MLS-C01 Modeling Practice Test 47 — 15 Questions

Question 1

A data scientist is using Amazon SageMaker to train a model with the built-in XGBoost algorithm. The training job is taking a long time, and the scientist wants to reduce the training time without significantly impacting model accuracy. The dataset is large, and the scientist has already tried increasing the instance count. Which hyperparameter should the scientist tune to speed up training by reducing the number of boosting rounds?

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

Accepted Answer

num_round. The number of boosting rounds (num_round) is directly proportional to training time. Reducing it will shorten training, though it may affect accuracy. Other hyperparameters like max_depth, eta, and subsample can influence training time per tree or the number of rounds needed, but num_round is the most direct control over the total number of trees built.

Answer

max_depth

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eta

Answer

subsample

Question 2

A machine learning engineer is training a linear regression model in Amazon SageMaker using the built-in Linear Learner algorithm. The training data contains a highly skewed numerical feature with extreme outliers. The engineer wants to reduce the impact of these outliers on the learned model. Which approach should the engineer take?

Accepted Answer

Apply a logarithmic transformation to the skewed feature before training.. The correct approach is to apply a logarithmic transformation to the skewed feature. This reduces the effect of extreme values on the linear model's coefficients, addressing the outlier issue directly. Other options either do not mitigate outliers or introduce unnecessary changes. The transformation is a standard, effective preprocessing technique for skewed numerical data.

Answer

Remove all records where the skewed feature exceeds two standard deviations from the mean.

Answer

Increase the number of epochs in the Linear Learner hyperparameters.

Answer

Switch to the XGBoost algorithm with default hyperparameters.

Question 3

A machine learning engineer is training a deep neural network for a regression task using Amazon SageMaker's built-in Linear Learner algorithm. The model's predictions are consistently biased, and after inspecting the training data, the engineer discovers that one input feature has a mean of 500 and a standard deviation of 200, while all other features have means near 0 and standard deviations near 1. To improve model convergence and accuracy without changing the algorithm, what should the engineer do?

Accepted Answer

Apply feature scaling to the dataset before training, such as standardization or normalization.. Feature scaling is essential for algorithms like Linear Learner that rely on gradient descent. When one feature has a much larger scale, it can dominate the learning process, leading to slow convergence and biased models. Standardization or normalization brings all features to a comparable range, improving optimization and model accuracy without altering the algorithm.

Answer

Increase the number of epochs in the training job to allow the model more time to learn the feature's scale.

Answer

Switch to the XGBoost algorithm, which is inherently robust to unscaled features.

Answer

Remove the large-scale feature from the dataset to eliminate its influence on the model.

Question 4

A financial institution is using Amazon SageMaker to train a binary classification model to detect fraudulent transactions. The dataset is highly imbalanced, with only 0.5% of transactions being fraudulent. The data scientist wants to improve the model's ability to identify fraud without significantly increasing false positives. Which of the following approaches is most appropriate when using the SageMaker built-in XGBoost algorithm?

Accepted Answer

Set the 'scale_pos_weight' hyperparameter to the ratio of negative to positive samples.. For imbalanced binary classification with XGBoost, setting 'scale_pos_weight' to the ratio of negative to positive samples is a standard and effective technique. It adjusts the gradient updates to give more weight to the minority class, improving recall without drastically increasing false positives. Other options do not directly address the imbalance.

Answer

Reduce the number of training epochs to prevent overfitting.

Answer

Use the 'objective' hyperparameter set to 'binary:logistic' and rely on default settings.

Answer

Increase the 'max_depth' hyperparameter to allow the model to learn more complex patterns.

Question 5

A financial services company is training a gradient boosting model on Amazon SageMaker to predict loan defaults. The dataset is highly imbalanced: only 1.5% of loans default. After training with the built-in XGBoost algorithm and default hyperparameters, the model achieves 98% accuracy but fails to identify almost any defaulters. A machine learning specialist needs to improve the model's ability to detect defaults while maintaining reasonable precision. Which approach is MOST appropriate?

Accepted Answer

Set the scale_pos_weight hyperparameter in the XGBoost algorithm to the ratio of negative to positive examples.. The core issue is class imbalance, and SageMaker's built-in XGBoost provides the scale_pos_weight hyperparameter specifically for this purpose. Setting it to the negative-to-positive ratio reweights the positive class, making the model more sensitive to defaults. This is a direct, efficient fix that requires no additional data processing and is well-documented for imbalanced classification.

Answer

Use SageMaker Automatic Model Tuning to optimize the area under the ROC curve (AUC) metric.

Answer

Apply SMOTE to the training data using a SageMaker Processing job, then train the model on the balanced dataset.

Answer

Increase the number of boosting rounds to 1000 and reduce the learning rate to 0.01.

Question 6

A data scientist is using Amazon SageMaker to train a model with the built-in XGBoost algorithm. The training job is taking a long time, and the scientist wants to reduce the training time without significantly impacting model accuracy. The dataset is large, and the scientist has already tried increasing the instance count. Which hyperparameter should the scientist tune to speed up training by reducing the number of boosting rounds?

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

Accepted Answer

Adjust the scale_pos_weight parameter in the XGBoost hyperparameters to a higher value.. For imbalanced binary classification with XGBoost, the scale_pos_weight hyperparameter is designed to handle class imbalance by weighting the positive class more heavily. Setting it to the ratio of negative to positive instances helps the model pay more attention to the minority class, improving recall. Other methods like resampling or using evaluation metrics like AUC-PR can also help, but scale_pos_weight is a direct and effective hyperparameter adjustment.

Answer

Apply feature selection to remove noisy features that may be causing the model to misclassify positives.

Answer

Increase the number of boosting rounds to allow the model to learn the positive class better.

Answer

Use a different evaluation metric such as accuracy instead of recall to guide hyperparameter tuning.

Question 7

A data scientist is training a deep learning model for image segmentation using Amazon SageMaker. The model is a fully convolutional network (FCN) with a ResNet-50 backbone. The training dataset contains 10,000 images with pixel-level annotations. The data scientist notices that the model performs well on the training set but poorly on the validation set. Which technique should the data scientist apply to reduce overfitting?

Accepted Answer

Add dropout layers after each convolutional block and apply L2 regularization.. Overfitting occurs when a model memorizes training data but fails to generalize. For a deep convolutional network like FCN with ResNet-50, adding dropout layers and L2 regularization are standard techniques to reduce overfitting. Dropout prevents co-adaptation of neurons, while L2 penalizes large weights. Together, they constrain the model's capacity and improve validation performance without sacrificing training accuracy excessively.

Answer

Increase the learning rate and use a larger batch size.

Answer

Reduce the image resolution and use data augmentation.

Answer

Use batch normalization and increase the number of epochs.

Question 8

A machine learning engineer is training a gradient boosting model on a tabular dataset with 500,000 rows and 120 features using Amazon SageMaker. The training job is taking excessively long, and the engineer suspects that the feature set contains many irrelevant or highly correlated features. The engineer wants to reduce the training time while preserving model accuracy. Which approach is MOST appropriate to address this issue?

Accepted Answer

Apply feature selection using techniques like variance threshold or recursive feature elimination before training, then retrain the model with the reduced feature set.. The excessive training time is likely due to a large number of features, many of which are irrelevant or correlated. Feature selection reduces the feature space, leading to faster training and potentially better model performance. Other options either do not address the dimensionality issue or may compromise accuracy. Thus, applying feature selection before retraining is the most effective and appropriate solution.

Answer

Switch to a linear learner algorithm, which is inherently faster for high-dimensional data.

Answer

Increase the instance count of the training job to distribute the workload across more machines.

Answer

Use SageMaker Automatic Model Tuning (AMT) with a large search space to find the best hyperparameters.

Question 9

A data scientist is using Amazon SageMaker to train a model with the built-in XGBoost algorithm. The training job is taking a long time, and the scientist wants to reduce the training time without significantly impacting model accuracy. The dataset is large, and the scientist has already tried increasing the instance count. Which hyperparameter should the scientist tune to speed up training by reducing the number of boosting rounds?

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

Accepted Answer

Use the 'balanced' value for the positive_example_weight_mult parameter.. SageMaker Linear Learner provides the positive_example_weight_mult parameter to handle imbalanced binary classification. Setting it to a value greater than 1 or to 'balanced' increases the weight of positive examples, making the model more sensitive to the minority class. This directly counteracts the bias toward the majority class and improves the model's ability to detect positive instances. Other hyperparameters like learning rate or feature reduction do not address the underlying class imbalance.

Answer

Set the predictor_type to 'regressor' instead of 'binary_classifier'.

Answer

Increase the value of the learning_rate hyperparameter.

Answer

Reduce the number of features using PCA before training.

Question 10

A machine learning engineer is tuning a gradient boosting model on Amazon SageMaker. The team has a fixed budget of 30 total training jobs and wants to explore a wide hyperparameter space early while narrowing toward promising regions later. They also want to automatically stop jobs that are clearly underperforming. Which SageMaker automatic model tuning configuration should the engineer use?

Accepted Answer

Use Bayesian optimization with early stopping enabled via a SageMaker automatic model tuning regular expression on the objective metric, so underperforming training jobs are terminated before completion.. Bayesian optimization is designed to model the objective function and progressively focus trials on promising hyperparameter regions, satisfying the explore-then-exploit requirement. Pairing it with automatic model tuning early stopping and a metric regular expression allows SageMaker to terminate weak training jobs, preserving the fixed thirty-job budget and reducing wasted compute.

Answer

Use Bayesian optimization but disable early stopping and instead reduce MaxNumberOfTrainingJobs to a very small value so each trial gets more time.

Answer

Use random search with early stopping disabled and set MaxNumberOfTrainingJobs equal to MaxParallelTrainingJobs so every job runs to completion.

Answer

Use grid search with a coarse hyperparameter grid and rely on the HyperparameterTuningJob objective metric to select the best job after all trials finish.

Question 11

A financial institution is training a binary classification model to detect fraudulent transactions. The dataset is highly imbalanced, with only 0.5% of transactions being fraudulent. The data scientist uses Amazon SageMaker's built-in XGBoost algorithm with default hyperparameters and achieves 99.5% accuracy on the validation set, but the model fails to identify any fraudulent cases. Which modification to the training process is MOST likely to improve the model's ability to detect fraud?

Accepted Answer

Use the scale_pos_weight hyperparameter in XGBoost to assign a higher weight to the positive (fraudulent) class.. The model achieves high accuracy by predicting all transactions as non-fraudulent, which is useless. The core problem is the severe class imbalance, causing the loss function to favor the majority class. Using scale_pos_weight in XGBoost increases the weight of the positive class, forcing the model to learn from fraudulent examples. This directly improves the model's ability to detect fraud without changing the algorithm or data pipeline.

Answer

Deploy the model to a real-time endpoint and use a higher threshold for classification.

Answer

Increase the number of training epochs to allow the model to learn more from the minority class.

Answer

Apply principal component analysis (PCA) to reduce dimensionality before training.

Question 12

A machine learning engineer is training a SageMaker built-in Object Detection model on a dataset of 10,000 images with 20 object classes. The training job uses a p3.8xlarge instance and completes successfully, but validation mAP is only 0.15. The engineer suspects the default anchor box configuration is poorly matched to the dataset. Which SageMaker Object Detection hyperparameter should be adjusted to improve anchor box alignment with the ground truth bounding boxes?

Accepted Answer

aspect_ratios. In the SageMaker built-in Object Detection algorithm, anchor boxes are defined by scales and aspect ratios. When the default aspect ratios do not match the shapes of objects in the dataset, the anchors have low overlap with ground truth boxes, causing poor localization and low mAP. Adjusting aspect_ratios to reflect the actual object dimensions improves anchor alignment. Other hyperparameters like num_classes, mini_batch_size, and learning_rate do not change anchor geometry.

Answer

learning_rate

Answer

num_classes

Answer

mini_batch_size

Question 13

A team trains a binary classifier with Amazon SageMaker and the model outputs a probability score. The business requires that at most 2% of predicted positives be false positives, and within that constraint the team wants to maximize recall. Which evaluation and threshold strategy should the team apply?

Accepted Answer

Plot the precision-recall curve, select the operating point whose precision is at least 0.98, and among those points pick the one with the highest recall.. The constraint is expressed as a maximum false positive rate among predicted positives, which is precisely precision. A precision-recall curve exposes the achievable recall at each precision level, so selecting the highest-recall point with precision at least 0.98 enforces the business rule while maximizing recall.

Answer

Plot the ROC curve, choose the point closest to the top-left corner, and use its threshold for deployment.

Answer

Train the model with class weights inversely proportional to class frequency and deploy with the default 0.5 threshold.

Answer

Choose the decision threshold that maximizes accuracy on the validation set, then deploy the model with that fixed threshold.

Question 14

A data scientist is using Amazon SageMaker to train a model with the built-in XGBoost algorithm. The training job is taking a long time, and the scientist wants to reduce the training time without significantly impacting model accuracy. The dataset is large, and the scientist has already tried increasing the instance count. Which hyperparameter should the scientist tune to speed up training by reducing the number of boosting rounds?

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

Accepted Answer

RecordIO. The SageMaker built-in Image Classification algorithm is designed to work with RecordIO (or image files with a list file). RecordIO is the preferred format because it stores multiple images in a single file, improving I/O efficiency during training. Other formats like TFRecord, CSV, and Parquet are not natively supported for this algorithm.

Answer

CSV

Answer

Parquet

Answer

TFRecord

Question 15

A data scientist is using Amazon SageMaker to train a model with the built-in XGBoost algorithm. The training job is taking a long time, and the scientist wants to reduce the training time without significantly impacting model accuracy. The dataset is large, and the scientist has already tried increasing the instance count. Which hyperparameter should the scientist tune to speed up training by reducing the number of boosting rounds?

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

Accepted Answer

The best training job is the one that achieved the highest value of the objective metric across all completed training jobs, provided the metric is maximized.. SageMaker Automatic Model Tuning selects the best training job by comparing the objective metric across all completed jobs according to the specified goal (Maximize or Minimize). For 'validation:auc', higher is better. The DescribeHyperParameterTuningJob API returns a BestTrainingJob field that includes the name of the best job. The scientist can then use DescribeTrainingJob to retrieve its hyperparameters and model artifacts. The best job is not necessarily the last one, and manual comparison is not required.

Answer

The best training job is always the last training job launched by the tuning job, regardless of its objective metric value.

Answer

The scientist must manually compare the objective metric values of all training jobs in the AWS Management Console to determine the best one.

Answer

The best training job is determined by averaging the objective metric across all epochs, not by the final epoch value.