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| Section | Weight | Objectives |
|---|---|---|
| Claude Code | 3.1% | - Claude Code configuration and usage |
| Security and Safety | 8.1% | - AI application security - Guardrails and safety controls |
| Agents and Workflows | 14.7% | - Agent architecture principles - Workflow vs autonomous agents - Memory and context management - Claude Agent SDK usage |
| Model Selection and Optimization | 16.8% | - Claude model family characteristics - Latency and performance trade-offs - Cost and token optimization |
| Tools and Model Context Protocol (MCP) | 10.6% | - Tool integration and usage - MCP server development |
| Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
| Applications and Integration | 33.1% | - Streaming and Batch API - SDK and third-party integration - Claude Messages API - Vision capabilities |
| Prompt and Context Engineering | 11% | - Context window management - Prompt design and structuring - Structured output handling |
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NEW QUESTION # 48
You are deciding between Claude models for a task. The team has identified three relevant tradeoff dimensions: quality, latency, and cost.
The right model is the one that...
Answer: C
Explanation:
The supplied Claude Certified Developer Foundations source marks C . Model selection is a multidimensional engineering decision. There is no universally correct Claude model independent of workload requirements; the application must satisfy the required capability or quality while remaining within acceptable latency and cost envelopes.
Anthropic's official model-selection guidance explicitly identifies capabilities, speed, and cost as core considerations and recommends testing models against workload-specific benchmarks rather than selecting them from a single metric. The guidance further recommends evaluating actual prompts and data, comparing response accuracy, quality, and edge-case behavior, and then weighing the resulting performance and cost tradeoffs.
Options A, B, and D each establish one or two dimensions as primary and effectively defer the remainder.
That can lead to a technically unsuitable model-for example, a cheap model that fails the quality threshold or a high-quality model whose latency makes the user experience unacceptable.
The correct method is to define minimum acceptable thresholds across all relevant dimensions and benchmark candidate models against the actual workload.
Relevant Claude Developer topics: Claude App Design, model selection, capability, quality, latency, cost, benchmarking, workload evaluation, tradeoff analysis, and production optimization .
NEW QUESTION # 49
You are configuring Claude Code for a new project. The team needs to set permissions, default model selections, and environment-specific behavior at the project level so the configuration is consistent across all developers working on the repository.
The Claude Code mechanism you would use is...
Answer: A
Explanation:
D is directly aligned with Claude Code's configuration model. The supplied exam source marks D . Anthropic documents settings.json as the official mechanism for configuring Claude Code through hierarchical scopes.
Shared project configuration is stored in .claude/settings.json , which is intended to be checked into source control and shared with the team.
Project settings provide consistent repository-level behavior while still participating in Claude Code's configuration precedence. They can configure permissions, environment variables, tool behavior, and other supported project policies. Claude Code additionally distinguishes shared project settings from .claude
/settings.local.json, which is intentionally excluded from source control and is suitable for developer-specific experimentation or machine-local overrides.
A spreadsheet provides documentation but no executable configuration enforcement. B allows every developer's configuration to diverge and is appropriate only for values that genuinely belong to the local environment. C requires manual repetition and cannot reliably establish repository-wide policy.
Therefore, shared project settings.json is the appropriate configuration-as-code mechanism.
Relevant Claude Developer topics: Confia Management, Claude Code configuration, settings scopes, project settings, permissions, environment configuration, team consistency, and source-controlled configuration .
NEW QUESTION # 50
A teammate has submitted a pull request that adds a Claude-powered feature to your service. The code works, but the prompt and model selection are hard-coded inline, error handling is missing, and there are no tests for the integration.
What would you request during code review?
Answer: A
Explanation:
D is the only option that addresses all three identified production-readiness defects. The supplied Claude Developer item explicitly selects D. A functioning happy path is insufficient for a maintainable Claude integration.
Prompt and model selection are configuration concerns that will change as prompts are evaluated, model versions evolve, or environments require different behavior. They should therefore be separated from unrelated business logic rather than scattered as inline constants. Claude API failures must also be handled deliberately. Anthropic documents typed SDK exceptions and defined HTTP error categories, including invalid requests, authentication failures, rate limits, server errors, overload, and timeouts. The official SDKs additionally retry appropriate transient failures.
Tests are required to verify the integration boundary, including successful behavior, malformed or unexpected responses, API error handling, and critical user workflows. Approving code without those controls pushes known reliability debt directly into production.
A and B knowingly merge incomplete production behavior. C improves configuration and test coverage but leaves a known API-failure path unhandled.
Relevant Claude Developer topics: SW Eng Foundations, code review, separation of configuration, error handling, integration testing, API resilience, maintainability, and production readiness .
NEW QUESTION # 51
You maintain a Claude application that uses Claude Sonnet 4.5 across several production workflows.
Anthropic released Claude Sonnet 4.7, which your evaluation suite shows performing 8% better on your highest-volume task. However, this version produces different output formatting on two of your structured- extraction prompts that downstream consumers parse with regex-based code.
To roll out the upgrade, you would...
Answer: C
Explanation:
Option A is correct because a model upgrade should be treated as a controlled application change, not a simple identifier substitution. Anthropic's evaluation guidance recommends defining measurable success criteria and running task-specific evaluations that mirror real production behavior, including edge cases. Its model-migration guidance likewise recommends testing replacement models before moving production workloads.
Here, the new model improves the highest-volume task but changes output formatting on structured-extraction prompts. That means the migration has both a quality benefit and a compatibility risk. The correct response is to tighten the output contract, re-run evaluations against the parsing/schema boundary, then deploy progressively with a feature flag and a per-workflow rollback path. This limits blast radius and preserves a known-good recovery option.
Option B pushes an unverified behavior change directly into production. Option C makes downstream parsing permissive, which can conceal schema drift instead of enforcing a stable contract. Option D permanently preserves a fragile implementation and discards the measured quality improvement.
Taking the model version stated in the question as the scenario, A is the correct lifecycle strategy. Relevant Study Guide topics: model migration, regression evaluation, structured output, compatibility testing, progressive rollout, rollback, and production change management.
NEW QUESTION # 52
Your team is integrating Claude into an existing REST API service. The service handles concurrent requests, and you are deciding how to structure the Claude API calls within the existing async codebase.
How would you structure the Claude calls?
Answer: B
Explanation:
Option C is the natural integration model for an application that already uses asynchronous I/O. Claude API calls are network-bound operations, so blocking the application's event loop while waiting for responses would reduce concurrency and impair throughput. The Anthropic Python SDK explicitly provides AsyncAnthropic, and its documented usage awaits client.messages.create() directly. It also supports an aiohttp backend when improved asynchronous concurrency is desired.
This means Claude requests can participate in the same cooperative asynchronous execution model as other database, HTTP, or service calls. While one request waits for remote I/O, the runtime can continue serving other work rather than dedicating the event loop to an idle blocking operation.
A can be used when integrating unavoidable blocking libraries into async software, but it adds thread-pool management when an official asynchronous client already exists. B needlessly redesigns a concurrent REST service around a synchronous integration. D is specifically harmful because blocking the event loop prevents normal concurrent request processing.
Therefore, C uses the abstraction provided for exactly this architecture. Relevant Study Guide topics: Claude SDK clients, asynchronous APIs, awaitable I/O, concurrency, REST-service integration, and scalable application architecture.
NEW QUESTION # 53
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