MLS-C01 Modeling Practice Test 25 — 15 Questions

Question 1

A data scientist is using SageMaker to train a deep learning model for image classification. The training job is taking too long. Which approach can reduce training time?

Accepted Answer

Use SageMaker's distributed data parallelism. SageMaker's distributed data parallelism splits the training data across multiple GPUs or instances, allowing each worker to process a different subset of the data simultaneously. This reduces the wall-clock time per epoch by parallelizing the computation, which directly addresses the 'taking too long' issue for deep learning image classification models.

Answer

Use SageMaker Neo to compile the model

Answer

Increase the number of epochs

Answer

Use a smaller image size

Question 2

A data scientist is building a model to predict customer churn. The dataset contains categorical features with high cardinality (e.g., ZIP code, customer ID). Which encoding method is MOST suitable?

Accepted Answer

Target encoding. Target encoding is most suitable for high-cardinality categorical features because it replaces each category with the mean of the target variable for that category, effectively capturing the predictive signal while keeping the feature space dense. This avoids the curse of dimensionality from one-hot encoding and the arbitrary ordinality of label encoding, which can mislead tree-based models.

Answer

One-hot encoding

Answer

Label encoding

Answer

Hashing encoding

Question 3

A data scientist is training a model and wants to monitor training progress. Which AWS service can be used to track metrics like loss and accuracy in real time?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch is the correct service because it provides real-time monitoring of metrics such as loss and accuracy during model training. When using SageMaker, training jobs automatically emit metrics to CloudWatch via the CloudWatch agent, allowing you to view logs and set alarms on metric thresholds in near real-time.

Answer

Amazon SageMaker Ground Truth

Answer

Amazon SageMaker Automatic Model Tuning

Answer

AWS Glue

Question 4

A company is using SageMaker to train a model, but the training job fails with an out-of-memory error. Which action should the data scientist take to resolve this issue?

Accepted Answer

Decrease the batch size. Decreasing the batch size reduces the memory footprint per training step, directly addressing the out-of-memory (OOM) error. In SageMaker, the training instance's GPU or CPU memory is shared between model parameters, activations, and the batch data; a smaller batch size lowers the peak memory usage, allowing the training job to complete without exceeding the instance's memory limit.

Answer

Use a larger instance type for training

Answer

Increase the learning rate

Answer

Increase the number of layers

Question 5

A data scientist is building a recommender system using collaborative filtering. The dataset is sparse (99% missing values). Which algorithm is best suited?

Accepted Answer

Matrix Factorization (e.g., SVD). Matrix factorization (e.g., SVD) is best suited for sparse collaborative filtering because it learns latent factors that capture underlying user-item interactions, effectively handling the 99% missing values by generalizing patterns rather than relying on explicit pairwise similarities. Unlike memory-based methods, it decomposes the sparse user-item matrix into lower-dimensional representations, enabling accurate predictions even when most entries are unobserved.

Answer

Random Forest

Answer

K-Nearest Neighbors

Answer

Hidden Markov Model

Question 6

A data scientist wants to automate the selection of optimal hyperparameters for a model. Which SageMaker feature should be used?

Accepted Answer

SageMaker Automatic Model Tuning. SageMaker Automatic Model Tuning (AMT) is the correct feature because it automates hyperparameter optimization by running multiple training jobs with different hyperparameter combinations, using algorithms like Bayesian optimization or random search to find the best set. This directly addresses the requirement to automate selection of optimal hyperparameters.

Answer

SageMaker Debugger

Answer

SageMaker Model Monitor

Answer

SageMaker Experiments

Question 7

Which TWO of the following are valid techniques to handle missing data in a dataset?

Accepted Answer

Mean imputation. Mean imputation (Option C) is a valid technique for handling missing data because it replaces missing values with the mean of the observed values for that feature, preserving the overall mean of the dataset. This approach is simple and effective for numerical data that is missing completely at random (MCAR), as it does not introduce bias in the mean estimate.

Answer

Normalizing the data

Answer

Adding a constant value of 0

Answer

Synthetic Minority Over-sampling (SMOTE)

Question 8

Which THREE of the following are common causes of overfitting in machine learning models?

Accepted Answer

Using a complex model like a deep neural network on a small dataset. A complex model like a deep neural network has high capacity and can easily memorize noise and patterns specific to a small dataset, rather than learning generalizable features. With limited training samples, the model fails to capture the underlying data distribution, leading to poor performance on unseen data.

Answer

Having a large dataset with many samples

Answer

Using regularization techniques

Question 9

Which TWO of the following are examples of unsupervised learning tasks?

Accepted Answer

Dimensionality reduction using PCA. Principal Component Analysis (PCA) is an unsupervised learning technique used for dimensionality reduction. It works by identifying the directions (principal components) that maximize variance in the data, without requiring any labeled target variable. This makes it a classic example of unsupervised learning, as the algorithm learns patterns solely from the input features.

Answer

Classifying emails as spam or not spam

Answer

Sentiment analysis of product reviews

Answer

Predicting house prices

Question 10

A data scientist is training a binary classification model on an imbalanced dataset where the positive class is rare. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the data scientist prioritize to improve model performance?

Accepted Answer

F1 score. The F1 score is the harmonic mean of precision and recall, making it the best single metric to optimize when the positive class is rare and both false positives and false negatives are costly. With 95% accuracy but only 10% recall, the model is likely predicting the majority class almost exclusively, so improving recall without sacrificing precision is critical — the F1 score directly balances this trade-off.

Answer

AUC-ROC

Answer

Precision

Answer

Accuracy

Question 11

A machine learning engineer is deploying a model to Amazon SageMaker for real-time inference. The model requires low latency and must handle variable traffic patterns. Which SageMaker feature should the engineer use to automatically scale the number of instances based on demand?

Accepted Answer

SageMaker automatic scaling. SageMaker automatic scaling (Application Auto Scaling) is the correct feature because it allows the engineer to define scaling policies (e.g., based on CPU utilization or request latency) that automatically adjust the number of instances behind a SageMaker endpoint in response to real-time traffic patterns. This ensures low latency by maintaining sufficient capacity during spikes and reducing costs during lulls, without manual intervention.

Answer

Amazon EC2 Auto Scaling

Answer

Elastic Inference

Answer

SageMaker Batch Transform

Question 12

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 wants to speed up training by using distributed training across multiple GPUs. Which SageMaker feature or configuration should the data scientist use?

Accepted Answer

SageMaker Data Parallelism library. The SageMaker Data Parallelism library is specifically designed to distribute training across multiple GPUs by splitting the input data across workers, which reduces per-GPU computation time and accelerates training for deep learning models. This library uses optimized all-reduce algorithms (e.g., Ring AllReduce) to synchronize gradients efficiently, making it ideal for speeding up image classification tasks that are data-intensive.

Answer

SageMaker Debugger

Answer

Model parallelism in SageMaker

Answer

SageMaker hyperparameter tuning

Question 13

A company is building a recommendation system for an e-commerce platform. The data includes user-item interactions and features such as user demographics and item categories. Which algorithm would be most appropriate for generating personalized recommendations?

Accepted Answer

Factorization Machines. Factorization Machines (FM) are specifically designed for recommendation tasks with sparse, high-dimensional data like user-item interactions. They model pairwise feature interactions (e.g., user demographics × item categories) using factorized parameters, enabling personalized recommendations even when many user-item pairs are unobserved. This makes FM far more effective than tree-based or clustering methods for collaborative filtering and feature-rich recommendation scenarios.

Answer

XGBoost

Answer

k-means clustering

Answer

Principal Component Analysis (PCA)

Question 14

A data scientist notices that a linear regression model trained on a dataset has high variance. The model performs well on the training data but poorly on the test data. Which action is most likely to reduce the variance?

Accepted Answer

Apply L2 regularization to the model. High variance indicates the model is overfitting to the training data. L2 regularization (ridge regression) adds a penalty proportional to the square of the magnitude of the coefficients, which shrinks them toward zero. This reduces the model's sensitivity to noise in the training data, thereby lowering variance and improving generalization to the test set.

Answer

Decrease the amount of training data

Answer

Increase the number of gradient descent iterations

Answer

Add more features to the model

Question 15

A machine learning team is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket. The team wants to ensure that the training job can access the data securely without using long-lived AWS credentials. Which approach should the team use?

Accepted Answer

Specify an IAM role in the SageMaker training job configuration. SageMaker training jobs can assume an IAM role specified in the job configuration to obtain temporary security credentials via AWS Security Token Service (STS). This allows the training job to access the S3 bucket securely without embedding long-lived AWS access keys in code or configuration files.

Answer

Store AWS access keys in the training script

Answer

Use an S3 bucket policy that allows public access

Answer

Create a new IAM user for each training job