AI-300 ML Model Lifecycle And Operations Practice Question
You are configuring a batch scoring job. You need to ensure that the job processes data in parallel to reduce completion time. What property should you adjust in the 'ParallelRunConfig'?
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
✓
Adjust 'process_count_per_node' and 'node_count'.
The 'node_count' and 'process_count_per_node' are the key parameters in 'ParallelRunConfig' that dictate the degree of parallelism.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the 'output_action' to 'append_row'.
Why it's wrong here
This dictates how output files are merged.
- ✓
Adjust 'process_count_per_node' and 'node_count'.
Why this is correct
These define how many instances run the script concurrently.
- ✗
Enable 'distributed_training'.
Why it's wrong here
This is for training, not inference.
- ✗
Set 'min_nodes' to 1.
Why it's wrong here
This is for scaling the cluster, not the job parallelism.
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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.