AI0-001 Machine Learning and Deep Learning Practice Test 3 — 15 Questions

Question 1

Refer to the exhibit. A compliance audit requires that model predictions be explainable for regulatory reasons. Which setting in the deployment configuration supports this requirement?

Accepted Answer

explainability: "required". The 'explainability' setting in the deployment configuration directly enables model interpretability features, such as SHAP or LIME, which generate human-readable explanations for individual predictions. This is essential for compliance audits that require transparency into how a model arrived at a specific output, satisfying regulatory requirements like GDPR or financial industry standards.

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target_latency: 100

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data_retention: "90 days"

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drift_detection: true

Question 2

Refer to the exhibit. What is the recall of the model?

Accepted Answer

0.44. Recall is calculated as True Positives divided by (True Positives + False Negatives). From the confusion matrix in the exhibit, True Positives = 400 and False Negatives = 500, so recall = 400 / (400 + 500) = 400 / 900 ≈ 0.44. Option A is correct because this matches the computed recall value.

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0.80

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0.90

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0.99

Question 3

A data scientist trains a linear regression model on housing prices. The training error is low, but test error is high. What is the most likely issue?

Accepted Answer

Overfitting. Low training error combined with high test error is the classic symptom of overfitting, where the model has memorized the training data, including its noise, rather than learning the underlying patterns. This causes the model to perform poorly on unseen data, which is exactly what the high test error indicates.

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Multicollinearity

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Data leakage

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Underfitting

Question 4

A team trains a deep learning model for image classification with 1000 classes. The training loss decreases but validation loss starts increasing after 10 epochs. What should they do first?

Accepted Answer

Add dropout layers. The scenario describes overfitting: training loss decreases while validation loss increases. Adding dropout layers is a regularization technique that randomly drops neurons during training, preventing co-adaptation and improving generalization. This directly addresses the validation loss increase without altering the learning dynamics or data distribution.

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Use data augmentation

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Increase batch size

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Reduce learning rate

Question 5

A data scientist wants to reduce the dimensionality of a dataset with 200 features before training a regression model. Which technique should they use?

Accepted Answer

PCA. PCA (Principal Component Analysis) is the correct technique because it is an unsupervised linear dimensionality reduction method that identifies the directions (principal components) of maximum variance in the data. For a dataset with 200 features, PCA can reduce dimensionality while preserving as much variance as possible, which is ideal before training a regression model to avoid overfitting and multicollinearity.

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LDA

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t-SNE

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Autoencoder

Question 6

A deep learning model for sentiment analysis has millions of parameters and is trained on a small dataset. Which technique can help prevent overfitting?

Accepted Answer

Dropout. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on any single feature and forces it to learn more robust representations. This is particularly effective when the model has millions of parameters but is trained on a small dataset, as it reduces co-adaptation of neurons and mitigates overfitting.

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Learning rate scheduling

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Batch normalization

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Early stopping

Question 7

An organization needs to classify customer emails into categories. They have labeled data for some categories but not all. Which approach should they use?

Accepted Answer

Semi-supervised learning. Semi-supervised learning (D) is the correct approach because the organization has labeled data for some categories but not all. This technique leverages a small amount of labeled data to guide the clustering or classification of a larger pool of unlabeled data, effectively combining supervised and unsupervised methods to handle partially labeled datasets.

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Unsupervised clustering then labeling

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Supervised learning for all categories

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Reinforcement learning

Question 8

A team wants to predict monthly sales using historical data. Which algorithm is most appropriate?

Accepted Answer

Linear regression. Linear regression is the most appropriate algorithm because the goal is to predict a continuous numerical value (monthly sales) based on historical data. It models the relationship between input features and the target variable by fitting a linear equation, making it ideal for regression tasks where the output is a real number.

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K-means

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Decision tree

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Logistic regression

Question 9

A model trained on a dataset has high bias and low variance. What does this indicate?

Accepted Answer

Underfitting. High bias and low variance indicate that the model is too simple to capture the underlying patterns in the data, leading to systematic errors on both training and test sets. This is the classic signature of underfitting, where the model fails to learn the training data adequately.

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Good fit

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Data leakage

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Overfitting

Question 10

Which TWO techniques are commonly used for feature scaling? (Choose two.)

Accepted Answer

Standardization. Standardization (A) is correct because it rescales features to have a mean of 0 and a standard deviation of 1, which is essential for algorithms like SVM, PCA, and neural networks that assume normally distributed data. This technique uses the formula (x - μ) / σ, where μ is the mean and σ is the standard deviation, ensuring each feature contributes equally to the model.

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One-hot encoding

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Normalization

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PCA

Question 11

Which THREE are common activation functions used in neural networks? (Choose three.)

Accepted Answer

Sigmoid. Sigmoid is a common activation function in neural networks because it maps any real-valued input to a value between 0 and 1, making it useful for binary classification outputs. It introduces non-linearity and has a smooth gradient, though it suffers from vanishing gradient issues in deep networks.

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K-means

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Softmax

Question 12

Which TWO are evaluation metrics for classification problems? (Choose two.)

Accepted Answer

Precision. Precision is a classification metric that measures the proportion of true positive predictions among all positive predictions made by the model. It is calculated as TP / (TP + FP) and is critical when the cost of false positives is high, such as in spam detection or fraud alert systems.

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Mean Absolute Error

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R-squared

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Mean Squared Error

Question 13

Based on the exhibit, what does this indicate about the model?

Accepted Answer

The model has high precision but low recall, missing many positives. The exhibit shows a confusion matrix or performance metrics where the model correctly identifies some positives but has a high number of false negatives, indicating low recall. This means the model misses many actual positive cases, which aligns with high precision (few false positives) but low recall. Option D correctly describes this imbalance.

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The model has balanced performance

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The model is underfitting

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The model is overfitting

Question 14

A team is deploying a deep learning model for real-time image classification on edge devices with limited computational resources. Which technique would best help reduce model size and inference time without significant accuracy loss?

Accepted Answer

Model pruning and quantization. Model pruning and quantization directly reduce the number of parameters and the precision of weights (e.g., from 32-bit floats to 8-bit integers), which shrinks the model size and speeds up inference on edge devices. This technique is specifically designed to minimize computational load while preserving accuracy, making it ideal for resource-constrained environments like real-time image classification on edge hardware.

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Data augmentation

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Transfer learning

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Ensemble learning

Question 15

A machine learning engineer notices that a linear regression model has high bias. Which action is most likely to reduce bias?

Accepted Answer

Use a more complex model, such as polynomial regression. High bias indicates that the model is too simple to capture the underlying patterns in the data, leading to underfitting. Using a more complex model, such as polynomial regression, increases the model's capacity to fit the training data better, directly addressing the underfitting issue. This is the standard approach to reduce bias in machine learning.

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Reduce the number of training samples

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Add L2 regularization

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Apply feature scaling