MLS-C01 Modeling Practice Test 33 — 15 Questions

Question 1

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.

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Data augmentation

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Batch normalization

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Early stopping

Question 2

A company wants to build a machine learning model to predict customer churn. The dataset includes customer demographics, usage patterns, and support interactions. The data is stored in Amazon S3. The data scientist needs to perform feature engineering, including creating aggregate features from support interactions and encoding categorical variables. Which AWS service is most suitable for building the feature engineering pipeline?

Accepted Answer

Amazon SageMaker Processing. Amazon SageMaker Processing is the most suitable service because it is purpose-built for data preprocessing and feature engineering within the SageMaker ecosystem. It allows you to run custom Python scripts (e.g., using pandas or PySpark) on managed infrastructure to create aggregate features from support interactions and encode categorical variables, and it integrates seamlessly with SageMaker for model training and deployment.

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AWS Glue

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Amazon EMR

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AWS Batch

Question 3

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.

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Reduce the amount of training data

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Increase the regularization strength

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Use a simpler model, such as ridge regression

Question 4

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.

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

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

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

Question 5

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.

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K-Means

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DeepAR

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XGBoost

Question 6

A data scientist is training a classification model on a dataset with missing values in several features. The data scientist wants to use SageMaker to train the model. Which TWO approaches can the data scientist use to handle missing data within the SageMaker training pipeline? (Choose two.)

Accepted Answer

Use the SageMaker built-in XGBoost algorithm, which can handle missing values by default.. Option A is correct because the SageMaker built-in XGBoost algorithm has a built-in mechanism to handle missing values by default. It learns the best direction (left or right branch) to route missing values during training, so no explicit imputation is needed. This makes it a seamless choice for datasets with missing data within the SageMaker training pipeline.

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Use the SageMaker BlazingText algorithm, which automatically imputes missing values.

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Use SageMaker Inference Pipeline to handle missing values at inference time.

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Use SageMaker PCA algorithm, which automatically handles missing values.

Question 7

A data scientist is developing a deep learning model for object detection using Amazon SageMaker. The training dataset has 50,000 labeled images. The data scientist wants to improve model generalization without collecting more data. Which TWO techniques can be applied? (Choose two.)

Accepted Answer

Apply data augmentation techniques such as random cropping and horizontal flipping.. Options A and D are correct. Option A: Data augmentation (e.g., random crops, flips) effectively increases dataset diversity. Option D: Using a pre-trained model (transfer learning) improves generalization. Option B (increasing batch size) may hurt generalization. Option C (increasing learning rate) can cause divergence. Option E (increasing epochs) may lead to overfitting.

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Increase the learning rate to speed up convergence.

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Increase the number of training epochs to ensure convergence.

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Increase the batch size to reduce variance.

Question 8

A data scientist is evaluating a binary classification model. The model's confusion matrix shows: True Positives=80, False Positives=20, True Negatives=900, False Negatives=0. Which THREE metrics can be calculated from this confusion matrix? (Choose three.)

Accepted Answer

Precision. Precision is calculated as TP/(TP+FP) = 80/(80+20) = 0.80. This metric measures the proportion of positive identifications that were actually correct, which is directly derivable from the confusion matrix values.

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AUC-ROC

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Root Mean Squared Error (RMSE)

Question 9

A data scientist is assigned an IAM policy as shown. The data scientist attempts to create a SageMaker endpoint to deploy a model, but the request fails. What is the most likely reason?

Accepted Answer

The data scientist does not have permission to create an endpoint.. The IAM policy shown does not include the `sagemaker:CreateEndpoint` action, which is required to create a SageMaker endpoint. Even if the data scientist has permissions for other SageMaker actions like `CreateModel` or `CreateEndpointConfig`, the explicit absence of `CreateEndpoint` in the policy will cause the request to fail with an access denied error. AWS IAM policies must explicitly grant each action needed for the operation.

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The data scientist does not have permission to upload the model to S3.

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The data scientist does not have permission to create a training job.

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The data scientist does not have permission to pass roles.

Question 10

A company is training a deep learning model on a large dataset using Amazon SageMaker. The training script uses TensorFlow and requires GPUs. The training job is failing with an out-of-memory error. Which configuration change should be made to resolve this issue?

Accepted Answer

Use a larger instance type with more GPU memory.. The training job is failing with an out-of-memory error, which indicates that the model or batch size exceeds the GPU memory capacity of the current instance. Using a larger instance type with more GPU memory directly addresses this by providing additional VRAM, allowing the model to fit in memory and the training to proceed without failure.

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Increase the number of instances in the training job.

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Switch to using spot instances to reduce cost.

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Enable distributed training across multiple instances.

Question 11

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains missing values. Which preprocessing step should be applied before training?

Accepted Answer

Impute missing values with the mean of the column.. Option B is correct because linear regression models in Amazon SageMaker cannot handle missing values natively; they require complete numerical input. Imputing missing values with the column mean is a standard preprocessing technique that preserves the overall distribution and avoids introducing bias, ensuring the SageMaker built-in Linear Learner algorithm can train without errors.

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Ignore missing values; linear regression can handle them.

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Replace missing values with zeros.

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Remove all rows containing missing values.

Question 12

A company is building a real-time fraud detection system using Amazon SageMaker. The model must have low latency (under 10ms) and high throughput (thousands of predictions per second). The team has trained a gradient boosting model using XGBoost. Which SageMaker inference option is MOST suitable?

Accepted Answer

Deploy the model on a SageMaker real-time endpoint with a multi-model endpoint.. A multi-model endpoint (MME) on SageMaker is the most suitable option because it allows you to host multiple XGBoost models on a single endpoint, sharing the underlying instance to maximize throughput and minimize latency. MMEs keep models loaded in memory and route requests to the correct model with sub-10ms overhead, meeting the low-latency and high-throughput requirements for real-time fraud detection.

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Use SageMaker asynchronous inference.

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Deploy the model on a SageMaker serverless endpoint.

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Use a batch transform job.

Question 13

A machine learning team is using Amazon SageMaker to tune hyperparameters for a neural network. They have defined a hyperparameter tuning job with a random search strategy. The training time per job is very long. Which strategy can reduce the total tuning time?

Accepted Answer

Enable early stopping to terminate poorly performing jobs.. Enabling early stopping allows SageMaker to terminate training jobs that are unlikely to produce better results based on the objective metric, which directly reduces total tuning time by freeing up compute resources for more promising hyperparameter combinations. This is especially effective with random search, where many trials may converge slowly or plateau.

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Use a larger instance type for each training job.

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Switch to Bayesian optimization.

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Increase the number of training jobs.

Question 14

A data scientist is using Amazon SageMaker to train a large language model with PyTorch. The training job is taking too long. The dataset is stored in S3 and the training script uses the SageMaker PyTorch container. Which change is MOST likely to reduce training time?

Accepted Answer

Use Pipe mode to stream data from S3 instead of downloading.. Option A is correct because SageMaker Pipe mode streams data directly from S3 to the training algorithm via a Unix FIFO (named pipe), eliminating the need to first download the entire dataset to the training instance's local storage. This reduces I/O wait time and disk usage, which is especially beneficial for large language models where dataset sizes can be in terabytes, thereby significantly cutting total training time.

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Increase the number of instances in the training job.

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Change the optimizer to AdamW.

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Switch to spot instances to reduce cost.

Question 15

A company is using Amazon SageMaker to build a binary classification model for customer churn. The dataset is highly imbalanced (90% no churn, 10% churn). Which technique is MOST effective for handling class imbalance?

Accepted Answer

Use SMOTE to generate synthetic samples for the minority class.. SMOTE (Synthetic Minority Oversampling Technique) is the most effective option because it generates synthetic samples for the minority class by interpolating between existing minority instances, thereby balancing the dataset without discarding valuable majority-class data. This approach directly addresses the class imbalance in a binary classification task on SageMaker, improving model recall for the churn class without the information loss caused by undersampling.

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Use accuracy as the evaluation metric.

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Undersample the majority class.

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Train a random forest model instead of logistic regression.