Machine Learning Flashcards
7 cards from real Microsoft Azure AI Fundamentals practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Machine Learning flashcards as text
Which Azure ML compute target is best suited for running large-scale, distributed training jobs?
Answer: Azure ML Compute Cluster
Azure ML Compute Clusters scale out automatically to multiple nodes, making them ideal for distributed, large-scale training workloads.
What does 'feature engineering' mean in the context of machine learning?
Answer: Transforming or creating input variables to improve model performance
Feature engineering involves creating, transforming, or selecting input variables (features) from raw data to help a model learn patterns more effectively.
In Azure AutoML, which task type should you select to predict a continuous numeric value such as house price?
Answer: Regression
Regression predicts continuous numeric outputs, making it the correct AutoML task type for scenarios like predicting house prices or temperatures.
What is the purpose of a validation dataset during model training?
Answer: To tune hyperparameters and monitor model performance during training
A validation dataset is used during training to tune hyperparameters and detect overfitting, while the test set provides the final unbiased evaluation.
Which ML concept describes using a model trained on one task as a starting point for a different but related task?
Answer: Transfer learning
Transfer learning reuses knowledge from a pretrained model (e.g., ImageNet-trained CNN) and fine-tunes it for a new related task with less data.
In Azure ML Designer, what is a 'pipeline' composed of?
Answer: Modules connected in a graph that represent data processing and model training steps
Azure ML Designer pipelines are visual graphs made up of connected modules (components) representing steps like data prep, training, and evaluation.
What does AUC (Area Under the Curve) measure in model evaluation?
Answer: The model's ability to distinguish between classes across all thresholds
AUC measures the area under the ROC curve, summarizing the model's ability to discriminate between positive and negative classes across all classification thresholds.