MLS-C01 Mock Exam 80 — 25 Questions

Question 1

A company is building a recommendation system using Amazon SageMaker Factorization Machines. The dataset includes user IDs, item IDs, and implicit feedback (clicks). The data is sparse with millions of users and items. The model needs to capture interactions between users and items. Which hyperparameter tuning strategy should be used to improve model performance?

Accepted Answer

Increase the number of factors (num_factors) to capture more latent features.. Option A is correct because increasing the number of factors allows the model to capture more complex interactions. Option B (learning rate) helps convergence but not specifically interaction complexity. Option C (batch size) affects speed, not capacity. Option D (regularization) prevents overfitting but does not increase interaction capacity. Option E (activation function) is not relevant for factorization machines (linear model).

Answer

Increase L2 regularization to prevent overfitting.

Answer

Increase the batch size to speed up training.

Answer

Decrease the learning rate to improve convergence.

Answer

Change the activation function to ReLU.

Question 2

Which THREE techniques help reduce overfitting in a neural network? (Select THREE.)

Accepted Answer

Dropout. Dropout randomly drops units during training, L2 regularization penalizes large weights, and early stopping halts training when validation error increases. Data augmentation can also help but is not listed. Batch normalization may help but primarily for training stability.

Answer

Increasing the number of layers

Answer

Using a larger batch size

Question 3

A company is using Amazon SageMaker to train a machine learning model. The training job is configured to use the File mode to download data from S3 to the training instances. The training data is stored in a single S3 bucket with multiple prefixes. Which TWO actions are required to ensure the training job can access the data? (Choose TWO.)

Accepted Answer

Grant the SageMaker execution role s3:GetObject permission for the data bucket.. Options A and D are correct. Option A: The IAM role must have s3:GetObject permission for the bucket. Option D: The input data channel must specify the S3 URI with the correct prefix. Option B is wrong because File mode does not require RecordIO. Option C is wrong because Pipe mode is not used. Option E is wrong because File mode does not require data in a single file.

Answer

Configure the training job to use Pipe mode.

Answer

Concatenate all data files into a single file.

Answer

Convert the data to RecordIO-protobuf format.

Question 4

A data scientist is using SageMaker to train a model using the built-in XGBoost algorithm. The training job fails with the error 'AlgorithmError: Framework error: No module named 'xgboost''. What is the most likely cause?

Accepted Answer

The training job is using a custom container that does not have XGBoost installed.. Option B is correct because the built-in XGBoost algorithm requires the 'xgboost' Python package; SageMaker's built-in algorithms provide the necessary environment, but if the container is overridden or the wrong image is used, the module may be missing. Option A is wrong because the error is about missing module, not data format. Option C is wrong because the error is not about permissions. Option D is wrong because the error is not about output path.

Answer

The training data is not in CSV format.

Answer

The IAM role does not have permission to access SageMaker.

Answer

The S3 output path is incorrect.

Question 5

A data scientist is using Amazon SageMaker to train a linear regression model. The dataset has 500 features and 50,000 observations. The model converges but has high bias. Which technique should the data scientist use to reduce bias?

Accepted Answer

Add polynomial features or interaction terms to the feature set.. Option B is correct because adding interaction features or polynomial features allows the linear model to capture non-linear relationships, reducing bias. Option A (regularization) reduces variance, not bias. Option C (more data) helps variance. Option D (feature selection) reduces complexity, may increase bias. Option E (reduce learning rate) affects convergence speed, not bias.

Answer

Apply L2 regularization (Ridge) to penalize large coefficients.

Answer

Decrease the learning rate.

Answer

Use feature selection to remove irrelevant features.

Answer

Increase the number of training epochs.

Question 6

Refer to the exhibit. An IAM policy is attached to a SageMaker execution role. A data scientist tries to create a training job that reads training data from s3://my-bucket/confidential/data.csv. What will happen?

Accepted Answer

The training job will fail with an access denied error. The policy allows s3:GetObject on my-bucket/* but explicitly denies s3:GetObject on my-bucket/confidential/*. Since explicit Deny overrides any Allow, the training job will fail with an access denied error.

Answer

The training job will succeed because there is an Allow on my-bucket/*

Answer

The training job will succeed because the Deny statement is invalid

Answer

The training job will fail because the role lacks sagemaker:CreateTrainingJob

Question 7

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance. The data scientist runs a training job that reads from s3://my-bucket/training-data/ and writes to s3://my-bucket/output/. The training job fails with an access denied error. What is the most likely cause?

Accepted Answer

The policy does not allow s3:PutObject on the output location. The policy allows s3:PutObject only for the training-data prefix, not the output prefix. The training job needs write access to the output bucket. Option A is wrong because the policy does include s3:GetObject. Option B is wrong because SageMaker actions are allowed. Option D is wrong because the policy allows specific actions.

Answer

The policy does not allow sagemaker:CreateTrainingJob

Answer

The policy is missing the sagemaker:InvokeEndpoint action

Answer

The policy does not allow s3:GetObject on the training data

Question 8

A healthcare company needs to predict patient readmission risk using clinical notes. Which AWS service can be used to preprocess the text data into numerical features for a machine learning model?

Accepted Answer

Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that can extract entities, key phrases, and sentiment. It is suitable for preprocessing clinical notes into features. SageMaker Ground Truth is for data labeling. Rekognition is for images. Translate is for translation.

Answer

Amazon SageMaker Ground Truth

Answer

Amazon Translate

Answer

Amazon Rekognition

Question 9

A data scientist is tuning hyperparameters for an XGBoost model on a large dataset using Amazon SageMaker. The training job is taking too long, and they want to speed up the tuning process. Which strategy is most effective?

Accepted Answer

Use Bayesian optimization. Option B is correct because Bayesian optimization is more efficient than grid search, especially for large datasets. Option A is wrong because grid search is exhaustive and slow. Option C is wrong because random search is faster but less efficient. Option D is wrong because reducing max depth may reduce accuracy.

Answer

Use grid search with a fine-grained grid

Answer

Use random search with more iterations

Answer

Reduce the max depth of trees

Question 10

A company wants to build a model to detect fraudulent transactions. The dataset has a highly imbalanced class distribution. Which technique should be used during training to handle class imbalance?

Accepted Answer

Use SageMaker's built-in fraud detection algorithm that applies random under-sampling. Option A is correct because the built-in fraud detection algorithm in SageMaker uses random under-sampling of the majority class. Option B is wrong because adding more features does not directly handle imbalance. Option C is wrong because increasing tree depth may overfit. Option D is wrong because reducing the learning rate does not address imbalance.

Answer

Add more features to the dataset

Answer

Reduce the learning rate

Answer

Increase the tree depth in XGBoost

Question 11

A team is training a deep learning model using TensorFlow on a single GPU instance in SageMaker. The GPU utilization is below 30%. Which change will MOST improve GPU utilization?

Accepted Answer

Increase the batch size. Increasing the batch size makes more efficient use of GPU memory and parallel processing, improving utilization.

Answer

Reduce the number of epochs

Answer

Use SageMaker Distributed Training with multiple GPUs

Answer

Switch to a CPU instance

Question 12

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 13

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance role. A data scientist is trying to train a model using the SageMaker built-in XGBoost algorithm with training data in 'my-bucket/training-data/' and expects output in 'my-bucket/output/'. The training job fails with an access denied error. What is the most likely missing permission?

Accepted Answer

iam:PassRole on the SageMaker execution role.. The training job uses the SageMaker built-in algorithm, which downloads the training data from S3 and uploads output. The policy allows s3:GetObject on training-data and s3:PutObject on output. However, SageMaker also needs to read the algorithm image from ECR (elasticcontainerregistry). The missing permission is likely ecr:GetDownloadUrlForLayer or ecr:BatchGetImage. Also, SageMaker needs to pass roles. But the error is 'access denied', likely from ECR. Option A (ecr:GetAuthorizationToken) is needed to authenticate, but typically SageMaker uses the role to pull images. Option B (s3:ListBucket) is needed if the training job lists objects. Option C (sagemaker:DescribeTrainingJob) is not needed for execution. Option D (iam:PassRole) is needed for the training job to assume the role. Actually, SageMaker needs iam:PassRole to pass the execution role to the training job. But the error 'access denied' could be due to missing iam:PassRole. However, the policy does not include iam:PassRole. The most likely missing permission is iam:PassRole. Let's check: The policy allows creating training job, but the training job also needs to pass a role to SageMaker. Without iam:PassRole, the API call fails. So D is correct. But also ECR permissions might be needed. However, IAM PassRole is a common missing permission. I'll go with D.

Answer

ecr:GetAuthorizationToken on the ECR repository.

Answer

s3:ListBucket on the S3 bucket.

Answer

sagemaker:DescribeTrainingJob on the training job.

Question 14

A team is using SageMaker to train a model with hyperparameter tuning. The training jobs are taking too long. The team wants to reduce time without sacrificing model quality. Which approach should they take?

Accepted Answer

Enable early stopping in the hyperparameter tuning job.. Option A is correct because early stopping terminates poor performing jobs early, saving time. Option B is wrong because reducing max runtime may not allow convergence. Option C is wrong because increasing max jobs would increase time. Option D is wrong because random search is faster but may miss optimal hyperparameters; however, early stopping directly reduces time on bad trials.

Answer

Use random search instead of Bayesian optimization.

Answer

Increase the maximum number of training jobs.

Answer

Reduce the maximum runtime per training job.

Question 15

A company is building a sentiment analysis model for customer reviews. The dataset includes 10,000 positive and 10,000 negative reviews. The data scientist splits the data into 70% training, 15% validation, and 15% test sets. After training, the model achieves 99% accuracy on training set but only 82% on validation set. What is the most likely issue?

Accepted Answer

The model is overfitting. Option B is correct because a large gap between training and validation accuracy indicates overfitting. Option A is wrong because the dataset is balanced. Option C is wrong because underfitting would show low training accuracy. Option D is wrong because data leakage is not indicated by accuracy gap alone.

Answer

There is data leakage from validation to training

Answer

The dataset is imbalanced

Answer

The model is underfitting

Question 16

A company is building a binary classifier to detect fraudulent transactions. The dataset is highly imbalanced (99% legitimate, 1% fraudulent). Which metric is most appropriate for evaluating the model?

Accepted Answer

F1-score. Precision and recall (or F1-score) are more informative for imbalanced datasets than accuracy, because a model predicting all legitimate would achieve 99% accuracy but be useless. F1-score balances precision and recall.

Answer

Accuracy

Answer

Mean Squared Error

Answer

Area Under the ROC Curve (AUC-ROC)

Question 17

A team is training a neural network for image classification using Amazon SageMaker. The training loss decreases rapidly but the validation loss starts increasing after a few epochs. Which action should the team take?

Accepted Answer

Implement early stopping based on validation loss. Option A is correct because early stopping prevents overfitting by stopping when validation loss increases. Option B is wrong because increasing learning rate may worsen overfitting. Option C is wrong because adding more layers increases model complexity. Option D is wrong because reducing batch size may slow training but not prevent overfitting.

Answer

Reduce the batch size

Answer

Add more convolutional layers

Answer

Increase the learning rate

Question 18

A machine learning engineer is deploying a model to SageMaker for real-time inference. The model is a TensorFlow SavedModel. Which SageMaker capability should be used to create an endpoint?

Accepted Answer

SageMaker hosting with TensorFlow Serving container. Option A is correct because SageMaker provides managed TensorFlow serving containers. Option B is wrong because SageMaker Ground Truth is for labeling data. Option C is wrong because SageMaker Model Monitor is for monitoring. Option D is wrong because SageMaker Pipelines is for ML workflows.

Answer

SageMaker Pipelines

Answer

SageMaker Model Monitor

Answer

SageMaker Ground Truth

Question 19

A team is training a large deep learning model on Amazon SageMaker. The training job is taking too long and they want to reduce training time without changing the model architecture. Which action is MOST effective?

Accepted Answer

Use a GPU instance (e.g., p3.2xlarge) for training. Using a SageMaker managed training instance with GPU (e.g., p3.2xlarge) provides significant acceleration for deep learning models due to parallel processing.

Answer

Switch to a compute-optimized instance like c5.4xlarge

Answer

Increase the batch size and learning rate proportionally

Answer

Use SageMaker Automatic Model Tuning with hyperparameter optimization

Question 20

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

Answer

Root Mean Square Error (RMSE)