MLS-C01 Modeling Practice Test 43 — 15 Questions

Question 1

A data scientist is troubleshooting a failed SageMaker training job that uses a custom Docker image. The failure reason shows 'unrecognized arguments: --sagemaker_program'. What is the most likely cause?

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

Accepted Answer

The custom Docker image does not use the SageMaker training toolkit and thus does not accept SageMaker hyperparameters. The error 'unrecognized arguments: --sagemaker_program' indicates that the custom Docker image does not include the SageMaker Training Toolkit. The SageMaker Training Toolkit is a Python library that provides a default entry point to parse and handle SageMaker-specific hyperparameters (like --sagemaker_program, --sagemaker_submit_directory, etc.). Without this toolkit, the container's entry point does not recognize these arguments, causing the training job to fail.

Answer

The Docker image is tagged incorrectly and cannot be pulled

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The training job is in a different region than the ECR repository

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The input mode is File mode, but the container expects Pipe mode

Question 2

A data scientist is reviewing the training logs from a SageMaker training job. The logs show training and validation loss per epoch. Based on the exhibited logs, which statement is correct?

Accepted Answer

The model is overfitting because training loss decreases while validation loss does not. The training logs show a classic overfitting pattern: training loss consistently decreases across epochs, indicating the model is memorizing the training data, while validation loss does not decrease (or may even increase), indicating poor generalization to unseen data. In SageMaker, monitoring both losses during training is critical to detect overfitting early, often prompting regularization or early stopping.

Answer

The model is not learning because the loss is not decreasing

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The model is underfitting because both losses are high

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The model is performing well because validation loss is stable

Question 3

A data scientist is training a deep learning model on Amazon SageMaker for image classification. The training is taking a long time and the GPU utilization is consistently below 30%. What should the data scientist do to improve GPU utilization and reduce training time?

Accepted Answer

Increase the batch size.. Low GPU utilization (below 30%) indicates that the GPU is spending most of its time waiting for data to process, often due to small batch sizes that underutilize the GPU's parallel compute capacity. Increasing the batch size allows the GPU to process more samples per forward/backward pass, improving arithmetic intensity and hardware utilization, which directly reduces total training time on SageMaker.

Answer

Use early stopping to stop training earlier.

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Switch to a CPU-only instance.

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Reduce the number of layers in the model.

Question 4

A machine learning engineer is building a binary classification model to predict customer churn. The dataset is highly imbalanced (5% churn). The engineer wants to use Amazon SageMaker's built-in XGBoost algorithm. Which combination of hyperparameters is most appropriate for this scenario?

Accepted Answer

scale_pos_weight=19, subsample=0.8. In a highly imbalanced dataset with only 5% churn, the ratio of negative to positive classes is 95:5, or 19:1. The `scale_pos_weight` hyperparameter in XGBoost should be set to this ratio (19) to penalize misclassifications of the minority class more heavily. A `subsample` of 0.8 introduces stochasticity and helps prevent overfitting, which is especially important when the minority class is small.

Answer

scale_pos_weight=0.05, subsample=0.8

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scale_pos_weight=19, subsample=1.0

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scale_pos_weight=1, subsample=1.0

Question 5

A company wants to deploy a machine learning model that provides real-time inference with low latency. The model is a small ensemble of three tree-based models. Which Amazon SageMaker approach is most appropriate?

Accepted Answer

Use a SageMaker real-time endpoint with a single inference container.. A SageMaker real-time endpoint with a single inference container is the most appropriate approach because it provides persistent, low-latency inference by keeping the model loaded in memory and handling requests synchronously. For a small ensemble of three tree-based models, a single container can host all models (e.g., using a custom inference script or a multi-model endpoint) and deliver sub-second response times, meeting the real-time requirement.

Answer

Use a SageMaker batch transform job.

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Use AWS Lambda with the model packaged in a layer.

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Use a SageMaker Serverless Inference endpoint.

Question 6

A data scientist is using Amazon SageMaker to train a custom TensorFlow model. The training job is failing with the error: 'OutOfRangeError: End of sequence'. The input data is stored in TFRecord format in S3. What is the most likely cause?

Accepted Answer

The number of training steps or epochs specified exceeds the dataset size.. The 'OutOfRangeError: End of sequence' error in TensorFlow occurs when the training loop attempts to read more data than is available in the dataset. This typically happens when the number of training steps or epochs specified exceeds the total number of records in the TFRecord files, causing the iterator to reach the end of the dataset prematurely.

Answer

The TFRecord files are corrupted.

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The instance type does not have enough memory.

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The shuffle buffer size is too large.

Question 7

A company is using Amazon SageMaker to train a model on a dataset with many categorical features. They want to use SageMaker's built-in Linear Learner algorithm. What preprocessing step is required for the categorical features?

Accepted Answer

Apply one-hot encoding to convert them to numerical vectors.. The SageMaker Linear Learner algorithm requires numerical input features. Categorical features must be converted to numerical vectors, typically via one-hot encoding, because the algorithm performs linear regression or classification on numerical data. Without this preprocessing, the algorithm cannot interpret categorical values directly.

Answer

Use label encoding to assign integers to categories.

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Normalize the categorical features using min-max scaling.

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Remove categorical features with high cardinality.

Question 8

A data scientist is tuning a neural network on a small dataset and observes that the training loss decreases but validation loss increases after a few epochs. Which technique should be applied to mitigate this issue?

Accepted Answer

Add dropout layers to the model.. The observed behavior—training loss decreasing while validation loss increasing—is a classic sign of overfitting. Adding dropout layers randomly drops a fraction of neurons during training, which forces the network to learn more robust features and reduces co-adaptation, thereby mitigating overfitting. This technique is specifically designed to improve generalization on small datasets.

Answer

Increase the learning rate.

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Remove regularization terms from the loss function.

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Increase the number of epochs.

Question 9

A company is building a recommendation system using Amazon SageMaker's Factorization Machines algorithm. The dataset includes user IDs, item IDs, and ratings. The data is sparse. Which data format should be used for training?

Accepted Answer

RecordIO-protobuf format with sparse feature vectors.. Factorization Machines (FM) in SageMaker are optimized for sparse, high-dimensional data. The RecordIO-protobuf format allows you to directly specify sparse feature vectors using integer keys and float values, which avoids the memory overhead of dense representations and enables efficient distributed training. This format is the recommended input for SageMaker's built-in FM algorithm.

Answer

CSV format with one row per rating.

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JSON lines format with nested structures.

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Parquet format with columns for each feature.

Question 10

A company uses Amazon SageMaker to train a model. The training job runs successfully but the model artifacts are not saved to the specified S3 output path. What is a likely cause?

Accepted Answer

The training script does not save the model to /opt/ml/model.. Amazon SageMaker expects the training script to save the model artifacts to the `/opt/ml/model` directory. After the training job completes, SageMaker automatically copies the contents of this directory to the specified S3 output path. If the script saves the model elsewhere (e.g., `/tmp` or a custom path), no artifacts will be uploaded, resulting in an empty or missing S3 output.

Answer

The model size exceeds the S3 bucket limit.

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The training job used spot instances.

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The S3 bucket is in a different AWS Region.

Question 11

A data scientist is training a deep learning model on Amazon SageMaker and wants to reduce the training time. Which TWO actions would help achieve this?

Accepted Answer

Use distributed training across multiple instances.. Option B is correct because distributed training across multiple instances (e.g., using SageMaker's distributed data parallel or model parallel libraries) splits the compute workload across nodes, directly reducing wall-clock training time for large deep learning models. Option D is correct because GPU-based instance types (e.g., ml.p3, ml.p4d, ml.g4dn) provide massively parallel floating-point throughput and high-bandwidth memory that accelerate the matrix operations dominating deep learning training compared to CPU instances. Option A is not correct because data augmentation increases the volume and variety of training data, which typically adds preprocessing and training overhead rather than reducing training time. Option C is not correct because SageMaker Automatic Model Tuning (hyperparameter tuning) runs many training jobs to find optimal hyperparameters, which increases overall time and cost rather than reducing a single training run's duration. Option E is not correct because SageMaker Managed Spot Training reduces cost by using spare EC2 capacity, not training time, and can even introduce interruptions and checkpoint-restart overhead.

Answer

Enable data augmentation.

Answer

Use SageMaker Automatic Model Tuning.

Answer

Use SageMaker Managed Spot Training.

Question 12

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains outliers. Which THREE techniques can mitigate the impact of outliers?

Accepted Answer

Remove observations with outlier values from the dataset.. Option A is correct because removing observations with outlier values eliminates the extreme data points entirely, so they can no longer distort the fitted linear regression coefficients. Option D is correct because winsorization caps extreme feature values at a specified percentile (e.g., the 5th and 95th percentiles), reducing the leverage of outliers while retaining those observations. Option E is correct because a robust loss function such as Huber loss behaves quadratically for small residuals but linearly for large residuals, limiting the influence of outliers on the learned parameters. Option B is not appropriate because adding layers increases model capacity and would tend to fit outliers even more closely, worsening their impact on a linear regression task. Option C is not correct in this context because standardization only rescales features to mean zero and unit variance; it does not reduce the relative influence of extreme values, so outliers remain outliers after transformation.

Answer

Increase the number of layers in the model.

Answer

Standardize the features to have mean zero and unit variance.

Question 13

A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. The model is evaluated using accuracy, but the accuracy is 99% even though the model predicts all instances as negative. Which metric should the data scientist use to properly evaluate the model?

Accepted Answer

F1 score. The F1 score is the harmonic mean of precision and recall, making it robust to class imbalance. With 99% negative instances, accuracy is misleadingly high even if the model never predicts the positive class. F1 captures both false positives and false negatives, providing a balanced evaluation of the minority class performance.

Answer

Root mean squared error (RMSE)

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Mean squared error (MSE)

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Accuracy

Question 14

A machine learning team is deploying a time-series forecasting model using Amazon SageMaker. The model is trained on historical data and needs to be updated daily with new data. The team wants to automate the retraining pipeline and avoid manual intervention. Which approach is the most efficient?

Accepted Answer

Use Amazon SageMaker Pipelines with a scheduled Lambda function to trigger retraining daily.. Amazon SageMaker Pipelines provides a fully managed, end-to-end orchestration service for building, training, and deploying machine learning models. By combining it with a scheduled AWS Lambda function, the team can automate daily retraining without manual intervention, leveraging SageMaker's native integration for step sequencing, artifact tracking, and model registry updates.

Answer

Use AWS Step Functions to orchestrate retraining, but require a manual approval step.

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Use SageMaker training jobs manually triggered by the team each day.

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Use a cron job on an EC2 instance to run a training script.

Question 15

A data scientist is training a linear regression model. After training, the model has a high bias and low variance. Which technique should the data scientist use to reduce bias?

Accepted Answer

Add more relevant features. High bias indicates the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding more relevant features increases model complexity, allowing it to learn more from the data and reduce bias. This directly addresses the underfitting issue without increasing variance excessively, provided the features are meaningful.

Answer

Decrease the model complexity

Answer

Apply L2 regularization (Ridge)

Answer

Reduce the amount of training data