AI0-001 Machine Learning and Deep Learning — 50 Questions

Question 1

A data engineer is designing a pipeline to train a linear regression model on a dataset with 10 million rows and 50 features. The dataset fits in memory. Which approach should the engineer use to train the model efficiently?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Stochastic gradient descent. Stochastic gradient descent (SGD) is the most efficient approach for training a linear regression model on a dataset with 10 million rows and 50 features because it updates the model parameters using only one training example per iteration, leading to much faster convergence per epoch compared to batch methods. Since the dataset fits in memory, SGD can still be implemented efficiently without the overhead of loading data in batches from disk, and it scales well to large datasets where the normal equation or batch gradient descent would be computationally prohibitive.

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Normal equation

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Batch gradient descent

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Principal component analysis

Question 2

A data scientist is training a convolutional neural network (CNN) for object detection. The training loss decreases rapidly but then plateaus at a high value, and the validation loss starts increasing. Which action should the scientist take to improve the model?

Accepted Answer

Reduce the model complexity. The training loss decreasing rapidly then plateauing at a high value while validation loss increases is classic overfitting. Reducing model complexity (Option C) directly addresses overfitting by decreasing the number of parameters or applying regularization (e.g., dropout, L2), which forces the network to learn more generalizable features rather than memorizing noise in the training data.

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Increase the learning rate

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Increase the number of epochs

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Add more convolutional layers

Question 3

A data scientist is training a multi-class classifier with 10 classes. The training log shows the above output for the first two epochs. What is the most likely cause?

Accepted Answer

The learning rate is set to zero. When the learning rate is set to zero, the optimizer makes no updates to the model weights regardless of the computed gradients. The training loss remains constant across epochs because the parameters never change, which matches the log showing identical loss values for both epochs. This is a common debugging scenario where a misconfigured learning rate prevents any learning from occurring.

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Batch normalization is disabled

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The dataset is imbalanced

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The model is overfitting

Question 4

A team is reviewing a neural network model summary. The input layer expects 784 features (e.g., 28x28 images). How many parameters does the first dense layer have?

Accepted Answer

100,352. The first dense layer has 128 neurons. The parameter count is computed as input_dim * units = 784 * 128 = 100,352. This model summary excludes bias parameters, so the weight count alone is 100,352.

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100,224

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109,258

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8,256

Question 5

A data scientist is training a neural network to classify images of handwritten digits. The model achieves 99% accuracy on training data but only 85% on validation data. Which technique should the scientist apply first to address this issue?

Accepted Answer

Apply L2 regularization to the network weights. The model shows high training accuracy (99%) but lower validation accuracy (85%), which is a classic sign of overfitting. L2 regularization (option C) adds a penalty term to the loss function proportional to the squared magnitude of the weights, discouraging the network from learning overly complex patterns that do not generalize. This directly addresses overfitting without reducing the model's capacity too aggressively.

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Remove one or more hidden layers from the network

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Increase the number of training epochs

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Add more features to the input data

Question 6

A company is deploying a machine learning model to predict customer churn. The dataset is highly imbalanced (95% non-churn, 5% churn). The model achieves 96% accuracy, but the F1-score for the churn class is only 0.2. Which metric should the team prioritize to evaluate model performance for this business problem?

Accepted Answer

F1-score. In a highly imbalanced dataset (95% non-churn, 5% churn), accuracy is misleading because a model can achieve 96% accuracy by simply predicting the majority class for all instances. The F1-score, which is the harmonic mean of precision and recall, specifically measures the model's performance on the minority (churn) class. A low F1-score of 0.2 indicates the model fails to correctly identify churners, which is the critical business outcome, making F1-score the correct metric to prioritize.

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Accuracy

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Log loss

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AUC-ROC

Question 7

An autonomous vehicle system uses a deep reinforcement learning agent to navigate. The agent's reward function gives +1 for reaching the destination and -0.1 for each time step. After training, the agent learns to circle the block repeatedly without reaching the destination. Which modification is most likely to fix this behavior?

Accepted Answer

Increase the time penalty to -1 per step. The agent learns to circle the block because the cumulative penalty for each time step (-0.1) is too small relative to the reward for reaching the destination (+1). By increasing the time penalty to -1 per step, the agent will incur a much larger cost for delaying, making it optimal to reach the destination quickly rather than looping indefinitely. This directly addresses the reward structure imbalance that causes the undesirable behavior.

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Increase the reward for reaching the destination to +10

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Use a discount factor closer to 0

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Add a penalty for each turn the vehicle makes

Question 8

Which TWO techniques are commonly used to prevent overfitting in deep neural networks?

Accepted Answer

Dropout. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and forces it to learn more robust features. This reduces overfitting by introducing noise and effectively training an ensemble of sub-networks.

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Using a larger learning rate

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L1 regularization

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Increasing the number of layers

Question 9

Refer to the exhibit. A data scientist is training a binary classifier. Based on the training log, which problem is the model experiencing?

Accepted Answer

Overfitting. The training log shows that the model's training accuracy continues to improve while the validation accuracy plateaus or degrades after a certain number of epochs. This divergence between training and validation performance is the hallmark of overfitting, where the model memorizes the training data noise rather than learning generalizable patterns.

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Underfitting

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Data leakage

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Vanishing gradient

Question 10

A healthcare startup is developing a deep learning model to detect diabetic retinopathy from retinal fundus images. The dataset contains 50,000 images, but only 5% are labeled as positive for the disease. The team uses a convolutional neural network (CNN) with a final sigmoid layer and binary cross-entropy loss. After training for 20 epochs, the model achieves 95% accuracy on the test set, but the recall for the positive class is only 10%. The team suspects the model is biased toward the negative class due to class imbalance. The data is stored in a secure environment, and no additional labeled data can be obtained. The team has access to the following techniques: oversampling the minority class, undersampling the majority class, using class weights in the loss function, applying data augmentation, and using a different architecture. Which course of action is most likely to improve recall for the positive class while maintaining reasonable overall performance?

Accepted Answer

Assign higher class weights to the positive class in the loss function. Assigning higher class weights to the positive class in the loss function directly penalizes misclassifications of the minority class during training. This forces the model to pay more attention to positive samples without altering the dataset distribution, which is critical when no additional labeled data can be obtained and the data is in a secure environment. It improves recall by increasing the gradient contribution from positive samples, while maintaining overall performance because the model still sees the original data distribution.

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Undersample the majority class to balance the dataset

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Oversample the minority class using synthetic image generation

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Replace the CNN with a transformer-based architecture

Question 11

A company is preparing a dataset for training a supervised machine learning model. The dataset contains missing values, outliers, and categorical features. Which two preprocessing steps are typically performed to prepare the data? (Choose two.)

Accepted Answer

Impute missing values with the mean. Imputing missing values with the mean is a standard technique to handle incomplete data, ensuring the model can process all records without discarding potentially valuable information. Option C is correct because one-hot encoding converts categorical features into a binary vector representation, which is required by most machine learning algorithms that expect numerical input.

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Normalize numerical features to a standard range

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Remove all features with low variance

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Increase the number of features using PCA

Question 12

A data scientist is evaluating a trained binary classification model. The model has high accuracy but the precision is low and recall is high. Which three actions are most appropriate to improve precision? (Choose three.)

Accepted Answer

Collect more training data for the minority class. Collecting more training data for the minority class (Option A) helps improve precision because it provides the model with more representative examples of the positive class, reducing the likelihood of false positives. In binary classification, low precision indicates many false positives, often due to class imbalance where the minority class is underrepresented. By enriching the minority class, the model can learn more distinct decision boundaries, thereby reducing false positive predictions.

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Apply oversampling to the majority class

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Decrease the classification threshold

Question 13

Refer to the exhibit. A data scientist is training a neural network and observes the training log above. What is the most likely cause?

Accepted Answer

The learning rate is too high. The training log shows a loss that initially decreases but then suddenly spikes and oscillates wildly, which is a classic sign of divergence caused by a learning rate that is too high. A high learning rate causes the optimizer to overshoot the minima in the loss landscape, leading to instability and failure to converge.

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The model is overfitting

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The model is underfitting

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The batch size is too large

Question 14

Refer to the exhibit. An AI specialist reviews the model evaluation report for a binary classifier. The specialist wants to improve recall. Which action is most likely effective?

Accepted Answer

Collect more training data for the minority class. Collecting more training data for the minority class directly addresses class imbalance, which is a common cause of low recall. By providing more examples of the positive (minority) class, the model can learn better decision boundaries and reduce false negatives, thereby improving recall without altering the classification threshold.

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Decrease the classification threshold

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Increase the classification threshold

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Add more features

Question 15

Refer to the exhibit. An AI developer implements the above neural network architecture for handwritten digit recognition. The model achieves 85% training accuracy and 83% test accuracy. Which modification is most likely to improve training accuracy?

Accepted Answer

Remove the dropout layer. The model achieves 85% training accuracy and 83% test accuracy, indicating slight overfitting (training accuracy is higher than test accuracy). Removing the dropout layer reduces regularization, allowing the model to fit the training data more closely, which directly improves training accuracy. Dropout randomly drops neurons during training to prevent overfitting, but if the model is already underfitting or the gap is small, removing it can boost training performance.

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Increase the dropout rate to 0.7

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Increase the number of filters in the first Conv2D layer

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Add another dense layer before the output

Question 16

A team trains a random forest model on a dataset with 50 features. The model's performance on the test set is significantly worse than on the training set. Which technique is most appropriate to address this issue?

Accepted Answer

Apply cross-validation to tune hyperparameters and reduce overfitting. The model's significantly worse test performance compared to training performance is a classic symptom of overfitting. Cross-validation is the most appropriate technique to address this because it evaluates the model's performance on multiple subsets of the data, providing a more robust estimate of generalization. Tuning hyperparameters (e.g., max_depth, min_samples_split) using cross-validation directly reduces overfitting by constraining model complexity, which is the core issue here.

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Increase the number of trees in the forest

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Use feature scaling

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Perform PCA to reduce dimensions

Question 17

A data scientist is using an ensemble method to combine multiple models. Which three statements about bagging (Bootstrap Aggregating) are true? (Select THREE.)

Accepted Answer

It reduces variance without increasing bias. Bagging reduces variance by training models on different bootstrap samples of the data and averaging their predictions. Since each model is trained independently on a random sample with replacement, the ensemble's variance decreases without introducing additional bias, as the expected prediction remains unbiased. This is a key property that distinguishes bagging from boosting, which reduces both bias and variance.

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It requires the base models to be of different types

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It reduces the error by combining weak learners

Question 18

Refer to the exhibit. A developer is using the above configuration for a multi-class classification task. The model performs well on training data but poorly on validation data. Which modification could help?

Accepted Answer

Add L2 regularization to the dense layers. The model is overfitting, as indicated by good performance on training data but poor on validation data. Adding L2 regularization to the dense layers penalizes large weights, reducing model complexity and improving generalization. This directly addresses overfitting without disrupting the training dynamics.

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Remove dropout

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Increase the dropout rate

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Increase the learning rate

Question 19

Refer to the exhibit. The training pod is using 2 GPUs. During training, the GPU utilization is only 30% each. What is the most likely cause?

Accepted Answer

The batch size is too small to fully utilize GPUs. With a batch size that is too small, each GPU receives insufficient data per training step to saturate its compute cores. This leads to low utilization (e.g., 30%) because the GPUs spend most of their time idling while waiting for the next batch to be loaded and processed. Increasing the batch size allows each GPU to process more data in parallel, improving throughput and utilization.

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The learning rate is too high

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The image is missing CUDA libraries

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The number of epochs is too high

Question 20

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