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| Section | Weight | Objectives |
|---|---|---|
| Claude Code | 3.1% | - Claude Code configuration and usage |
| Model Selection and Optimization | 16.8% | - Cost and token optimization - Claude model family characteristics - Latency and performance trade-offs |
| Applications and Integration | 33.1% | - Streaming and Batch API - Claude Messages API - Vision capabilities - SDK and third-party integration |
| Agents and Workflows | 14.7% | - Memory and context management - Claude Agent SDK usage - Agent architecture principles - Workflow vs autonomous agents |
| Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Prompt and Context Engineering | 11% | - Context window management - Prompt design and structuring - Structured output handling |
| Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
Question 1
You are implementing a custom tool for your Claude agent. The tool needs to interact with an external pricing service that returns product data.
Which of the following best practices would you apply as you develop this tool?
A. Define the tool with a clear schema, write a precise description for when to call it, and handle pricing service errors explicitly.
B. Define the tool with a loose schema and let the model interpret the inputs flexibly on each call the agent makes.
C. Implement the tool with no error handling and let the agent loop catch failures whenever the pricing service returns an error during operation.
D. Omit the tool description and let the model infer when to use the tool based on the tool's name and the rest of the prompt context.
Question 2
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...
A. Keep the older model in production to preserve the fragile parsing layer that may risk breaking with any model change.
B. Adjust the prompts to constrain output format, re-run evaluations against the expected parsing-layer schema, then roll the model out with a feature flag and the option to revert per workflow.
C. Rewrite the downstream parsing code to accept a wider range of output formats to ensure the application is resilient to future model upgrades without requiring prompt changes.
D. Switch the model identifier in the application's configuration to the new model, then monitor production for parsing failures to be fixed as failures surface.
Question 3
A teammate has asked how to extend Claude Code with a custom Skill that the team can invoke during sessions. The Skill consists of a set of instructions and a few support scripts the team wants Claude to be able to call when the Skill is loaded.
Where is the right place to define the Skill?
A. Define the Skill as a long inline instruction at the top of every CLAUDE.md file in the team's repositories so Claude has access to it on every session.
B. Define the Skill inside the application's source code as a regular library module and call it from the application code instead of from Claude Code.
C. Define the Skill in a personal scratch directory on each developer's machine and load it manually before each Claude Code session that needs it.
D. Define the Skill in a Skills directory recognized by Claude Code, where Claude can discover and load it during sessions for the team's repositories.
Question 4
Your Claude application validates structured output but has been treating validation failures as terminal errors. Each validation failure causes the entire user request to fail. The team wants to handle validation failures more gracefully.
How would you handle the validation failures?
A. Treat validation failures as a recognized error path that triggers retry, repair, or fallback logic before failing the user request.
B. Disable output validation until the underlying cause of validation failures has been identified and addressed in a future release.
C. Pass validation failures directly to downstream systems and let each downstream system decide how to handle the malformed output.
D. Tell users that validation failures are unavoidable and instruct them to perform manual accuracy checks before relying on outputs.
Question 5
The team is debating whether to integrate with the Claude API directly or through a third-party abstraction layer that supports multiple LLM providers. The team has identified that all current and projected use cases run on Claude, no internal customer has requested LLM portability, and the team's product roadmap does not mention multi-provider support over the next two years. The third-party abstraction would add roughly 15 percent overhead in code complexity and introduce one additional dependency.
Which integration approach would you recommend?
A. Both integration paths in parallel, where the application uses each path on different runs to compare which performs better in production over time.
B. A custom multi-provider abstraction layer the team builds in-house so that the team controls every part of the abstraction the application uses for its API calls.
C. The third-party abstraction layer, on the grounds that multi-provider support is valuable for any application as a matter of long-term flexibility across vendors.
D. Direct integration with Claude through its SDK, because no multi-provider need exists and abstraction would add complexity that does not pay off.
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: D | Question 4 Answer: A | Question 5 Answer: D |
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