MLS-C01 Modeling Practice Test 23 — 15 Questions

Question 1

A company uses Amazon SageMaker to deploy a model for real-time inference. The endpoint uses an ml.m5.large instance with automatic scaling based on CPU utilization. The team notices that during traffic spikes, the endpoint returns 5xx errors. What should the team do to improve the endpoint's availability?

Accepted Answer

Increase the instance type to ml.c5.2xlarge.. The correct answer is A because upgrading the instance type from ml.m5.large to ml.c5.2xlarge provides more CPU and memory resources, which directly addresses the root cause of 5xx errors during traffic spikes — insufficient compute capacity to handle the request load. Automatic scaling based on CPU utilization may not react quickly enough to sudden spikes, leading to request queuing and timeouts that manifest as 5xx errors. A larger instance type increases the baseline throughput, reducing the likelihood of resource exhaustion before scaling can take effect.

Answer

Reduce the scaling cooldown period.

Answer

Place an Application Load Balancer in front of the endpoint.

Answer

Use Amazon API Gateway to throttle requests.

Question 2

A data scientist is training a model using SageMaker's built-in XGBoost algorithm with a large dataset stored in CSV format. The training job is using File mode. The data scientist wants to reduce the time it takes to start training. Which approach would be most effective?

Accepted Answer

Use Pipe mode for the input data channel.. Pipe mode streams data directly from Amazon S3 into the training container, eliminating the need to first download the entire dataset to the EBS volume. This reduces the startup time significantly because training can begin as soon as the first records arrive, rather than waiting for the full download to complete.

Answer

Increase the size of the EBS volume.

Answer

Convert the data to Parquet format.

Answer

Increase the number of training instances.

Question 3

A data scientist is training a binary classifier using imbalanced data. Which TWO techniques can help improve model performance on the minority class? (Choose two.)

Accepted Answer

Use the F1 score as the evaluation metric.. The F1 score is the harmonic mean of precision and recall, making it a robust evaluation metric for imbalanced datasets because it captures both false positives and false negatives. Unlike accuracy, which can be misleadingly high when the majority class dominates, the F1 score provides a balanced measure of model performance on the minority class.

Answer

Undersample the majority class randomly.

Answer

Use accuracy as the evaluation metric.

Answer

Apply L1 regularization to the model.

Question 4

A company is using SageMaker to deploy a model for real-time inference. The model requires GPU for low latency. Which THREE configurations should the company consider for high availability and cost optimization? (Choose three.)

Accepted Answer

Use a multi-model endpoint to share GPU instances among multiple models.. Option B is correct because a multi-model endpoint allows multiple models to be hosted on the same GPU-backed instance, sharing the GPU resources and reducing idle time. This improves cost efficiency by maximizing GPU utilization while still providing low-latency inference for each model. It is a recommended pattern for serving many models with GPU requirements without provisioning separate endpoints.

Answer

Use Spot instances for the endpoint.

Answer

Use SageMaker Batch Transform for inference.

Question 5

A data scientist is using SageMaker to train a deep learning model. The training job runs on a single GPU instance and is taking too long. Which THREE actions can the data scientist take to reduce training time? (Choose three.)

Accepted Answer

Switch to Pipe input mode to reduce I/O waiting time.. Option C is correct because Pipe input mode streams training data directly from Amazon S3 to the GPU instance, reducing I/O waiting time compared to the default File mode, which downloads the entire dataset to the EBS volume first. This minimizes the time the GPU spends idle waiting for data, especially for large datasets that cannot fit entirely in memory.

Answer

Increase the size of the EBS volume attached to the instance.

Answer

Increase the number of instances but keep the same total data.

Question 6

A company is deploying a machine learning model for real-time fraud detection. The model must respond within 100ms. Which SageMaker endpoint deployment strategy should be used?

Accepted Answer

Deploy the model to a SageMaker Real-Time Inference endpoint with a Multi-Model Endpoint configuration.. Option B is correct because a SageMaker Real-Time Inference endpoint with a Multi-Model Endpoint configuration provides low-latency (sub-100ms) responses by keeping models loaded in memory and routing requests efficiently. This architecture is ideal for real-time fraud detection where multiple models may be needed, and it meets the strict latency requirement without the cold-start overhead of serverless options.

Answer

Deploy the model to a SageMaker Serverless Inference endpoint.

Answer

Deploy the model as an AWS Lambda function with an API Gateway trigger.

Answer

Use SageMaker Batch Transform to process requests in batches.

Question 7

A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). Which metric is most appropriate for evaluating model performance?

Accepted Answer

Area Under the ROC Curve (AUC-ROC). AUC-ROC is the most appropriate metric for imbalanced binary classification because it evaluates the model's ability to distinguish between positive and negative classes across all classification thresholds, without being biased by the 95% negative majority. It measures the trade-off between true positive rate and false positive rate, making it robust to class imbalance.

Answer

R-squared

Answer

Mean Squared Error (MSE)

Answer

Accuracy

Question 8

A team is training a deep learning model on SageMaker using a custom Docker container. The training job fails with 'OutOfMemoryError'. The instance type is ml.p3.2xlarge with 61 GB memory. Which change should increase available memory?

Accepted Answer

Set the 'shm-size' parameter in the SageMaker training container to a larger value.. The 'OutOfMemoryError' in a SageMaker training container often stems from insufficient shared memory (/dev/shm) for data-loading workers, especially with PyTorch or TensorFlow dataloaders that use multiprocessing. Increasing the 'shm-size' parameter allocates more shared memory to the container, resolving the error without altering the model or instance type.

Answer

Reduce the batch size to use less memory.

Answer

Use SageMaker distributed data parallelism to distribute the model across multiple instances.

Answer

Mount an Amazon FSx for Lustre file system to offload data.

Question 9

A company wants to deploy a machine learning model that requires GPU acceleration for inference. The model is small and can fit on a single GPU. Which SageMaker endpoint configuration is MOST cost-effective?

Accepted Answer

Use a ml.p3.2xlarge instance with 1 GPU and enable automatic scaling.. Option D is the most cost-effective because it uses a single-GPU ml.p3.2xlarge instance, which matches the requirement that the model fits on one GPU, and enables automatic scaling to handle variable traffic without over-provisioning. This avoids paying for unused GPU capacity while still providing the necessary GPU acceleration for inference.

Answer

Use a ml.p3.16xlarge instance with 8 GPUs.

Answer

Use a SageMaker Serverless Inference endpoint.

Answer

Use a Multi-Model Endpoint on a ml.g4dn.xlarge instance.

Question 10

A data scientist is training a linear regression model and notices high bias in the training set. What action is most likely to reduce bias?

Accepted Answer

Add more relevant features to the model.. High bias indicates that the model is underfitting the training data, meaning it is too simple to capture the underlying patterns. Adding more relevant features increases the model's capacity to learn complex relationships, directly reducing bias. This is a standard approach in linear regression to address underfitting.

Answer

Apply L1 regularization.

Answer

Increase the learning rate.

Answer

Increase the amount of training data.

Question 11

A machine learning engineer is tuning a gradient boosting model using SageMaker Hyperparameter Tuning. The objective is to minimize MAE. The tuning job uses 20 training jobs. After 10 jobs, the best objective value is 5.2. Which action should the engineer take to potentially improve the result?

Accepted Answer

Increase the total number of training jobs to 50.. Option C is correct because increasing the total number of training jobs from 20 to 50 gives the Bayesian optimization algorithm more opportunities to explore the hyperparameter space and exploit promising regions. With only 10 jobs completed, the tuning job may not have converged to the global minimum of MAE, and additional jobs can refine the search, especially since Bayesian search builds a probabilistic model that improves with more observations.

Answer

Set early stopping to avoid overfitting.

Answer

Change the objective metric to RMSE.

Answer

Switch the tuning strategy from Bayesian to Random search.

Question 12

A company has a time series forecasting problem with daily sales data. The data shows both trend and seasonality. Which Amazon SageMaker built-in algorithm is most suitable?

Accepted Answer

DeepAR. DeepAR is a supervised learning algorithm for time series forecasting that explicitly models both trend and seasonality using recurrent neural networks (RNNs). It is designed to handle multiple related time series, incorporate additional features like holidays or promotions, and produce probabilistic forecasts, making it the most suitable choice for daily sales data with trend and seasonal patterns.

Answer

K-Means

Answer

Linear Learner

Answer

XGBoost

Question 13

A data scientist is using SageMaker to train a model. The training data is stored in an S3 bucket in a different AWS account. What is required to allow SageMaker to access the data?

Accepted Answer

Configure the SageMaker execution role with a policy that grants cross-account access to the S3 bucket.. Option A is correct because SageMaker uses an IAM execution role to access resources. To allow cross-account access to an S3 bucket, the SageMaker execution role must have an IAM policy that grants s3:GetObject and s3:ListBucket permissions for the bucket, and the S3 bucket policy must also grant cross-account access to that role. This is the standard AWS mechanism for cross-account resource access.

Answer

Set up VPC peering between the two accounts.

Answer

Create a SageMaker notebook instance in the same account as the S3 bucket.

Answer

Launch the training job from a SageMaker notebook in the account containing the S3 bucket.

Question 14

A company is deploying a model for real-time inference with SageMaker. The endpoint receives spiky traffic, with occasional bursts of 10x normal load. Which scaling policy is MOST cost-effective while maintaining availability?

Accepted Answer

Use a combination of scheduled scaling for predictable peaks and simple scaling for additional bursts.. Option C is correct because it combines scheduled scaling for predictable traffic patterns (e.g., known peak hours) with simple scaling to handle unexpected bursts, ensuring availability during 10x load spikes without over-provisioning. This hybrid approach is more cost-effective than always-on large instances, as it dynamically adjusts capacity only when needed, aligning with SageMaker's automatic scaling capabilities.

Answer

Provision a large instance type that can handle the peak load at all times.

Answer

Manually scale the endpoint based on historical traffic patterns.

Answer

Use a target tracking scaling policy based on average latency.

Question 15

A data scientist is training a neural network for image classification. The training loss decreases but validation loss increases after a few epochs. Which TWO actions should be taken to address this?

Accepted Answer

Implement early stopping based on validation loss.. Option A is correct because early stopping monitors validation loss and halts training when it stops improving, preventing overfitting. This directly addresses the symptom of decreasing training loss with increasing validation loss, which is a classic sign of overfitting.

Answer

Increase the learning rate.

Answer

Increase the number of epochs.

Answer

Add more convolutional layers.