MLS-C01 Modeling Practice Test 16 — 15 Questions

Question 1

A data scientist is using Amazon SageMaker to train a model with a large dataset that does not fit into memory on a single instance. The training algorithm supports distributed training. Which approach should the scientist use to train the model efficiently?

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

Accepted Answer

Use SageMaker Pipe mode to stream data directly from S3. SageMaker Pipe mode streams data from S3 directly to the training algorithm without writing to disk, enabling processing of large datasets beyond memory.

Answer

Use SageMaker File mode and increase the instance volume size

Answer

Use Amazon EMR to preprocess data and then train on a smaller sample

Answer

Split the data into smaller files and use multiple training jobs sequentially

Question 2

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 3

A team is using Amazon SageMaker to train a model and wants to automatically stop training when the model stops improving to save costs. Which SageMaker feature should they use?

Accepted Answer

SageMaker Debugger. SageMaker Debugger provides built-in rules, such as `LossNotDecreasing`, that automatically monitor training metrics and can halt a training job when the model stops improving. This directly addresses the requirement to stop training when improvement plateaus, saving costs. While Managed Spot Training (C) reduces cost by using spot instances, it does not inherently provide automatic early stopping based on model performance; early stopping must be implemented separately. SageMaker Experiments (A) track and compare runs but do not stop training. Automatic Model Tuning (D) can apply early stopping to hyperparameter tuning jobs, but it is not a feature of a single training job.

Answer

SageMaker Experiments

Answer

SageMaker Managed Spot Training with early stopping

Answer

SageMaker Automatic Model Tuning

Question 4

A data scientist is training a gradient boosting model using SageMaker. The model is overfitting to the training data. Which TWO actions can help reduce overfitting? (Choose 2)

Accepted Answer

Increase the minimum child weight. Increasing the learning rate actually worsens overfitting; increasing max_depth increases model complexity. Reducing max_depth and increasing min_child_weight both regularize the model.

Answer

Increase the number of boosting rounds

Answer

Increase the learning rate

Answer

Use a larger training dataset

Question 5

A company uses a SageMaker endpoint for real-time inference. They need to ensure high availability during deployment updates. Which THREE steps achieve this? (Choose 3)

Accepted Answer

Use blue/green deployment with a new endpoint configuration. Blue/green deployment, multiple instances, and traffic shifting are standard practices for zero-downtime updates.

Answer

Use a single instance to save costs

Answer

Delete the old endpoint before creating the new one

Question 6

A data scientist is building a classification model and wants to evaluate its performance. Which TWO metrics are appropriate for a multi-class classification problem? (Choose 2)

Accepted Answer

Recall. Both precision and recall can be extended to multi-class via micro/macro averaging. R-squared is for regression; RMSE is for regression; Mean Absolute Error is for regression.

Answer

Mean Absolute Error (MAE)

Answer

R-squared

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Root Mean Square Error (RMSE)

Question 7

A company is training a deep learning model on Amazon SageMaker. The training job is taking a long time and the data scientist suspects that the model is overfitting. Which of the following actions can help reduce overfitting and improve generalization?

Accepted Answer

Add dropout layers to the model architecture.. Adding dropout layers is a regularization technique that randomly drops neurons during training to prevent overfitting. Increasing the number of epochs (Option B) would likely worsen overfitting. Using a larger batch size (Option C) can sometimes help generalization but is not a direct regularization technique. Removing regularization (Option D) would increase overfitting.

Answer

Increase the batch size used during training.

Answer

Increase the number of training epochs.

Answer

Remove regularization terms from the loss function.

Question 8

A data scientist is building a binary classification model to predict customer churn. The dataset is highly imbalanced, with only 5% of customers churning. The scientist evaluates several models using accuracy, precision, recall, and F1 score. Which metric is most appropriate for comparing model performance in this scenario?

Accepted Answer

F1 score. F1 score is the harmonic mean of precision and recall and is suitable for imbalanced datasets where accuracy can be misleading. Accuracy would be high even if the model predicts no churn ever (95% accuracy). Precision and recall each consider only one aspect, but F1 balances both.

Answer

Accuracy

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Precision

Answer

Recall

Question 9

A company is using Amazon SageMaker to train a linear regression model. The data scientist notices that the training loss is decreasing but the validation loss has started to increase after a few epochs. What is the most likely cause?

Accepted Answer

The model is overfitting the training data.. When training loss decreases but validation loss increases, the model is overfitting to the training data. This is a classic sign of overfitting. Underfitting would show both losses high. Learning rate too high would cause divergence. Data leakage would cause both losses to be artificially low.

Answer

The model is underfitting the training data.

Answer

There is data leakage from the validation set into the training set.

Answer

The learning rate is too high.

Question 10

A data scientist is using Amazon SageMaker to train a model with a large dataset that does not fit into memory on a single instance. The training algorithm supports distributed training. Which approach should the scientist use to train the model efficiently?

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

Accepted Answer

Increase the max_depth parameter.. High bias (underfitting) can be reduced by increasing the model complexity. Increasing max_depth allows more complex trees. Decreasing max_depth would increase bias. Increasing gamma increases regularization and bias. Reducing num_round (number of trees) reduces complexity.

Answer

Reduce the num_round parameter.

Answer

Increase the gamma parameter.

Answer

Decrease the max_depth parameter.

Question 11

A company is building a sentiment analysis model using Amazon SageMaker BlazingText. The training data consists of 100,000 product reviews. The data scientist wants to use the Word2Vec algorithm to generate word embeddings. Which configuration is required to use the continuous bag-of-words (CBOW) architecture?

Accepted Answer

Set the mode parameter to 'cbow'.. In BlazingText, the 'mode' parameter controls the training objective. Setting 'mode' to 'cbow' enables the continuous bag-of-words architecture. 'skipgram' is for skip-gram. 'batch_skipgram' is for large-scale skip-gram. 'supervised' is for text classification.

Answer

Set the mode parameter to 'supervised'.

Answer

Set the mode parameter to 'batch_skipgram'.

Answer

Set the mode parameter to 'skipgram'.

Question 12

A data scientist is using Amazon SageMaker to train a model with a large dataset that does not fit into memory on a single instance. The training algorithm supports distributed training. Which approach should the scientist use to train the model efficiently?

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

Accepted Answer

Increase the batch size used during training.. Increasing the batch size allows the model to process more samples per gradient update, reducing the number of iterations per epoch and thus speeding up training. This is a common technique to reduce training time without changing the algorithm or hardware. While it may affect convergence, it is the most direct way among the options to shorten training time. Adding regularization (B) introduces extra computation and does not reduce time. Data augmentation (C) increases the dataset size, which increases training time. Reducing epochs (D) also reduces training time, but it decreases the number of times the model sees the data, which can significantly harm model performance, making it less desirable than increasing batch size.

Answer

Add regularization to the loss function.

Answer

Use data augmentation to increase the dataset size.

Answer

Reduce the number of training epochs.

Question 13

A data scientist is training a neural network on Amazon SageMaker. The network has many layers and the training is very slow. The scientist suspects that the gradients are vanishing. Which technique is most specifically designed to mitigate the vanishing gradient problem?

Accepted Answer

Use batch normalization.. Batch normalization helps by normalizing the activations, which reduces the problem of vanishing/exploding gradients. Dropout is for regularization. Data augmentation increases data. Gradient clipping deals with exploding gradients, not vanishing.

Answer

Use gradient clipping.

Answer

Use data augmentation.

Answer

Use dropout layers.

Question 14

A company is using Amazon SageMaker to deploy a model for real-time inference. The model has a latency requirement of less than 100 milliseconds. During testing, the latency is around 150 milliseconds. Which action can most likely reduce the latency to meet the requirement?

Accepted Answer

Use a larger instance type for the endpoint.. Enabling data capture adds overhead and increases latency. Using a larger instance type would provide more compute and reduce latency, but may increase cost. Reducing the batch size for inference (if batching is used) can reduce latency because the model processes fewer requests at once. However, the question implies a real-time endpoint which typically processes one request at a time; batch size might be 1. Increasing the variant weight for the production variant is for traffic routing, not latency. The most direct is to use a more powerful instance type. But also consider that increasing batch size (if using multi-record) increases latency. Reducing batch size reduces latency. However, for a real-time endpoint, the instance type is key. I'll go with using a larger instance type.

Answer

Reduce the batch size for inference.

Answer

Enable data capture for the endpoint.

Answer

Increase the initial variant weight for the production variant.

Question 15

A data scientist is using Amazon SageMaker to train a model with a large dataset that does not fit into memory on a single instance. The training algorithm supports distributed training. Which approach should the scientist use to train the model efficiently?

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

Accepted Answer

Target encoding. One-hot encoding creates many binary columns, which can cause the curse of dimensionality for high-cardinality features. Label encoding assigns arbitrary integers, which linear models may interpret as ordinal. Target encoding (mean encoding) replaces categories with the mean of the target variable, which captures information without expanding dimensionality. This is often used for high-cardinality features. Ordinal encoding is similar to label encoding.

Answer

Label encoding

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One-hot encoding

Answer

Ordinal encoding