CCAR-P Practice Question: Developer Productivity and Operational Enablement
A company is scaling its Claude-powered applications globally. Which strategy best optimizes for both latency and cost?
⚠ Common exam trap
Candidates frequently assume the most powerful model should be used for every task, ignoring cost-efficiency strategies like routing simpler tasks to smaller models.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Route complex tasks to Sonnet and simpler tasks to Haiku.
Selecting the appropriate model based on task complexity (right-sizing) combined with regional deployment of application logic minimizes latency. By routing simpler tasks to smaller, faster models and reserving the most capable models for complex reasoning, the organization maximizes cost-efficiency. This operational strategy ensures that developers can build high-performance applications while staying within budget constraints, which is vital for long-term project sustainability and scalability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the largest available Claude model for every API request globally.
Why it's wrong here
Using the largest model for all tasks is inefficient and costly. It increases latency and unnecessary expenditure for simple operations that could be handled by smaller models. This approach ignores the operational benefits of model right-sizing, which is essential for managing costs and response times at global scale.
- ✓
Route complex tasks to Sonnet and simpler tasks to Haiku.
Why this is correct
Right-sizing models based on task complexity is a highly effective optimization strategy. It reduces operational costs by leveraging smaller models for lightweight tasks while maintaining performance for complex reasoning. This architectural choice improves developer productivity by providing a balanced toolkit that addresses diverse use cases with optimal efficiency and speed.
- ✗
Cache all API responses indefinitely to eliminate future costs.
Why it's wrong here
Indefinite caching is impractical for dynamic AI applications where context and user intent vary significantly. It can lead to stale or irrelevant information being returned to users, negatively impacting the application's utility. Caching should be strategic and TTL-limited to ensure accuracy while providing minor performance improvements in specific areas.
- ✗
Deploy all applications in a single region to simplify infrastructure.
Why it's wrong here
Centralizing all infrastructure in one region significantly increases latency for global users, leading to a degraded user experience. To optimize for global performance, latency must be minimized by deploying application components closer to the end user, even if it adds some complexity to the underlying infrastructure management and networking.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Anthropic exam blueprint
This CCAR-P practice question is part of Courseiva's free Anthropic certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CCAR-P exam.