MLS-C01 Practice Test 19 — 15 Questions

Question 1

A data scientist is working on a binary classification problem with a highly imbalanced dataset (1% positive class). They have applied oversampling using SMOTE and trained a logistic regression model. The model achieves 99% accuracy on the test set, but the recall for the positive class is only 5%. What is the most likely cause?

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

Accepted Answer

SMOTE was applied before splitting the data into training and test sets. Applying SMOTE before splitting the data causes data leakage. SMOTE generates synthetic samples based on the entire dataset, including the test set, so synthetic versions of test samples can appear in the training set. This inflates training accuracy artificially but does not improve the model's ability to generalize to unseen data, leading to poor recall on the true test set. Option B is incorrect because while overfitting due to lack of regularization can cause poor generalization, the specific pattern of high accuracy but very low recall is characteristic of data leakage from preprocessing before splitting. Option C is incorrect because accuracy is indeed a poor metric for imbalanced data, but the low recall (5%) indicates a fundamental issue with the model's ability to detect positives, which goes beyond metric choice. Option D is incorrect because logistic regression can be effective for imbalanced datasets when properly handled (e.g., with class weights or resampling); the problem here stems from the improper application of SMOTE, not the algorithm itself.

Answer

The model is overfitting due to lack of regularization

Answer

Accuracy is not a suitable metric for imbalanced data

Answer

Logistic regression is inappropriate for imbalanced datasets

Question 2

A data scientist is using Amazon SageMaker to train a model. Training is taking longer than expected. The scientist notices that the training job is using a single instance type with limited GPU memory. Which action will MOST likely reduce training time?

Accepted Answer

Configure the training job to use distributed data parallelism across multiple instances.. The training job is bottlenecked by limited GPU memory on a single instance. Distributed data parallelism splits the dataset across multiple instances, each processing a subset of the data in parallel, which directly reduces wall-clock training time by leveraging aggregate GPU memory and compute. This is the most effective action to address the stated problem of slow training due to limited GPU memory.

Answer

Use SageMaker Managed Spot Training to lower cost.

Answer

Use batch normalization layers.

Answer

Enable SageMaker Debugger for real-time monitoring.

Question 3

A data scientist needs to run ad-hoc SQL queries on a large dataset stored in Amazon S3 (Parquet format, 2 TB). The queries are interactive and require sub-second response times. Which service should they use?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless, interactive query service designed for ad-hoc SQL queries on data stored in Amazon S3, with no infrastructure to manage. It natively supports Parquet format and can achieve sub-second response times on 2 TB datasets through columnar projection, predicate pushdown, and data partitioning, making it ideal for this use case.

Answer

Amazon Redshift Spectrum

Answer

Amazon QuickSight

Answer

Amazon EMR with Spark SQL

Question 4

A data scientist is training a model using Amazon SageMaker and notices that training is taking much longer than expected. The training job uses a single ml.p3.2xlarge instance. The data is stored in S3 and is about 50 GB in size. Which action would MOST likely reduce training time?

Accepted Answer

Change the input mode from File to Pipe.. Changing the input mode from File to Pipe reduces training time by streaming data directly from S3 to the training algorithm without first downloading it to the local disk. In File mode, SageMaker first downloads the entire 50 GB dataset to the instance's Amazon EBS volume, which adds significant I/O latency and storage overhead. Pipe mode uses a Linux FIFO (named pipe) to feed data on-the-fly, eliminating the download step and allowing the GPU to start processing sooner.

Answer

Enable automatic data sharding in the SageMaker training job.

Answer

Enable S3 server-side encryption on the training data.

Answer

Use a larger instance type, such as ml.p3.16xlarge.

Question 5

A data scientist is analyzing a large dataset of images stored in Amazon S3. The dataset is used to train a computer vision model. Which THREE EDA steps are appropriate for this image dataset?

Accepted Answer

Compute the distribution of image dimensions (height and width).. The appropriate EDA steps for an image dataset include analyzing image dimensions (A) to understand size variability and potential resizing needs, checking for corrupted or unreadable files (B) to ensure data integrity, and visualizing sample images per class (D) to verify label accuracy and detect labeling errors. Option C (time series decomposition) is irrelevant because timestamps, while possibly present, are not a primary focus of standard image EDA; it would be relevant for time-series data. Option E (tokenization and stop word removal) applies to text data, not images.

Answer

Decompose the time series of image timestamps to detect seasonality.

Answer

Perform tokenization and stop word removal on image filenames.

Question 6

Which TWO techniques are used for feature scaling? (Choose 2.)

Accepted Answer

Standardization (Z-score normalization). Standardization (Z-score normalization) is a feature scaling technique that transforms data to have a mean of 0 and a standard deviation of 1, using the formula z = (x - μ) / σ. This is essential for algorithms like SVM, k-means, and PCA that assume normally distributed features and are sensitive to feature magnitudes.

Answer

One-hot encoding

Answer

Principal Component Analysis (PCA)

Answer

Label encoding

Question 7

Which THREE statements about data leakage in machine learning are correct? (Select THREE.)

Accepted Answer

Using the target variable to filter features before splitting leads to data leakage. Using the target variable to filter features before splitting allows test set information to influence feature selection, causing data leakage. Option B is correct: applying SMOTE after splitting the dataset into training and test sets prevents leakage that would occur if SMOTE were applied before splitting, as synthetic samples would then be generated using information from the entire dataset. While SMOTE after splitting does not prevent all forms of leakage, the statement 'prevents data leakage' is interpreted in the context of the specific leakage that SMOTE can introduce, making it a correct practice. Option C is correct: standardizing the entire dataset before splitting uses statistics computed from both training and test data, which leaks information about the test set into the training process. Option D is incorrect because cross-validation does not eliminate all leakage; if preprocessing steps like scaling are applied to the entire dataset before cross-validation, leakage still occurs. Option E is incorrect because for time series data, a random train-test split ignores the temporal order and can cause future information to leak into past predictions; a time-based split is recommended.

Answer

Using cross-validation eliminates all possible data leakage

Answer

For time series data, using a random train-test split is recommended to avoid data leakage

Question 8

A company is using Amazon SageMaker to deploy a model for real-time inference. The model receives requests with varying payload sizes. The company observes occasional latency spikes. Which feature can help mitigate this?

Accepted Answer

Amazon SageMaker Inference Recommender. SageMaker Inference Recommender provides load testing and recommendations for instance type and endpoint configuration. It can help identify optimal settings to reduce latency spikes. Multi-model endpoints are for hosting multiple models, not directly for latency spikes. Elastic Inference is for accelerating deep learning inference, not general latency. Automatic scaling adjusts capacity but not per-request latency.

Answer

Multi-model endpoints

Answer

Amazon Elastic Inference

Answer

Automatic scaling

Question 9

Refer to the exhibit. A data scientist is trying to create a SageMaker training job but receives an access denied error. The IAM policy shown is attached to the user. What is the likely issue?

Accepted Answer

The policy is missing s3:ListBucket permission. The policy allows s3:GetObject and s3:PutObject on the bucket, but SageMaker training jobs also require s3:ListBucket permission to read objects. Without it, access is denied. Option A is wrong because the policy includes sagemaker:CreateTrainingJob. Option C is wrong because the error is about creating a training job, not a model. Option D is wrong because the policy does allow s3:PutObject.

Answer

The policy does not allow sagemaker:CreateTrainingJob

Answer

The policy does not allow sagemaker:CreateModel

Answer

The policy does not allow s3:PutObject

Question 10

A machine learning team is developing a model to predict housing prices. They have a dataset with numerical features like square footage and number of bedrooms, and categorical features like neighborhood. Which preprocessing step is essential before training a linear regression model?

Accepted Answer

One-hot encode categorical features. One-hot encoding converts categorical features into binary columns, which linear regression requires. Option A is wrong because scaling is important but not the only essential step; encoding is needed first. Option B is wrong because removing correlated features can help with multicollinearity but is not essential for linear regression. Option D is wrong because PCA reduces dimensionality but is optional and not a preprocessing step required before training.

Answer

Normalize all numerical features to have zero mean and unit variance

Answer

Remove highly correlated features

Answer

Apply Principal Component Analysis (PCA) to reduce dimensionality

Question 11

A data scientist is working on a binary classification problem with a highly imbalanced dataset (1% positive class). They have applied oversampling using SMOTE and trained a logistic regression model. The model achieves 99% accuracy on the test set, but the recall for the positive class is only 5%. What is the most likely cause?

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

Accepted Answer

Use oversampling techniques like SMOTE to create synthetic samples of the minority class. SMOTE (Synthetic Minority Oversampling Technique) creates synthetic samples for the minority class by interpolating between existing minority instances, which increases the representation of the default class in the training data. This directly addresses the low recall (20%) by providing the logistic regression model with more balanced class distributions, enabling it to learn decision boundaries that capture more true positives without discarding majority class information. Unlike simple oversampling, SMOTE reduces overfitting risk by generating novel samples rather than duplicating existing ones, which helps maintain precision while improving recall.

Answer

Use random undersampling of the majority class to balance the dataset

Answer

Change the decision threshold to 0.3

Answer

Increase the regularization strength (C) in logistic regression

Question 12

A data scientist is using Amazon SageMaker to train a custom image classification model using a PyTorch script. The training job runs successfully but the model accuracy is lower than expected. The scientist wants to debug the training process by inspecting gradients and layer outputs. Which SageMaker feature should be used to capture this internal state during training?

Accepted Answer

Use SageMaker Debugger to capture tensors and gradients.. SageMaker Debugger is specifically designed to capture internal model state such as tensors, gradients, and weights during training. It allows you to set rules to monitor for issues like vanishing gradients or overfitting, and to save these tensors for later analysis. This directly addresses the need to inspect gradients and layer outputs to diagnose low accuracy.

Answer

Use SageMaker Experiments to track hyperparameters and metrics.

Answer

Use SageMaker Profiler to profile system bottlenecks.

Answer

Use SageMaker Model Monitor to detect data drift.

Question 13

A healthcare company is building a model to predict patient readmission within 30 days of discharge. The dataset includes 10,000 patient records with 200 features, including lab results, demographics, and historical admissions. The target variable is highly imbalanced: only 8% of patients are readmitted. The data scientist splits the data into 80% training and 20% test sets, ensuring the same proportion of readmissions in each. The scientist trains a logistic regression model and a random forest model. The logistic regression achieves 92% accuracy but recall of 10% for the readmitted class. The random forest achieves 90% accuracy but recall of 25%. The business requirement is to achieve at least 60% recall for readmissions while maintaining reasonable precision. The scientist also has access to a large collection of unlabeled patient records from other hospitals. Which strategy should the data scientist use to meet the business requirement?

Accepted Answer

Use SMOTE to oversample the minority class in the training set.. Using SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which can improve recall. Option A is wrong because collecting more data may not be feasible and may not help if imbalance persists. Option C is wrong because undersampling reduces data and may lose information. Option D is wrong because changing to a deep learning model may not help with limited data.

Answer

Collect more labeled data from other hospitals.

Answer

Use random undersampling of the majority class in the training set.

Answer

Switch to a deep neural network with more layers.

Question 14

A data scientist is training a binary classifier using logistic regression. The dataset has 100 features and 1 million samples. After training, the model achieves AUC of 0.85 on the test set. The business wants to understand which features contribute most to predictions. Which technique should the data scientist use?

Accepted Answer

Use the coefficients of the logistic regression model as feature importance. Coefficients of logistic regression are natural measures of feature importance.

Answer

Use t-SNE to visualize feature importance

Answer

Use a random forest model and its feature importance attribute

Answer

Use Principal Component Analysis (PCA) to find important components

Question 15

A startup is building a recommendation system for an e-commerce platform using collaborative filtering. They have a dataset of user-item interactions (ratings) with 1 million users and 100,000 items. The data is sparse (99% missing ratings). They need to train a model on Amazon SageMaker that can handle large-scale sparse data efficiently. Which approach should they use?

Accepted Answer

Use the built-in Factorization Machines algorithm in SageMaker. SageMaker's Factorization Machines handle sparse data efficiently and are designed for recommendation tasks.

Answer

Use PCA to reduce dimensionality and then apply k-nearest neighbors

Answer

Use the built-in XGBoost algorithm with one-hot encoding for user and item IDs

Answer

Implement a neural network with dense layers using the built-in MXNet framework