MLS-C01 Modeling Practice Test 30 — 15 Questions

Question 1

A data scientist is using Amazon SageMaker to train a classification model. The dataset contains categorical features with high cardinality. Which encoding method is most appropriate for handling high-cardinality categorical features in a linear model?

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

Accepted Answer

Target encoding. One-hot encoding creates many binary columns, which can cause the curse of dimensionality for high-cardinality features. Label encoding assigns arbitrary integers, which linear models may interpret as ordinal. Target encoding (mean encoding) replaces categories with the mean of the target variable, which captures information without expanding dimensionality. This is often used for high-cardinality features. Ordinal encoding is similar to label encoding.

Answer

Label encoding

Answer

One-hot encoding

Answer

Ordinal encoding

Question 2

A data scientist is building a deep learning model using Amazon SageMaker. The model is overfitting the training data. Which THREE actions can help reduce overfitting?

Accepted Answer

Add L2 regularization to the loss function.. Option A is correct because adding L2 regularization (weight decay) to the loss function penalizes large weights, which constrains model complexity and reduces overfitting. Option B is correct because data augmentation synthetically expands the training set with transformed samples, giving the model more varied data and improving generalization. Option E is correct because dropout layers randomly deactivate neurons during training, preventing the network from relying on specific units and acting as a strong regularizer. Option C is not correct because increasing the number of layers raises model capacity, which typically worsens overfitting. Option D is not correct because reducing the learning rate only affects optimization speed and stability, not the model's tendency to overfit.

Answer

Increase the number of layers in the network.

Answer

Reduce the learning rate.

Question 3

A data scientist wants to use a linear regression model to predict house prices. After training, the model shows high bias and low variance. Which action would most likely improve the model's performance?

Accepted Answer

Add polynomial features to capture non-linear relationships.. High bias and low variance indicate underfitting: the model is too simple to capture the underlying patterns in the data. Adding polynomial features increases model capacity, allowing linear regression to fit non-linear relationships such as the curved effect of square footage on price. This directly addresses the underfitting by enriching the feature space while keeping the model interpretable.

Answer

Increase L2 regularization strength.

Answer

Use a simpler model, such as linear regression without interaction terms.

Answer

Reduce the amount of training data.

Question 4

A company uses SageMaker to deploy a model for real-time inference. The model is a large ensemble that requires 8 GB of memory and has high latency. The team wants to reduce latency without increasing cost. Which strategy is most effective?

Accepted Answer

Use SageMaker Neo to compile the model for the target instance.. SageMaker Neo compiles and optimizes the model for the target hardware, reducing latency and memory footprint without increasing cost. Option A is wrong because a larger instance would increase cost. Option B is wrong because deploying on multiple instances increases cost and complexity without necessarily reducing latency. Option D is wrong because batch transform is for offline inference, not real-time, and does not address the low latency requirement.

Answer

Use a larger instance type with more memory.

Answer

Deploy the model on multiple instances behind a load balancer.

Answer

Switch from real-time inference to batch transform.

Question 5

During training, a binary classification model has an AUC of 0.99 on the training set but only 0.72 on the validation set. Which of the following is the most likely cause?

Accepted Answer

Overfitting.. A large gap between training AUC (0.99) and validation AUC (0.72) is the classic signature of overfitting: the model has memorized training data including noise, so it performs excellently on seen data but generalizes poorly. The high training score combined with degraded validation performance is diagnostic. Regularization, more data, or simpler models would typically be the remedy.

Answer

Class imbalance in the training set.

Answer

Underfitting.

Answer

Data leakage from validation to training.

Question 6

A data scientist is using Amazon SageMaker to train a classification model. The dataset contains categorical features with high cardinality. Which encoding method is most appropriate for handling high-cardinality categorical features in a linear model?

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

Accepted Answer

Use a SageMaker processing job to convert the CSV data to recordIO-protobuf format.. A SageMaker processing job is the most efficient way to convert CSV to recordIO-protobuf because it runs the conversion at scale within AWS, close to the data in S3, and integrates natively with the SageMaker pipeline. This avoids local bandwidth bottlenecks and keeps the workflow reproducible and automated. The processing job can use a built-in or custom container to perform the conversion and write the output back to S3 for training.

Answer

Change the inference data to recordIO-protobuf format.

Answer

Use a boto3 script to convert the CSV files locally and upload.

Answer

Switch to a different algorithm that accepts CSV format.

Question 7

A data scientist is training a recurrent neural network (RNN) for time series forecasting. The model's training loss is not decreasing, and the gradients are vanishing. Which technique should the scientist apply to address vanishing gradients?

Accepted Answer

Replace the RNN cells with LSTM or GRU units.. Replacing RNN cells with LSTM or GRU units directly addresses vanishing gradients because these architectures include gating mechanisms that regulate the flow of information, allowing gradients to propagate over many time steps without diminishing. LSTMs and GRUs are specifically designed to capture long-term dependencies, making them the standard solution for vanishing gradients in recurrent networks.

Answer

Apply gradient clipping.

Answer

Add batch normalization layers.

Answer

Increase the learning rate.

Question 8

A team is using SageMaker to train a model with hyperparameter tuning. The training jobs are taking too long. The team wants to reduce time without sacrificing model quality. Which approach should they take?

Accepted Answer

Enable early stopping in the hyperparameter tuning job.. Enabling early stopping in hyperparameter tuning jobs allows SageMaker to automatically stop training jobs that are not improving on the objective metric, saving time and resources without sacrificing model quality. This is a built-in feature that monitors performance and terminates underperforming trials early.

Answer

Use random search instead of Bayesian optimization.

Answer

Increase the maximum number of training jobs.

Answer

Reduce the maximum runtime per training job.

Question 9

A data scientist is using Amazon SageMaker to train a classification model. The dataset contains categorical features with high cardinality. Which encoding method is most appropriate for handling high-cardinality categorical features in a linear model?

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

Accepted Answer

Use a real-time endpoint with an instance that has sufficient memory.. A real-time endpoint is the only SageMaker hosting option designed for low-latency, synchronous inference where the client waits for the response. The scientist can select an instance type (e.g., ml.g4dn.2xlarge with 32 GB memory) that meets the 16 GB requirement, ensuring the model loads and serves predictions with minimal delay. Asynchronous inference, serverless inference, and batch transform all introduce additional queuing, cold-start, or job-scheduling overhead that increases latency.

Answer

Use an asynchronous inference endpoint.

Answer

Use SageMaker Serverless Inference.

Answer

Use a batch transform job.

Question 10

Which TWO techniques are used to handle missing values in a dataset before training? (Choose 2.)

Accepted Answer

Mean or median imputation.. Mean or median imputation (A) is a standard missing-value handling technique: it replaces missing entries in a numeric feature with the column's mean (for roughly symmetric distributions) or median (robust to outliers), preserving the row count so the model can still train on that record. Removing rows or columns with missing values (C), often called listwise deletion or column dropping, is another direct missing-data technique that eliminates incomplete data before training, which is valid when the missingness is limited and dropping does not bias the dataset. The other options address different preprocessing goals: min-max scaling (B) rescales numeric features to a fixed range such as [0,1] but does not fill or remove missing entries, one-hot encoding (D) converts categorical variables into binary indicator columns and cannot represent missing values without a separate imputation step, and principal component analysis (E) is a dimensionality-reduction method that generally requires a complete numeric matrix and is not itself a missing-value treatment.

Answer

Min-max scaling.

Answer

One-hot encoding.

Answer

Principal component analysis (PCA).

Question 11

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents only 5% of the data. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the scientist focus on to improve the model's ability to detect the positive class?

Accepted Answer

Recall. Recall measures the proportion of actual positives the model correctly identifies (TP / (TP + FN)). With only 10% recall on the positive class, the model is missing 90% of true positives — exactly the problem the scientist needs to fix. Optimizing recall (e.g., via class weighting, resampling, or threshold tuning) directly improves the model's ability to detect the minority positive class.

Answer

Accuracy

Answer

Precision

Answer

AUC-ROC

Question 12

An e-commerce company wants to build a recommendation system. They have user-item interaction data (clicks, purchases) and user demographic data. The goal is to recommend items that a user is likely to purchase. Which approach should be used?

Accepted Answer

Factorization Machines using user-item interactions and user features.. Factorization Machines (FM) can model interactions between users and items while also incorporating user demographic features, making them ideal for this scenario. They capture higher-order interactions and handle sparse data well, which is common in recommendation systems.

Answer

Linear regression on user and item features.

Answer

Collaborative filtering using matrix factorization.

Answer

Content-based filtering using item features.

Question 13

A data scientist is training a deep learning model for image classification using TensorFlow on Amazon SageMaker. The model trains slowly, and the GPU utilization is below 20%. Which action will MOST effectively increase GPU utilization and reduce training time?

Accepted Answer

Increase the batch size to better saturate the GPU.. Increasing batch size provides more work per GPU step, improving utilization. Option A is wrong because reducing dataset size does not directly improve GPU utilization and may lead to underfitting. Option C is wrong because switching to CPU would be slower. Option D is wrong because decreasing batch size reduces the work per step, lowering GPU utilization and increasing training time.

Answer

Reduce the training dataset size.

Answer

Switch to a CPU-only instance.

Answer

Decrease the batch size to reduce memory pressure.

Question 14

A company uses Amazon SageMaker to train a model and wants to track metrics like loss and accuracy in real-time. Which SageMaker feature should be used?

Accepted Answer

SageMaker metrics and CloudWatch dashboards. SageMaker's built-in metrics integration with CloudWatch allows real-time tracking of training metrics such as loss and accuracy. Option A (SageMaker Model Monitor) is for monitoring inference quality and data drift after deployment, not real-time training metrics. Option C (SageMaker Experiments) is for organizing, tracking, and comparing different experiment runs, but it does not provide real-time streaming of metrics during training. Option D (SageMaker Debugger) is used for debugging training issues like gradient anomalies, not for real-time metric tracking.

Answer

SageMaker Model Monitor

Answer

SageMaker Experiments

Answer

SageMaker Debugger

Question 15

A data scientist is using Amazon SageMaker to train a classification model. The dataset contains categorical features with high cardinality. Which encoding method is most appropriate for handling high-cardinality categorical features in a linear model?

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

Accepted Answer

Fine-tune a pre-trained BERT-base model using a GPU instance.. Fine-tuning a pre-trained BERT-base model on a GPU instance is the most cost-effective approach because transfer learning leverages the pre-trained model's existing language understanding, requiring far less compute and data than training from scratch. BERT-base has 110M parameters and is well-suited for text classification with 100,000 samples across 20 classes. GPU instances accelerate the fine-tuning process significantly compared to CPU, reducing overall cost.

Answer

Train a BERT model from scratch using a larger instance.

Answer

Use a pre-trained BERT-large model with a larger instance.

Answer

Train a CNN model from scratch using CPU instances.