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| Section | Weight | Objectives |
|---|---|---|
| Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Model Selection and Optimization | 16.8% | - Latency and performance trade-offs - Cost and token optimization - Claude model family characteristics |
| Tools and Model Context Protocol (MCP) | 10.6% | - Tool integration and usage - MCP server development |
| Prompt and Context Engineering | 11% | - Context window management - Structured output handling - Prompt design and structuring |
| Evaluation, Testing, and Debugging | 2.6% | - Output evaluation and validation - Error handling and debugging |
| Agents and Workflows | 14.7% | - Claude Agent SDK usage - Memory and context management - Agent architecture principles - Workflow vs autonomous agents |
| Claude Code | 3.1% | - Claude Code configuration and usage |
| Applications and Integration | 33.1% | - Claude Messages API - Streaming and Batch API - Vision capabilities - SDK and third-party integration |
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
Your Claude application has been running for several conversation turns, and you notice the model occasionally references information that was discussed many turns ago but is no longer relevant. You suspect context drift is causing the model to weight stale content too heavily.
How would you address the drift?
A. Reset the conversation after every turn so the model loses all prior turns when generating a response.
B. Increase the context window size so all turns of the conversation remain visible to the model in full detail.
C. Truncate the conversation so the model sees only the most recent turn during each subsequent response.
D. Apply compaction to summarize older portions of the conversation so the gist remains while the specifics carry less weight.
Question 3
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?
A. Build the application against the direct Anthropic API now and migrate to Bedrock in a follow-up release once the team has more experience with the Bedrock API.
B. Build the application against the direct Anthropic API and ignore the contractual requirement to route Claude calls through Amazon Bedrock.
C. Build two parallel implementations of every call, one for the direct Anthropic API and one for Bedrock, and pick the faster one at runtime.
D. Configure the application to invoke Claude through the Bedrock-compatible API path while keeping the application's logic provider-agnostic.
Question 4
Your team uses several plugins across multiple Claude applications, and a recent plugin update introduced a regression. The team had not been tracking plugin versions, so the team cannot easily identify which version was previously working. How would you address this?
A. Stop using all plugins until the team can rebuild equivalent functionality directly into the application code, treating plugin avoidance as a way to remove version-related risk.
B. Upgrade every plugin to the latest version on a regular cadence to keep version drift small, on the grounds that drift contributes to regression risk.
C. Add explicit plugin version tracking to the project's configuration so the team can identify, pin, and upgrade plugin versions deliberately.
D. Treat plugins as untrackable third-party code and rely on plugin authors to communicate breaking changes when they happen, with no internal version tracking.
Question 5
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...
A. Meets the task's cost target within a defined latency budget, with quality validated against a representative sample of inputs after the model is selected.
B. Satisfies the task's latency requirement first, then is evaluated against quality and cost thresholds to confirm the selection is acceptable across all three dimensions.
C. Fits the task's quality, latency, and cost requirements together, recognizing that improving one dimension typically affects the others.
D. Meets the task's quality requirements at an acceptable latency, with cost reviewed separately once the quality and latency bar has been established.
Solutions:
| Question 1 Answer: A | Question 2 Answer: D | Question 3 Answer: D | Question 4 Answer: C | Question 5 Answer: C |
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