it is almost-but not entirely-sufficient.
Relevant Claude Developer topics: built-in tools, custom tools, capability-gap analysis, tool contracts, maintenance cost, extensibility, and agent construction decisions .
NEW QUESTION # 19
A teammate is reviewing the team's threat model for a Claude application and has asked you to identify the categories of AI-specific threats that the model should cover. The teammate has already listed traditional web application threats and wants to know what additional categories apply to a Claude application.
Which AI-specific threat categories would you add?
Answer: D
Explanation:
Option C correctly identifies threat categories introduced or significantly amplified by LLM-based application architecture. Prompt injection attempts to manipulate Claude into following adversarial instructions.
Jailbreaks seek to circumvent behavioral or application safeguards. Data leakage can expose confidential information contained in system prompts, retrieved context, tool results, or conversation state. Unsafe model output becomes particularly serious when output is consumed by downstream systems or translated into tool actions.
Anthropic's official guardrail documentation explicitly distinguishes jailbreaks and direct prompt injection from indirect prompt injection. It recommends input screening, hardened system prompts, structured handling of untrusted tool content, least-privilege access, output screening, and continuous monitoring. Anthropic also warns that sensitive prompt/context data can leak and recommends post-processing and output filtering where required.
Traditional threats such as SQL injection or XSS remain relevant to the surrounding application, but they are not the additional AI-specific categories requested. B similarly describes conventional infrastructure threats.
D is far too narrow because the SDK is only one component of the attack surface.
The supplied exam source explicitly marks C. Relevant topics: Claude App Design, threat modeling, prompt injection, jailbreaks, data leakage, output safety, least privilege, and defense in depth.
NEW QUESTION # 20
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