MLS-C01 Modeling Practice Test 40 — 15 Questions

Question 1

A team is training a linear regression model to predict house prices. After training, they observe that the model has high bias (underfitting). Which action is most likely to reduce bias?

Accepted Answer

Add more relevant features and increase model complexity.. High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more relevant features and increasing model complexity (e.g., using polynomial features or more interaction terms) gives the linear regression model greater capacity to fit the training data, directly reducing bias. This aligns with the bias-variance tradeoff, where increasing complexity lowers bias at the cost of potentially increasing variance.

Answer

Increase the regularization strength.

Answer

Reduce the amount of training data.

Answer

Decrease the number of model parameters.

Question 2

A machine learning engineer is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket encrypted with AWS KMS. The SageMaker training job fails with an AccessDenied error when trying to read the data. Which IAM policy addition should resolve the issue?

Accepted Answer

Add kms:Decrypt permission for the KMS key.. When an S3 bucket is encrypted with AWS KMS, the SageMaker training job's execution role must have the `kms:Decrypt` permission for the specific KMS key to read the encrypted objects. Without this permission, the job fails with an AccessDenied error, even if `s3:GetObject` is granted, because SageMaker must decrypt the data before reading it.

Answer

Add s3:GetObject permission for the bucket.

Answer

Add kms:GenerateDataKey permission for the key.

Answer

Attach the AmazonSageMakerFullAccess policy.

Question 3

A data scientist is building a recommendation system using matrix factorization. The dataset has 1 million users and 100,000 items, with a sparse user-item interaction matrix. The scientist wants to minimize training time on Amazon SageMaker. Which algorithm would be most appropriate?

Accepted Answer

Factorization Machines. Factorization Machines (B) are specifically designed for sparse, high-dimensional datasets like the user-item interaction matrix in recommendation systems. They extend matrix factorization by modeling pairwise feature interactions, which is ideal for collaborative filtering tasks. On Amazon SageMaker, the built-in Factorization Machines algorithm is optimized for sparse data and can train efficiently on 1 million users and 100,000 items, minimizing training time compared to general-purpose algorithms.

Answer

Linear Learner

Answer

K-Means

Answer

XGBoost

Question 4

A team is training a binary classifier and obtains a confusion matrix with 100 true positives, 10 false positives, 20 false negatives, and 200 true negatives. What is the precision of the model?

Accepted Answer

0.91. Precision is calculated as TP / (TP + FP). With 100 true positives and 10 false positives, precision = 100 / (100 + 10) = 100 / 110 ≈ 0.909, which rounds to 0.91. This metric measures how many of the positive predictions were actually correct.

Answer

0.87

Answer

0.94

Answer

0.83

Question 5

A data scientist is using Amazon SageMaker to train a model with a custom Docker container. The training job fails with an error: 'Container exited with code 137'. What is the most likely cause?

Accepted Answer

The training instance ran out of memory.. Exit code 137 (128+9) indicates the container was killed by the SIGKILL signal, which typically occurs when the Linux Out-Of-Memory (OOM) killer terminates a process that has exceeded its memory allocation. In Amazon SageMaker, training instances have finite memory, and if the training algorithm or data loading exceeds that limit, the OOM killer forcibly stops the container, resulting in exit code 137.

Answer

The training data was corrupted.

Answer

The training job exceeded the maximum runtime.

Answer

The Docker entrypoint script was not found.

Question 6

Which TWO of the following are best practices for training deep learning models on Amazon SageMaker? (Select TWO.)

Accepted Answer

Use Pipe input mode to stream data directly from S3 to the algorithm.. Option B is correct because Pipe input mode streams data directly from Amazon S3 to the training algorithm without downloading the full dataset to the instance's disk, which reduces startup time and disk usage for large datasets. Option D is correct because SageMaker managed spot training uses spare EC2 capacity at up to a 90% discount, and SageMaker automatically manages the spot interruptions and resumes training from checkpoints, making it a cost-optimization best practice. Option A is not a general best practice for training itself; SageMaker Processing is for preprocessing jobs, and data augmentation is typically done there or in the training container, but it is not one of the two targeted best practices here. Option C is wrong because storing training data on attached EBS volumes is not a SageMaker best practice; data should reside in S3 and be accessed via File, FastFile, or Pipe modes. Option E is wrong because disabling checkpointing is not a best practice; checkpointing is essential for spot training and for resuming long jobs, and it does not meaningfully slow training.

Answer

Use SageMaker Processing to perform data augmentation before training.

Answer

Store training data on Amazon EBS volumes attached to the training instance.

Answer

Disable checkpointing to improve training speed.

Question 7

Which TWO of the following are valid Amazon SageMaker built-in algorithms for regression tasks? (Select TWO.)

Accepted Answer

XGBoost. XGBoost (B) is a valid SageMaker built-in algorithm that supports regression via the reg:squarederror (and related) objectives, making it appropriate for predicting continuous numeric targets. Linear Learner (E) is also a SageMaker built-in algorithm explicitly designed for both classification and regression, learning a linear function to predict continuous values. BlazingText (A) is a built-in algorithm for text classification and word embeddings, not regression. Image Classification (C) is a supervised vision algorithm for assigning discrete labels to images, so it is not a regression algorithm. Object Detection (D) is a vision algorithm that predicts bounding boxes and classes, which is a detection task rather than regression.

Answer

BlazingText

Answer

Image Classification

Answer

Object Detection

Question 8

Refer to the exhibit. The training job 'my-job' failed with the error 'Unable to pull image from ECR'. What is the most likely cause?

Accepted Answer

The IAM role does not have permission to pull images from the ECR repository.. The error 'Unable to pull image from ECR' indicates that the SageMaker training job could not retrieve the custom Docker image stored in Amazon ECR. The most likely cause is that the IAM role associated with the training job lacks the `ecr:GetDownloadUrlForLayer` and `ecr:BatchGetImage` permissions required to pull images from the ECR repository. Without these permissions, SageMaker cannot authenticate and download the container image, even if the repository and image exist.

Answer

The instance type ml.m5.large does not support custom images.

Answer

The S3 bucket for training data is in a different account.

Answer

The role ARN is incorrect.

Question 9

A data scientist is training a binary classification model on a highly imbalanced dataset (99% negative class, 1% positive class). The model currently achieves 99% accuracy but only identifies 0.5% of true positives. Which metric should the data scientist focus on to improve model performance?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified, which is critical when the dataset is highly imbalanced (99% negative, 1% positive) and the model fails to detect most positives (only 0.5% true positives). Improving recall directly addresses the model's inability to capture the minority class, even if it reduces precision or accuracy. In binary classification with severe class imbalance, accuracy is misleading because a model can achieve 99% accuracy by simply predicting the majority class, as seen here.

Answer

Precision

Answer

Root Mean Squared Error (RMSE)

Answer

Accuracy

Question 10

A data scientist is building a recommendation system for an e-commerce platform using Amazon SageMaker. The system needs to provide personalized product recommendations based on user purchase history and product metadata. The dataset contains 10 million users and 1 million products. Which algorithm should the data scientist use as the core of the recommendation engine?

Accepted Answer

Factorization Machines. Factorization Machines (FM) are specifically designed for high-dimensional sparse data like user-item interactions, making them ideal for recommendation systems with 10 million users and 1 million products. FM can capture pairwise feature interactions (e.g., user-product affinities) efficiently using factorized parameters, which scales well to large datasets and supports personalized recommendations from purchase history and metadata.

Answer

Linear Learner

Answer

XGBoost

Answer

K-Means

Question 11

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 use distributed training across multiple GPUs to speed up the process. Which SageMaker feature should the data scientist use?

Accepted Answer

SageMaker Distributed Training Libraries. SageMaker Distributed Training Libraries provide optimized implementations of data parallelism and model parallelism that automatically partition the model and data across multiple GPUs, reducing training time for deep learning models. This is the correct choice because the question specifically asks for a feature to enable distributed training across multiple GPUs, which is exactly what these libraries are designed for.

Answer

SageMaker Managed Spot Training

Answer

SageMaker Hyperparameter Tuning

Answer

SageMaker Automatic Model Tuning

Question 12

A data scientist is training a text classification model using Amazon SageMaker. The dataset consists of 100,000 labeled documents. The data scientist notices that the model performs well on the training set but poorly on the validation set. Which regularization technique should the data scientist apply to reduce overfitting?

Accepted Answer

Dropout. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on any single feature and forces it to learn more robust representations. This directly addresses the overfitting symptom of high training accuracy and low validation accuracy by reducing the model's capacity to memorize noise in the training data.

Answer

Data augmentation

Answer

Batch normalization

Answer

Early stopping

Question 13

A data scientist is training a linear regression model to predict house prices. The dataset contains 10 features. After training, the data scientist notices that the model has high bias (underfitting). Which action should the data scientist take to reduce bias?

Accepted Answer

Add more features, such as polynomial features. High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more features, such as polynomial features, increases model complexity, allowing the linear regression model to fit non-linear relationships and reduce bias. This directly addresses the underfitting issue by giving the model more expressive power.

Answer

Reduce the amount of training data

Answer

Increase the regularization strength

Answer

Use a simpler model, such as ridge regression

Question 14

A data scientist is using Amazon SageMaker to train a neural network. The training job fails with the error 'ResourceLimitExceeded: The account-level service limit for ml.p3.8xlarge for training job usage is 0.' What is the most likely cause and solution?

Accepted Answer

The account has not requested a limit increase for ml.p3.8xlarge; submit a limit increase request via AWS Support.. The error message explicitly states that the account-level service limit for ml.p3.8xlarge for training job usage is 0, which means the account has not been granted any capacity for that instance type. AWS enforces service quotas (limits) per account per region, and for GPU-intensive instances like ml.p3.8xlarge, the default limit is often 0 unless a limit increase request has been submitted and approved. Therefore, the correct solution is to request a limit increase via AWS Support.

Answer

The training job is using spot instances; switch to on-demand instances.

Answer

The instance type is not available in the current region; switch to a different region.

Answer

The instance type is too large; use a smaller instance type like ml.m5.large.

Question 15

A company is building a machine learning model to detect anomalies in industrial sensor data. The data is time-series with seasonal patterns. The data scientist wants to use Amazon SageMaker to train a model. Which algorithm is most suitable for this task?

Accepted Answer

Random Cut Forest (RCF). Random Cut Forest (RCF) is the most suitable algorithm because it is designed for unsupervised anomaly detection on streaming and time-series data. It works by constructing an ensemble of random trees that isolate anomalies based on how quickly a data point can be separated from the rest, making it effective for detecting outliers in sensor data with seasonal patterns without requiring labeled training data.

Answer

K-Means

Answer

DeepAR

Answer

XGBoost