MLS-C01 Modeling Practice Test 38 — 15 Questions

Question 1

A company uses Amazon SageMaker to train a linear regression model. After training, the model shows high bias on the training set. Which action is MOST likely to reduce bias?

Accepted Answer

Add more features. High bias indicates that the model is underfitting the training data, meaning it is too simple to capture the underlying patterns. Adding more features increases the model's capacity to learn complex relationships, directly addressing underfitting by reducing bias. In SageMaker, this can be done by engineering additional input columns or using feature transformations before training.

Answer

Collect more training data

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Apply L2 regularization

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Deploy the model to a larger instance

Question 2

A machine learning team is building a recommendation system for an e-commerce platform. They have user-item interaction data (clicks, purchases). They need to choose an algorithm that can capture both user and item latent factors and handle missing data. Which algorithm should they use?

Accepted Answer

Matrix factorization. Matrix factorization is the correct choice because it decomposes the user-item interaction matrix into lower-dimensional latent factors for users and items, capturing underlying patterns in preferences. It naturally handles missing data by learning from observed interactions only, making it ideal for recommendation systems with sparse data.

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Linear regression

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Principal component analysis (PCA)

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Convolutional neural network (CNN)

Question 3

A data scientist is training a decision tree classifier and notices that the model performs well on training data but poorly on test data. Which technique should the data scientist use to address this issue?

Accepted Answer

Prune the tree. Pruning the tree reduces overfitting by removing branches that have little statistical significance or that capture noise in the training data. This technique improves generalization to unseen test data, which directly addresses the symptom of high training accuracy and low test accuracy.

Answer

Use a different split criterion

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Apply L1 regularization

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Increase tree depth

Question 4

A data scientist is building a model to predict customer churn. The dataset has 20 features, including categorical variables with high cardinality (e.g., ZIP code). The data scientist wants to use a linear model. Which feature engineering technique is MOST appropriate for the high-cardinality categorical features?

Accepted Answer

Target encoding. Target encoding is the most appropriate technique for high-cardinality categorical features when using a linear model because it replaces each category with the mean of the target variable for that category, creating a numeric feature that captures the relationship between the category and the target without exploding the feature space. One-hot encoding would create an unmanageable number of binary columns (e.g., thousands of ZIP codes), leading to the curse of dimensionality and making the linear model unstable or computationally infeasible.

Answer

One-hot encoding

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TF-IDF

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Standard scaling

Question 5

A machine learning engineer needs to deploy a model that requires low latency (under 10 ms) for real-time inference. The model is a small ensemble of decision trees. Which Amazon SageMaker endpoint configuration is MOST appropriate?

Accepted Answer

Real-time endpoint. Real-time endpoints in Amazon SageMaker are designed for low-latency inference (typically under 10 ms) and are the correct choice for deploying a small ensemble of decision trees that needs to respond to individual prediction requests in real time. They keep the model loaded and ready, providing a persistent HTTPS endpoint that can serve predictions with minimal overhead.

Answer

Batch transform

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Training job

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Multi-model endpoint

Question 6

A data scientist is training a deep learning model using TensorFlow on Amazon SageMaker. The training job uses a single GPU instance but the GPU utilization is low. Which action is MOST likely to improve GPU utilization?

Accepted Answer

Increase the batch size. Increasing the batch size allows the GPU to process more data in parallel per training step, which keeps the GPU compute units busier and reduces idle time. In TensorFlow on SageMaker, a small batch size can cause the GPU to finish computation quickly and then wait for the next batch to be loaded, leading to low utilization. This is the most direct way to improve GPU throughput without changing the instance or model architecture.

Answer

Use a smaller instance type

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Add more features

Answer

Decrease the number of epochs

Question 7

A company uses Amazon SageMaker to train a linear regression model. After training, the model shows high bias on the training set. Which action is MOST likely to reduce bias?

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

Accepted Answer

Use class weights in the loss function. SMOTE generates synthetic samples for the minority class, but it does not directly address the model's tendency to prioritize the majority class during training. By assigning higher class weights to the minority class in the loss function, the model penalizes misclassifications of minority samples more heavily, which directly improves recall on that class. This technique is especially effective when combined with oversampling, as it forces the optimizer to focus on the underrepresented class during gradient updates.

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One-vs-rest encoding

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L1 regularization

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Principal component analysis (PCA)

Question 8

Which TWO metrics are appropriate for evaluating a binary classification model trained on imbalanced data? (Select TWO.)

Accepted Answer

F1 score. The F1 score (B) is appropriate because it is the harmonic mean of precision and recall, so it reflects performance on the minority class rather than being dominated by the majority class as accuracy would be. The precision-recall curve (D) is appropriate because it plots precision against recall across thresholds and is far more informative than ROC curves when the positive class is rare, since it does not include the large number of true negatives. Log loss (A) measures probabilistic calibration and is not a threshold-based classification metric tailored to class imbalance. Accuracy (C) is misleading on imbalanced data because a trivial majority-class predictor can score very high. ROC-AUC (E) can look optimistically high under severe imbalance because the false positive rate is diluted by the large number of negatives, making it less suitable than precision-recall analysis.

Answer

Log loss

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Accuracy

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ROC-AUC

Question 9

Which THREE techniques can help reduce overfitting in a neural network? (Select THREE.)

Accepted Answer

Dropout. Dropout (B) is correct because randomly deactivating a fraction of neurons during each training iteration prevents the network from relying on specific units and forces redundant, more generalizable feature representations. Early stopping (C) is correct because it halts training when validation loss stops improving, preventing the model from continuing to fit noise in the training data. L2 regularization (E) is correct because adding a penalty proportional to the squared magnitude of weights (weight decay) constrains the model's parameters and reduces variance. Increasing training epochs (A) is not correct because more epochs typically allow the network to fit the training data even more closely, worsening overfitting. Increasing the number of layers (D) is not correct because adding depth increases model capacity, which makes overfitting more likely rather than less.

Answer

Increase training epochs

Answer

Increase the number of layers

Question 10

Which TWO actions are valid ways to handle missing data in a dataset before training a machine learning model? (Select TWO.)

Accepted Answer

Delete rows with missing values. Option A (Delete rows with missing values) is correct because listwise deletion removes incomplete records so the remaining dataset contains only complete cases, which is a standard and valid preprocessing approach when missingness is limited. Option E (Impute missing values with the mean) is correct because mean imputation replaces missing numeric entries with the feature's average, preserving the row count and producing a fully populated dataset suitable for training. Option B is not appropriate as a general rule because dropping every feature with any missing values can discard highly predictive variables and severely reduce the dataset's information. Option C is not a standard valid method because using the maximum value introduces bias and distorts the feature distribution unless there is a specific domain justification. Option D is invalid because most machine learning algorithms cannot natively handle missing values and will error out or produce incorrect results if they are simply ignored.

Answer

Remove all features that have any missing values

Answer

Replace missing values with the maximum value

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Ignore missing values and train the model

Question 11

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance role. When the data scientist tries to run a training job that writes model artifacts to 's3://my-bucket/models/', the job fails with an access denied error. What is the MOST likely cause?

Accepted Answer

Missing s3:PutObject permission for the output S3 bucket. The error occurs because the IAM policy attached to the SageMaker notebook instance role does not grant the s3:PutObject permission on the 's3://my-bucket/models/' path. SageMaker training jobs require this permission to write model artifacts to the specified S3 output bucket. Without it, the API call to upload the model fails with an access denied error, even if other S3 actions are allowed.

Answer

The IAM role does not have a trust policy

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The policy does not include any S3 actions

Answer

The sagemaker:CreateTrainingJob action is not allowed on the specific resource

Question 12

Refer to the exhibit. A SageMaker training job using the built-in Linear Learner algorithm fails with 'Loss function returned NaN'. Which hyperparameter change is MOST likely to resolve this issue?

Accepted Answer

Reduce learning_rate to 0.01. The 'Loss function returned NaN' error in SageMaker's built-in Linear Learner algorithm typically occurs when the learning rate is too high, causing gradient updates to overshoot optimal parameters and diverge. Reducing the learning rate to 0.01 stabilizes training by ensuring smaller, more controlled weight updates, preventing numerical instability that leads to NaN loss.

Answer

Increase the learning rate to 0.5

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Increase mini_batch_size to 2000

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Decrease epochs to 5

Question 13

A data scientist is training a regression model on a dataset with 50 features. After training a linear regression model, the model achieves an R-squared of 0.85 on the training set but only 0.55 on the test set. Which technique is most likely to reduce the generalization error?

Accepted Answer

Apply L2 regularization (Ridge regression). The model exhibits high variance (overfitting): high training R² (0.85) but much lower test R² (0.55). L2 regularization (Ridge regression) shrinks coefficients toward zero, reducing model complexity and penalizing large weights, which directly combats overfitting and improves generalization to unseen data.

Answer

Add more features

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Remove highly correlated features

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Increase the polynomial degree of the model

Question 14

A data scientist is training a deep learning model for image classification using Amazon SageMaker. The training job is taking too long. The data scientist notices that GPU utilization is low (around 30%). Which action is most likely to improve GPU utilization and reduce training time?

Accepted Answer

Increase the batch size. Low GPU utilization (around 30%) indicates that the GPU is spending too much time idle while waiting for data batches to be processed. Increasing the batch size allows each training step to process more samples per forward/backward pass, which increases computational load on the GPU and improves hardware utilization. This directly reduces the number of steps needed per epoch, thereby decreasing overall training time.

Answer

Use a smaller instance type

Answer

Increase the learning rate

Answer

Reduce the batch size

Question 15

A company has a time series dataset of daily sales for the past 5 years. They want to forecast sales for the next 30 days. The data shows weekly seasonality and a slight upward trend. Which Amazon SageMaker algorithm is most appropriate for this task?

Accepted Answer

DeepAR. DeepAR is purpose-built for time series forecasting with seasonal patterns and trends. It uses a recurrent neural network (RNN) to model the conditional distribution of future values given past observations, and it natively handles multiple time series, missing data, and known seasonal periods (e.g., weekly). The weekly seasonality and upward trend in the daily sales data are exactly the kind of patterns DeepAR is designed to capture.

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Linear Learner

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XGBoost

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K-Means