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| Section | Weight | Objectives |
|---|---|---|
| Security and Safety | 8.1% | - Secure Application Design
|
| Claude Code | 3.1% | - Claude Code Configuration and Usage
|
| Agents and Workflows | 14.7% | - Claude Agent SDK and Agent Loops
|
| Eval, Testing, and Debugging | 2.6% | - Evaluation
|
| Prompt and Context Engineering | 11% | - Context Engineering
|
| Applications and Integration | 33.1% | - Claude API and Client SDKs
|
| Model Selection and Optimization | 16.8% | - Model Selection
|
| Tools and MCPs | 10.6% | - Model Context Protocol
|
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NEW QUESTION # 25
A new Claude model release includes performance improvements for several reasoning tasks but has changed the format of its responses to system prompts that use multi-section instructions. Your application uses multi- section system prompts heavily. Initial evaluation on the application's actual workload shows the new model performs 8 percent better on reasoning tasks but produces malformed output on roughly 3 percent of requests because of the format change. The team is debating whether to upgrade.
How would you decide?
Answer: A
Explanation:
The supplied examination page marks B . The scenario already demonstrates why model upgrades must be treated as evaluated software changes rather than automatic replacements: the new model improves one metric while introducing a regression in another.
Anthropic's official model-selection guidance recommends creating benchmark tests specific to the application's use case, testing models with the application's actual prompts and data, comparing response quality and edge-case performance, and weighing performance against operational tradeoffs. Therefore, the correct action is to adapt the multi-section system prompt to the new model's behavior and repeat the evaluation. Only after the formatting regression is eliminated-or reduced below an explicitly acceptable threshold-should the upgrade proceed.
A incorrectly assumes that an 8% reasoning improvement numerically compensates for a 3% malformed- output rate; these metrics measure different consequences and cannot simply be subtracted. C treats the known incompatibility only downstream instead of first correcting the prompt/model interaction. D permanently rejects future improvement and is inconsistent with controlled lifecycle evolution.
The engineering principle is migration through regression testing and adaptation , not blind upgrading or permanent version avoidance.
Relevant Claude Developer topics: Systems Life Cycle, model migration, regression evaluation, prompt adaptation, compatibility testing, deployment gates, and continuous evolution .
NEW QUESTION # 26
You are designing an agent that processes vendor invoices. The work involves a small number of well- understood steps, but occasionally an invoice arrives in an unexpected format that requires the system to decide between rerouting, requesting clarification, or flagging for human review.
The most appropriate architecture for this system is...
Answer: B
Explanation:
Option D applies the correct hybrid pattern. Anthropic distinguishes workflows-where LLMs and tools execute along predefined code paths-from agents, where the model dynamically determines how to proceed.
Anthropic recommends choosing the simplest architecture that meets the requirement: workflows provide predictability and consistency for well-understood tasks, while agents are valuable where flexible model- driven decisions are necessary.
Standard invoice processing is explicitly described as a small number of known steps. That makes a deterministic workflow the appropriate default because the path can be tested, monitored, and reproduced.
The unexpected-format branch is different: the system must reason among rerouting, requesting clarification, or human escalation. That decision point is where agentic flexibility adds value.
A makes the entire process autonomous even though most steps do not require autonomy, increasing cost, latency, and unpredictability. B introduces multiple specialized agents despite no demonstrated need for that complexity. C collapses deterministic processing and exception handling into one monolithic model call, making validation and debugging harder.
Therefore, D combines workflow predictability with targeted agentic reasoning. Relevant Study Guide topics:
workflows versus agents, routing, exception handling, hybrid agentic architecture, escalation, and complexity minimization.
NEW QUESTION # 27
A teammate has asked you to explain when a Skill would be the right choice over an MCP server. The teammate is unsure how the two differ in practice when both can be reused across teams.
How would you explain the distinction?
Answer: C
Explanation:
The supplied Claude Developer question explicitly identifies D . The two mechanisms solve different extension problems. A Skill is a reusable package of domain expertise and workflow guidance. Anthropic describes Agent Skills as modular, filesystem-based capabilities containing instructions, metadata, and optional supporting resources such as scripts and templates, which Claude can load when relevant.
MCP, by contrast, defines a standardized mechanism for exposing external capabilities and context.
Anthropic's MCP integration supports MCP tools and, through client-side helpers, MCP prompts and resources. This makes MCP appropriate when Claude must communicate with an external server or reusable service interface rather than simply load packaged expertise.
Thus, Skills are appropriate for packaging repeatable instructions, procedures, scripts, templates, or domain knowledge. MCP servers are appropriate for exposing callable operations, external data resources, and shared service capabilities using the Model Context Protocol.
A incorrectly treats them as interchangeable. B incorrectly describes the difference as merely generational. C invents a universal efficiency advantage that is not the architectural distinction.
Relevant Claude Developer topics: Agent Construction, Agent Skills, MCP, reusable capabilities, resources, prompts, tools, extension architecture, and cross-team component reuse .
NEW QUESTION # 28
You have just shipped a new Claude-powered application to production. The development phase is complete, and the system is now in active use by internal teams.
The next phase of work for this system is...
Answer: B
Explanation:
Production deployment does not terminate the software lifecycle. Once a Claude application is actively serving users, it enters an operations and maintenance phase in which developers and operators monitor performance, evaluate model behavior, respond to failures, control costs, manage security, and evolve the implementation as requirements or model capabilities change.
Anthropic's official developer documentation explicitly separates the journey into build, evaluate-and-ship, and operate stages. The operating stage includes workspace administration, API-key management, usage monitoring, and model migration. This confirms that production deployment is a transition into ongoing operation rather than the endpoint of development.
For LLM applications, maintenance is especially important because production traffic can expose input distributions and failure modes that were not fully represented during pre-release evaluation. Operational data should feed back into evaluations, prompt improvements, guardrails, architecture decisions, and model- version planning.
A incorrectly treats deployment as final. B may be useful but is only one governance activity, not the overall lifecycle phase. D assumes a mandatory organizational separation that is neither required nor generally desirable.
The supplied exam source marks C. Relevant topics: Systems Life Cycle, production operations, monitoring, maintenance, incident response, evaluation, and continuous system evolution.
NEW QUESTION # 29
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: B
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 # 30
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