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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tools and Model Context Protocol (MCP) | 10.6% | - Tool integration and usage - MCP server development |
| Topic 2: Claude Code | 3.1% | - Claude Code configuration and usage |
| Topic 3: Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
| Topic 4: Prompt and Context Engineering | 11% | - Context window management - Prompt design and structuring - Structured output handling |
| Topic 5: Model Selection and Optimization | 16.8% | - Cost and token optimization - Latency and performance trade-offs - Claude model family characteristics |
| Topic 6: Applications and Integration | 33.1% | - Streaming and Batch API - SDK and third-party integration - Claude Messages API - Vision capabilities |
| Topic 7: Agents and Workflows | 14.7% | - Claude Agent SDK usage - Agent architecture principles - Memory and context management - Workflow vs autonomous agents |
| Topic 8: Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
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NEW QUESTION # 91
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: D
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 # 92
Your Claude agent has too many tools, and many of them have overlapping functionality. The agent often picks an inappropriate tool when several could plausibly handle a request.
How would you address the tool selection problem?
Answer: A
Explanation:
Option D addresses the architectural cause of the failure: an ambiguous tool surface. Claude chooses tools partly from their names, descriptions, schemas, and the relationship between the request and the capability described. If multiple tools appear to perform substantially the same task, selection becomes unnecessarily difficult.
Anthropic's tool-definition guidance explicitly recommends consolidating related operations into fewer tools and making descriptions clear about both what a tool does and when it should be used. The documentation notes that fewer, more capable tools reduce selection ambiguity and make the available tool surface easier for Claude to navigate.
Option A makes the ambiguity worse by expanding an already overlapping tool set. B destroys required application capabilities. C could improve selection somewhat, because examples can clarify complex inputs, but it leaves the underlying duplication intact. Examples complement good tool design; they are not a substitute for distinct tool responsibilities.
Therefore, D is the strongest solution: remove unused tools, merge functions that represent the same conceptual operation, and write discriminative descriptions defining appropriate and inappropriate usage.
Relevant Study Guide topics: tool design, tool selection, descriptions, tool consolidation, agent construction, and reducing ambiguity.
NEW QUESTION # 93
Your Claude application's error handling currently logs every API error with the same severity level. The team wants to differentiate between errors that should page an on-call engineer and errors that should be logged for later review. How would you structure the error handling?
Answer: A
Explanation:
Option C is correct because operational error handling should distinguish failures by impact, urgency, and recoverability. Anthropic's API documentation already separates error conditions by type and documents different recovery behaviors. For example, transient connection failures, rate limits, and many 5xx errors are automatically retried by official SDKs with exponential backoff, while other failures require application correction or investigation. Treating all of those conditions as the same operational severity creates either alert fatigue or missed incidents.
A production design should classify errors using criteria such as user impact, data integrity risk, security exposure, persistence after retry, affected request volume, and whether automatic recovery succeeds. High- severity failures that threaten service availability or correctness can page the on-call engineer. Recoverable or isolated failures can be logged with structured metadata, request IDs, and metrics for later review.
Option A pages on everything and quickly makes alerts noisy. Option B destroys observability for non-paging errors. Option D preserves the original defect by assigning the same severity to every condition. Therefore, C follows sound Claude application operations. Relevant Study Guide topics: API errors, retries, observability, incident response, severity classification, structured logging, and production operations.
NEW QUESTION # 94
Your Claude application is deployed to development, staging, and production environments. Each environment uses a different model version, different prompt versions, and different plugin dependencies, but the configuration is currently scattered across environment variables, hardcoded values, and undocumented setup scripts.
How would you manage the configuration?
Answer: C
Explanation:
Option C provides the required configuration-management discipline. Development, staging, and production may legitimately use different models, prompts, plugins, permissions, or service endpoints, but those differences must be explicit, reproducible, and auditable rather than scattered across undocumented mechanisms.
Claude Code documentation follows the same configuration-as-code principle. Project-level configuration can live in source-controlled files such as .claude/settings.json, while project instructions are maintained in repository-level CLAUDE.md. Anthropic specifically distinguishes shared project settings from local developer configuration.
A introduces uncontrolled model changes and regression risk. B hides configuration in application logic and makes environment differences harder to review. D ignores the fact that environments often require deliberate differences-for example, production credentials or pinned release versions.
The correct strategy is therefore to define configuration centrally, pin compatibility-sensitive dependencies, record environment-specific overrides, review modifications through source control, and retain rollback history. Relevant Study Guide topics: configuration management, environment isolation, model versioning, prompt versioning, dependency management, reproducibility, and controlled deployment.
NEW QUESTION # 95
You are building a Claude application that processes 10,000 customer emails overnight to extract structured data. The work is non-interactive, runs once daily, and has a flexible completion window of several hours.
Which Claude API would you use?
Answer: C
Explanation:
Option A is correct because the Message Batches API is designed for asynchronous, high-volume processing where results do not need to be returned interactively. Anthropic's API reference states that a Message Batch can contain many independent Messages requests and may take up to 24 hours to complete. That makes it appropriate for 10,000 overnight email-extraction jobs with a several-hour completion window.
Streaming in B solves a different requirement: it exposes partial response events while a single request is being generated, which is valuable for interactive user experiences or long-running synchronous requests, but it does not provide the workload-management advantages of a batch job. C processes items sequentially and unnecessarily sacrifices throughput. D can increase throughput with concurrent real-time calls, but it adds concurrency management and rate-limit pressure when the workload explicitly tolerates asynchronous completion.
The batch design also lets each request carry a custom identifier so results can be matched back to source emails even if completion order differs. Therefore, A is the intended Claude API choice. Relevant Study Guide topics: Message Batches API, asynchronous processing, high-volume workloads, request correlation, throughput, and non-interactive application design.
NEW QUESTION # 96
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