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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security and Safety | 8.1% | - AI application security - Guardrails and safety controls |
| Topic 2: Agents and Workflows | 14.7% | - Workflow vs autonomous agents - Claude Agent SDK usage - Memory and context management - Agent architecture principles |
| Topic 3: Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Topic 4: Model Selection and Optimization | 16.8% | - Cost and token optimization - Claude model family characteristics - Latency and performance trade-offs |
| Topic 5: Claude Code | 3.1% | - Claude Code configuration and usage |
| Topic 6: Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
| Topic 7: Prompt and Context Engineering | 11% | - Structured output handling - Prompt design and structuring - Context window management |
| Topic 8: Applications and Integration | 33.1% | - Streaming and Batch API - Claude Messages API - Vision capabilities - SDK and third-party integration |
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NEW QUESTION # 30
Your team is preparing to roll out a configuration change that updates several prompt versions across a Claude application used by multiple downstream systems. The change has already been tested in staging, but the team has not assessed how the prompt change will affect each downstream system that depends on the application's output.
What would you do before rolling out the change?
Answer: A
Explanation:
A is the appropriate configuration-change control. The supplied examination item selects A . A prompt is not merely editorial text; in a Claude application it functions as executable behavioral configuration. Changing a prompt can alter output structure, field population, language, classifications, tool-use decisions, refusal behavior, or other assumptions on which downstream consumers depend.
Staging success therefore proves only the scenarios actually covered by staging. Before production rollout, the team must perform impact analysis across every dependent system, identify contractual expectations, run representative regressions, and coordinate changes where a consumer may be affected. This is especially important where downstream applications parse structured output or expect stable semantics.
Anthropic's Structured Outputs guidance illustrates why interface contracts matter: missing fields, inconsistent types, and schema violations can break consuming applications. Even when output remains syntactically valid, prompt changes can produce semantic changes that require consumer validation.
B assumes staging coverage is universal. C communicates the change without determining its consequences.
D arbitrarily defers systems instead of assessing them.
Relevant Claude Developer topics: Confia Management, configuration impact assessment, prompt versioning, dependency management, change control, downstream contracts, regression testing, and coordinated deployment .
NEW QUESTION # 31
Your Claude application uses structured output that is consumed by downstream code. The team wants to handle malformed or unexpected output gracefully so it does not crash downstream systems.
The best choice for handling this issue would be to...
Answer: A
Explanation:
Option D applies a fundamental production engineering principle: treat externally generated data as potentially malformed and parse it defensively before use. Downstream application logic should not assume that every field exists, every type is correct, or every unexpected property can safely be ignored. Instead, the parser should validate expected structures, handle optional or missing values deliberately, reject incompatible types, and convert failures into controlled application errors rather than process crashes.
Anthropic's Structured Outputs documentation identifies exactly these failure classes for unconstrained model output: malformed JSON, missing required fields, inconsistent types, and schema violations can break downstream applications. Current Structured Outputs and strict tool-use capabilities can eliminate many schema-level failures through constrained decoding, but defensive handling remains an important software boundary when unexpected data can still arise from external services, legacy paths, or semantic validation requirements.
A creates unnecessary outages. B hides failures and discards potentially recoverable data without observability. C deliberately postpones a reliability requirement until after deployment.
The supplied question identifies D as correct. Relevant topics: Claude App Design, defensive programming, structured output, schema validation, parsing, error handling, downstream reliability, and type safety.
NEW QUESTION # 32
Your application uses the Messages API to handle multi-turn conversations. Each new turn resends the entire conversation history, and your token costs are growing as conversations get longer. You suspect there is a more efficient approach.
How would you address this?
Answer: B
Explanation:
Option A is the technically verified answer. Anthropic documents that multi-turn agentic requests resend the growing conversation context on subsequent turns. Prompt caching allows repeated prompt prefixes-such as system instructions, tool definitions, and prior unchanged conversation content-to be reused at the lower cache-read rate rather than repeatedly charged as ordinary uncached input. Anthropic specifically identifies caching repeated context as a major production cost optimization.
B is incorrect even though the supplied PDF contains a duplicated screenshot in which B appears selected.
The Batch API is designed for asynchronous workloads that can tolerate delayed completion; it is not the appropriate mechanism for every interactive turn in a multi-turn conversation. Because the user requested verified answers rather than blindly reproducing marked selections, A is retained.
C discards potentially essential conversation state arbitrarily. D is a valid context-management technique in some long-running workflows, but summarizing after every turn creates additional model work and loses detail; it does not exploit repeated-prefix caching.
Therefore, A directly addresses the stated cost pattern while preserving conversation fidelity. Relevant Study Guide topics: Messages API, stateless conversation history, prompt caching, cached input tokens, multi-turn applications, token economics, and cost optimization.
NEW QUESTION # 33
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: A
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 # 34
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: C
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 # 35
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