AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
What is 'stochastic gradient descent' (SGD) and how does it work?
⚠ Common exam trap
Many candidates confuse 'stochastic' with 'random sampling of data' (Option A) or 'random model selection' (Option C), when in fact SGD's stochasticity refers to using random mini-batches to compute gradients, not random data selection or architecture choice.
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
✓
An optimisation algorithm that updates weights using gradients computed on random data mini-batches
Stochastic Gradient Descent (SGD) is an optimization algorithm used to train machine learning models by iteratively updating model weights. It computes the gradient of the loss function on a randomly selected mini-batch of training data (not the entire dataset), which introduces noise but significantly speeds up convergence and reduces memory usage. This mini-batch approach is the core of SGD and distinguishes it from batch gradient descent.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A random sampling method for selecting training data without replacement
Why it's wrong here
This describes a data-subsetting strategy, such as without-replacement sampling used when partitioning a dataset into training and validation folds. SGD selects random mini-batches only to estimate the loss gradient cheaply for a single weight update; the mini-batches are not permanently withheld from training and the procedure is not a general method for dataset selection.
- ✓
An optimisation algorithm that updates weights using gradients computed on random data mini-batches
Why this is correct
Stochastic Gradient Descent (SGD) is an iterative optimization algorithm where each update step uses a randomly drawn mini-batch of training examples to compute an unbiased estimate of the loss gradient. Because the mini-batch is small and randomly chosen, the gradient estimate is noisy, but it is far cheaper to compute than the full-batch gradient, allowing SGD to scale to large datasets and still converge by taking many noisy steps.
- ✗
A technique for randomly selecting which model architecture to use for AutoML
Why it's wrong here
This describes model search in AutoML, where different neural network architectures (such as ResNet versus EfficientNet) are evaluated for a task. Stochastic Gradient Descent (SGD) is not an architecture selection technique; it is an optimizer that adjusts the weights of an already chosen architecture by taking steps in the direction of the negative gradient of the loss function.
- ✗
Randomly descending through decision tree branches to make predictions
Why it's wrong here
Traversing a trained decision tree from root to leaf by applying feature-value comparisons at each branch is how predictions are made at inference time. SGD, by contrast, is a weight-update procedure that iteratively minimizes a loss function using gradient information, and it has no relation to how tree-based models make predictions.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Batch
Batch is a cloud computing service that runs large numbers of computing jobs as a group, or batch, without needing to manage individual servers.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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