MLS-C01 Modeling Practice Test 11 — 15 Questions

Question 1

A machine learning team is using Amazon SageMaker to train a model using a custom Docker container. The training job fails with an error: 'Unable to write to /opt/ml/model'. The container does not have root access. What is the most likely cause?

Accepted Answer

The container process does not have write permission to /opt/ml/model. SageMaker expects training containers to write the model artifact to /opt/ml/model. The container process must have write permissions to that directory. Root access is not required; the container runs with the 'sagemaker' user. The directory may not exist or permissions are wrong. The error indicates a write issue, likely permissions.

Answer

The /opt/ml/model directory does not exist in the container

Answer

The container is using an unsupported operating system

Answer

The container does not have internet access

Question 2

A data scientist is building a classification model to predict customer churn. The dataset has 10,000 samples with 100 features. After training a logistic regression model, the scientist observes that the model has high variance (overfitting). Which technique can reduce overfitting?

Accepted Answer

Use L2 regularization (Ridge). L2 regularization (Ridge) adds a penalty on large coefficients, reducing overfitting. Removing features may help but is not the best practice. Increasing model complexity (polynomial features) would worsen overfitting. Increasing training data helps but not listed.

Answer

Remove the regularization term

Answer

Add polynomial features

Answer

Use a smaller learning rate

Question 3

Which TWO of the following are valid approaches to handle missing values in a dataset for a machine learning model?

Accepted Answer

Impute missing values with the mean of the column. Removing rows with missing values is a valid approach (listwise deletion). Imputing with the mean is also valid. Using a neural network to predict missing values is possible but not standard. Standardization does not handle missing values. One-hot encoding is for categorical variables.

Answer

Use a neural network to predict missing values

Answer

Standardize the features to handle missing values

Answer

Apply one-hot encoding to convert missing values

Question 4

Which THREE of the following are best practices for training a deep learning model on Amazon SageMaker?

Accepted Answer

Use Pipe mode for large datasets to reduce I/O overhead. Profiling GPU utilization helps identify bottlenecks. Using Pipe mode for large datasets reduces I/O. Setting up automatic model tuning (hyperparameter optimization) is a best practice. Training on a single instance is not a best practice for large models. Debugger is for monitoring, not for training acceleration.

Answer

Use SageMaker Debugger to automatically fix training errors

Answer

Train on a single instance to avoid distributed training overhead

Question 5

Which TWO of the following are appropriate use cases for Amazon SageMaker built-in algorithms?

Accepted Answer

Classifying customer churn using tabular data. XGBoost is suitable for tabular classification. BlazingText is for text classification on word embeddings. Image classification using custom CNNs may use built-in but not necessarily. Time series forecasting is not a built-in algorithm (use DeepAR). Reinforcement learning is not a built-in algorithm.

Answer

Reinforcement learning using Q-learning

Answer

Image classification using a custom CNN architecture

Answer

Time series forecasting using ARIMA

Question 6

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 1% of the data. The model needs to maximize recall while keeping precision above 0.7. Which sampling strategy should the data scientist use?

Accepted Answer

SMOTE from imbalanced-learn to generate synthetic samples for the minority class.. Option C is correct because SMOTE generates synthetic samples for the minority class, which can improve recall without discarding data. Option A (RandomUnderSampler) may discard too many majority samples, reducing precision. Option B (RandomOverSampler) can cause overfitting. Option D (NearMiss) focuses on hard samples and may reduce recall. Option E (TomekLinks) only removes noisy instances, not addressing imbalance effectively.

Answer

NearMiss from imbalanced-learn to undersample the majority class based on distance to minority samples.

Answer

RandomUnderSampler from imbalanced-learn to undersample the majority class.

Answer

TomekLinks from imbalanced-learn to remove overlapping samples.

Answer

RandomOverSampler from imbalanced-learn to oversample the minority class.

Question 7

A data scientist is training a gradient boosting model on a large dataset (100 GB) stored in Amazon S3. The training job uses a SageMaker built-in XGBoost algorithm with a single ml.p3.2xlarge instance. The job fails with a memory error. Which solution should the data scientist adopt to resolve the memory issue?

Accepted Answer

Increase the number of training instances to use distributed training with XGBoost.. Option C is correct because increasing the number of instances allows distributed training, reducing per-instance memory pressure. Option A may not fully load the dataset. Option B uses a different algorithm with different memory characteristics, but XGBoost can handle large data with distributed training. Option D reduces features but may degrade model quality. Option E uses a different algorithm but not necessarily more memory-efficient.

Answer

Reduce the number of features by using PCA before training.

Answer

Use SageMaker Pipe mode to stream data directly from S3 instead of downloading it.

Answer

Use SageMaker BlazingText algorithm with negative sampling.

Answer

Switch to SageMaker Linear Learner algorithm, which requires less memory.

Question 8

A data scientist wants to evaluate the performance of a multiclass classification model. The model outputs probabilities for 10 classes. Which metric is most appropriate for evaluating the model's ranking performance across all classes?

Accepted Answer

Log loss (cross-entropy). Option D is correct because log loss measures the performance of a classification model where the prediction is a probability value, and it penalizes false classifications. Option A (Accuracy) ignores probability calibration. Option B (ROC AUC) is for binary classification. Option C (F1 score) is for binary or per-class. Option E (Mean Absolute Error) is for regression.

Answer

F1 score (macro-averaged)

Answer

Accuracy

Answer

Mean Absolute Error

Answer

ROC AUC (one-vs-rest macro-averaged)

Question 9

A company is building a recommendation system using Amazon SageMaker Factorization Machines. The dataset includes user IDs, item IDs, and implicit feedback (clicks). The data is sparse with millions of users and items. The model needs to capture interactions between users and items. Which hyperparameter tuning strategy should be used to improve model performance?

Accepted Answer

Increase the number of factors (num_factors) to capture more latent features.. Option A is correct because increasing the number of factors allows the model to capture more complex interactions. Option B (learning rate) helps convergence but not specifically interaction complexity. Option C (batch size) affects speed, not capacity. Option D (regularization) prevents overfitting but does not increase interaction capacity. Option E (activation function) is not relevant for factorization machines (linear model).

Answer

Increase L2 regularization to prevent overfitting.

Answer

Increase the batch size to speed up training.

Answer

Decrease the learning rate to improve convergence.

Answer

Change the activation function to ReLU.

Question 10

A data scientist is training a neural network on image data using TensorFlow with GPU instances on SageMaker. The training is slow because the GPU utilization is low. The data pipeline uses tf.data with a large number of preprocessing operations. Which action would most likely increase GPU utilization?

Accepted Answer

Increase the prefetch buffer size in the tf.data pipeline.. Option D is correct because prefetching allows the CPU to prepare batches while the GPU is computing, reducing idle time. Option A (reduce batch size) may decrease utilization. Option B (increase CPU instances) may not help if bottleneck is data pipeline. Option C (use smaller images) reduces computation but may not improve utilization percentage. Option E (increase learning rate) does not affect data throughput.

Answer

Increase the learning rate to converge faster.

Answer

Reduce the batch size to speed up each step.

Answer

Increase the number of CPU instances in the training job.

Answer

Use smaller image sizes to reduce computation.

Question 11

A data scientist is using Amazon SageMaker to train a linear regression model. The dataset has 500 features and 50,000 observations. The model converges but has high bias. Which technique should the data scientist use to reduce bias?

Accepted Answer

Add polynomial features or interaction terms to the feature set.. Option B is correct because adding interaction features or polynomial features allows the linear model to capture non-linear relationships, reducing bias. Option A (regularization) reduces variance, not bias. Option C (more data) helps variance. Option D (feature selection) reduces complexity, may increase bias. Option E (reduce learning rate) affects convergence speed, not bias.

Answer

Apply L2 regularization (Ridge) to penalize large coefficients.

Answer

Decrease the learning rate.

Answer

Use feature selection to remove irrelevant features.

Answer

Increase the number of training epochs.

Question 12

A data science team is deploying a machine learning model to production using SageMaker. The model is a PyTorch model that requires custom inference logic including image preprocessing. The team needs to ensure that the endpoint can handle variable batch sizes and has low latency. Which deployment approach should the team use?

Accepted Answer

Use SageMaker Inference Pipelines with a preprocessing container followed by the PyTorch model container.. Option A is correct because SageMaker Inference Pipelines allow chaining of preprocessing and prediction containers, enabling custom logic and efficient batching. Option B (Lambda) is not designed for real-time inference with large models. Option C (SageMaker Batch Transform) is for offline batch inference, not real-time. Option D (multi-model endpoints) is for hosting multiple models, not custom inference logic. Option E (SageMaker SDK's Predictor) is a client, not a deployment approach.

Answer

Deploy the model as an AWS Lambda function and use API Gateway.

Answer

Use the SageMaker Python SDK's Predictor class with the model artifact.

Answer

Use a SageMaker multi-model endpoint to host the model with a custom container.

Answer

Use SageMaker Batch Transform for real-time inference.

Question 13

A data scientist is building a fraud detection model using a highly imbalanced dataset. The model uses a random forest classifier. The recall for the minority class is 0.6, and precision is 0.9. The business requires recall above 0.8. Which action should the data scientist take to improve recall?

Accepted Answer

Decrease the probability threshold for classifying a transaction as fraudulent.. Option D is correct because decreasing the classification threshold for the positive class increases recall (more positives predicted) at the cost of precision. Option A (more trees) reduces variance, may not improve recall. Option B (class weights) can help but is already used. Option C (feature selection) may reduce recall. Option E (increase max depth) could lead to overfitting and not necessarily improve recall.

Answer

Perform feature selection to remove noisy features.

Answer

Increase the maximum depth of the trees.

Answer

Increase the class weight for the minority class in the algorithm.

Answer

Increase the number of trees in the random forest.

Question 14

A data scientist trains a convolutional neural network (CNN) for image classification. The training loss decreases steadily, but the validation loss starts increasing after 10 epochs. Which technique should the data scientist use to address this problem?

Accepted Answer

Use early stopping to halt training when validation loss stops decreasing.. Option D is correct because early stopping halts training when validation loss stops improving, preventing overfitting. Option A (more data) is not directly addressing the current overfitting. Option B (data augmentation) can help but is already used. Option C (increase learning rate) may cause divergence. Option E (more layers) increases model complexity, likely worsening overfitting.

Answer

Add more data augmentation to the training set.

Answer

Increase the number of training epochs.

Answer

Add more convolutional layers to increase model capacity.

Answer

Increase the learning rate.

Question 15

A data scientist is training a gradient boosting model using SageMaker's built-in XGBoost algorithm. The dataset has missing values in several features. Which TWO actions should the data scientist take to handle missing values effectively? (Choose two.)

Accepted Answer

Impute missing values with the median of each feature using a preprocessing step.. Option A and Option D are correct. XGBoost can learn the best direction to handle missing values by using the missing parameter. Alternatively, using a constant imputation (like median) is a standard approach. Option B (remove rows) loses data. Option C (one-hot encode) is for categorical. Option E (PCA) is for dimensionality reduction.

Answer

Use one-hot encoding to create binary columns indicating missingness.

Answer

Remove all rows with missing values from the training dataset.

Answer

Apply PCA to reduce dimensionality and ignore missing values.