MLS-C01 Modeling Practice Test 44 — 15 Questions

Question 1

A company is building a recommendation system for an e-commerce platform. The system needs to suggest products to users based on past purchases and browsing history. Which approach would be most appropriate for this use case?

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

Accepted Answer

Collaborative filtering using past user-item interactions. Collaborative filtering is the most appropriate approach because it leverages past user-item interactions (e.g., purchases, clicks) to identify patterns and recommend items that similar users have liked. This method directly captures user behavior and preferences without requiring explicit product metadata, making it ideal for e-commerce recommendation systems where implicit feedback is abundant.

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Content-based filtering using product descriptions

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K-means clustering of users based on demographics

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Matrix factorization on user-item ratings

Question 2

A data scientist is training a deep learning model for image classification. The model is overfitting on the training data. Which combination of techniques will most effectively reduce overfitting?

Accepted Answer

Add dropout layers and use data augmentation. Dropout layers randomly deactivate a fraction of neurons during training, which forces the network to learn more robust features and prevents co-adaptation. Data augmentation artificially expands the training dataset by applying transformations (e.g., rotation, flipping, cropping), which reduces the model's ability to memorize spurious patterns and improves generalization. Together, these techniques directly counteract overfitting by increasing regularization and effective training diversity.

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Reduce the batch size

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Train for more epochs without early stopping

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Increase the number of layers and neurons

Question 3

A machine learning engineer is evaluating a binary classification model. The model has a high recall but low precision. Which of the following is the most likely consequence?

Accepted Answer

The model has many false positives.. High recall means the model correctly identifies most positive cases (few false negatives), but low precision indicates that among the cases predicted as positive, many are actually negative. This directly implies a high number of false positives, as precision = TP/(TP+FP) and a low precision with high recall forces FP to be large relative to TP.

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The model has few false negatives.

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The model misses many positive cases.

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The model has few false positives.

Question 4

A data scientist is working on a regression problem with a dataset that contains outliers. The data scientist is choosing between mean squared error (MSE) and mean absolute error (MAE) as the loss function. Which loss function is more robust to outliers?

Accepted Answer

MAE, because it treats all errors equally.. MAE is more robust to outliers because it uses the absolute difference between predicted and actual values, which does not disproportionately penalize large errors. In contrast, MSE squares the errors, causing outliers to have a much larger influence on the loss and model updates. This makes MAE less sensitive to extreme values in regression tasks.

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Both are equally robust to outliers.

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MSE, because it penalizes large errors more heavily.

Answer

Neither is robust; use Huber loss instead.

Question 5

A team is using Amazon SageMaker to train a model. The training job repeatedly fails with a 'ResourceLimitExceeded' error. Which action should the team take to resolve this issue?

Accepted Answer

Request a service limit increase for SageMaker resources.. The 'ResourceLimitExceeded' error in Amazon SageMaker indicates that the AWS account has reached a service quota for SageMaker resources, such as the number of concurrent training jobs, total instance count, or specific instance types. The correct action is to request a service limit increase via the AWS Service Quotas console or by contacting AWS Support, as this directly addresses the quota cap causing the failure.

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Reduce the size of the training dataset.

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Switch to using Spot Instances.

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Use a different instance type with less memory.

Question 6

A data scientist is using a decision tree algorithm for a classification task. The tree is very deep and achieves 100% accuracy on the training set but performs poorly on the test set. Which technique should the data scientist use to improve generalization?

Accepted Answer

Prune the decision tree.. A deep decision tree that achieves 100% training accuracy but poor test accuracy is overfitting the training data. Pruning the tree removes branches that have little statistical power, reducing complexity and improving generalization to unseen data.

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Add more features to the dataset.

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Reduce the number of training samples.

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Increase the maximum depth of the tree.

Question 7

A data scientist is training a neural network for a multi-class classification problem. The model is overfitting. Which TWO of the following techniques can help reduce overfitting? (Choose two.)

Accepted Answer

Add dropout layers after the hidden layers.. Option B is correct because dropout layers randomly deactivate a fraction of neurons during each training iteration, which prevents the network from relying on specific neurons and forces it to learn more robust, generalizable features, directly reducing overfitting. Option D is correct because L2 regularization adds a penalty proportional to the squared magnitude of the weights to the loss function, discouraging large weights and thereby constraining model complexity to improve generalization. Option A is not correct because increasing hidden layers raises model capacity, which typically worsens overfitting rather than reducing it. Option C is not correct because decreasing the learning rate only affects the optimization step size and convergence speed, not the model's tendency to overfit. Option E is not correct because reducing batch size changes gradient noise and training dynamics but does not by itself serve as a regularization technique to reduce overfitting.

Answer

Increase the number of hidden layers.

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Decrease the learning rate.

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Reduce the batch size.

Question 8

A machine learning team is using Amazon SageMaker to train a model with a large dataset stored in S3. The training job is taking too long. Which THREE of the following actions can reduce training time? (Choose three.)

Accepted Answer

Use a GPU instance with more powerful GPUs.. Option B is correct because selecting a SageMaker training instance type with more powerful GPUs (e.g., moving from ml.p3.2xlarge to ml.p4d.24xlarge) increases FLOPS and GPU memory bandwidth, allowing each epoch's forward/backward passes to complete faster. Option C is correct because SageMaker distributed training (e.g., data parallelism or model parallelism libraries) spreads the workload across multiple instances, so the dataset or model is processed in parallel and wall-clock training time is reduced. Option D is correct because Pipe input mode streams data directly from S3 to the training container instead of downloading the full dataset to local EBS first, eliminating the initial download bottleneck and improving throughput for large datasets. Option A is incorrect because decreasing the batch size means more iterations per epoch and typically slower overall training. Option E is incorrect because simply increasing the batch size does not guarantee reduced training time and can hurt convergence, requiring more epochs.

Answer

Decrease the batch size.

Answer

Increase the batch size.

Question 9

A company is using Amazon SageMaker to train a deep learning model for image segmentation. The training job uses a single ml.p3.2xlarge instance and takes 48 hours to complete. The team needs to reduce training time to under 12 hours to meet a deadline. The dataset is 50 GB of images stored in S3. The team currently uses File mode to download the data to the training instance. The model architecture is a convolutional neural network (CNN) with 50 layers. The team has access to multiple instances of the same type. Which approach will most effectively reduce training time?

Accepted Answer

Use SageMaker's distributed data parallelism with multiple instances.. SageMaker's distributed data parallelism splits the 50 GB dataset across multiple ml.p3.2xlarge instances, allowing each instance to process a subset of the data in parallel. This can reduce training time from 48 hours to under 12 hours, assuming near-linear scaling with the number of instances (e.g., 4 instances for a 4x speedup). The approach directly addresses the need to reduce wall-clock time without altering the model architecture or data loading method.

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Reduce the number of layers in the CNN to speed up training.

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Increase the batch size on the single instance to process more data per iteration.

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Switch to Pipe mode to stream data from S3, reducing data loading time.

Question 10

A data scientist is building a regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, year built, and location. After training a linear regression model, the data scientist notices that the residuals have a clear pattern when plotted against predicted values: they increase with predicted values. The model also has high RMSE. Which action should the data scientist take to improve the model?

Accepted Answer

Apply a log transformation to the target variable.. The residuals increasing with predicted values indicate heteroscedasticity, which violates a core linear regression assumption and inflates RMSE. When the target (house prices) is right-skewed and its variance grows with magnitude, a log transformation stabilizes the variance and makes the relationship more linear, directly addressing the pattern observed.

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Remove outliers from the dataset.

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Use L1 regularization (Lasso) to reduce overfitting.

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Add interaction terms between features.

Question 11

A company is building a text classification model to categorize customer support tickets. The dataset is highly imbalanced with 95% of tickets belonging to 'General Inquiry' and 5% to 'Complaint'. The data scientist is using a random forest classifier. Which metric is most appropriate for evaluating model performance on the minority class?

Accepted Answer

F1-score for 'Complaint'. In a highly imbalanced dataset (95% General Inquiry, 5% Complaint), accuracy is misleading because a model that predicts 'General Inquiry' for every ticket would achieve 95% accuracy but completely fail on the minority class. The F1-score for 'Complaint' is the harmonic mean of precision and recall, providing a balanced evaluation of the model's ability to correctly identify complaints without being skewed by the majority class. For a random forest classifier, this metric directly addresses the minority class performance, which is the primary concern.

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Accuracy

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ROC AUC

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Precision for 'Complaint'

Question 12

A data scientist is training a deep learning model for image segmentation using a U-Net architecture. The model overfits severely. The scientist tries L2 regularization, dropout, and data augmentation, but validation loss remains high while training loss approaches zero. Which additional strategy is most likely to reduce overfitting?

Accepted Answer

Implement early stopping based on validation loss. Early stopping monitors validation loss and halts training when it stops improving, directly addressing overfitting by preventing the model from memorizing noise after it has learned generalizable features. Since the training loss is near zero but validation loss remains high, the model has already started overfitting, and early stopping can cut training at the point just before overfitting worsens.

Answer

Increase the batch size

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Use a larger learning rate

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Add more convolutional layers to increase model capacity

Question 13

A company wants to build a real-time anomaly detection system for IoT sensor data. The data arrives as a stream of numerical values. The model should adapt to concept drift over time. Which approach is most suitable?

Accepted Answer

Train an online learning model, such as stochastic gradient descent (SGD) with a sliding window. Online learning with stochastic gradient descent (SGD) using a sliding window allows the model to continuously update its parameters as new IoT sensor data arrives, adapting to concept drift without retraining from scratch. The sliding window ensures that the model focuses on the most recent data distribution, discarding outdated patterns, which is essential for real-time anomaly detection in streaming environments.

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Use a static deep learning model trained once on historical data

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Use a stateful LSTM with fixed weights

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Batch train a random forest model monthly

Question 14

A company uses Amazon SageMaker to deploy a model that predicts customer churn. The model is retrained weekly. The data scientist notices that the model's accuracy remains high, but the business reports that the model is not capturing new churn patterns. What is the most likely cause?

Accepted Answer

The model is experiencing concept drift. Concept drift occurs when the statistical properties of the target variable change over time, causing the model's predictions to become less relevant even if accuracy metrics remain high. In this scenario, the model is retrained weekly but still fails to capture new churn patterns because the underlying customer behavior has shifted—a classic sign of concept drift rather than a data or overfitting issue. Amazon SageMaker's built-in Model Monitor can detect such drift by comparing inference data distributions against a baseline.

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The model is underfitting the data

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The model has data leakage from future data

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The model is overfitting to the training data

Question 15

A data scientist is building a recommendation system for an e-commerce platform. The dataset includes user-item interactions (clicks, purchases, ratings). The scientist wants to use matrix factorization. Which approach is most appropriate for handling implicit feedback (e.g., clicks) rather than explicit ratings?

Accepted Answer

Use weighted alternating least squares (WALS) with confidence weights. Weighted Alternating Least Squares (WALS) is specifically designed for implicit feedback scenarios because it treats unobserved interactions as negative signals with low confidence, rather than missing values. By assigning confidence weights (e.g., based on click frequency or dwell time), WALS can factorize the implicit feedback matrix effectively, avoiding the bias introduced by treating all zeros as true negatives.

Answer

Use k-means clustering to segment users and then use item popularity within clusters

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Use singular value decomposition (SVD) on the interaction matrix with missing values filled with 0

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Use a deep neural network with a softmax output to predict item probabilities