AML Technology & Digital Tools 2

Question 1

Which distributed computing framework is most commonly used for training large-scale ML models across multiple GPUs on a single node?

Accepted Answer

PyTorch Distributed Data Parallel (DDP)

Answer

PyTorch DDP synchronizes gradients across GPUs within a single node using an all-reduce operation, making it the standard for single-node multi-GPU training.

Question 2

In MLflow, what is the purpose of the 'Model Registry' component?

Accepted Answer

To manage model versioning, staging, and production transitions

Answer

MLflow Model Registry provides a centralized hub for managing the full lifecycle of ML models including versioning and stage transitions (Staging, Production, Archived).

Question 3

What is the primary advantage of using ONNX (Open Neural Network Exchange) format for ML models?

Accepted Answer

It enables interoperability between different ML frameworks

Answer

ONNX defines a common format so models trained in one framework (e.g., PyTorch) can be deployed using a different runtime or framework (e.g., TensorFlow, ONNX Runtime).

Question 4

When using Kubernetes for ML workload orchestration, what resource type is typically used to run a one-time batch training job?

Accepted Answer

Job

Answer

A Kubernetes Job creates one or more pods to run a task to completion and terminates them afterward, making it ideal for finite ML training runs.

Question 5

Which technique does TensorFlow's tf.data API primarily optimize?

Accepted Answer

Data ingestion and preprocessing pipeline throughput

Answer

tf.data optimizes the ETL pipeline feeding data into training by enabling parallelism, prefetching, and caching to prevent GPU starvation.

(AML) Advanced Machine Learning Certification Practice Test

(AML) Advanced Machine Learning Certification Practice Test

AML Technology & Digital Tools 2