MLS-C01 Modeling Practice Test 8 — 15 Questions

Question 1

A data scientist is building a text classification model using a bag-of-words approach. The dataset contains 100,000 documents with a vocabulary of 50,000 unique words. The model is overfitting. Which THREE techniques can help reduce overfitting? (Choose THREE.)

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

Accepted Answer

Apply L1 or L2 regularization. Regularization (L1/L2), reducing n-gram range to unigrams, and feature selection (removing rare words) all reduce model complexity and help prevent overfitting. Option A (increasing max_features) increases complexity and can worsen overfitting. Option E (TF-IDF) is a weighting scheme, not a regularization technique.

Answer

Increase max_features to include more words

Answer

Use TF-IDF instead of raw counts

Question 2

A company is deploying a machine learning model using Amazon SageMaker. The model requires GPUs for inference. Which THREE configurations can the company use to meet this requirement? (Choose THREE.)

Accepted Answer

Real-time endpoints with ml.p3 instance types. Real-time endpoints (option B) support GPU instances like ml.p3. Batch Transform (option C) also supports GPU instances. Elastic Inference (option E) provides GPU acceleration without a full GPU instance. Option A (SageMaker Serverless Inference) does not support GPU. Option D (SageMaker Studio) is an IDE, not an inference option.

Answer

SageMaker Serverless Inference

Answer

SageMaker Studio

Question 3

A data scientist wants to build a binary classifier to predict customer churn. The dataset has 10,000 records with 500 churners (5%). Which technique should the data scientist use to address class imbalance?

Accepted Answer

Use SMOTE (Synthetic Minority Over-sampling Technique) to create synthetic samples.. SMOTE (Synthetic Minority Over-sampling Technique) creates synthetic samples for the minority class, effectively balancing the dataset without losing information. Option A (Randomly undersampling the majority class) discards potentially useful data. Option C (Assigning higher class weights to the minority class) is a valid approach but not a data-level technique; it adjusts the loss function. Option D (Downsampling the majority class) also loses data and is similar to undersampling.

Answer

Randomly undersample the majority class.

Answer

Assign higher class weights to the minority class.

Answer

Downsample the majority class to match the minority class size.

Question 4

A company is training a deep learning model on SageMaker using a large dataset stored in S3. The training job is taking a long time due to I/O bottlenecks. Which action would MOST effectively reduce the I/O bottleneck?

Accepted Answer

Use Pipe input mode in the SageMaker estimator.. Pipe input mode streams training data directly from S3 to the algorithm without writing intermediate files to disk, eliminating I/O wait time. Option A (using Amazon EFS) introduces network file system latency, which would not reduce I/O bottleneck. Option B (using Amazon FSx for Lustre) could improve throughput but is more complex to set up and may not be as effective as Pipe mode for streaming. Option C (increasing the number of training instances) might distribute computation but does not directly reduce per-instance I/O bottleneck.

Answer

Use Amazon EFS as the data source.

Answer

Use Amazon FSx for Lustre as the data source.

Answer

Increase the number of training instances.

Question 5

A data scientist is tuning a gradient boosting model using SageMaker automatic model tuning. The hyperparameter 'num_round' ranges from 50 to 500. The tuning job uses 'ObjectiveMetric' = 'validation:auc'. After 50 training jobs, the best objective value is 0.95. The data scientist suspects overfitting. What should the data scientist do?

Accepted Answer

Add an early stopping round and increase the range for regularization hyperparameters like 'gamma' and 'lambda'.. Adding an early stopping round prevents training after validation performance stops improving, and increasing the range of regularization hyperparameters like 'gamma' (minimum loss reduction) and 'lambda' (L2 regularization) helps penalize overly complex models, reducing overfitting. Option A (increasing 'max_depth') would allow deeper trees that can memorize noise, worsening overfitting. Option C (increasing 'num_round' to 1000) with no regularization and no early stopping would likely lead to further overfitting. Option D (decreasing 'num_round' to 10-100) might underfit, but it does not address the root cause of overfitting and could reduce performance.

Answer

Increase 'max_depth' to capture more complex patterns.

Answer

Increase 'num_round' to 1000 and keep other hyperparameters unchanged.

Answer

Decrease the range of 'num_round' to 10-100.

Question 6

A company uses Amazon SageMaker to train a linear regression model. The training data includes a feature 'age' with values ranging from 0 to 100. The model's loss is not converging. What is the MOST likely cause?

Accepted Answer

The features are not normalized.. Normalizing features (e.g., scaling 'age' to a similar range as other features) is critical for linear regression convergence. With 'age' ranging 0-100, if other features are in smaller ranges, the optimizer may struggle. Option B (outliers) can cause issues but is less likely than lack of normalization. Option C (instance type too small) typically affects training time, not convergence. Option D (learning rate too high) can cause divergence, but normalization is a more common first step.

Answer

There are outliers in the target variable.

Answer

The instance type is too small.

Answer

The learning rate is too high.

Question 7

A machine learning engineer is deploying a PyTorch model on SageMaker for real-time inference. The model requires GPU for low latency. Which instance type and configuration should the engineer choose?

Accepted Answer

Deploy to an ml.p3.2xlarge instance with a SageMaker endpoint.. SageMaker real-time endpoints support GPU instances like ml.p3.2xlarge. Option A (ml.c5.4xlarge with batch transform) is a CPU instance and batch transform is for offline inference, not real-time. Option B (ml.m5.large with endpoint) is a CPU instance and not suitable for GPU-accelerated inference. Option D (ml.p3.2xlarge with batch transform) uses a GPU instance but batch transform is not real-time; a SageMaker endpoint is required for real-time inference.

Answer

Deploy to an ml.c5.4xlarge instance with SageMaker batch transform.

Answer

Deploy to an ml.m5.large instance with a SageMaker model endpoint.

Answer

Deploy to an ml.p3.2xlarge instance with SageMaker batch transform.

Question 8

A data scientist is using SageMaker to train a random forest model. The dataset has 100 features and 1 million rows. The training job fails with a 'ResourceLimitExceeded' error. What is the MOST likely cause?

Accepted Answer

The account has reached its limit on the number of SageMaker training instances.. The 'ResourceLimitExceeded' error indicates that the account has reached its limit on the number of SageMaker training instances or vCPUs. Option A (S3 bucket region) would cause a different error, not a resource limit. Option B (GPU memory) is unlikely because random forest models typically use CPU instances. Option C (wrong algorithm) would result in an algorithm-specific error, not a resource limit. Option D correctly identifies that the account limit has been exceeded.

Answer

The S3 bucket containing the training data is not in the same region.

Answer

The instance type selected does not have enough GPU memory.

Answer

The wrong algorithm was specified for the training job.

Question 9

A company wants to use Amazon SageMaker to automatically tune hyperparameters for a XGBoost model. Which built-in SageMaker feature should be used?

Accepted Answer

SageMaker Automatic Model Tuning. SageMaker Automatic Model Tuning performs hyperparameter optimization. Option A (SageMaker Debugger) monitors training. Option B (SageMaker Model Monitor) detects drift. Option C (SageMaker Experiments) tracks trials. Option D (SageMaker Automatic Model Tuning) correctly performs hyperparameter tuning.

Answer

SageMaker Debugger

Answer

SageMaker Model Monitor

Answer

SageMaker Experiments

Question 10

A data scientist trains a model using SageMaker and notices that the training loss decreases but validation loss increases after a few epochs. What is the MOST likely issue?

Accepted Answer

The model is overfitting the training data.. Overfitting occurs when the model performs well on training data but poorly on validation data. Option D is correct. Option A is incorrect because a low learning rate would slow convergence, not cause validation loss to increase while training loss decreases. Option B is incorrect because data leakage would cause both training and validation loss to be artificially low. Option C is incorrect because underfitting would show high training loss.

Answer

The learning rate is too low.

Answer

There is data leakage from validation to training.

Answer

The model is underfitting the training data.

Question 11

A company is using SageMaker to train a deep learning model with TensorFlow. The training job is running on an ml.p3.16xlarge instance. The data scientist wants to maximize GPU utilization. Which configuration should be used?

Accepted Answer

Use Pipe mode or Fast File mode with a large batch size that fits in GPU memory.. To maximize GPU utilization on an ml.p3.16xlarge instance, the data pipeline must keep GPUs busy. Option D is correct because Pipe mode or Fast File mode reduce I/O bottlenecks by streaming data directly to the GPU, and a large batch size that fits in GPU memory ensures efficient parallel processing. Option A (single GPU, more epochs) wastes GPU resources by not using all available GPUs. Option B (CPU instance for training) is counterproductive for GPU utilization. Option C (File mode, small batch size) may cause GPU idle time due to I/O bottlenecks and underutilization.

Answer

Use a single GPU and increase the number of epochs.

Answer

Use a CPU-only instance for training and then deploy on GPU.

Answer

Use File mode input and a small batch size.

Question 12

A data scientist is building a binary classifier using logistic regression. The dataset has 10 features and 100,000 observations. The model achieves 99% accuracy on the test set, but the precision is 50% and recall is 90%. Which TWO actions should the data scientist take to improve model performance? (Choose 2.)

Accepted Answer

Adjust the decision threshold to increase precision at the cost of recall.. The model has high recall (90%) but low precision (50%), indicating many false positives. Two effective approaches are: adjusting the decision threshold (Option B) to require a higher predicted probability for the positive class, which reduces false positives and increases precision at the cost of some recall. Switching to a random forest classifier (Option C) can capture complex interactions and non-linearities, often improving precision by better separating classes. Option A: Increasing regularization strength (i.e., decreasing C in logistic regression) may help reduce overfitting but does not directly target precision; it may marginally help but is not a primary action. Option D: Collecting more data does not address the underlying class separation issue; it might even amplify imbalance. Option E: Removing features with low correlation could discard valuable information and worsen performance.

Answer

Increase the regularization strength (C) in logistic regression.

Answer

Collect more training data.

Answer

Remove features that have low correlation with the target.

Question 13

A company is deploying a machine learning model for real-time fraud detection. The model must have extremely low latency (<10 ms) and high throughput. Which THREE design choices meet these requirements? (Choose 3.)

Accepted Answer

Use GPU instances (e.g., ml.p3) for the endpoint.. GPU instances like ml.p3 provide massively parallel compute capability that accelerates matrix operations common in deep learning models, enabling inference latencies under 10 ms. For real-time fraud detection, the GPU's high throughput and low latency are essential for processing thousands of transactions per second without bottlenecks.

Answer

Use one endpoint per model to avoid interference.

Answer

Use SageMaker Batch Transform for real-time predictions.

Question 14

A data scientist is training a deep learning model on SageMaker using a custom container. The training job fails with an 'OutOfMemory' error. Which THREE actions could resolve this issue? (Choose 3.)

Accepted Answer

Use gradient accumulation to simulate larger batch sizes.. An OutOfMemory error occurs when the model and data exceed the GPU memory. Reducing batch size (C) directly lowers memory per iteration. Gradient accumulation (A) allows using a larger effective batch size without increasing memory by splitting it into micro-batches. Using an instance with more memory (D) provides additional capacity. Reducing epochs (B) does not affect per-batch memory; it only shortens training. Increasing learning rate (E) can cause instability but does not reduce memory usage.

Answer

Reduce the number of training epochs.

Answer

Increase the learning rate.

Question 15

A data scientist is training a binary classification model on a dataset with 100 features and 10,000 samples. The model achieves 99% accuracy on the training set but only 65% on the test set. Which technique should be applied first to address this issue?

Accepted Answer

Apply L2 regularization to the model. The symptoms indicate overfitting. Regularization (L1/L2) is a direct method to reduce overfitting by penalizing large coefficients. Option A is wrong because reducing the size of the training dataset would worsen overfitting. Option B is wrong because increasing the number of trees in a random forest could help reduce overfitting in some cases, but it's not the first technique to apply; regularization is more direct. Option D is wrong because adding more features would increase model complexity and worsen overfitting.

Answer

Reduce the size of the training dataset

Answer

Increase the number of trees in a random forest

Answer

Add more features to the model