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| Section | Weight | Objectives |
|---|---|---|
| Tools and Model Context Protocol (MCP) | 10.6% | - Tool integration and usage - MCP server development |
| Agents and Workflows | 14.7% | - Claude Agent SDK usage - Workflow vs autonomous agents - Memory and context management - Agent architecture principles |
| Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
| Security and Safety | 8.1% | - AI application security - Guardrails and safety controls |
| Model Selection and Optimization | 16.8% | - Cost and token optimization - Claude model family characteristics - Latency and performance trade-offs |
| Prompt and Context Engineering | 11% | - Prompt design and structuring - Context window management - Structured output handling |
| Claude Code | 3.1% | - Claude Code configuration and usage |
| Applications and Integration | 33.1% | - Claude Messages API - Streaming and Batch API - SDK and third-party integration - Vision capabilities |
Question 1
You are running Claude Code as part of an automated continuous integration pipeline. The pipeline needs Claude Code to execute a set of well-defined tasks without prompting for confirmation, and the output needs to be captured for downstream processing.
How would you configure the pipeline?
A. Run Claude Code in headless mode with the required permissions configured in settings.json and capture its output for downstream processing.
B. Disable Claude Code's confirmation prompts globally across all environments so the pipeline runs without interruption from any prompt.
C. Replace Claude Code with a different tool that does not require any configuration to operate without confirmation prompts in the pipeline.
D. Run Claude Code in interactive mode and have a developer manually approve every confirmation prompt while the pipeline executes its tasks.
Question 2
A teammate is reviewing the team's threat model for a Claude application and has asked you to identify the categories of AI-specific threats that the model should cover. The teammate has already listed traditional web application threats and wants to know what additional categories apply to a Claude application.
Which AI-specific threat categories would you add?
A. Supply chain attacks on the Claude SDK because the SDK itself is the only point of vulnerability that a Claude application introduces beyond traditional web application threats.
B. Prompt injection, data leakage from prompts or context, jailbreak attempts, and unsafe model output that bypasses application controls.
C. Network-level denial of service and physical infrastructure attacks, because these categories cover the threats most likely to affect any Claude application in production.
D. Cross-site scripting and SQL injection, because these traditional web application threats apply with equal weight to any application that uses Claude in any way.
Question 3
Your team is preparing to roll out a configuration change that updates several prompt versions across a Claude application used by multiple downstream systems. The change has already been tested in staging, but the team has not assessed how the prompt change will affect each downstream system that depends on the application's output.
What would you do before rolling out the change?
A. Document the prompt version changes in the application changelog and proceed with the rollout, treating the staging test results as sufficient evidence of impact across all downstream systems.
B. Assess the configuration impact on each downstream system before rolling out, and coordinate with downstream system owners as needed.
C. Limit the rollout to systems that were explicitly included in staging testing, and defer all other downstream systems until a later release cycle.
D. Notify downstream system owners that a change is coming and schedule the rollout for the following week, without conducting a formal impact assessment.
Question 4
Your Claude application uses structured output that is consumed by downstream code. The team wants to handle malformed or unexpected output gracefully so it does not crash downstream systems.
The best choice for handling this issue would be to...
A. Apply defensive parsing to handle unexpected fields, missing values, and type mismatches that the downstream code might otherwise crash on.
B. Silently drop any output that does not match expectations so downstream systems do not see the malformed output the application receives from the model.
C. Use the output the model produces and leave schema enforcement to a later phase of production after the application is more complete.
D. Crash on any unexpected output and require manual recovery on affected requests the application handles during normal operation.
Question 5
Your team's Claude application has been in production for a year, and the team has decided to formalize its testing strategy. Currently, the team writes ad-hoc tests for individual features but has no overall testing approach.
What testing approach would you formalize?
A. Define unit tests for individual functions, integration tests for the Claude integration, and end-to-end tests for critical user flows, applied consistently across the codebase.
B. Continue writing ad-hoc tests as features ship and introduce a peer review step to ensure each test adequately covers the feature being released.
C. Adopt a test-driven development practice where unit tests are written before each feature is implemented and must pass before code is merged.
D. Define a single testing approach that uses end-to-end tests and apply it consistently across all new features as they are added to the codebase.
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: B | Question 4 Answer: A | Question 5 Answer: A |
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