AI-300 ML Model Lifecycle And Operations Practice Question
You are running a distributed training job using the 'PyTorch' framework on Azure Machine Learning. You need to configure the 'DistributionConfiguration'. Which setting is mandatory for multi-node training?
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
✓
'process_count' or 'node_count' in the configuration.
When using 'PyTorch' distribution, you must specify the 'process_count' or 'node_count' to correctly distribute the workload across the compute cluster.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
'process_count' or 'node_count' in the configuration.
Why this is correct
This defines the parallel distribution parameters.
- ✗
'framework' set to 'TensorFlow'.
Why it's wrong here
This is for a different framework.
- ✗
'shm_size' set to 1GB.
Why it's wrong here
This is for shared memory, not training distribution.
- ✗
'enable_gpu' set to False.
Why it's wrong here
GPU is not mandatory, but configuration is.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-300 practice question is part of Courseiva's free Microsoft 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 AI-300 exam.