MLS-C01 Modeling Practice Test 37 — 15 Questions

Question 1

A company wants to deploy a machine learning model that provides real-time inference with low latency. The model is a small ensemble of three tree-based models. Which Amazon SageMaker approach is most appropriate?

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

Accepted Answer

Use a SageMaker real-time endpoint with a single inference container.. A SageMaker real-time endpoint with a single inference container is the most appropriate approach because it provides persistent, low-latency inference by keeping the model loaded in memory and handling requests synchronously. For a small ensemble of three tree-based models, a single container can host all models (e.g., using a custom inference script or a multi-model endpoint) and deliver sub-second response times, meeting the real-time requirement.

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Use a SageMaker batch transform job.

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Use AWS Lambda with the model packaged in a layer.

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Use a SageMaker Serverless Inference endpoint.

Question 2

A data scientist is using Amazon SageMaker to train a custom TensorFlow model. The training job is failing with the error: 'OutOfRangeError: End of sequence'. The input data is stored in TFRecord format in S3. What is the most likely cause?

Accepted Answer

The number of training steps or epochs specified exceeds the dataset size.. The 'OutOfRangeError: End of sequence' error in TensorFlow occurs when the training loop attempts to read more data than is available in the dataset. This typically happens when the number of training steps or epochs specified exceeds the total number of records in the TFRecord files, causing the iterator to reach the end of the dataset prematurely.

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The TFRecord files are corrupted.

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The instance type does not have enough memory.

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The shuffle buffer size is too large.

Question 3

A company is using Amazon SageMaker to train a model on a dataset with many categorical features. They want to use SageMaker's built-in Linear Learner algorithm. What preprocessing step is required for the categorical features?

Accepted Answer

Apply one-hot encoding to convert them to numerical vectors.. The SageMaker Linear Learner algorithm requires numerical input features. Categorical features must be converted to numerical vectors, typically via one-hot encoding, because the algorithm performs linear regression or classification on numerical data. Without this preprocessing, the algorithm cannot interpret categorical values directly.

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Use label encoding to assign integers to categories.

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Normalize the categorical features using min-max scaling.

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Remove categorical features with high cardinality.

Question 4

A data scientist is tuning a neural network on a small dataset and observes that the training loss decreases but validation loss increases after a few epochs. Which technique should be applied to mitigate this issue?

Accepted Answer

Add dropout layers to the model.. The observed behavior—training loss decreasing while validation loss increasing—is a classic sign of overfitting. Adding dropout layers randomly drops a fraction of neurons during training, which forces the network to learn more robust features and reduces co-adaptation, thereby mitigating overfitting. This technique is specifically designed to improve generalization on small datasets.

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Increase the learning rate.

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Remove regularization terms from the loss function.

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Increase the number of epochs.

Question 5

A company is building a recommendation system using Amazon SageMaker's Factorization Machines algorithm. The dataset includes user IDs, item IDs, and ratings. The data is sparse. Which data format should be used for training?

Accepted Answer

RecordIO-protobuf format with sparse feature vectors.. Factorization Machines (FM) in SageMaker are optimized for sparse, high-dimensional data. The RecordIO-protobuf format allows you to directly specify sparse feature vectors using integer keys and float values, which avoids the memory overhead of dense representations and enables efficient distributed training. This format is the recommended input for SageMaker's built-in FM algorithm.

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CSV format with one row per rating.

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JSON lines format with nested structures.

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Parquet format with columns for each feature.

Question 6

A data scientist needs to evaluate a binary classification model. The dataset is balanced. Which metric is most appropriate to compare model performance?

Accepted Answer

Accuracy. For a balanced binary classification dataset, accuracy is the most appropriate metric because it directly measures the proportion of correct predictions (true positives and true negatives) out of all predictions. Since the class distribution is equal, accuracy is not misleadingly high due to class imbalance, making it a reliable and straightforward measure of overall model performance.

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Recall

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F1 score

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Precision

Question 7

A company uses Amazon SageMaker to train a model. The training job runs successfully but the model artifacts are not saved to the specified S3 output path. What is a likely cause?

Accepted Answer

The training script does not save the model to /opt/ml/model.. Amazon SageMaker expects the training script to save the model artifacts to the `/opt/ml/model` directory. After the training job completes, SageMaker automatically copies the contents of this directory to the specified S3 output path. If the script saves the model elsewhere (e.g., `/tmp` or a custom path), no artifacts will be uploaded, resulting in an empty or missing S3 output.

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The model size exceeds the S3 bucket limit.

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The training job used spot instances.

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The S3 bucket is in a different AWS Region.

Question 8

A company is deploying a machine learning model for fraud detection. The model outputs a probability score. The cost of false negatives is very high. Which TWO metrics should the company focus on optimizing?

Accepted Answer

F1 score. Recall (true positive rate) measures ability to find positives; minimizing false negatives is optimizing recall. AUC-ROC summarizes overall performance but not specific to false negatives. Precision focuses on false positives. FPR is about false positives. F1 balances precision and recall, but recall directly addresses false negatives.

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Precision

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False positive rate (FPR)

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Area under the ROC curve (AUC-ROC)

Question 9

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains outliers. Which THREE techniques can mitigate the impact of outliers?

Accepted Answer

Remove observations with outlier values from the dataset.. Removing observations with outlier values directly eliminates data points that can disproportionately influence the linear regression coefficients, leading to a more stable and representative model. In Amazon SageMaker, this can be done during data preprocessing using built-in algorithms or custom scripts in a SageMaker Processing job.

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Increase the number of layers in the model.

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Standardize the features to have mean zero and unit variance.

Question 10

A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. The model is evaluated using accuracy, but the accuracy is 99% even though the model predicts all instances as negative. Which metric should the data scientist use to properly evaluate the model?

Accepted Answer

F1 score. The F1 score is the harmonic mean of precision and recall, making it robust to class imbalance. With 99% negative instances, accuracy is misleadingly high even if the model never predicts the positive class. F1 captures both false positives and false negatives, providing a balanced evaluation of the minority class performance.

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Root mean squared error (RMSE)

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Mean squared error (MSE)

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Accuracy

Question 11

A machine learning team is deploying a time-series forecasting model using Amazon SageMaker. The model is trained on historical data and needs to be updated daily with new data. The team wants to automate the retraining pipeline and avoid manual intervention. Which approach is the most efficient?

Accepted Answer

Use Amazon SageMaker Pipelines with a scheduled Lambda function to trigger retraining daily.. Amazon SageMaker Pipelines provides a fully managed, end-to-end orchestration service for building, training, and deploying machine learning models. By combining it with a scheduled AWS Lambda function, the team can automate daily retraining without manual intervention, leveraging SageMaker's native integration for step sequencing, artifact tracking, and model registry updates.

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Use AWS Step Functions to orchestrate retraining, but require a manual approval step.

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Use SageMaker training jobs manually triggered by the team each day.

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Use a cron job on an EC2 instance to run a training script.

Question 12

A data scientist is training a linear regression model. After training, the model has a high bias and low variance. Which technique should the data scientist use to reduce bias?

Accepted Answer

Add more relevant features. High bias indicates the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding more relevant features increases model complexity, allowing it to learn more from the data and reduce bias. This directly addresses the underfitting issue without increasing variance excessively, provided the features are meaningful.

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Decrease the model complexity

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Apply L2 regularization (Ridge)

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Reduce the amount of training data

Question 13

A company is building a recommendation system for an e-commerce platform. The system needs to suggest products to users based on past purchases and browsing history. Which approach would be most appropriate for this use case?

Accepted Answer

Collaborative filtering using past user-item interactions. Collaborative filtering is the most appropriate approach because it leverages past user-item interactions (e.g., purchases, clicks) to identify patterns and recommend items that similar users have liked. This method directly captures user behavior and preferences without requiring explicit product metadata, making it ideal for e-commerce recommendation systems where implicit feedback is abundant.

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Content-based filtering using product descriptions

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K-means clustering of users based on demographics

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Matrix factorization on user-item ratings

Question 14

A data scientist is training a deep learning model for image classification. The model is overfitting on the training data. Which combination of techniques will most effectively reduce overfitting?

Accepted Answer

Add dropout layers and use data augmentation. Dropout layers randomly deactivate a fraction of neurons during training, which forces the network to learn more robust features and prevents co-adaptation. Data augmentation artificially expands the training dataset by applying transformations (e.g., rotation, flipping, cropping), which reduces the model's ability to memorize spurious patterns and improves generalization. Together, these techniques directly counteract overfitting by increasing regularization and effective training diversity.

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Reduce the batch size

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Train for more epochs without early stopping

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Increase the number of layers and neurons

Question 15

A machine learning engineer is evaluating a binary classification model. The model has a high recall but low precision. Which of the following is the most likely consequence?

Accepted Answer

The model has many false positives.. High recall means the model correctly identifies most positive cases (few false negatives), but low precision indicates that among the cases predicted as positive, many are actually negative. This directly implies a high number of false positives, as precision = TP/(TP+FP) and a low precision with high recall forces FP to be large relative to TP.

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The model has few false negatives.

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The model misses many positive cases.

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The model has few false positives.