CCAR-P Practice Question: Developer Productivity and Operational Enablement
Which THREE factors should be prioritized when selecting an Anthropic model for a production-grade application?
⚠ Common exam trap
Candidates often focus only on model 'intelligence' or 'capability,' ignoring operational realities like cost and latency which are critical for production-grade, scalable applications.
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
✓
Latency requirements of the specific user task.
Balancing performance, latency, and cost is fundamental to operational enablement. A professional architect must consider the specific requirements of the task—whether high-level reasoning or rapid response—to ensure that the chosen model delivers value efficiently. These factors dictate the system's scalability and overall budget, making them the primary drivers for architectural decisions when building and maintaining reliable AI-powered solutions in a corporate environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Latency requirements of the specific user task.
Why this is correct
Latency is a critical factor for user experience. Real-time applications require lower-latency models like Haiku, while complex analysis tasks can afford higher latency in exchange for reasoning performance. Aligning model choice with the user task is essential for building a performant, well-architected application.
- ✗
The total number of parameters in the model.
Why it's wrong here
The parameter count is an internal implementation detail and is not directly exposed or useful for architectural decision-making. Architects should focus on functional capabilities, performance metrics, and cost, rather than the underlying model architecture, which is subject to change by the provider.
- ✓
Reasoning capabilities required for the task complexity.
Why this is correct
Task complexity dictates the required intelligence level of the model. Choosing a model that is too simple will lead to incorrect outputs, while choosing one that is too complex increases costs and latency. Matching the model's reasoning capabilities to the application's needs is fundamental for operational quality.
- ✓
Cost efficiency per token generated.
Why this is correct
Cost efficiency is essential for the long-term sustainability of the application. Using cost-effective models for simpler tasks allows the organization to allocate their budget towards more complex use cases. Maintaining a clear understanding of cost-per-token is a requirement for professional financial and operational management of AI apps.
- ✗
Whether the model is open-source or proprietary.
Why it's wrong here
The licensing model (open-source vs. proprietary) is a secondary concern compared to the performance, cost, and reliability of the model provider. For most enterprises, the operational support, security guarantees, and consistent performance of a managed proprietary model are more important than the open-source status of the weights.
About these practice questions
This CCAR-P question is part of Courseiva's 262-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.