MLS-C01 Amazon Web Services AWS Certified Machine Learning - Specialty Free Practice Exam Questions (2025 Updated)

The solution D meets the requirements with the least operational overhead because it uses Amazon SageMaker Autopilot, which is a fully managed service that automates the end-to-end process of building, training, and deploying machine learning models. Amazon SageMaker Autopilot can handle data preprocessing, feature engineering, algorithm selection, hyperparameter tuning, and model deployment. The company only needs to create an IAM role for Amazon SageMaker with access to the S3 bucket, create a SageMaker AutoML job pointing to the bucket with the dataset, specify the price as the target attribute, and wait for the job to complete. Amazon SageMaker Autopilot will generate a list of candidate models with different configurations and performance metrics, and the company can deploy the best model for predictions1.

The other options are not suitable because:

Option A: Creating a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket, creating an ECS cluster based on an AWS Deep Learning Containers image, writing the code to perform the feature engineering, training a logistic regression model for predicting the price, and performing the inferences will incur more operational overhead than using Amazon SageMaker Autopilot. The company will have to manage the ECS cluster, the container image, the code, the model, and the inference endpoint. Moreover, logistic regression may not be the best algorithm for predicting the price, as it is more suitable for binary classification tasks2.

Option B: Creating an Amazon SageMaker notebook with a new IAM role that is associated with the notebook, pulling the dataset from the S3 bucket, exploring different combinations of feature engineering transformations, regression algorithms, and hyperparameters, comparing all the results in the notebook, and deploying the most accurate configuration in an endpoint for predictions will incur more operational overhead than using Amazon SageMaker Autopilot. The company will have to write the code for the feature engineering, the model training, the model evaluation, and the model deployment. The company will also have to manually compare the results and select the best configuration3.

Option C: Creating an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda, creating a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset, specifying the price as the target feature, loading the model artifact to a Lambda function for inference on prices of new houses will incur more operational overhead than using Amazon SageMaker Autopilot. The company will have to create and manage the Lambda function, the model artifact, and the inference endpoint. Moreover, XGBoost may not be the best algorithm for predicting the price, as it is more suitable for classification and ranking tasks4.

1: Amazon SageMaker Autopilot

2: Amazon Elastic Container Service

3: Amazon SageMaker Notebook Instances

4: Amazon SageMaker XGBoost Algorithm

To prepare and transform historical data efficiently with minimal setup, Amazon SageMaker Data Wrangler is the optimal tool. Data Wrangler simplifies data preprocessing and exploratory data analysis (EDA) by providing a graphical interface for transformations and insights. By first uploading the CSV data to Amazon S3, the data becomes easily accessible to SageMaker and can be imported directly into Data Wrangler.

Once in Data Wrangler, the team can perform required data transformations and generate insights in a single workflow, avoiding the need for additional tools like Amazon QuickSight or further notebook configuration. This approach provides the simplest and most integrated solution for the data science team.

Option A is correct because SageMaker Pipelines is a service that enables you to create and manage automated workflows for your machine learning projects. You can use SageMaker Pipelines to orchestrate the steps of data extraction, model training, and model deployment in a repeatable and scalable way1.

Option B is correct because SageMaker Model Monitor is a service that monitors the quality of your models in production and alerts you when there are deviations in the model quality. You can use SageMaker Model Monitor to set an accuracy threshold for your model and configure a CloudWatch alarm that triggers when the threshold is exceeded. You can then connect the alarm to the workflow in SageMaker Pipelines to automatically initiate retraining and deployment of a new version of the model2.

Option C is incorrect because it is not the most operationally efficient way to maintain the model’s accuracy. Creating a daily SageMaker Processing job that reads the predictions from Amazon S3 and checks for changes in model prediction accuracy is a manual and time-consuming process. It also requires you to write custom code to perform the data analysis and send the email notification. Moreover, it does not automatically retrain and deploy the model when the accuracy drops.

Option D is incorrect because it is not the most operationally efficient way to maintain the model’s accuracy. Rerunning the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model is a manual and error-prone process. It also requires you to monitor the model’s performance and initiate the retraining and deployment steps yourself. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.

Option E is incorrect because it is not the most operationally efficient way to maintain the model’s accuracy. Exporting the training and deployment code from the SageMaker Studio notebooks into a Python script and packaging the script into an Amazon ECS task that an AWS Lambda function can initiate is a complex and cumbersome process. It also requires you to manage the infrastructure and resources for the Amazon ECS task and the AWS Lambda function. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.

1: SageMaker Pipelines - Amazon SageMaker

2: Monitor data and model quality - Amazon SageMaker

The Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. However, the Data Scientist observes that there is a wide gap between the training and validation set accuracy, which indicates that the model is overfitting the data and generalizing poorly to new data.

To improve the model performance and satisfy the Marketing team’s needs, the Data Scientist can use the following methods:

Add L1 regularization to the classifier: L1 regularization is a technique that adds a penalty term to the loss function of the logistic regression model, proportional to the sum of the absolute values of the coefficients. L1 regularization can help reduce overfitting by shrinking the coefficients of the less important features to zero, effectively performing feature selection. This can simplify the model and make it more interpretable, as well as improve the validation accuracy.

Perform recursive feature elimination: Recursive feature elimination (RFE) is a feature selection technique that involves training a model on a subset of the features, and then iteratively removing the least important features one by one until the desired number of features is reached. The idea behind RFE is to determine the contribution of each feature to the model by measuring how well the model performs when that feature is removed. The features that are most important to the model will have the greatest impact on performance when they are removed. RFE can help improve the model performance by eliminating the irrelevant or redundant features that may cause noise or multicollinearity in the data. RFE can also help the Marketing team understand the direct impact of the relevant features on the model outcome, as the remaining features will have the highest weights in the model.

Regularization for Logistic Regression

Recursive Feature Elimination

The best solution for encrypting images at rest and in transit, and opting out of data usage for service improvement, is to use the following steps:

Enable server-side encryption on the S3 bucket. This will encrypt the images stored in the bucket using AWS Key Management Service (AWS KMS) customer master keys (CMKs). This will protect the data at rest from unauthorized access1

Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support. This will prevent AWS from storing or using the images processed by Amazon Rekognition for service development or enhancement purposes. This will protect the data privacy and ownership2

Use HTTPS to call the Amazon Rekognition CompareFaces API operation. This will encrypt the data in transit between the client and the server using SSL/TLS protocols. This will protect the data from interception or tampering3

The other options are incorrect because they either do not encrypt the images at rest or in transit, or do not opt out of data usage for service improvement. For example:

Option B switches to using an Amazon Rekognition collection to store the images. A collection is a container for storing face vectors that are calculated by Amazon Rekognition. It does not encrypt the images at rest or in transit, and it does not opt out of data usage for service improvement. It also requires changing the API operations from CompareFaces to IndexFaces and SearchFacesByImage, which may not have the same functionality or performance4

Option C switches to using the AWS GovCloud (US) Region for Amazon S3 and Amazon Rekognition. The AWS GovCloud (US) Region is an isolated AWS Region designed to host sensitive data and regulated workloads in the cloud. It does not automatically encrypt the images at rest or in transit, and it does not opt out of data usage for service improvement. It also requires migrating the data and the application to a different Region, which may incur additional costs and complexity5

Option D enables client-side encryption on the S3 bucket. This means that the client is responsible for encrypting and decrypting the images before uploading or downloading them from the bucket. This adds extra overhead and complexity to the client application, and it does not encrypt the data in transit when calling the Amazon Rekognition API. It also does not opt out of data usage for service improvement.

1: Protecting Data Using Server-Side Encryption with AWS KMS–Managed Keys (SSE-KMS) - Amazon Simple Storage Service

2: Opting Out of Content Storage and Use for Service Improvements - Amazon Rekognition

3: HTTPS - Wikipedia

4: Working with Stored Faces - Amazon Rekognition

5: AWS GovCloud (US) - Amazon Web Services

Protecting Data Using Client-Side Encryption - Amazon Simple Storage Service

The correct solution for changing the scaling behavior of the SageMaker instances is to increase the cooldown period for the scale-out activity. The cooldown period is the amount of time, in seconds, after a scaling activity completes before another scaling activity can start. By increasing the cooldown period for the scale-out activity, the ML team can ensure that the new instances are ready before launching additional instances. This will prevent over-scaling and reduce costs1

The other options are incorrect because they either do not solve the issue or require unnecessary steps. For example:

Option A decreases the cooldown period for the scale-in activity and increases the configured maximum capacity of instances. This option does not address the issue of launching additional instances before the new instances are ready. It may also cause under-scaling and performance degradation.

Option B replaces the current endpoint with a multi-model endpoint using SageMaker. A multi-model endpoint is an endpoint that can host multiple models using a single endpoint. It does not affect the scaling behavior of the SageMaker instances. It also requires creating a new endpoint and updating the application code to use it2

Option C sets up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint. Amazon API Gateway is a service that allows users to create, publish, maintain, monitor, and secure APIs. AWS Lambda is a service that lets users run code without provisioning or managing servers. These services do not affect the scaling behavior of the SageMaker instances. They also require creating and configuring additional resources and services34

1: Automatic Scaling - Amazon SageMaker

2: Create a Multi-Model Endpoint - Amazon SageMaker

3: Amazon API Gateway - Amazon Web Services

4: AWS Lambda - Amazon Web Services

XGBoost is a machine learning framework that can be used for classification, regression, ranking, and other tasks. It is based on the gradient boosting algorithm, which builds an ensemble of weak learners (usually decision trees) to produce a strong learner. XGBoost has several advantages over other algorithms, such as scalability, parallelization, regularization, and sparsity handling. For categorizing new products using the provided dataset, an XGBoost model would be a suitable choice, because it can handle multiple features and multiple classes efficiently and accurately. To train an XGBoost model for multi-class classification, the objective parameter should be set to multi: softmax, which means that the model will output a probability distribution over the classes and predict the class with the highest probability. Alternatively, the objective parameter can be set to multi: softprob, which means that the model will output the raw probability of each class instead of the predicted class label. This can be useful for evaluating the model performance or for post-processing the predictions. References:

XGBoost: A tutorial on how to use XGBoost with Amazon SageMaker.

XGBoost Parameters: A reference guide for the parameters of XGBoost.

The solution C is the best option to identify and address training issues with the least development effort. The solution C involves the following steps:

Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues. SageMaker Debugger is a feature of Amazon SageMaker that allows data scientists to monitor, analyze, and debug machine learning models during training. SageMaker Debugger provides a set of built-in rules that can automatically detect common issues and anomalies in model training, such as vanishing or exploding gradients, overfitting, underfitting, low GPU utilization, and more1. The data scientist can use the vanishing_gradient rule to check if the gradients are becoming too small and causing the training to not converge. The data scientist can also use the LowGPUUtilization rule to check if the GPU resources are underutilized and causing the training to be inefficient2.

Launch the StopTrainingJob action if issues are detected. SageMaker Debugger can also take actions based on the status of the rules. One of the actions is StopTrainingJob, which can terminate the training job if a rule is in an error state. This can help the data scientist to save time and money by stopping the training early if issues are detected3.

The other options are not suitable because:

Option A: Using CPU utilization metrics that are captured in Amazon CloudWatch and configuring a CloudWatch alarm to stop the training job early if low CPU utilization occurs will not identify and address training issues effectively. CPU utilization is not a good indicator of model training performance, especially for GPU instances. Moreover, CloudWatch alarms can only trigger actions based on simple thresholds, not complex rules or conditions4.

Option B: Using high-resolution custom metrics that are captured in Amazon CloudWatch and configuring an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected will incur more development effort than using SageMaker Debugger. The data scientist will have to write the code for capturing, sending, and analyzing the custom metrics, as well as for invoking the Lambda function and stopping the training job. Moreover, this solution may not be able to detect all the issues that SageMaker Debugger can5.

Option D: Using the SageMaker Debugger confusion and feature_importance_overweight built-in rules and launching the StopTrainingJob action if issues are detected will not identify and address training issues effectively. The confusion rule is used to monitor the confusion matrix of a classification model, which is not relevant for a regression model that predicts prices. The feature_importance_overweight rule is used to check if some features have too much weight in the model, which may not be related to the convergence or resource utilization issues2.

1: Amazon SageMaker Debugger

2: Built-in Rules for Amazon SageMaker Debugger

3: Actions for Amazon SageMaker Debugger

4: Amazon CloudWatch Alarms

5: Amazon CloudWatch Custom Metrics

The data sources that the data scientist should use to augment the dataset of reviews are those that contain relevant and diverse customer feedback about the company or its products. Emails exchanged by customers and the company’s customer service agents can provide valuable insights into the issues and complaints that customers have, as well as the solutions and responses that the company offers. Social media posts containing the name of the company or its products can capture the opinions and sentiments of customers and potential customers, as well as their reactions to marketing campaigns and product launches. A publicly available collection of customer reviews can provide a large and varied sample of feedback from different online platforms and marketplaces, which can help to generalize the ML models and avoid bias.

Detect sentiment from customer reviews using Amazon Comprehend | AWS Machine Learning Blog

How to Apply Machine Learning to Customer Feedback

The Hyperband tuning strategy is a multi-fidelity based tuning strategy that dynamically reallocates resources to the most promising hyperparameter configurations. Hyperband uses both intermediate and final results of training jobs to stop under-performing jobs and reallocate epochs to well-utilized hyperparameter configurations. Hyperband can provide up to three times faster hyperparameter tuning compared to other strategies1. Setting a lower value for the MaxNumberOfTrainingJobs parameter can also reduce the computation time for the hyperparameter tuning job by limiting the number of training jobs that the tuning job can launch. This can help avoid unnecessary or redundant training jobs that do not improve the objective metric.

The other options are not effective ways to reduce the computation time for the hyperparameter tuning job. Increasing the number of hyperparameters will increase the complexity and dimensionality of the search space, which can result in longer computation time and lower performance. Using the grid search tuning strategy will also increase the computation time, as grid search methodically searches through every combination of hyperparameter values, which can be very expensive and inefficient for large search spaces. Setting a lower value for the MaxParallelTrainingJobs parameter will reduce the number of training jobs that can run in parallel, which can slow down the tuning process and increase the waiting time.

The XGBoost algorithm is a popular machine learning technique for classification problems. It is based on the idea of boosting, which is to combine many weak learners (decision trees) into a strong learner (ensemble model).

The XGBoost algorithm can handle imbalanced data by using the scale_pos_weight parameter, which controls the balance of positive and negative weights in the objective function. A typical value to consider is the ratio of negative cases to positive cases in the data. By increasing this parameter, the algorithm will pay more attention to the minority class (positive) and reduce the number of false negatives.

The XGBoost algorithm can also use different evaluation metrics to optimize the model performance. The default metric is error, which is the misclassification rate. However, this metric can be misleading for imbalanced data, as it does not account for the different costs of false positives and false negatives. A better metric to use is AUC, which is the area under the receiver operating characteristic (ROC) curve. The ROC curve plots the true positive rate against the false positive rate for different threshold values. The AUC measures how well the model can distinguish between the two classes, regardless of the threshold. By changing the eval_metric parameter to AUC, the algorithm will try to maximize the AUC score and reduce the number of false negatives.

Therefore, the combination of steps that should be taken to reduce the number of false negatives are to increase the scale_pos_weight parameter and change the eval_metric parameter to AUC.

XGBoost Parameters

XGBoost for Imbalanced Classification

 The XGBoost model is a supervised machine learning algorithm, which means it requires labeled data to learn from. The customers’ current segmentation is unknown, so there is no label to train the XGBoost model on. Moreover, the XGBoost model is designed for classification or regression tasks, not for clustering. Clustering is a type of unsupervised machine learning, which means it does not require labeled data. Clustering algorithms try to find natural groups or clusters in the data based on their similarity or distance. A common clustering algorithm is k-means, which partitions the data into K clusters, where each data point belongs to the cluster with the nearest mean. To meet the current requirements, the data scientist should train a k-means model with K = 5 on the same dataset to predict a segment for each customer. This way, the data scientist can identify five customer segments based on age, income, and location, without needing any labels. References:

What is XGBoost? - Amazon SageMaker

What is Clustering? - Amazon SageMaker

K-Means Algorithm - Amazon SageMaker

To retrieve only the latest version of a record in real-time for a single entity (like a customer), the correct method is to use the GetRecord API provided by SageMaker Feature Store.

“Use the GetRecord API to retrieve the latest feature values for a specific record identifier from the online store. This operation returns the latest version of the record only.”

This is optimal for low-latency, single-record lookups, such as real-time inference scenarios. BatchGetRecord is used for multiple records, and Athena queries are for offline/batch analysis and not suited for real-time access.

A confusion matrix is a matrix that summarizes the performance of a machine learning model on a set of test data. It displays the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) produced by the model on the test data1. For multi-class classification, the matrix shape will be equal to the number of classes i.e for n classes it will be nXn1. The diagonal values represent the number of correct predictions for each class, and the off-diagonal values represent the number of incorrect predictions for each class1.

The BlazingText algorithm is a proprietary machine learning algorithm for forecasting time series using causal convolutional neural networks (CNNs). BlazingText works best with large datasets containing hundreds of time series. It accepts item metadata, and is the only Forecast algorithm that accepts related time series data without future values2.

From the confusion matrices for the training and test sets, we can observe the following:

The model has a high accuracy on the training set, as most of the diagonal values are high and the off-diagonal values are low. This means that the model is able to learn the patterns and features of the training data well.

However, the model has a lower accuracy on the test set, as some of the diagonal values are lower and some of the off-diagonal values are higher. This means that the model is not able to generalize well to the unseen data and makes more errors.

The model has a particularly high error rate for classes B and E on the test set, as the values of M_22 and M_55 are much lower than the values of M_12, M_21, M_15, M_25, M_51, and M_52. This means that the model is confusing classes B and E with other classes more often than it should.

The model has a relatively low error rate for classes A, C, and D on the test set, as the values of M_11, M_33, and M_44 are high and the values of M_13, M_14, M_23, M_24, M_31, M_32, M_34, M_41, M_42, and M_43 are low. This means that the model is able to distinguish classes A, C, and D from other classes well.

These results indicate that the model is overfitting for classes B and E, meaning that it is memorizing the specific features of these classes in the training data, but failing to capture the general features that are applicable to the test data. Overfitting is a common problem in machine learning, where the model performs well on the training data, but poorly on the test data3. Some possible causes of overfitting are:

The model is too complex or has too many parameters for the given data. This makes the model flexible enough to fit the noise and outliers in the training data, but reduces its ability to generalize to new data.

The data is too small or not representative of the population. This makes the model learn from a limited or biased sample of data, but fails to capture the variability and diversity of the population.

The data is imbalanced or skewed. This makes the model learn from a disproportionate or uneven distribution of data, but fails to account for the minority or rare classes.

Some possible solutions to prevent or reduce overfitting are:

Simplify the model or use regularization techniques. This reduces the complexity or the number of parameters of the model, and prevents it from fitting the noise and outliers in the data. Regularization techniques, such as L1 or L2 regularization, add a penalty term to the loss function of the model, which shrinks the weights of the model and reduces overfitting3.

Increase the size or diversity of the data. This provides more information and examples for the model to learn from, and increases its ability to generalize to new data. Data augmentation techniques, such as rotation, flipping, cropping, or noise addition, can generate new data from the existing data by applying some transformations3.

Balance or resample the data. This adjusts the distribution or the frequency of the data, and ensures that the model learns from all classes equally. Resampling techniques, such as oversampling or undersampling, can create a balanced dataset by increasing or decreasing the number of samples for each class3.

Confusion Matrix in Machine Learning - GeeksforGeeks

BlazingText algorithm - Amazon SageMaker

Overfitting and Underfitting in Machine Learning - GeeksforGeeks

In cases where survey questions allow multiple choices per question, one-hot encoding is an effective way to represent responses as binary features. Each possible option for each question is transformed into a separate binary column (1 if selected, 0 if not), providing a comprehensive and machine-readable format that logistic regression models can interpret effectively.

This approach ensures that each respondent’s selections are accurately captured in a format suitable for training, offering a straightforward representation for multi-choice responses.

 Regularization and dropouts are techniques that can help reduce overfitting in deep learning models. Overfitting occurs when the model learns too much from the training data and fails to generalize well to new data. Regularization adds a penalty term to the loss function that penalizes the model for having large or complex weights. This prevents the model from memorizing the noise or irrelevant features in the training data. L1 and L2 are two types of regularization that differ in how they calculate the penalty term. L1 regularization uses the absolute value of the weights, while L2 regularization uses the square of the weights. Dropouts are another technique that randomly drops out some units or neurons from the network during training. This creates a thinner network that is less prone to overfitting. Dropouts also act as a form of ensemble learning, where multiple sub-models are combined to produce a better prediction. By applying regularization and dropouts to the training, the web-based company can improve the generalization and accuracy of its deep learning model on the test and validation data. References:

Regularization: A video that explains the concept and benefits of regularization in deep learning.

Dropout: A video that demonstrates how dropout works and why it helps reduce overfitting.

 The best feature engineering strategy for this scenario is to apply dimensionality reduction by using the principal component analysis (PCA) algorithm. PCA is a technique that transforms a large set of correlated features into a smaller set of uncorrelated features called principal components. This can help reduce the complexity and noise in the data, improve the performance and interpretability of the model, and avoid overfitting. Amazon SageMaker provides a built-in PCA algorithm that can be used to perform dimensionality reduction on tabular data. The ML specialist can use Amazon SageMaker to train and deploy the PCA model, and then use the output of the PCA model as the input for the classification model.

Dimensionality Reduction with Amazon SageMaker

Amazon SageMaker PCA Algorithm

 Amazon SageMaker is a service that allows users to build, train, and deploy ML models on AWS. Amazon SageMaker endpoints are scalable and secure web services that can be used to perform real-time inference on ML models. An endpoint configuration defines the models that are deployed and the resources that are used by the endpoint. An endpoint configuration can have multiple production variants, each representing a different version or variant of a model. Users can specify the portion of the inferences served by each production variant using the initialVariantWeight parameter. Users can also programmatically update the endpoint configuration to change the portion of the inferences served by each production variant using the UpdateEndpointWeightsAndCapacities API. Therefore, option B is the best solution to satisfy the requirements with minimal effort.

Option A is incorrect because creating multiple endpoints for each model would incur more cost and complexity than using a single endpoint with multiple production variants. Moreover, controlling the invocation of different models at the application layer would require more custom logic and coordination than using the UpdateEndpointWeightsAndCapacities API. Option C is incorrect because Amazon SageMaker Neo is a service that allows users to optimize ML models for different hardware platforms, such as edge devices. It is not relevant to the problem of running multiple versions of a model in parallel for long periods of time. Option D is incorrect because Amazon SageMaker batch transform is a service that allows users to perform asynchronous inference on large datasets. It is not suitable for the problem of performing real-time inference on streaming data from device users.

Deploying models to Amazon SageMaker hosting services - Amazon SageMaker

Update an Amazon SageMaker endpoint to accommodate new models - Amazon SageMaker

UpdateEndpointWeightsAndCapacities - Amazon SageMaker

Data normalization is a data preprocessing technique that scales the features to a common range, such as [0, 1] or [-1, 1]. This helps reduce the impact of features with high magnitude on the cost function and improves the convergence during backpropagation. Data normalization can be done using different methods, such as min-max scaling, z-score standardization, or unit vector normalization. Data normalization is different from dimensionality reduction, which reduces the number of features; model regularization, which adds a penalty term to the cost function to prevent overfitting; and data augmentation, which increases the amount of data by creating synthetic samples. References:

Data processing options for AI/ML | AWS Machine Learning Blog

Data preprocessing - Machine Learning Lens

How to Normalize Data Using scikit-learn in Python

Normalization | Machine Learning | Google for Developers