MLS-C01 Modeling Practice Test 21 — 15 Questions

Question 1

A company uses Amazon SageMaker to train a time-series forecasting model using the built-in DeepAR algorithm. The training data consists of daily sales for 1000 products over 2 years. The model performs well on most products, but for a few products with intermittent demand (sporadic sales), the predictions are poor. Which action should the data scientist take to improve predictions for these products?

Accepted Answer

Create a separate forecasting model specifically for intermittent demand products, using a model designed for such patterns (e.g., Croston's method).. Option A is correct. Creating separate models for different demand patterns allows specialized treatment. Option B is wrong because the dataset is already long enough. Option C is wrong because using a linear model may underfit. Option D is wrong because increasing training data does not help with intermittent patterns.

Answer

Use a linear regression model for all products.

Answer

Increase the context length of the DeepAR model to capture longer history.

Answer

Add more training data by including additional product categories.

Question 2

A data scientist is using Amazon SageMaker to train a linear learner model for regression. After reviewing the training logs, the data scientist notices that the loss is not decreasing and remains high. The learning rate is set to 0.01. The data is normalized. What should the data scientist do to improve convergence?

Accepted Answer

Try different learning rates, such as 0.001 or 0.1.. Option B is correct. The learning rate may be too high causing oscillation or too low causing slow convergence. Adjusting it can help. Option A is wrong because more epochs may not help if the learning rate is inappropriate. Option C is wrong because the data is already normalized. Option D is wrong because reducing batch size increases noise but may not resolve convergence issues.

Answer

Normalize the data again.

Answer

Reduce the mini-batch size.

Answer

Increase the number of epochs.

Question 3

A company uses Amazon SageMaker to host a model for real-time inference. The model is a large ensemble that takes 2 seconds to load into memory. To reduce cold start latency, the data scientist uses SageMaker's managed warm pools. However, they notice that during a sudden traffic spike, new instances still experience high latency. What is the BEST way to ensure consistently low latency for all requests?

Accepted Answer

Configure auto scaling based on the number of active invocations to maintain a buffer of warmed instances.. Option C is correct. Using auto scaling with a target metric keeps a buffer of warm instances. Option A is wrong because increasing instance size reduces per-request time but cold start still occurs. Option B is wrong because Serverless Inference has its own cold start issues. Option D is wrong because scaling down aggressively increases cold starts.

Answer

Use a larger instance type to reduce model loading time.

Answer

Reduce the number of instances to minimize cold start frequency.

Answer

Switch to SageMaker Serverless Inference.

Question 4

A data scientist is training a text classification model using Amazon SageMaker's BlazingText algorithm. The dataset consists of 1 million documents, each labeled with one of 10 categories. The model achieves 92% accuracy on a held-out test set. However, when deployed, the model performs poorly on documents containing slang and typos. What should the data scientist do to improve model robustness?

Accepted Answer

Augment the training data by introducing common slang replacements and typos.. Option A is correct. Data augmentation with noise helps the model generalize to variations. Option B is wrong because removing such documents reduces training data. Option C is wrong because a larger embedding dimension may not help with slang. Option D is wrong because increasing epochs may lead to overfitting.

Answer

Remove all documents with slang or typos from the training set.

Answer

Increase the embedding dimension from 100 to 300.

Answer

Increase the number of training epochs.

Question 5

A company wants to use Amazon SageMaker to train a model, but the training data contains personally identifiable information (PII). The data scientist needs to ensure that the PII is not accessible during training. The data is stored in S3. What is the MOST secure approach?

Accepted Answer

Use SageMaker's built-in mechanisms to encrypt data at rest and in transit, and ensure the training container does not have direct S3 access.. Option D is correct. Using SageMaker with a SageMaker-managed KMS key encrypts data at rest and in transit, and the training container cannot access S3 directly. Option A is wrong because encryption alone does not prevent access. Option B is wrong because Redshift adds complexity. Option C is wrong because a VPC alone does not encrypt data.

Answer

Configure a VPC for the SageMaker notebook and training job.

Answer

Load the data into Amazon Redshift and use Redshift ML.

Answer

Use server-side encryption with S3-managed keys (SSE-S3).

Question 6

A data scientist is using Amazon SageMaker to train a model using the built-in XGBoost algorithm. The training job is taking a long time. The data scientist notices that the input data is in CSV format and the training job is using File mode. The data size is 50 GB. What is the BEST way to reduce training time?

Accepted Answer

Switch the input mode to Pipe.. Option B is correct. Pipe mode streams data directly from S3, reducing I/O overhead. Option A is wrong because increasing instance count may not help if I/O is bottleneck. Option C is wrong because converting to Parquet may not be supported directly by XGBoost. Option D is wrong because reducing data harms accuracy.

Answer

Use a larger instance type with more vCPUs.

Answer

Convert the data to Parquet format.

Answer

Reduce the number of features in the dataset.

Question 7

A data scientist is training a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (5% positive class). Which TWO techniques should the scientist use to address the class imbalance? (Choose TWO.)

Accepted Answer

Use SMOTE to oversample the minority class. Option A (SMOTE) generates synthetic samples for the minority class. Option C (class_weight='balanced') adjusts loss function weights. Option B (undersampling majority) can be used but is not always preferred; Option D (oversampling with replacement) may cause overfitting; Option E (threshold tuning) is post-training.

Answer

Tune the decision threshold after training

Answer

Randomly undersample the majority class to match minority size

Answer

Oversample the minority class by duplicating existing samples

Question 8

A team is building a regression model to predict house prices. They observe that the model performs well on training data but poorly on validation data. Which THREE actions can help reduce overfitting? (Choose THREE.)

Accepted Answer

Reduce model complexity by selecting fewer features. Option A (increase regularization) penalizes large coefficients. Option C (reduce model complexity) like using fewer features or a simpler algorithm. Option D (add more training data) helps generalization. Option B (increase tree depth) increases overfitting. Option E (feature engineering) may not reduce overfitting directly.

Answer

Increase the maximum depth of decision trees

Answer

Add more interaction features

Question 9

A machine learning engineer is tuning a Gradient Boosting model for a regression task. The dataset contains 50 features and 100,000 samples. The engineer wants to speed up training without sacrificing predictive performance significantly. Which THREE hyperparameters should the engineer consider adjusting? (Choose THREE.)

Accepted Answer

Reduce the subsample ratio (e.g., from 1.0 to 0.5). Option A (subsample) uses a fraction of samples per tree, reducing overfitting and training time. Option B (max_features) limits features considered for splits, reducing computation. Option C (learning_rate) with more estimators trades off; lowering learning rate often requires more estimators, not faster. Option D (max_depth) lower depth speeds up training. Option E (n_estimators) increasing slows training.

Answer

Increase learning_rate and decrease n_estimators proportionally

Answer

Increase the number of estimators

Question 10

A data scientist is training a linear regression model using Amazon SageMaker's built-in Linear Learner algorithm. The dataset has 500 features and 1 million rows. After training, the model's training RMSE is 2.5 and validation RMSE is 2.6, which is acceptable. However, the scientist notices that many feature coefficients are very small but non-zero, and the model takes a long time to train. The scientist wants to reduce training time while maintaining similar accuracy. Which action should the scientist take?

Accepted Answer

Increase the L1 regularization strength. Option B (increase L1 regularization) will drive many coefficients to zero, reducing effective features and thus training time. Option A (increase L2) shrinks coefficients but doesn't zero them out, so less impact on speed. Option C (increase batch size) may speed training but could affect convergence. Option D (use a different algorithm) is unnecessary.

Answer

Increase the mini-batch size

Answer

Switch to a neural network model

Answer

Increase the L2 regularization strength

Question 11

A company is building a fraud detection model using a random forest classifier. The dataset is highly imbalanced with 99% legitimate transactions and 1% fraudulent. The model currently achieves 99% accuracy on the test set, but the fraud recall is only 10%. The business requires at least 80% recall for fraud. The data scientist has tried oversampling the minority class and adjusting class weights, but recall remains below 40%. The dataset contains millions of transactions with hundreds of features. Which approach should the data scientist try next to improve fraud recall?

Accepted Answer

Use a gradient boosting machine (e.g., XGBoost) with scale_pos_weight parameter. Option D (gradient boosting with scale_pos_weight) is effective for imbalance. Option A (logistic regression) likely underperforms. Option B (undersampling) loses too much data. Option C (PCA) reduces features but may lose signal.

Answer

Randomly undersample the majority class to a 50:50 ratio

Answer

Apply PCA to reduce dimensionality before training

Answer

Use a logistic regression model with L2 regularization

Question 12

A research team is training a deep learning model for image classification using Amazon SageMaker. The model is a convolutional neural network (CNN) with 50 layers. The team uses a single ml.p3.2xlarge instance. After 10 hours of training, the model has not converged and the loss is decreasing very slowly. The team suspects vanishing gradients. They want to diagnose and fix the issue without significant code changes. Which action should they take?

Accepted Answer

Modify the architecture to include residual connections (skip connections). Option A (use residual connections) directly addresses vanishing gradients. Option B (increase learning rate) may cause divergence. Option C (add more layers) worsens the problem. Option D (use batch normalization) helps but residual connections are more targeted for vanishing gradients.

Answer

Add more convolutional layers to increase model capacity

Answer

Use batch normalization after each convolutional layer

Answer

Increase the learning rate by a factor of 10

Question 13

A data scientist is building a binary classification model to predict whether a customer will subscribe to a service. The dataset contains 20 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). The scientist uses a logistic regression model and obtains a training AUC of 0.85 and a test AUC of 0.60. The scientist suspects overfitting due to high cardinality features. Which approach should the scientist use to address this issue?

Accepted Answer

Apply target encoding with smoothing to the zip code feature. Option C (target encoding with smoothing) reduces cardinality while preserving predictive power. Option A (one-hot encoding) increases dimensionality drastically. Option B (label encoding) may introduce ordinality issues. Option D (remove zip code) may lose important information.

Answer

Apply label encoding to the zip code feature

Answer

Remove the zip code feature entirely

Answer

Apply one-hot encoding to the zip code feature

Question 14

A machine learning engineer is deploying a sentiment analysis model using Amazon SageMaker. The model is a BERT-based transformer that takes up to 512 tokens. The engineer notices that inference latency is high (over 500 ms per request) on a single ml.c5.xlarge instance. The application requires latency under 100 ms. The model has already been optimized using half-precision (FP16). Which action should the engineer take to reduce latency?

Accepted Answer

Use a GPU instance such as ml.g4dn.xlarge. Option B (use a GPU instance) accelerates inference for transformers. Option A (increase batch size) can help throughput but not latency for single requests. Option C (reduce max sequence length) may hurt accuracy. Option D (use SageMaker Neo) is for compilation but may not achieve sub-100ms.

Answer

Reduce the maximum sequence length to 128

Answer

Increase the batch size for inference requests

Answer

Use SageMaker Neo to compile the model for the target instance

Question 15

A data scientist is training a time series forecasting model using Amazon SageMaker's DeepAR algorithm. The dataset contains daily sales data for 10,000 products over 2 years. The scientist splits the data chronologically: training on the first 18 months, validation on the next 3 months, and test on the last 3 months. The model performs well on validation but poorly on test. The data scientist suspects the model is overfitting to the validation period. Which action should the scientist take to improve test performance?

Accepted Answer

Use time series cross-validation with an expanding window. Option D (use cross-validation respecting time order) such as expanding window. Option A (increase training data) may help but not specifically address overfitting to validation. Option B (add more features) may worsen overfitting. Option C (reduce context length) may lose long-term patterns.

Answer

Reduce the context length to 30 days

Answer

Add more exogenous features like holidays and promotions

Answer

Use the entire dataset for training and ignore validation