MLS-C01 Practice Test 6 — 10 Questions

Question 1

A data scientist is tuning a gradient boosting model using Amazon SageMaker's Automatic Model Tuning (hyperparameter optimization). The objective metric is validation:auc. After 50 training jobs, the best model still has a validation AUC of only 0.65. The scientist suspects overfitting because the training AUC is 0.99. Which hyperparameter configuration is MOST likely to reduce overfitting?

Accepted Answer

Increase lambda from 1 to 10. Increasing lambda (L2 regularization) from 1 to 10 adds a stronger penalty on the magnitude of leaf weights in the gradient boosting model. This directly reduces overfitting by discouraging the model from fitting noise in the training data, which is consistent with the observed gap between training AUC (0.99) and validation AUC (0.65). In XGBoost, lambda controls the L2 regularization term on weights, and a higher value forces the model to be simpler and more generalizable.

Answer

Increase num_round from 100 to 500

Answer

Increase max_depth from 6 to 12

Answer

Increase subsample from 0.5 to 1.0

Question 2

A company is building a recommendation system for an e-commerce platform. They have user-item interaction data and want to use matrix factorization. However, the dataset is sparse (99% missing interactions). Which approach should the data scientist take to train the model effectively?

Accepted Answer

Use alternating least squares (ALS) with implicit feedback and assign lower confidence to unobserved interactions. Option B is correct because Alternating Least Squares (ALS) with implicit feedback is specifically designed to handle sparse implicit feedback datasets by assigning lower confidence to unobserved interactions (e.g., confidence = 1 + alpha * r_ui, where r_ui is 0 for unobserved). This avoids the pitfalls of treating missing values as zeros (which distorts the factorization) and scales well to 99% sparsity by leveraging weighted regularization.

Answer

Impute missing values with zeros and use singular value decomposition (SVD)

Answer

Remove all users and items with fewer than 10 interactions to reduce sparsity

Answer

Use item-based collaborative filtering with cosine similarity

Question 3

A data scientist is training a binary classification model on a dataset with 10,000 features. The model overfits severely. Which technique is MOST appropriate to reduce overfitting?

Accepted Answer

Apply L1 regularization (Lasso). L1 regularization (Lasso) can shrink some feature coefficients to zero, effectively performing feature selection and reducing overfitting. Early stopping is more for iterative algorithms. PCA reduces dimensionality but may lose interpretability. Increasing max depth would worsen overfitting.

Answer

Use early stopping during training

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Use PCA to reduce dimensionality

Answer

Increase the max depth of the model

Question 4

A data scientist is training a model using Amazon SageMaker and wants to automatically stop training when the model stops improving. Which feature should be used?

Accepted Answer

Enable early stopping in the training job configuration.. Option D is correct because SageMaker's built-in early stopping feature automatically halts a training job when the model's objective metric (e.g., loss or accuracy) ceases to improve over a specified number of steps or epochs. This is configured directly in the training job's `StoppingCondition` parameter, which monitors the metric defined in the `MetricDefinitions` and stops training if no improvement is detected, saving compute time and avoiding overfitting.

Answer

Use SageMaker Debugger to monitor the loss metric.

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Configure a CloudWatch alarm on the training job's CPU utilization.

Answer

Use SageMaker Hyperparameter Tuning with random search.

Question 5

A company is using Amazon SageMaker to train a model with a custom algorithm. The training script reads data from an S3 bucket using boto3. The training job fails with an 'AccessDenied' error when trying to access the S3 bucket. The IAM role attached to the SageMaker notebook instance has full S3 access. What is the most likely cause?

Accepted Answer

The SageMaker execution role used for the training job does not have S3 access permissions.. The IAM role attached to the SageMaker notebook instance is used for interactive development, but training jobs run under a separate SageMaker execution role. Even if the notebook role has full S3 access, the training job's execution role must also have explicit S3 permissions. The 'AccessDenied' error indicates that the execution role lacks the necessary s3:GetObject or s3:ListBucket actions for the S3 bucket.

Answer

The S3 bucket has a bucket policy that denies access from the SageMaker service.

Answer

The training script is using an incorrect S3 bucket name.

Answer

The SageMaker training job is not configured to use the S3 VPC endpoint.

Question 6

A company is using Amazon SageMaker to train a deep learning model. The training job is failing with an error 'CUDA out of memory'. The training instance is an ml.p3.2xlarge with 16 GB GPU memory. The model architecture and batch size are appropriate for this instance size. What is the most likely cause of this error?

Accepted Answer

Enable automatic mixed precision (AMP) training to reduce memory usage.. Option C is correct because enabling automatic mixed precision (AMP) training reduces GPU memory usage by storing tensors in half-precision (FP16) where possible, while keeping critical operations in full precision (FP32). This directly addresses the 'CUDA out of memory' error on an ml.p3.2xlarge instance (16 GB GPU memory) without changing the model architecture or batch size, which are already appropriate.

Answer

Reduce the number of epochs.

Answer

Increase the number of GPUs by using a distributed training instance type.

Answer

Use a smaller instance type to force lower memory usage.

Question 7

A company is using Amazon SageMaker to train a deep learning model on a large dataset stored in S3. The training job is failing with an OutOfMemory error. The data scientist wants to minimize cost while resolving the issue. Which action should the data scientist take?

Accepted Answer

Change the input mode from 'File' to 'Pipe'.. The OutOfMemory error occurs because the 'File' input mode downloads the entire training dataset to the instance's local storage before training begins, consuming significant memory. Switching to 'Pipe' mode streams data directly from S3 to the training algorithm, reducing memory footprint and avoiding the need for larger instances. This minimizes cost by using the existing instance type while resolving the memory issue.

Answer

Increase the instance type to one with more memory.

Answer

Use the 'auto' setting for the input mode.

Answer

Reduce the batch size hyperparameter.

Question 8

A company is using Amazon SageMaker to train a XGBoost model on a large dataset. The training job is taking a long time. The data scientist wants to reduce training time without sacrificing model accuracy. The dataset is 100 GB in CSV format stored in S3. What is the most effective approach?

Accepted Answer

Use Pipe mode to stream data from S3 instead of downloading it first.. Option B is correct because SageMaker's Pipe mode streams data directly from S3 to the training algorithm without writing it to disk, eliminating the I/O bottleneck of downloading the full 100 GB dataset. This reduces training time significantly by overlapping data loading with computation, while preserving model accuracy since the entire dataset is still used.

Answer

Reduce the number of instances to avoid communication overhead.

Answer

Use random sampling to reduce the dataset size to 10 GB.

Answer

Use SageMaker Managed Spot Training to reduce cost, but training time may increase due to interruptions.

Question 9

A data scientist is deploying a model using Amazon SageMaker. The model endpoint needs to handle real-time inference requests with low latency. The model is a large ensemble of 10 deep learning models, each approximately 500 MB. What is the most cost-effective deployment strategy that meets the low-latency requirement?

Accepted Answer

Use a SageMaker multi-model endpoint to host all models on one or more instances.. A SageMaker multi-model endpoint (MME) allows hosting multiple models on a single or few instances, dynamically loading them from Amazon S3 into memory as needed. This is the most cost-effective option for a large ensemble of 500 MB models because it avoids the expense of separate endpoints or multiple instances per model, while still supporting low-latency real-time inference by keeping frequently used models cached.

Answer

Deploy each model to a separate endpoint and use a load balancer.

Answer

Use a single endpoint with multiple instances behind it.

Answer

Use a SageMaker batch transform job to process inference requests in batches.

Question 10

An IAM policy is attached to a SageMaker execution role. A data scientist tries to create a training job using a custom algorithm stored in an ECR repository. The training job fails with an 'AccessDenied' error when pulling the Docker image from ECR. What is the missing permission?

Accepted Answer

ecr:GetDownloadUrlForLayer and ecr:BatchGetImage on the ECR repository. When SageMaker pulls a custom Docker image from ECR during training job creation, the execution role needs permissions to download the image layers. The required actions are ecr:GetDownloadUrlForLayer (to generate pre-signed URLs for each layer) and ecr:BatchGetImage (to retrieve image metadata and layer manifests). Without these, the 'AccessDenied' error occurs because SageMaker cannot authenticate or fetch the container image from the ECR repository.

Answer

ecr:PutImage on the ECR repository

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s3:GetObject on the ECR repository

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sagemaker:CreateTrainingJob on the ECR resource