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| Section | Weight | Objectives |
|---|---|---|
| Agents and Workflows | 14.7% | - Subagents and Agentic Frameworks
|
| Tools and MCPs | 10.6% | - Model Context Protocol
|
| Prompt and Context Engineering | 11% | - Prompt Engineering
|
| Applications and Integration | 33.1% | - Application Development and Integration
|
| Eval, Testing, and Debugging | 2.6% | - Evaluation
|
| Security and Safety | 8.1% | - Safety and Guardrails
|
| Model Selection and Optimization | 16.8% | - Model Selection
|
| Claude Code | 3.1% | - Claude Code Configuration and Usage
|
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NEW QUESTION # 50
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: D
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 # 51
A Claude application is producing outputs that drift away from the expected JSON format after several conversation turns. The first few turns produce correctly formatted output, but later turns gradually lose structure.
How would you address the drift?
Answer: B
Explanation:
Option A correctly treats this as a context-dependent format drift problem rather than an arbitrary model failure. Because the application initially produces valid JSON and progressively loses structure over subsequent turns, the investigation should focus on how accumulated conversation history changes the instructions and examples visible to Claude. Earlier malformed outputs, conflicting instructions, tool results, or unnecessary historical content can gradually reduce adherence to the intended format.
The application should therefore inspect the conversation state, preserve the authoritative formatting requirement, prune irrelevant content where appropriate, and validate outputs at the application boundary.
Anthropic's Structured Outputs capability is specifically designed to prevent malformed structured responses by constraining Claude to a supplied JSON Schema. Official guidance states that Structured Outputs provide valid, parseable, schema-compliant results for downstream processing.
B destroys the response rather than correcting the failure mode. C resets state but prevents meaningful multi- turn operation and does not diagnose why context causes drift. D changes model characteristics without establishing that the model is responsible.
The supplied question on page 12 identifies A as correct. Relevant topics: Agent Architecture, context engineering, format drift, structured outputs, context evolution, schema validation, and long-running conversations.
NEW QUESTION # 52
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 # 53
You are choosing between using STDIO-based communication and HTTP-based communication for an MCP server. The server will be invoked by a Claude Code session running locally.
Which communication pattern would you use?
Answer: D
Explanation:
Option D is directly supported by both the supplied examination material and Claude Code's MCP documentation. The examination source selects STDIO for this local-process scenario.
Claude Code's official MCP guidance distinguishes remote HTTP servers from local STDIO servers .
Anthropic states that STDIO servers run as local processes and communicate through standard input and standard output. They are particularly suitable for local tools requiring direct machine access or for custom scripts. Claude Code can launch the process itself and communicate with it without creating, exposing, securing, or maintaining a network listener.
HTTP is appropriate when the MCP server exists as a separately reachable network service, especially for cloud-hosted or shared services. That requirement is absent here: the server is being invoked locally from a Claude Code session.
A creates duplicate transport infrastructure without a stated availability requirement. B incorrectly assumes one transport is universally superior regardless of deployment topology. C describes HTTP polling rather than the normal MCP transport relationship and introduces unnecessary request overhead.
Therefore, local execution strongly favors STDIO; remote/shared deployment generally favors HTTP.
Relevant Claude Developer topics: MCP architecture, STDIO transport, HTTP transport, Claude Code integrations, local process communication, and deployment topology .
NEW QUESTION # 54
You are designing an agent that handles a multi-step research task. You want the agent to break the task into smaller pieces, hand each piece to a focused subagent, and consolidate the results.
The agent pattern you would apply is...
Answer: B
Explanation:
The supplied Claude Developer source explicitly marks A . The scenario contains the defining elements of an orchestrator/subagent architecture : decomposition of a larger objective, delegation of independent subtasks to specialized workers, and aggregation of their outputs by a coordinating agent.
This architecture is appropriate when subtasks can be performed with focused context or specialized tools.
Instead of forcing one agent to carry every intermediate detail, the orchestrator can formulate assignments, launch suitable subagents, receive condensed results, identify missing information, and synthesize the final research product. This also enables context isolation and potentially parallel execution.
Anthropic's published multi-agent architecture uses this orchestrator-worker approach for research workloads:
a lead agent decomposes a query, delegates work to specialized subagents, and then integrates their findings.
This is particularly useful where exploration is broad and individual subtasks benefit from independent context windows. The broader model-selection guidance also identifies orchestrator strategies as useful where work can be partitioned among worker models.
B describes storage rather than delegation. C is a context-management technique, not a decomposition pattern.
D centralizes all responsibilities in a single loop.
Relevant Claude Developer topics: Agent Patterns, orchestration, subagents, task decomposition, specialization, delegation, context isolation, and result consolidation .
NEW QUESTION # 55
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