AI-900 Describe fundamental principles of machine learning on Azure Practice Test 11 — 15 Questions

Question 1

What is 'overfitting' in machine learning and how does Azure ML help prevent it?

Accepted Answer

When a model learns training data too specifically and fails to generalise to new data. Overfitting occurs when a machine learning model learns the training data too precisely, including noise and outliers, resulting in poor performance on unseen data. Azure ML helps prevent overfitting through automated machine learning (AutoML) which applies regularization, cross-validation, and early stopping techniques, as well as by enabling easy configuration of train/test splits and hyperparameter tuning.

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When a model is trained on too much data and becomes too accurate

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When a model's predictions exceed the acceptable numerical range

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When Azure ML runs training for longer than the allocated compute budget

Question 2

What is the difference between 'precision' and 'recall' as model evaluation metrics?

Accepted Answer

Precision measures correctness of positive predictions; recall measures coverage of actual positives. Precision measures the proportion of positive identifications that were actually correct (true positives / (true positives + false positives)), while recall measures the proportion of actual positives that were correctly identified (true positives / (true positives + false negatives)). In Azure Machine Learning, these metrics are critical for evaluating classification models, especially when dealing with imbalanced datasets, as they provide distinct insights into model performance.

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Precision is the speed of prediction; recall is the model's memory usage

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Precision and recall are both the same metric, just calculated on different datasets

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Recall is higher than precision whenever the model has seen more training data

Question 3

What is 'feature engineering' and why does it matter for machine learning models?

Accepted Answer

Creating and transforming input variables using domain knowledge to improve model performance. Feature engineering is the process of creating new input variables or transforming existing ones using domain knowledge to help machine learning models better capture patterns in the data. It directly impacts model performance by making the underlying relationships more explicit, reducing noise, and enabling algorithms to learn more effectively. In Azure Machine Learning, this is often done through automated feature engineering tools or custom Python scripts within pipelines.

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Building physical infrastructure features (GPU clusters) for model training

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The process of selecting which machine learning algorithm to use for a task

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Adding new computing nodes to a training cluster to speed up training

Question 4

What is 'automated machine learning' (AutoML) in Azure Machine Learning?

Accepted Answer

Automatically iterating through algorithms and hyperparameters to find the best model for a dataset. Automated machine learning (AutoML) in Azure Machine Learning automates the process of selecting the best machine learning algorithm and tuning its hyperparameters for a given dataset. It iterates through multiple combinations of algorithms and hyperparameter values, evaluating each model's performance to identify the optimal solution without manual intervention. This is why option B is correct.

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A system that automatically retrains models on a fixed daily schedule

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Automatically labelling training data using existing model predictions

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A robot that physically connects GPU hardware for distributed training

Question 5

What is 'hyperparameter tuning' in Azure Machine Learning?

Accepted Answer

Searching for the optimal algorithm settings (learning rate, batch size) that maximise model performance. Hyperparameter tuning in Azure Machine Learning is the process of searching for the optimal set of algorithm settings, such as learning rate, batch size, or number of epochs, to maximize model performance. Azure ML provides automated hyperparameter tuning via HyperDrive, which uses techniques like Bayesian sampling, random sampling, or grid search to efficiently explore the hyperparameter space. This is a core step in training a model to achieve the best accuracy or other metrics, not a hardware or compression task.

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Adjusting the physical voltage supplied to GPU hardware during training

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Training the model to predict hyper-specific rare events in the data

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Compressing model weights to reduce inference latency

Question 6

What is the purpose of a 'validation dataset' in machine learning?

Accepted Answer

A held-out data split used during development to tune hyperparameters and compare models. A validation dataset is a held-out subset of the training data used during model development to tune hyperparameters and compare different models without bias. In Azure Machine Learning, this split is typically performed using the `train_test_split` function or automated via AutoML's cross-validation settings, ensuring that the model's performance on unseen data is accurately estimated before final evaluation on the test set.

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Validating that the training data complies with data privacy regulations

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The original dataset before any preprocessing transformations are applied

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Data that has been manually verified as 100% correct by domain experts

Question 7

What is 'cross-validation' and when should it be used in machine learning?

Accepted Answer

Dividing data into k folds and training k times to get a more reliable performance estimate. Cross-validation is a resampling technique used to evaluate machine learning models by partitioning the original dataset into k equal-sized folds. The model is trained on k-1 folds and validated on the remaining fold, repeating this process k times so each fold serves as the validation set once. This provides a more robust and less biased estimate of model performance compared to a single train-test split, especially when data is limited.

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Validating that a model works correctly across different Azure regions

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Comparing two different models' predictions on the same test set

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Checking whether training labels are consistent across different human annotators

Question 8

What is 'label imbalance' in a classification dataset and how does it affect model training?

Accepted Answer

When one class greatly outnumbers others, causing models to be biased toward the majority class. Label imbalance refers to a situation in classification datasets where one class (the majority class) has significantly more samples than other classes (minority classes). This causes the model to become biased toward predicting the majority class, as it minimizes overall loss by ignoring minority classes, leading to poor generalization and low recall for underrepresented classes.

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When labels in the training data contain spelling errors

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When training labels are applied inconsistently by different human annotators

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When a model produces predictions that don't match any of the training labels

Question 9

What is 'gradient boosting' and how does it differ from random forests?

Accepted Answer

Gradient boosting trains trees sequentially to correct prior errors; random forests trains trees independently in parallel. Gradient boosting is an ensemble technique that builds trees sequentially, where each new tree attempts to correct the errors (residuals) of the previous trees by optimizing a loss function via gradient descent. In contrast, random forests build multiple decision trees independently in parallel using bootstrapped samples and random feature selection, then average their predictions. This sequential error-correction process is the key difference, making option B correct.

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Gradient boosting uses deep neural networks; random forests use shallow trees

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Random forests always outperform gradient boosting for structured data

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Gradient boosting requires GPUs; random forests work only on CPUs

Question 10

What is 'model deployment' in Azure Machine Learning?

Accepted Answer

Making a trained model available as a callable endpoint for applications to use. Model deployment in Azure Machine Learning is the process of taking a trained model and hosting it as a web service endpoint (e.g., via Azure Kubernetes Service or Azure Container Instances) so that applications can send data to it and receive predictions in real time or batch mode. This makes the model operational and accessible for inference, which is the core purpose of deployment.

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Uploading training data to Azure Blob Storage for model training

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Distributing the training job across multiple compute nodes

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Publishing a model to the Azure Marketplace for other organisations to purchase

Question 11

What is 'model registry' in Azure Machine Learning?

Accepted Answer

A centralised versioned store for tracking and managing trained models and their lineage. The model registry in Azure Machine Learning is a centralized, versioned store that tracks trained models along with their metadata, lineage, and lifecycle. It enables data scientists to register, version, and manage models, ensuring reproducibility and governance across the ML lifecycle.

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A public marketplace where organisations can buy pre-trained models from third parties

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A database of domain-specific vocabularies used for NLP model training

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A compliance register documenting AI models used by an organisation for audit purposes

Question 12

What is 'Azure Machine Learning designer' and who is it designed for?

Accepted Answer

A drag-and-drop visual interface for building ML pipelines without writing code. Azure Machine Learning designer is a drag-and-drop visual interface that allows users to build, test, and deploy machine learning pipelines without writing code. It is designed for data scientists and developers who prefer a low-code or no-code approach to creating ML workflows, enabling them to focus on model design rather than programming syntax.

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A tool for designing Azure network infrastructure diagrams

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A user interface design tool for building AI-powered mobile applications

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A visualisation tool for exploring and analysing completed model training runs

Question 13

What is 'causal inference' and how does it differ from correlation-based machine learning?

Accepted Answer

Causal inference determines whether X actually causes Y; ML finds correlations that predict outcomes. Causal inference specifically aims to determine whether a change in variable X directly causes a change in variable Y, often through controlled experiments or techniques like do-calculus. In contrast, correlation-based machine learning identifies statistical patterns and associations between variables to make predictions, but does not establish a cause-and-effect relationship. This distinction is fundamental in Azure Machine Learning when choosing between predictive modeling (e.g., regression) and causal analysis (e.g., using the DoWhy library).

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Causal inference uses larger training datasets; correlation-based ML uses smaller ones

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Causal inference is exclusively used in medical research; ML is used in business applications

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ML models always establish causal relationships; causal inference is needed only when data quality is poor

Question 14

What is 'regularisation' in machine learning and what problem does it solve?

Accepted Answer

Adding a penalty to the loss function to discourage overly complex models and reduce overfitting. Regularisation is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function. This penalty discourages the model from learning overly complex patterns, such as large or numerous coefficients, which helps the model generalise better to unseen data. In Azure Machine Learning, regularisation parameters like L1 (Lasso) or L2 (Ridge) can be configured in algorithms such as linear regression or neural networks to control model complexity.

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Standardising input features to the same scale before training

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Applying government regulations to ensure AI models comply with data privacy laws

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Converting irregular training data shapes into a uniform format for the algorithm

Question 15

What is 'Azure Machine Learning compute' and what types are available?

Accepted Answer

The managed cloud infrastructure (VMs, clusters) used to run ML training and inference workloads. Azure Machine Learning compute is a managed cloud infrastructure that provides on-demand virtual machines (VMs) and clusters for running machine learning training and inference workloads. It abstracts away the underlying hardware management, allowing you to dynamically scale compute resources up or down based on job requirements, and supports both CPU and GPU instances for different model types.

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The mathematical computations performed by the model during training

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The number of floating-point operations a model performs per second

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A billing calculator that estimates the cost of running machine learning workloads