MLS-C01 Practice Test 2 — 30 Questions

Question 1

A team is building a product recommendation system using matrix factorization in Amazon SageMaker. They notice that the model's training loss decreases steadily but validation loss starts increasing after 5 epochs. What is the most likely cause?

Accepted Answer

Overfitting. In matrix factorization for recommendation systems, a decreasing training loss with an increasing validation loss after several epochs is a classic sign of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, which degrades its performance on unseen validation data.

Answer

Underfitting

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Not enough training data

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Learning rate too high

Question 2

A company is using Amazon SageMaker to train a deep learning model on a large dataset. The training job is taking too long. The team wants to reduce training time without changing the model architecture. Which action should they take?

Accepted Answer

Use SageMaker's distributed training with multiple instances. SageMaker's distributed training with multiple instances splits the dataset and model computations across several machines, enabling parallel processing that significantly reduces wall-clock training time. This approach leverages data parallelism or model parallelism without altering the model architecture, directly addressing the need for faster training.

Answer

Increase the learning rate by a factor of 10

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Reduce the number of epochs

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Reduce the batch size

Question 3

A data scientist is deploying a regression model in Amazon SageMaker that predicts housing prices. The model shows high bias (underfitting). Which action is most likely to reduce bias?

Accepted Answer

Add more features or increase model complexity. High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more features or increasing model complexity (e.g., using polynomial features, deeper trees, or a more flexible algorithm) directly addresses underfitting by giving the model greater capacity to learn from the data. In Amazon SageMaker, this could involve using a more complex built-in algorithm like XGBoost with deeper trees or adding feature engineering transformations in a processing job.

Answer

Reduce the amount of training data

Answer

Increase regularization strength

Answer

Use a simpler model

Question 4

A machine learning engineer is training a neural network on Amazon SageMaker using a custom Docker container. The training job fails with an error: 'CUDA out of memory.' The training instance is an ml.p3.2xlarge with 16 GB GPU memory. The model and data fit into memory when using batch size 32, but the engineer wants to maximize GPU utilization. Which approach should the engineer use to fix the out-of-memory error while maintaining efficient training?

Accepted Answer

Implement gradient accumulation with a larger effective batch size. Gradient accumulation allows the engineer to simulate a larger effective batch size by accumulating gradients over multiple forward/backward passes before performing an optimizer step. This keeps the per-step memory footprint low (avoiding CUDA out-of-memory) while maintaining training dynamics similar to a larger batch, thus maximizing GPU utilization without crashing.

Answer

Enable mixed precision training

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Reduce batch size to 1

Answer

Use a CPU-only instance

Question 5

A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. The model currently achieves 99% accuracy but a recall of only 10% on the positive class. Which metric combination should the data scientist prioritize to evaluate model improvements?

Accepted Answer

F1 score and AUC-ROC. With a highly imbalanced dataset (1% positive class), 99% accuracy is misleading because the model can achieve it by simply predicting the majority class. The low recall (10%) indicates the model fails to identify most positive instances. The F1 score balances precision and recall, providing a single metric for minority class performance, while AUC-ROC evaluates the model's ability to distinguish between classes across all thresholds, making it robust to class imbalance.

Answer

Precision and recall at 90% precision

Answer

Accuracy and RMSE

Answer

Precision and RMSE

Question 6

A company wants to use Amazon SageMaker to train a deep learning model using a custom TensorFlow script. The data is stored in an S3 bucket. Which SageMaker API operation should be used to launch the training job?

Accepted Answer

CreateTrainingJob. The correct API operation to launch a training job in Amazon SageMaker is CreateTrainingJob. This operation specifies the training algorithm (or custom script), resource configuration (instance type and count), input data configuration (pointing to S3), and output location for the model artifacts. It directly initiates the training process on SageMaker-managed infrastructure.

Answer

CreateHyperParameterTuningJob

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CreateEndpoint

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CreateTransformJob

Question 7

A data scientist runs a SageMaker training job that fails with the above error. The S3 bucket and object exist, and the IAM role has s3:GetObject permission. What is the MOST likely cause?

Accepted Answer

The S3 object was uploaded with incorrect checksum or is corrupted. The error indicates that SageMaker cannot read the S3 object, even though the bucket and object exist and the IAM role has s3:GetObject permission. The most likely cause is that the object was uploaded with an incorrect checksum or is corrupted, which prevents SageMaker from verifying the integrity of the data during the training job initialization. SageMaker uses ETag (MD5 checksum) validation when reading objects, and a mismatch triggers a failure.

Answer

The training instance does not have internet access

Answer

The S3 bucket has versioning enabled and the object version is not specified

Answer

The IAM role lacks kms:Decrypt permission for an encrypted S3 object

Question 8

A data scientist is trying to run a SageMaker training job that writes output to an S3 bucket 'my-bucket'. The IAM policy is shown. The training job fails with an AccessDenied error when trying to write to S3. What is the reason?

Accepted Answer

The S3 bucket is encrypted with AWS KMS and the policy does not include kms:GenerateDataKey. The correct answer is A because the training job fails with an AccessDenied error when writing to an S3 bucket that is encrypted with AWS KMS. The IAM policy shown must include the `kms:GenerateDataKey` permission to allow the SageMaker training job to generate a data key for encrypting the output objects. Without this KMS permission, the S3 PutObject operation is denied even if the policy allows `s3:PutObject`, as KMS encryption requires explicit authorization to use the customer master key (CMK).

Answer

The policy does not allow s3:ListBucket

Answer

The policy does not allow s3:PutObject

Answer

The policy does not allow s3:PutObjectAcl

Question 9

A data scientist is building a multi-class classification model with 10 classes. The dataset has 100,000 samples. After training a random forest with 100 trees, the model achieves 85% accuracy on the test set. However, the data scientist notices that for one rare class (1% of data), recall is only 5%. Which technique is MOST likely to improve recall for the rare class without significantly reducing overall accuracy?

Accepted Answer

Apply SMOTE to oversample the rare class in the training data. SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the rare class by interpolating between existing minority instances, which directly addresses the class imbalance. This increases the model's exposure to the rare class during training, improving recall without discarding data or significantly altering the overall class distribution, thus preserving overall accuracy.

Answer

Increase the number of trees to 500

Answer

Use stratified sampling only for the test set

Answer

Reduce the decision threshold for the rare class to 0.1

Question 10

A company uses an XGBoost model to predict equipment failures. The model has high precision but low recall. The business impact of a false negative is very high (missing a failure). Which action would MOST effectively increase recall while keeping precision reasonably high?

Accepted Answer

Decrease the probability threshold for the positive class. Decreasing the probability threshold for the positive class means the model will classify a case as a failure at a lower predicted probability, which captures more true positives (increases recall). However, this also allows more false positives, so precision may drop, but the trade-off is acceptable given the high cost of false negatives. This is a standard post-training calibration technique for imbalanced classification problems.

Answer

Increase the regularization parameter lambda

Answer

Set the objective to 'reg:squarederror'

Answer

Increase the number of boosting rounds

Question 11

Which TWO metrics are MOST appropriate for evaluating a regression model that predicts house prices, where the business is most sensitive to large errors?

Accepted Answer

Root Mean Squared Error (RMSE). RMSE is most appropriate because it squares the errors before averaging, which heavily penalizes large errors. Since the business is most sensitive to large errors in house price predictions, RMSE directly aligns with this requirement by amplifying the impact of outliers, making it a suitable metric for evaluating model performance in this context.

Answer

Accuracy

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Mean Absolute Error (MAE)

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R-squared

Question 12

Which THREE techniques can help reduce overfitting in a neural network trained on a small dataset?

Accepted Answer

Apply L2 weight regularization. L2 weight regularization (also known as weight decay) penalizes large weights by adding a term to the loss function proportional to the sum of squared weights. This forces the network to learn simpler patterns and reduces sensitivity to noise in the training data, which is especially helpful when the dataset is small and prone to overfitting.

Answer

Increase the number of hidden layers

Answer

Train for more epochs

Question 13

A data scientist is training a neural network for image classification. The training loss is not decreasing significantly, and the validation loss is high. Which TWO actions should the scientist take to address potential vanishing gradients?

Accepted Answer

Use ReLU activation functions in hidden layers. ReLU activation functions help mitigate vanishing gradients because they output a constant gradient of 1 for positive inputs, preventing the gradient from shrinking as it propagates backward through many layers. This avoids the exponential decay of gradients that occurs with saturating activations like sigmoid or tanh, enabling effective training of deep networks.

Answer

Increase the learning rate

Answer

Switch activation functions from ReLU to sigmoid

Answer

Remove dropout layers

Question 14

A company is deploying a real-time fraud detection system using a gradient boosting model on AWS SageMaker. The model uses 200 features and is trained on 50 GB of data. The inference latency requirement is under 10 ms per request. During load testing, the endpoint shows average latency of 15 ms. Which change is MOST likely to reduce latency below 10 ms?

Accepted Answer

Reduce the number of features to the top 50 based on feature importance. Reducing the number of features from 200 to the top 50 directly decreases the amount of data each inference request must process, which lowers both feature engineering overhead and model evaluation time. For gradient boosting models on SageMaker, fewer features mean fewer decision tree splits to traverse per prediction, which can significantly reduce latency without requiring hardware changes. This is the most direct and cost-effective way to meet the 10 ms requirement.

Answer

Switch to a GPU-based instance type

Answer

Increase the number of trees in the model

Answer

Use a larger batch size for inference

Question 15

A data scientist is training a binary classifier to predict customer churn. The dataset has 10,000 samples, with 500 churners (positive class). The scientist trains a logistic regression model and obtains an F1-score of 0.6. To improve the F1-score, which approach is MOST likely to be effective?

Accepted Answer

Apply SMOTE to oversample the minority class. The dataset is highly imbalanced (500 churners out of 10,000 samples, a 5% positive rate). Logistic regression trained on such imbalance tends to bias toward the majority class, resulting in low recall for the minority class and a poor F1-score. SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority instances, which balances the class distribution and allows the model to learn a better decision boundary, directly improving recall and F1-score.

Answer

Increase the regularization strength (C)

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Apply PCA to reduce feature dimensionality

Answer

Use the original dataset without any modification

Question 16

Drag and drop the steps to train a model using Amazon SageMaker built-in algorithm in the correct order.

Question 17

Drag and drop the steps to deploy a model as a SageMaker endpoint for real-time inference in the correct order.

Question 18

Drag and drop the steps to create a data processing job using Amazon SageMaker Processing in the correct order.

Question 19

A company is using Amazon SageMaker to train a large language model. The training job is taking too long. The data scientist wants to reduce training time without sacrificing model accuracy. Which THREE strategies are MOST appropriate?

Accepted Answer

Use mixed precision training (float16). Mixed precision training (float16) reduces memory usage and accelerates computation by using half-precision floating-point numbers for most operations, while maintaining a single-precision copy of critical parameters to preserve accuracy. This directly reduces training time on compatible GPUs (e.g., NVIDIA V100, A100) without sacrificing model quality, as the loss scaling technique prevents underflow in gradients.

Answer

Switch from GPU instance to CPU instance

Answer

Increase the maximum sequence length

Question 20

A data scientist is training a binary classification model on a dataset with a severe class imbalance (95% negative, 5% positive). The model achieves 95% accuracy but only correctly identifies 10% of the positive class. Which metric should the data scientist use to evaluate model performance?

Accepted Answer

F1 score. The F1 score is the harmonic mean of precision and recall, making it robust to class imbalance. With 95% accuracy but only 10% recall on the positive class, the model is essentially a trivial classifier that predicts the majority class. F1 score captures both false positives and false negatives, providing a balanced view of performance on the minority class.

Answer

Log loss

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Accuracy

Answer

Area under the ROC curve (AUC)