MLS-C01 Mock Exam 84 — 25 Questions

Question 1

A data scientist is using Amazon SageMaker to train a random forest model for a binary classification task. The dataset has 50 features and 10,000 samples. The model achieves high training accuracy but poor test accuracy. Which TWO actions should the scientist take to improve generalization?

Accepted Answer

Reduce the max_depth of the trees.. The model is overfitting. Increasing max_depth would increase overfitting. Reducing the number of trees (n_estimators) generally increases bias and may reduce overfitting, but random forest is robust to overfitting with more trees. Typically, increasing trees reduces overfitting. However, reducing tree depth (max_depth) reduces model complexity. Also, increasing the minimum samples per leaf reduces overfitting. So correct actions are: reduce max_depth and increase min_samples_leaf. Option B (increase n_estimators) can also help, but it is not a direct fix for overfitting; random forest with more trees tends to generalize better. But the question says 'which TWO'. I'll go with A and D as they directly reduce complexity. However, increasing n_estimators is also a common practice to reduce overfitting. Let's check: In random forest, more trees reduce variance and overfitting. So B is also correct. But we need exactly 2 correct. The question says 'Which TWO'. I need to provide two correct options. I'll choose A and D. But I'll reconsider: Reducing max_depth (A) and increasing min_samples_leaf (D) are standard ways to reduce overfitting. Increasing n_estimators (B) also helps but may increase training time. The question likely expects A and D. I'll set A and D as correct.

Answer

Increase the max_samples parameter.

Answer

Increase the max_features parameter.

Answer

Increase the number of trees (n_estimators).

Question 2

A data scientist is using Amazon SageMaker to train a model using a built-in algorithm. The training job fails with an error indicating that the algorithm expects the data to be in recordIO-protobuf format, but the input is CSV. What is the most efficient way to resolve this?

Accepted Answer

Use a SageMaker processing job to convert the CSV data to recordIO-protobuf format.. Option A is correct because converting CSV to recordIO-protobuf format using a SageMaker processing job is efficient and scalable. Option B is wrong because changing the algorithm may not be desirable. Option C is wrong because recordIO-protobuf is not suitable for CSV. Option D is wrong because direct conversion via boto3 is more manual and less efficient than a processing job.

Answer

Change the inference data to recordIO-protobuf format.

Answer

Use a boto3 script to convert the CSV files locally and upload.

Answer

Switch to a different algorithm that accepts CSV format.

Question 3

A machine learning engineer is using Amazon SageMaker to train a model. The training job fails with an out-of-memory error. The training data size is 10 GB and the instance is ml.m5.xlarge (16 GB memory). Which change is MOST likely to resolve the issue without increasing cost?

Accepted Answer

Reduce the batch size in the training script.. Option A is correct because many algorithms allow you to set a batch size, and reducing it lowers memory usage. Option B is wrong because changing to GPU may not help and could increase cost. Option C is wrong because increasing instance type increases cost. Option D is wrong because decreasing the dataset size may lose information.

Answer

Switch to a GPU instance like p3.2xlarge.

Answer

Use a larger instance type like ml.m5.4xlarge.

Answer

Decrease the training dataset size.

Question 4

A team is training a large language model using SageMaker's distributed training. They notice that the training loss is not decreasing after the first few epochs. Which action is MOST likely to resolve this issue?

Accepted Answer

Reduce the learning rate. A learning rate that is too high can cause the loss to plateau or diverge. Reducing the learning rate often helps. Increasing batch size may stabilize training but not directly address plateau. Switching optimizer or adding regularization may help but are less direct.

Answer

Increase the batch size

Answer

Add L2 regularization

Answer

Switch from Adam to SGD optimizer

Question 5

A team deployed a SageMaker endpoint for real-time inference using a PyTorch model. After monitoring, they notice that the latency is highly variable, with p99 latency 10x the p50 latency. The endpoint uses a single ml.c5.2xlarge instance with auto-scaling based on average CPU utilization. Which change is most likely to reduce latency variability?

Accepted Answer

Change the auto-scaling metric to 'InvocationsPerInstance'. Option C is correct because high p99 latency often results from cold starts or queueing when traffic spikes. Scaling on invocations per instance ensures more instances are ready. Option A (GPU) may not help if model is CPU-bound. Option B (batch size) can increase latency. Option D (warm-up) helps cold starts but not queueing.

Answer

Increase the batch size for inference

Answer

Pre-warm the model by sending dummy requests every minute

Answer

Switch to a GPU instance type

Question 6

A company is using SageMaker to train a TensorFlow model for image classification. The training is slow on a single GPU instance. Which TWO strategies can reduce training time? (Choose TWO.)

Accepted Answer

Use SageMaker Pipe Mode for data ingestion. Options B and D are correct. Using SageMaker Pipe Mode streams data from S3, reducing download time. Using multiple GPUs (distributed training) parallelizes computation. Option A (batch size decrease) may slow training. Option C (larger images) increases computation. Option E (more epochs) increases training time.

Answer

Increase the image size

Answer

Increase the number of training epochs

Answer

Decrease the batch size

Question 7

A data scientist is training a regression model. The training loss is decreasing but the validation loss starts to increase after a few epochs. Which technique should the scientist use to address this issue?

Accepted Answer

Implement early stopping based on validation loss.. Option B is correct because early stopping halts training when validation loss stops improving, preventing overfitting. Option A is wrong because adding more layers may worsen overfitting. Option C is wrong because increasing learning rate can cause divergence. Option D is wrong because decreasing batch size may increase noise but not directly address overfitting.

Answer

Decrease the batch size.

Answer

Add more layers to the model.

Answer

Increase the learning rate.

Question 8

A data scientist is evaluating a regression model. The RMSE on the training set is 2.5, and on the test set is 2.7. The R² on the test set is 0.98. What does this indicate?

Accepted Answer

The model generalizes well with no severe overfitting. The model has low error and high R² on both sets, indicating good generalization without significant overfitting. The small difference between training and test RMSE suggests no severe overfitting.

Answer

The model has high bias

Answer

The model is underfitting because R² is too high

Answer

The model is overfitting because RMSE is lower on training data

Question 9

A company is using Amazon SageMaker to host a model for real-time inference. The model is a large ensemble of 10 XGBoost models, each 2 GB. The endpoint uses a single ml.c5.18xlarge instance. The inference latency is high (average 2 seconds). Which change would most effectively reduce latency?

Accepted Answer

Use SageMaker Multi-Model Endpoints to serve each model independently. Option A is correct because serialization/deserialization of large models is a bottleneck; SageMaker Multi-Model Endpoints can reduce overhead by loading only the requested model. Option B (GPU) may not help if the bottleneck is CPU. Option C (batch transform) is for offline inference. Option D (more instances) helps throughput but not per-request latency.

Answer

Switch to a GPU instance type

Answer

Add more instances behind a load balancer

Answer

Use SageMaker Batch Transform instead of real-time endpoint

Question 10

A machine learning engineer is using Amazon SageMaker to deploy a model for real-time inference. The model must respond within 100 milliseconds. The initial deployment uses a single ml.m5.large instance, but latency is too high. Which change should the engineer make to reduce latency?

Accepted Answer

Switch to a compute-optimized instance like ml.c5.2xlarge.. Option A is correct because using a more powerful instance reduces inference time. Option B is wrong because multi-model endpoint can lead to resource contention. Option C is wrong because batch transforms are for offline predictions. Option D is wrong because scaling down reduces resources.

Answer

Use batch transform instead of real-time endpoint.

Answer

Deploy to a single ml.t2.medium instance to reduce cost.

Answer

Deploy the model on a multi-model endpoint.

Question 11

A machine learning engineer is using SageMaker's built-in XGBoost algorithm for a multi-class classification problem. The training job completes but the model accuracy is low. Which THREE hyperparameters should the engineer tune to improve performance?

Accepted Answer

eta (learning rate). XGBoost hyperparameters: 'num_round' (number of boosting rounds), 'eta' (learning rate), and 'max_depth' (tree depth) are key for improving accuracy. 'subsample' can help but is less direct. 'min_child_weight' also important but these three are most common. 'colsample_bytree' is for feature subsampling.

Answer

subsample

Answer

colsample_bytree

Question 12

A data scientist is using Amazon SageMaker Autopilot to automatically build a model for a regression problem. The dataset has 100 features and 50,000 rows. Autopilot recommends a model with an R² of 0.85 on the validation set. However, when deployed to production, the model performs poorly (R² of 0.2). What is the most likely cause?

Accepted Answer

The production data distribution has shifted from the training data distribution. Option B is correct because a large discrepancy between validation and production performance often indicates data drift. Option A (overfitting) is possible but less likely given validation performance. Option C (feature importance) is not the direct cause. Option D (Autopilot bug) is rare.

Answer

The model is overfitting to the training data

Answer

The model is underfitting due to insufficient training

Answer

Autopilot selected the wrong features

Question 13

A company is building a binary classifier to predict customer churn. The dataset has 10,000 samples with 500 churners (5% positive class). After training a logistic regression model, the precision is 0.8 and recall is 0.2. Which metric should the data scientist focus on to improve the model's ability to identify churners while minimizing false positives?

Accepted Answer

Increase recall. Option A is correct because recall is very low (0.2), meaning the model misses most churners. Improving recall will capture more churners. Option B (precision) is already high but recall is low. Option C (accuracy) is misleading due to class imbalance. Option D (F1) balances precision and recall, but the primary concern is low recall.

Answer

Increase accuracy

Answer

Increase precision

Answer

Increase F1 score

Question 14

A data scientist is using Amazon SageMaker to train a linear regression model. The dataset has outliers. Which TWO techniques can help reduce the impact of outliers? (Choose TWO.)

Accepted Answer

Trim the dataset to remove extreme values. Options A and D are correct. Huber loss is robust to outliers, and trimming the dataset removes extreme values. Option B (standardization) does not handle outliers. Option C (L1 regularization) reduces overfitting but not outlier impact. Option E (more features) not relevant.

Answer

Add more features

Answer

Apply L1 regularization

Answer

Standardize the features

Question 15

A company uses Amazon SageMaker to train a linear regression model. During evaluation, they observe that the model has high bias (underfitting). Which THREE actions can reduce bias?

Accepted Answer

Add polynomial features.. Options A, B, and D are correct. Adding polynomial features increases model complexity. Using a more complex algorithm (random forest) can capture non-linear patterns. Reducing regularization allows the model to fit more closely. Option C is wrong because adding L2 regularization increases bias. Option E is wrong because reducing training data typically increases bias.

Answer

Increase L2 regularization.

Answer

Use a smaller training dataset.

Question 16

A data scientist is training a recurrent neural network (RNN) for time series forecasting. The training loss decreases steadily for the first 10 epochs, then plateaus. The validation loss starts increasing after epoch 10. What is the most appropriate action?

Accepted Answer

Stop training early and use the model from epoch 10. Option B is correct because validation loss increasing while training loss decreases indicates overfitting. Early stopping halts training before overfitting worsens. Option A (more epochs) would increase overfitting. Option C (batch size) is not directly addressing overfitting. Option D (more layers) may exacerbate overfitting.

Answer

Continue training for more epochs

Answer

Add more layers to the network

Answer

Increase the batch size

Question 17

A data scientist is using Amazon SageMaker to train a gradient boosting model on a dataset with categorical features. The dataset contains a column 'UserID' with over 1 million unique values. The training is taking very long and the model size is large. Which technique would MOST effectively reduce training time and model size while maintaining accuracy?

Accepted Answer

Apply feature hashing to UserID.. Option B is correct because hashing reduces the number of distinct categories to a fixed number of buckets, controlling dimensionality. Option A is wrong because one-hot encoding would explode the feature space. Option C is wrong because removing UserID likely loses important signal. Option D is wrong because label encoding creates ordinal relationships that may mislead the model.

Answer

Use one-hot encoding on UserID.

Answer

Use label encoding for UserID.

Answer

Remove UserID from the dataset.

Question 18

A company is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is failing with an error indicating insufficient memory. The model architecture and hyperparameters are fixed. Which change is MOST likely to resolve the issue without modifying the model code?

Accepted Answer

Enable SageMaker's distributed data parallelism.. Option C is correct because enabling data parallelism with SageMaker distributed training splits the data across multiple instances, reducing per-instance memory usage. Option A is wrong because increasing instance memory does not address root cause if training script uses memory inefficiently. Option B is wrong because using Pipe mode reduces disk usage but not memory. Option D is wrong because Spot instances do not affect memory.

Answer

Use managed Spot training to get cheaper compute.

Answer

Use a larger instance type with more memory.

Answer

Use Pipe mode for input data instead of File mode.

Question 19

A data scientist is training a binary classifier using a large dataset with class imbalance (90% negative, 10% positive). After training a logistic regression model, the F1 score is low but accuracy is high. Which TWO actions should the data scientist take to improve model performance? (Choose 2.)

Accepted Answer

Switch to evaluation metrics such as F1 score or AUC-ROC instead of accuracy.. Option A (resample training data) and Option C (use different evaluation metric) are correct because class imbalance causes the model to be biased toward the majority class, leading to high accuracy but poor F1. Resampling (e.g., SMOTE) balances classes, and using F1 or AUC-ROC focuses on minority class performance. Option B (feature scaling) is a general preprocessing step but doesn't directly address imbalance. Option D (increase regularization) might reduce overfitting but doesn't target imbalance. Option E (add more features) may not help if the model is already biased.

Answer

Apply feature scaling to ensure all features contribute equally.

Answer

Add more features to the model to improve its capacity.

Answer

Increase the regularization parameter to reduce overfitting.

Question 20

A data scientist is training a binary classification model using Amazon SageMaker's built-in XGBoost algorithm. The dataset is highly imbalanced (95% negative class, 5% positive class). The model achieves high accuracy but poor recall on the positive class. Which TWO actions should the data scientist take to improve recall without significantly sacrificing precision?

Accepted Answer

Set scale_pos_weight to the ratio of negative to positive samples.. Options B and E are correct. Using scale_pos_weight adjusts the weight of the positive class, directly addressing imbalance. SMOTE oversamples the minority class to balance the dataset. Option A is wrong because subsampling the majority class may lose information. Option C is wrong because increasing max_depth may overfit. Option D is wrong because reducing eta may slow convergence but not directly help imbalance.

Answer

Perform random undersampling of the majority class.

Answer

Increase the max_depth hyperparameter.

Answer

Reduce the learning rate (eta) and increase num_round.