MLS-C01 Modeling Practice Test 34 — 15 Questions

Question 1

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains missing values. Which preprocessing step should be applied before training?

Accepted Answer

Impute missing values with the mean of the column.. Linear regression models in Amazon SageMaker cannot handle missing values natively; they require complete numerical input. Imputing missing values with the column mean is a standard preprocessing technique that preserves the overall distribution and avoids introducing bias, ensuring the SageMaker built-in Linear Learner algorithm can train without errors.

Answer

Ignore missing values; linear regression can handle them.

Answer

Replace missing values with zeros.

Answer

Remove all rows containing missing values.

Question 2

A company is building a real-time fraud detection system using Amazon SageMaker. The model must have low latency (under 10ms) and high throughput (thousands of predictions per second). The team has trained a gradient boosting model using XGBoost. Which SageMaker inference option is MOST suitable?

Accepted Answer

Deploy the model on a SageMaker real-time endpoint with a multi-model endpoint.. A multi-model endpoint (MME) on SageMaker is the most suitable option because it allows you to host multiple XGBoost models on a single endpoint, sharing the underlying instance to maximize throughput and minimize latency. MMEs keep models loaded in memory and route requests to the correct model with sub-10ms overhead, meeting the low-latency and high-throughput requirements for real-time fraud detection.

Answer

Use SageMaker asynchronous inference.

Answer

Deploy the model on a SageMaker serverless endpoint.

Answer

Use a batch transform job.

Question 3

A machine learning team is using Amazon SageMaker to tune hyperparameters for a neural network. They have defined a hyperparameter tuning job with a random search strategy. The training time per job is very long. Which strategy can reduce the total tuning time?

Accepted Answer

Enable early stopping to terminate poorly performing jobs.. Enabling early stopping allows SageMaker to terminate training jobs that are unlikely to produce better results based on the objective metric, which directly reduces total tuning time by freeing up compute resources for more promising hyperparameter combinations. This is especially effective with random search, where many trials may converge slowly or plateau.

Answer

Use a larger instance type for each training job.

Answer

Switch to Bayesian optimization.

Answer

Increase the number of training jobs.

Question 4

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains missing values. Which preprocessing step should be applied before training?

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

Accepted Answer

Use Pipe mode to stream data from S3 instead of downloading.. SageMaker Pipe mode streams data directly from S3 to the training algorithm via a Unix FIFO (named pipe), eliminating the need to first download the entire dataset to the training instance's local storage. This reduces I/O wait time and disk usage, which is especially beneficial for large language models where dataset sizes can be in terabytes, thereby significantly cutting total training time.

Answer

Increase the number of instances in the training job.

Answer

Change the optimizer to AdamW.

Answer

Switch to spot instances to reduce cost.

Question 5

A company is using Amazon SageMaker to build a binary classification model for customer churn. The dataset is highly imbalanced (90% no churn, 10% churn). Which technique is MOST effective for handling class imbalance?

Accepted Answer

Use SMOTE to generate synthetic samples for the minority class.. SMOTE (Synthetic Minority Oversampling Technique) is the most effective option because it generates synthetic samples for the minority class by interpolating between existing minority instances, thereby balancing the dataset without discarding valuable majority-class data. This approach directly addresses the class imbalance in a binary classification task on SageMaker, improving model recall for the churn class without the information loss caused by undersampling.

Answer

Use accuracy as the evaluation metric.

Answer

Undersample the majority class.

Answer

Train a random forest model instead of logistic regression.

Question 6

A team has trained a deep learning model on Amazon SageMaker using a custom Docker container. They want to deploy the model to a SageMaker endpoint for real-time inference. Which format should the model artifacts be in?

Accepted Answer

A single .tar.gz file containing the model files.. Amazon SageMaker requires model artifacts to be packaged as a single .tar.gz file when using a custom Docker container for real-time inference. This compressed archive must contain the model files (e.g., model.pth, model.h5) and any necessary inference code, as SageMaker extracts the archive to the /opt/ml/model directory during deployment. The .tar.gz format ensures consistent extraction and compatibility with SageMaker's inference pipeline.

Answer

A folder on S3 with the model files.

Answer

No format requirement; any file works.

Answer

A .zip file containing the model files.

Question 7

A company is using Amazon SageMaker to train a time series forecasting model using the DeepAR algorithm. The training data contains multiple time series. The model is overfitting. Which action is LEAST likely to reduce overfitting?

Accepted Answer

Reduce the number of time series in the training set.. Reducing the number of time series in the training set reduces the diversity of training data, which typically increases overfitting rather than reducing it. DeepAR relies on learning patterns across multiple related time series to generalize well; fewer time series mean less shared statistical strength, making the model more likely to memorize noise in the remaining series.

Answer

Decrease the number of layers in the neural network.

Answer

Increase the dropout rate.

Answer

Decrease the context length.

Question 8

A data scientist is using Amazon SageMaker to train a model. The training job is using a large dataset stored in S3. Which data input mode provides the FASTEST data loading for training?

Accepted Answer

Pipe mode. Pipe mode is the fastest data loading mode for SageMaker training because it streams data directly from S3 to the training algorithm via a FIFO pipe, bypassing disk writes. This eliminates the I/O overhead of downloading files to the local storage, enabling near-zero latency data ingestion for large datasets.

Answer

FastFile mode

Answer

Augmented manifest file

Answer

File mode

Question 9

A machine learning engineer is deploying a model on Amazon SageMaker. Which TWO steps are required to create a SageMaker endpoint?

Accepted Answer

Create a SageMaker model. A is correct because creating a SageMaker model is the first required step to define the model artifacts, inference code, and container image that will be used for predictions. Without a model object, SageMaker has no executable artifact to deploy behind the endpoint.

Answer

Submit a training job

Answer

Create a SageMaker pipeline

Answer

Create a SageMaker notebook instance

Question 10

A data scientist is using Amazon SageMaker Debugger to monitor training. Which THREE types of issues can Debugger monitor?

Accepted Answer

Poor weight initialization. Amazon SageMaker Debugger can monitor training for poor weight initialization by analyzing tensors and gradients during the training process. It uses built-in rules to detect if weights are initialized with values that are too large or too small, which can lead to slow convergence or failure to learn. This is a core capability of Debugger's real-time monitoring of model parameters.

Answer

Hardware failures

Answer

Data drift

Question 11

A company is using Amazon SageMaker to train a model. Which TWO metrics should be used to evaluate a binary classification model?

Accepted Answer

AUC. AUC (Area Under the ROC Curve) is a threshold-independent metric that measures the model's ability to distinguish between positive and negative classes across all classification thresholds. For binary classification in SageMaker, AUC is robust to class imbalance and provides a single scalar value representing overall model performance, making it a standard evaluation metric.

Answer

Accuracy

Answer

Perplexity

Answer

Mean Absolute Error

Question 12

A machine learning engineer is trying to deploy a model using a SageMaker endpoint but receives an access denied error. The IAM policy attached to the role is shown in the exhibit. What is the MOST likely cause of the error?

Accepted Answer

The policy does not include iam:PassRole.. The error occurs because the IAM role used by SageMaker does not have the iam:PassRole permission, which is required to allow SageMaker to assume the role and access the necessary resources (e.g., S3 buckets, EC2 instances) during endpoint deployment. Without this permission, SageMaker cannot pass the role to the service, resulting in an access denied error even if other SageMaker actions are allowed.

Answer

The policy does not include sagemaker:CreateEndpoint.

Answer

The policy does not specify resource ARNs.

Answer

The policy does not include sagemaker:InvokeEndpoint.

Question 13

A data scientist runs a training job that fails. The CLI output is shown in the exhibit. What is the MOST likely cause of the failure?

Accepted Answer

The S3 bucket or prefix does not exist.. The CLI output shows an error indicating that the S3 bucket or prefix does not exist. This is a common failure when the training job's input data path is incorrect, as SageMaker attempts to read from the specified S3 location and fails if the bucket or prefix is missing. The error message directly points to this issue, making it the most likely cause.

Answer

The channel name is misspelled.

Answer

The instance type ml.m5.large is too small.

Answer

The IAM role does not have s3:GetObject permission.

Question 14

A data scientist is using an IAM role with the policy shown in the exhibit to train a model in SageMaker. The training job fails with a permissions error. What is the missing permission?

Accepted Answer

iam:PassRole. The training job fails because SageMaker needs to assume the IAM role specified in the training job configuration to access resources like S3 buckets. The `iam:PassRole` permission is required to allow the SageMaker service to pass that role to the training job. Without it, SageMaker cannot assume the role and thus cannot perform actions such as reading training data from S3.

Answer

sagemaker:InvokeEndpoint

Answer

sagemaker:DescribeTrainingJob

Answer

s3:ListBucket

Question 15

A data scientist is training a deep learning model on a large dataset using Amazon SageMaker. The training job is taking too long. Which action would MOST likely reduce training time without sacrificing model accuracy?

Accepted Answer

Enable data parallelism across multiple GPUs. Enabling data parallelism across multiple GPUs distributes the training workload across several devices, allowing larger batch sizes and faster gradient computation per epoch. Amazon SageMaker's distributed training libraries (e.g., SageMaker Data Parallelism) use all-reduce algorithms to synchronize gradients efficiently, which reduces wall-clock training time without altering the model architecture or loss function, thus preserving accuracy.

Answer

Use a smaller instance type for training

Answer

Implement early stopping with a low patience value

Answer

Reduce the number of epochs by half