MLS-C01 Mock Exam 81 — 25 Questions

Question 1

A data scientist is training a neural network on image data using TensorFlow with GPU instances on SageMaker. The training is slow because the GPU utilization is low. The data pipeline uses tf.data with a large number of preprocessing operations. Which action would most likely increase GPU utilization?

Accepted Answer

Increase the prefetch buffer size in the tf.data pipeline.. Option D is correct because prefetching allows the CPU to prepare batches while the GPU is computing, reducing idle time. Option A (reduce batch size) may decrease utilization. Option B (increase CPU instances) may not help if bottleneck is data pipeline. Option C (use smaller images) reduces computation but may not improve utilization percentage. Option E (increase learning rate) does not affect data throughput.

Answer

Increase the learning rate to converge faster.

Answer

Reduce the batch size to speed up each step.

Answer

Increase the number of CPU instances in the training job.

Answer

Use smaller image sizes to reduce computation.

Question 2

Which TWO services can be used to serve machine learning models for real-time inference? (Select TWO.)

Accepted Answer

Amazon SageMaker. Amazon SageMaker provides real-time endpoints. AWS Lambda can be used with a custom container for inference. Amazon Rekognition is a specialized service for image analysis, not general model serving. Amazon Comprehend is NLP service. Amazon Athena is query service.

Answer

Amazon Rekognition

Answer

Amazon Athena

Answer

Amazon Comprehend

Question 3

A company uses a linear regression model to predict house prices. The model's R-squared is 0.95 on the training set but 0.60 on the test set. Which of the following is the most likely cause?

Accepted Answer

Overfitting. Option A is correct because such a large gap between training and test R-squared indicates overfitting. Option B is wrong because if the model were underfitting, both would be low. Option C is wrong because data leakage would likely cause high test R-squared. Option D is wrong because multicollinearity affects both sets similarly.

Answer

Underfitting

Answer

Multicollinearity among features

Answer

Data leakage

Question 4

A data science team is training a binary classification model using Amazon SageMaker. The dataset is highly imbalanced (95% negative class, 5% positive class). The team wants to maximize the F1 score. Which built-in SageMaker algorithm is most appropriate?

Accepted Answer

XGBoost. XGBoost supports scale_pos_weight to handle class imbalance, directly optimizing for F1. Linear Learner with balanced class weights can also help but typically optimizes log loss. K-Means is unsupervised. PCA is for dimensionality reduction.

Answer

Linear Learner

Answer

PCA

Answer

K-Means

Question 5

Which TWO of the following are valid approaches to handle missing values in a dataset for a machine learning model?

Accepted Answer

Impute missing values with the mean of the column. Removing rows with missing values is a valid approach (listwise deletion). Imputing with the mean is also valid. Using a neural network to predict missing values is possible but not standard. Standardization does not handle missing values. One-hot encoding is for categorical variables.

Answer

Use a neural network to predict missing values

Answer

Standardize the features to handle missing values

Answer

Apply one-hot encoding to convert missing values

Question 6

A data scientist is building a fraud detection model using a highly imbalanced dataset. The model uses a random forest classifier. The recall for the minority class is 0.6, and precision is 0.9. The business requires recall above 0.8. Which action should the data scientist take to improve recall?

Accepted Answer

Decrease the probability threshold for classifying a transaction as fraudulent.. Option D is correct because decreasing the classification threshold for the positive class increases recall (more positives predicted) at the cost of precision. Option A (more trees) reduces variance, may not improve recall. Option B (class weights) can help but is already used. Option C (feature selection) may reduce recall. Option E (increase max depth) could lead to overfitting and not necessarily improve recall.

Answer

Perform feature selection to remove noisy features.

Answer

Increase the maximum depth of the trees.

Answer

Increase the class weight for the minority class in the algorithm.

Answer

Increase the number of trees in the random forest.

Question 7

Refer to the exhibit. A data scientist creates a SageMaker model using the configuration above. When deploying the model to an endpoint, the endpoint status remains 'Creating' for a long time and then fails. What is the most likely cause?

Accepted Answer

The IAM role lacks permission to pull the Docker image from ECR. The image URI points to an ECR repository in account 382416733822, which is not the customer's account. SageMaker expects the image to be in the same account or accessible via cross-account permissions. This URI is likely the AWS account for built-in algorithms, but if the region or repository is incorrect, it may fail. The most likely issue is that the image does not exist in that account or the role lacks permissions to pull it.

Answer

The S3 model artifact does not exist

Answer

The environment variable SAGEMAKER_REGION is incorrect

Answer

The model name is already in use

Question 8

A data scientist is building a regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, and location. After training a linear regression model, the scientist notices that the residuals have a pattern: they increase as the predicted value increases. Which action is most appropriate?

Accepted Answer

Apply a log transformation to the target variable. Patterned residuals (heteroscedasticity) violating linear regression assumptions. Log-transforming the target variable can stabilize variance. Adding polynomial features or interactions may help with non-linearity but not specifically for heteroscedasticity. Ridge regression is for multicollinearity, not for patterned residuals.

Answer

Remove outliers from the dataset

Answer

Use Ridge regression instead of linear regression

Answer

Add polynomial features to the model

Question 9

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 1% of the data. The model needs to maximize recall while keeping precision above 0.7. Which sampling strategy should the data scientist use?

Accepted Answer

SMOTE from imbalanced-learn to generate synthetic samples for the minority class.. Option C is correct because SMOTE generates synthetic samples for the minority class, which can improve recall without discarding data. Option A (RandomUnderSampler) may discard too many majority samples, reducing precision. Option B (RandomOverSampler) can cause overfitting. Option D (NearMiss) focuses on hard samples and may reduce recall. Option E (TomekLinks) only removes noisy instances, not addressing imbalance effectively.

Answer

NearMiss from imbalanced-learn to undersample the majority class based on distance to minority samples.

Answer

RandomUnderSampler from imbalanced-learn to undersample the majority class.

Answer

TomekLinks from imbalanced-learn to remove overlapping samples.

Answer

RandomOverSampler from imbalanced-learn to oversample the minority class.

Question 10

A data scientist needs to implement a recommendation system for an e-commerce website. Which Amazon service is specifically designed for building and deploying recommendation models?

Accepted Answer

Amazon Personalize. Amazon Personalize is a fully managed machine learning service that provides real-time personalized recommendations. It is purpose-built for recommendation systems. SageMaker is a general-purpose ML platform, but Personalize is specialized for recommendations.

Answer

Amazon SageMaker

Answer

Amazon Rekognition

Answer

Amazon Forecast

Question 11

Which TWO of the following are techniques used to reduce overfitting in a neural network?

Accepted Answer

L2 regularization. Options B and D are correct. B is correct because dropout randomly drops units, preventing co-adaptation. D is correct because L2 regularization penalizes large weights. A is wrong because increasing the number of layers increases model complexity, which can worsen overfitting. C is wrong because increasing learning rate may cause divergence, not reduce overfitting. E is wrong because batch normalization helps training but does not primarily reduce overfitting.

Answer

Increase the number of layers

Answer

Batch normalization

Answer

Increase the learning rate

Question 12

Which TWO metrics are appropriate for evaluating a binary classification model when the cost of false negatives is high? (Choose 2)

Accepted Answer

Recall. Option A (Recall) and Option D (Specificity) are correct. Recall measures true positives, and specificity measures true negatives, both relevant when false negatives are costly. Precision (B) and F1 (C) are not directly focused on false negatives. Accuracy (E) is misleading if imbalanced.

Answer

Accuracy

Answer

F1 score

Answer

Precision

Question 13

A machine learning engineer is monitoring a deployed model on SageMaker and notices that the prediction latency is increasing over time. The model is a linear regression with a small number of features. Which is the MOST likely cause?

Accepted Answer

The inference code has a memory leak. Memory leak or accumulation of model artifacts in inference code can cause latency growth over time.

Answer

The number of features is too large

Answer

The CPU utilization is too low

Answer

The model is overfitting to recent data

Question 14

A data scientist is using Amazon SageMaker to train a model. The training job is taking longer than expected. The scientist wants to reduce training time without changing the algorithm or the hardware. Which action is most likely to help?

Accepted Answer

Increase the batch size used during training.. Using data augmentation increases the dataset size, which would increase training time. Increasing the batch size can speed up training because it processes more samples per step, but it may affect convergence. Reducing the number of epochs reduces the number of passes over the data, directly reducing training time. However, this might affect model quality. Among the options, reducing epochs is a direct way to reduce time. But note: increasing batch size can also reduce time, but it's not guaranteed to be safe for model quality. The question says 'without changing the algorithm or the hardware', and asks for 'most likely to help'. Reducing epochs is straightforward. Data augmentation increases time. Changing optimizer could be considered changing algorithm. Adding regularization does not reduce time.

Answer

Add regularization to the loss function.

Answer

Use data augmentation to increase the dataset size.

Answer

Reduce the number of training epochs.

Question 15

A data scientist wants to use a linear regression model to predict house prices. After training, the model shows high bias and low variance. Which action would most likely improve the model's performance?

Accepted Answer

Add polynomial features to capture non-linear relationships.. Option C is correct because high bias indicates underfitting, and increasing model complexity (e.g., adding polynomial features or using a more complex algorithm) can reduce bias. Option A is wrong because adding L2 regularization increases bias. Option B is wrong because reducing training data can increase variance but not necessarily reduce bias. Option D is wrong because using a simpler model would increase bias.

Answer

Increase L2 regularization strength.

Answer

Use a simpler model, such as linear regression without interaction terms.

Answer

Reduce the amount of training data.

Question 16

Which THREE evaluation metrics are appropriate for a multi-class classification problem? (Choose 3.)

Accepted Answer

Confusion matrix.. Option A is correct because accuracy is common for multi-class. Option C is correct because confusion matrix gives detailed per-class performance. Option D is correct because F1 score macro/micro averaging is used. Option B is wrong because mean squared error is for regression. Option E is wrong because precision-recall curve is typically for binary.

Answer

Mean squared error.

Answer

Precision-recall curve.

Question 17

A data scientist is training a gradient boosting model on a large dataset (100 GB) stored in Amazon S3. The training job uses a SageMaker built-in XGBoost algorithm with a single ml.p3.2xlarge instance. The job fails with a memory error. Which solution should the data scientist adopt to resolve the memory issue?

Accepted Answer

Increase the number of training instances to use distributed training with XGBoost.. Option C is correct because increasing the number of instances allows distributed training, reducing per-instance memory pressure. Option A may not fully load the dataset. Option B uses a different algorithm with different memory characteristics, but XGBoost can handle large data with distributed training. Option D reduces features but may degrade model quality. Option E uses a different algorithm but not necessarily more memory-efficient.

Answer

Reduce the number of features by using PCA before training.

Answer

Use SageMaker Pipe mode to stream data directly from S3 instead of downloading it.

Answer

Use SageMaker BlazingText algorithm with negative sampling.

Answer

Switch to SageMaker Linear Learner algorithm, which requires less memory.

Question 18

A data scientist trains a neural network using TensorFlow on SageMaker. The training job fails with a 'CUDA out of memory' error. What is the most likely cause and solution?

Accepted Answer

The model is too large for the GPU. Use a smaller batch size.. CUDA out of memory indicates that the GPU memory is insufficient for the batch size or model size. Reducing the batch size is a common fix. Switching to CPU is not ideal for deep learning. Increasing the number of instances may help but requires distributed training setup. Upgrading to a larger instance type is another option, but reducing batch size is simpler.

Answer

The dataset is too large. Use SageMaker Pipe mode.

Answer

The training script has a bug. Use SageMaker Debugger.

Answer

The instance type is insufficient. Use distributed training across multiple instances.

Question 19

A data scientist trains a convolutional neural network (CNN) for image classification. The training loss decreases steadily, but the validation loss starts increasing after 10 epochs. Which technique should the data scientist use to address this problem?

Accepted Answer

Use early stopping to halt training when validation loss stops decreasing.. Option D is correct because early stopping halts training when validation loss stops improving, preventing overfitting. Option A (more data) is not directly addressing the current overfitting. Option B (data augmentation) can help but is already used. Option C (increase learning rate) may cause divergence. Option E (more layers) increases model complexity, likely worsening overfitting.

Answer

Add more data augmentation to the training set.

Answer

Increase the number of training epochs.

Answer

Add more convolutional layers to increase model capacity.

Answer

Increase the learning rate.

Question 20

A data scientist is training a binary classifier on an imbalanced dataset (95% negative, 5% positive). The model achieves 99% accuracy but only correctly identifies 2% of the positive samples. Which metric should the data scientist focus on to improve the model's performance?

Accepted Answer

Recall. Option B is correct because recall measures the proportion of actual positives correctly identified, which is critical for imbalanced datasets. Option A is wrong because accuracy is misleading when classes are imbalanced. Option C is wrong because RMSE is for regression. Option D is wrong because precision does not directly address the low identification of positives.

Answer

Precision

Answer

RMSE

Answer

Accuracy