Welcome to the most comprehensive practice resource for mastering AI System Design in 2026. This course is specifically engineered for engineers, architects, and data scientists who want to move beyond theoretical knowledge and prove their ability to build scalable, production-ready AI infrastructures.

Why Serious Learners Choose These Practice Exams

Navigating the world of AI System Design requires more than just knowing how to train a model. It requires an understanding of data pipelines, latency trade-offs, and hardware acceleration. Serious learners choose this course because it reflects the current 2026 landscape of Artificial Intelligence, focusing on Large Language Model (LLM) orchestration, vector database optimization, and distributed training efficiency. Our questions are crafted to simulate high-pressure technical interviews and real-world architectural challenges.

Course Structure

Our curriculum is divided into six strategic pillars to ensure a progressive learning experience:

Basics / Foundations: This section covers the essential building blocks, including data ingestion patterns, basic machine learning workflows, and the difference between batch and stream processing.
Core Concepts: Here, we dive into model selection, evaluation metrics for production, and fundamental scaling techniques for inference.
Intermediate Concepts: You will explore the nuances of feature stores, model versioning, and the integration of caching layers to reduce redundant computations.
Advanced Concepts: This module tackles complex topics such as distributed training strategies (data vs. model parallelism), quantization, and sharding strategies for massive vector embeddings.
Real-world Scenarios: We present architectural case studies, asking you to design systems for recommendation engines, real-time fraud detection, or multi-modal search engines.
Mixed Revision / Final Test: A comprehensive, timed exam that pulls from all previous sections to test your mental agility and readiness for certification or interviews.

Sample Practice Questions

QUESTION 1

When designing a low-latency recommendation system for a mobile app with 50 million active users, which architecture provides the best balance between freshness and inference speed?

Option 1: Calculating all recommendations in a nightly batch job and storing them in a relational database.
Option 2: A two-stage pipeline consisting of a fast retrieval (candidate generation) model followed by a complex ranking model.
Option 3: Running a deep neural network inference on the full product catalog every time a user opens the app.
Option 4: Utilizing a single-stage complex model that processes raw logs directly from the stream.
Option 5: Storing only the user’s last five clicks and using a rule-based lookup table.

CORRECT ANSWER: Option 2

CORRECT ANSWER EXPLANATION: The two-stage architecture is the industry standard for large-scale systems. The retrieval stage narrows down millions of items to a few hundred candidates using lightweight methods (like embeddings), and the ranking stage uses a more computationally expensive model to order those few candidates. This ensures low latency while maintaining high accuracy.

WRONG ANSWERS EXPLANATION:

Option 1: Nightly batches fail to incorporate "in-session" behavior, leading to stale recommendations that do not reflect the user's current intent.
Option 3: Running complex inference on the entire catalog is computationally prohibited and would result in seconds of latency, ruining the user experience.
Option 4: Processing raw logs through a complex model in a single stage without a retrieval step is too slow for real-time requirements at this scale.
Option 5: While fast, a rule-based lookup lacks the predictive power and personalization capabilities required for a modern high-performance system.

QUESTION 2

In the context of distributed training, when should an architect prefer Model Parallelism over Data Parallelism?

Option 1: When the dataset is too large to fit on a single machine's disk.
Option 2: When the model parameters are small enough to fit on a single GPU but the training needs to be faster.
Option 3: When the model size exceeds the memory capacity of a single accelerator (GPU/TPU).
Option 4: When using a simple Logistic Regression model on a very high-dimensional sparse dataset.
Option 5: Only when training on local CPUs instead of cloud-based GPU clusters.

CORRECT ANSWER: Option 3

CORRECT ANSWER EXPLANATION: Model Parallelism is required when the model itself (the weights and gradients) is too large for the memory (VRAM) of a single device. In this case, the model must be split across multiple devices to function.

WRONG ANSWERS EXPLANATION:

Option 1: Large datasets are handled by Data Parallelism or distributed file systems, not necessarily by splitting the model itself.
Option 2: If the model fits on one GPU, Data Parallelism is generally more efficient for speeding up training by processing different batches of data simultaneously.
Option 4: Simple models like Logistic Regression rarely require Model Parallelism; they are usually handled through data-parallel distributed systems.
Option 5: Parallelism strategies are most critical for specialized accelerators (GPUs/TPUs) where memory is a finite and expensive resource, not just for CPUs.

Enrollment Benefits

Welcome to the best practice exams to help you prepare for your AI System Design career. By joining this course, you gain access to a premium learning environment designed for success.

You can retake the exams as many times as you want.
This is a huge original question bank updated for 2026.
You get support from instructors if you have questions.
Each question has a detailed explanation for deep conceptual understanding.
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We hope that by now you are convinced! There are hundreds of additional questions waiting for you inside the course to help you bridge the gap between AI theory and professional system architecture.