Amazon  AWS Certified Machine Learning Engineer – Associate MLA-C01   Exam  Dumps and Practice Test Questions Set 3 Q 41- 60

Visit here for our full Amazon AWS Certified Machine Learning Engineer – Associate MLA-C01 exam dumps and practice test questions.

Question 41

A telecom company wants to detect abnormal patterns in customer call durations and drop rates to identify potential network issues. The dataset contains high-dimensional continuous metrics collected over time. Which AWS SageMaker approach is most suitable for this anomaly detection task?

A) Amazon SageMaker Random Cut Forest (RCF)

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker K-Means

D) Amazon SageMaker XGBoost

Answer

A) Amazon SageMaker Random Cut Forest (RCF)

Explanation

A) Amazon SageMaker Random Cut Forest is specifically designed for unsupervised anomaly detection in high-dimensional continuous data. For telecom metrics such as call durations, drop rates, and other continuous network KPIs, RCF identifies points that deviate from expected behavior by computing anomaly scores. The algorithm works well when anomalous patterns are rare and labels are unavailable, which is typical in network monitoring. It is scalable to large datasets, can handle correlated metrics, and provides interpretable anomaly scores. Real-time deployment of RCF enables immediate detection of unusual patterns, allowing the telecom company to respond quickly to network issues, optimize performance, and prevent service degradation. RCF’s ability to model evolving normal behavior is crucial in dynamic environments like telecom networks.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled examples to learn a predictive model. Since anomalies in network metrics are rare and often unlabeled, Linear Learner is impractical for real-time anomaly detection. Even with labeled data, it may fail to capture subtle or evolving abnormal patterns inherent in high-dimensional network metrics.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. K-Means may help group typical network behaviors or segment call patterns, but it does not produce anomaly scores or detect rare deviations directly. It is exploratory rather than predictive and cannot provide actionable real-time detection for abnormal metrics.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While XGBoost is powerful for classification and regression with labeled data, it is not suitable for unsupervised anomaly detection in real-time network metrics. Acquiring labeled anomalies for network data is challenging, and XGBoost would require extensive labeling and preprocessing.

Random Cut Forest is the most suitable solution for detecting abnormal patterns in continuous, high-dimensional network metrics due to its unsupervised nature, scalability, real-time deployment, and ability to handle evolving patterns. Other algorithms either assume supervision, are exploratory, or do not provide anomaly scoring.

Question 42

A bank wants to predict whether a customer will churn based on transaction history, demographics, and engagement metrics. The dataset contains numerical, categorical, and sparse features with imbalanced classes. Which AWS SageMaker algorithm is most appropriate?

A) Amazon SageMaker XGBoost

B) Amazon SageMaker Factorization Machines

C) Amazon SageMaker Linear Learner

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm that captures non-linear relationships and interactions among features. For customer churn prediction, XGBoost can handle dense numerical features, categorical variables (after encoding), and sparse engagement metrics. It supports weighting for imbalanced datasets, ensuring that minority churn events are accurately predicted. XGBoost provides probability estimates, interpretable feature importance metrics, and can scale to large customer datasets. These characteristics make it ideal for identifying at-risk customers and enabling proactive retention strategies. It also integrates seamlessly with SageMaker endpoints for batch or real-time predictions, which is essential for dynamic engagement campaigns.

B) Amazon SageMaker Factorization Machines are optimized for sparse, high-dimensional datasets focusing on pairwise interactions. While FM can model sparse engagement features, it is less effective for dense numerical data or capturing complex non-linear interactions. Factorization Machines may underfit the rich combination of features typically found in churn prediction datasets, reducing predictive accuracy.

C) Amazon SageMaker Linear Learner is a supervised algorithm suitable for regression or classification tasks. It assumes linear relationships between features and the target. While simple and interpretable, Linear Learner may underfit churn data with complex interactions among demographics, transaction history, and engagement metrics. Feature engineering could mitigate some limitations, but the model’s performance may still lag compared to XGBoost.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It could cluster customers based on behavior, but it cannot produce supervised churn predictions or probability scores. K-Means is exploratory, not predictive, and unsuitable for classification tasks with labeled outcomes.

XGBoost is the most appropriate choice due to its ability to handle mixed feature types, capture non-linear relationships, manage class imbalance, and provide interpretable results for customer churn prediction. Other algorithms either focus on sparse interactions, linear relationships, or unsupervised segmentation.

Question 43

A retail company wants to forecast weekly product demand across multiple stores using historical sales data, promotions, and seasonal information. Which AWS SageMaker algorithm is best suited for this multi-step time series forecasting task?

A) Amazon SageMaker Linear Learner

B) Amazon SageMaker DeepAR Forecasting

C) Amazon SageMaker XGBoost

D) Amazon SageMaker K-Means

Answer

B) Amazon SageMaker DeepAR Forecasting

Explanation

A) Amazon SageMaker Linear Learner is a regression algorithm suitable for structured datasets. While it could incorporate lag features, promotions, and seasonal variables, it assumes linear relationships and does not naturally capture temporal dependencies or sequential patterns in time series data. Multi-step forecasting would require extensive feature engineering, and the predictions would lack probabilistic estimates, which are important for inventory planning and risk management.

B) Amazon SageMaker DeepAR Forecasting is a recurrent neural network-based algorithm designed for multi-step, probabilistic time series forecasting. It can handle multiple related time series simultaneously, leveraging shared patterns across products and stores. DeepAR incorporates covariates such as promotions, holidays, and seasonal effects to improve forecast accuracy. It provides both point forecasts and prediction intervals, enabling risk-aware decision-making for inventory, supply chain, and demand planning. Its ability to scale to large datasets, adapt to new trends, and capture sequential dependencies makes it ideal for weekly product demand forecasting across multiple stores.

C) Amazon SageMaker XGBoost is optimized for structured tabular data. While it can be adapted for time series using lag features and rolling statistics, it does not naturally model sequential dependencies or multi-step forecasts. Capturing seasonality and correlations across multiple related series would require significant feature engineering and may reduce forecast accuracy compared to DeepAR.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It can segment products or stores with similar demand patterns but cannot generate numerical forecasts or multi-step predictions. K-Means is exploratory and does not provide actionable forecasts for inventory planning.

DeepAR is the most suitable solution for multi-step demand forecasting due to its ability to handle multiple related time series, incorporate covariates, capture temporal patterns, and provide probabilistic forecasts. Other algorithms are either linear, not sequential, or exploratory.

Question 44

A company wants to recommend movies to users based on sparse user-item interaction data and user demographics. Which AWS SageMaker algorithm is most suitable for building a recommendation engine?

A) Amazon SageMaker Factorization Machines

B) Amazon SageMaker XGBoost

C) Amazon SageMaker Linear Learner

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker Factorization Machines

Explanation

A) Amazon SageMaker Factorization Machines are specifically designed to handle sparse, high-dimensional datasets, making them ideal for recommendation systems. They model pairwise feature interactions, capturing latent relationships between users and items even when direct interactions are limited. FM can incorporate side information, such as user demographics and item attributes, improving recommendation quality. Its scalability enables handling millions of users and items efficiently. For movie recommendations, Factorization Machines can predict preferences for user-item pairs not previously observed, which is essential for collaborative filtering scenarios.

B) Amazon SageMaker XGBoost is designed for structured tabular data and non-linear classification/regression tasks. While XGBoost could be used for engineered interaction features, it cannot efficiently model latent factors for unseen user-item combinations. The preprocessing required for sparse collaborative filtering makes it less practical for large-scale recommendation systems.

C) Amazon SageMaker Linear Learner assumes linear relationships between features and the target variable. While it can work with sparse features, it cannot capture complex user-item interactions or latent factors, limiting recommendation quality. Linear Learner may underfit in high-dimensional sparse datasets typical of recommendation engines.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering could segment users or items, it does not provide individual-level recommendations or predict preferences. K-Means is exploratory and does not support collaborative filtering.

Factorization Machines are best suited for recommendation systems due to their ability to handle sparse interactions, model latent factors, incorporate side information, and scale efficiently. Other algorithms either focus on structured data, linear relationships, or clustering without personalization.

Question 45

A healthcare provider wants to predict patient readmission within 30 days using electronic health records (EHR) data containing numerical lab results, categorical demographics, and sparse diagnosis codes. Which AWS SageMaker algorithm is most suitable?

A) Amazon SageMaker XGBoost

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker Factorization Machines

D) Amazon SageMaker DeepAR Forecasting

Answer

A) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm suitable for heterogeneous structured data like EHRs. It handles numerical, categorical, and sparse features efficiently, capturing non-linear interactions between lab results, demographics, and diagnoses. XGBoost can also address class imbalance using weighting, which is important because readmissions are relatively rare. Its interpretability through feature importance allows healthcare providers to understand key risk factors, such as comorbidities or abnormal lab results. XGBoost scales well to large datasets and provides probability estimates for readmission risk, enabling targeted interventions and resource planning.

B) Amazon SageMaker Linear Learner assumes linear relationships and may underfit in datasets with complex interactions between numerical labs, categorical demographics, and sparse diagnosis codes. While simple and interpretable, Linear Learner would likely produce lower predictive accuracy without extensive feature engineering.

C) Amazon SageMaker Factorization Machines are designed for sparse high-dimensional datasets with pairwise interactions, commonly used in recommendations. EHR data is a mixture of dense and sparse features with higher-order interactions, which FM may not capture effectively. It may underperform in readmission prediction compared to XGBoost.

D) Amazon SageMaker DeepAR Forecasting is designed for sequential time series forecasting. Predicting readmission is a supervised classification problem with heterogeneous features rather than a temporal sequence prediction, making DeepAR unsuitable.

XGBoost provides the best combination of predictive power, handling of heterogeneous features, interpretability, and scalability for patient readmission prediction. Other algorithms either assume linearity, focus on sparse interactions, or are designed for time series forecasting.

Question 46

A company wants to detect anomalies in IoT sensor data from manufacturing machines. The dataset contains continuous sensor readings such as temperature, vibration, and pressure. Which AWS SageMaker algorithm is most suitable for real-time anomaly detection?

A) Amazon SageMaker Random Cut Forest (RCF)

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker K-Means

D) Amazon SageMaker XGBoost

Answer

A) Amazon SageMaker Random Cut Forest (RCF)

Explanation

A) Amazon SageMaker Random Cut Forest is an unsupervised algorithm specifically designed for anomaly detection in high-dimensional continuous datasets. For IoT sensor data, RCF identifies data points that deviate from normal patterns, computing anomaly scores for each observation. Its unsupervised nature makes it ideal for scenarios where labeled anomalies are rare or unavailable, which is typical in industrial environments. RCF can handle correlated features, detect both point and contextual anomalies, and is scalable to large streaming datasets. Real-time deployment via SageMaker endpoints allows continuous monitoring, immediate detection, and automated alerting for unusual sensor readings. This enables proactive maintenance, reducing downtime and equipment failures.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled anomalies for training, which are often unavailable in IoT sensor streams. Even with labels, Linear Learner may not capture the dynamic, high-dimensional interactions in continuous sensor data, limiting its effectiveness in real-time anomaly detection.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it could cluster normal operating states of machines, it does not provide anomaly scores or detect rare deviations. K-Means is exploratory and not suitable for real-time anomaly detection in industrial IoT data.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. It requires labeled anomalies for training and is not naturally suited for continuous streaming data. Obtaining sufficient labeled anomalies in real-world IoT environments is challenging, making XGBoost impractical for real-time detection.

Random Cut Forest is the most appropriate choice for IoT sensor anomaly detection due to its ability to handle high-dimensional continuous data, operate without labels, provide interpretable anomaly scores, and deploy in real-time. Other algorithms are either supervised, exploratory, or not optimized for anomaly detection.

Question 47

A retail company wants to forecast daily sales for multiple products across hundreds of stores, incorporating promotions, holidays, and seasonality. Which AWS SageMaker algorithm is most suitable?

A) Amazon SageMaker Linear Learner

B) Amazon SageMaker DeepAR Forecasting

C) Amazon SageMaker XGBoost

D) Amazon SageMaker K-Means

Answer

B) Amazon SageMaker DeepAR Forecasting

Explanation

A) Amazon SageMaker Linear Learner is a regression algorithm that assumes linear relationships between features and the target. While it could be used with engineered lag features and covariates, it cannot naturally capture temporal dependencies, trends, or seasonal patterns across multiple stores and products. Multi-step forecasting would be complex, and probabilistic estimates for uncertainty in demand would be limited, which is critical for inventory management.

B) Amazon SageMaker DeepAR Forecasting is a recurrent neural network-based algorithm designed for multi-step probabilistic time series forecasting. It can learn sequential patterns, trends, and seasonality across multiple related time series, making it ideal for hundreds of stores and products. DeepAR incorporates covariates such as promotions, holidays, and external events to improve forecast accuracy. Its ability to provide both point forecasts and prediction intervals supports risk-aware inventory planning, resource allocation, and supply chain optimization. Scaling across large datasets and adapting to evolving patterns ensures accuracy and operational efficiency.

C) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm suitable for structured tabular data. While it can be adapted for time series with lag features, it is not inherently sequential and does not naturally model multiple related time series or seasonality. Feature engineering is required for multi-step forecasting, reducing efficiency and possibly accuracy compared to DeepAR.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it may identify clusters of products or stores with similar demand patterns, it cannot generate forecasts or prediction intervals. K-Means is exploratory and does not meet the predictive requirements for sales forecasting.

DeepAR is the most suitable solution for daily sales forecasting across multiple stores and products due to its ability to handle sequential data, incorporate covariates, learn patterns, and provide probabilistic forecasts. Other algorithms either assume linearity, are not sequential, or are exploratory.

Question 48

A company wants to recommend personalized products to users based on sparse interaction data and user demographics. Which AWS SageMaker algorithm is most suitable?

A) Amazon SageMaker Factorization Machines

B) Amazon SageMaker XGBoost

C) Amazon SageMaker Linear Learner

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker Factorization Machines

Explanation

A) Amazon SageMaker Factorization Machines are specifically designed to handle sparse, high-dimensional datasets and model pairwise feature interactions. This makes them ideal for recommendation systems, where interactions between users and items are often sparse. FM learns latent representations for both users and items, enabling predictions for user-item pairs that have not been observed previously. It can also incorporate side information such as user demographics and product attributes to improve recommendation accuracy. Factorization Machines scale efficiently for large numbers of users and items, making them highly suitable for building personalized recommendation engines.

B) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While powerful for structured tabular data, XGBoost is not optimized for sparse user-item interactions and does not learn latent factors naturally. Using XGBoost for recommendation would require extensive feature engineering, reducing scalability and performance compared to FM.

C) Amazon SageMaker Linear Learner is a supervised algorithm assuming linear relationships between features and target variables. While it can handle sparse inputs, it cannot capture complex user-item interactions or latent factors, limiting recommendation quality. Linear Learner may underfit in high-dimensional sparse datasets typical of recommendation scenarios.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While K-Means can group users or products with similar behavior, it cannot generate individualized recommendations or predict preferences. K-Means is exploratory and does not support collaborative filtering.

Factorization Machines are the best choice for building personalized recommendation systems due to their ability to model latent factors, handle sparse interactions, incorporate side information, and scale efficiently. Other algorithms are either linear, structured-data focused, or exploratory.

Question 49

A bank wants to predict credit card fraud in real time using transaction data with numerical, categorical, and sparse features. Fraudulent transactions are rare. Which AWS SageMaker approach is most suitable?

A) Amazon SageMaker Random Cut Forest (RCF)

B) Amazon SageMaker Linear Learner without class weighting

C) Amazon SageMaker XGBoost

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker Random Cut Forest (RCF)

Explanation

A) Amazon SageMaker Random Cut Forest is an unsupervised anomaly detection algorithm ideal for detecting rare, unusual patterns in high-dimensional datasets. For credit card fraud, RCF can identify anomalies in transaction features such as amount, location, merchant, and frequency without requiring labeled fraud data. Its unsupervised nature is critical because labeled fraudulent transactions are scarce and continuously evolving. RCF assigns anomaly scores that can be used to trigger alerts for investigation, enabling real-time detection and proactive fraud prevention. Its scalability and ability to handle continuous streaming data make it suitable for deployment in high-volume financial environments.

B) Amazon SageMaker Linear Learner without class weighting is a supervised algorithm that assumes balanced classes. Given the rarity of fraudulent transactions, a standard Linear Learner would predict the majority class (legitimate transactions) most of the time, resulting in poor detection of fraud. Class imbalance handling is crucial for effective fraud detection.

C) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While effective for classification, it requires labeled fraud instances. In real-time settings, obtaining sufficient labeled examples is challenging, and the model may struggle to adapt to emerging fraud patterns. XGBoost also requires retraining frequently to handle evolving fraud tactics.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it may identify clusters of similar transactions, it does not provide anomaly scores or detect rare deviations reliably. K-Means is exploratory and not designed for real-time fraud detection.

Random Cut Forest is the most suitable approach for real-time credit card fraud detection due to its unsupervised design, ability to handle rare anomalies, scalability, and real-time deployment. Other algorithms either assume supervision, ignore class imbalance, or are exploratory.

Question 50

A healthcare provider wants to predict patient readmission within 30 days using EHR data that contains numerical lab results, categorical demographics, and sparse diagnosis codes. Which AWS SageMaker algorithm is most appropriate?

A) Amazon SageMaker XGBoost

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker Factorization Machines

D) Amazon SageMaker DeepAR Forecasting

Answer

A) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm suitable for structured, heterogeneous datasets like EHRs. It handles numerical, categorical, and sparse features effectively while capturing non-linear interactions between lab results, demographics, and diagnoses. XGBoost can address class imbalance using weighted loss functions, which is important because readmissions are relatively rare. Feature importance metrics provide interpretability, allowing healthcare providers to understand key risk factors and prioritize interventions. XGBoost scales efficiently to large datasets and supports real-time or batch predictions, enabling timely readmission risk assessment and resource allocation.

B) Amazon SageMaker Linear Learner assumes linear relationships between features and outcomes. While it can handle heterogeneous features, its linear assumption may underfit datasets with complex interactions between lab results, demographics, and sparse diagnoses. Extensive feature engineering would be required to improve performance, reducing efficiency.

C) Amazon SageMaker Factorization Machines are optimized for sparse, high-dimensional datasets with pairwise interactions, commonly used in recommendations. EHR datasets contain dense and sparse features with complex, higher-order interactions, which FM may not capture effectively. Its predictive performance would likely be lower than XGBoost for readmission prediction.

D) Amazon SageMaker DeepAR Forecasting is designed for sequential time series forecasting. Predicting patient readmission is a supervised classification problem, not sequential forecasting, making DeepAR unsuitable for this task.

XGBoost provides the best combination of predictive power, interpretability, handling of heterogeneous features, class imbalance, and scalability for predicting patient readmissions. Other algorithms either assume linearity, focus on sparse interactions, or are designed for time series forecasting.

Question 51

A logistics company wants to forecast daily shipment volumes for multiple regions using historical shipment data, weather, and holiday information. Which AWS SageMaker algorithm is most suitable for this multi-step time series forecasting task?

A) Amazon SageMaker DeepAR Forecasting

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker XGBoost

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker DeepAR Forecasting

Explanation

A) Amazon SageMaker DeepAR Forecasting is specifically designed for multi-step probabilistic time series forecasting. It uses recurrent neural networks to model temporal dependencies and patterns in sequential data. For forecasting daily shipment volumes across multiple regions, DeepAR can learn from multiple related time series, such as shipments in different regions or product categories. It incorporates covariates like weather conditions, holidays, and promotions, which can significantly impact shipment volumes. DeepAR provides probabilistic forecasts, giving both point predictions and uncertainty estimates, which are crucial for logistics planning, resource allocation, and risk management. Its ability to scale across large datasets and adapt to changing trends ensures accurate and robust forecasting for operational decisions.

B) Amazon SageMaker Linear Learner is a regression algorithm suitable for structured data. While it can incorporate lag features and covariates, it assumes linear relationships and does not naturally capture sequential dependencies or seasonality. Multi-step forecasting would require extensive feature engineering and still may not produce reliable probabilistic forecasts, making it less effective for daily shipment predictions.

C) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm for structured tabular data. While it can be adapted for time series with lag features and rolling statistics, it is not inherently sequential and does not naturally model multi-step forecasts or uncertainty. Capturing correlations across multiple regions and products would require significant feature engineering, reducing scalability and accuracy compared to DeepAR.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It may help group regions or days with similar shipment patterns but cannot produce numerical forecasts or uncertainty estimates. K-Means is exploratory and does not satisfy predictive requirements for operational planning.

DeepAR is the most suitable solution due to its ability to model sequential patterns, handle multiple related time series, incorporate covariates, and provide probabilistic multi-step forecasts. Other algorithms assume linearity, are not sequential, or are exploratory.

Question 52

A company wants to classify images of handwritten letters for an automated mail sorting system. The dataset contains labeled images with varying resolutions. Which AWS SageMaker algorithm is most appropriate?

A) Amazon SageMaker Image Classification (built-in CNN)

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker XGBoost

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker Image Classification (built-in CNN)

Explanation

A) Amazon SageMaker Image Classification uses convolutional neural networks (CNNs) optimized for image recognition tasks. It can process raw image pixels, handle varying resolutions, and automatically learn spatial hierarchies such as edges, shapes, and textures. For handwritten letters, CNNs are capable of capturing subtle variations in handwriting styles and features that are crucial for accurate classification. SageMaker’s built-in CNN provides flexibility in network architecture, supports data augmentation, and can leverage GPU acceleration for faster training. This ensures high accuracy in real-world applications like automated mail sorting.

B) Amazon SageMaker Linear Learner is a supervised classification algorithm for structured tabular data. It cannot process raw image pixels directly. Flattening image data for Linear Learner would lose spatial information critical for recognizing handwritten letters, resulting in poor accuracy. It is not suitable for image recognition tasks.

C) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm for structured tabular data. While feature engineering could convert images into numerical features, it would be inefficient, lose spatial relationships, and perform poorly compared to CNNs for image classification. XGBoost is not designed for raw image data.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It can group similar images but cannot classify them into predefined categories. K-Means is exploratory and cannot be used for supervised recognition of handwritten letters.

Image Classification with CNNs is the most suitable approach because it can handle raw images, learn spatial features, scale efficiently, and provide high accuracy for real-world classification tasks. Other algorithms are either tabular-based or unsupervised.

Question 53

A bank wants to detect anomalies in ATM transactions to identify potential fraud in real time. The dataset contains continuous transaction amounts, timestamps, and categorical merchant data. Which AWS SageMaker algorithm is most suitable?

A) Amazon SageMaker Random Cut Forest (RCF)

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker XGBoost

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker Random Cut Forest (RCF)

Explanation

A) Amazon SageMaker Random Cut Forest is an unsupervised anomaly detection algorithm designed for high-dimensional continuous data. For ATM transactions, RCF can detect unusual transactions based on deviations from normal patterns, considering features like transaction amount, frequency, merchant type, and timing. It works without labeled anomalies, which are scarce in real-world fraud detection, and assigns anomaly scores for each transaction. Real-time deployment allows immediate alerting and investigation, critical for preventing financial loss. RCF is scalable, handles correlated metrics effectively, and can detect both point and contextual anomalies, making it ideal for financial transaction monitoring.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled examples of fraudulent and non-fraudulent transactions. Acquiring sufficient labeled anomalies is challenging, and Linear Learner may not capture evolving fraud patterns in high-dimensional continuous and categorical features effectively.

C) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While powerful for structured classification, it requires labeled fraud data and frequent retraining to handle new types of fraud. Obtaining comprehensive labels for rare fraudulent transactions is difficult, limiting XGBoost’s real-time applicability.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group similar transaction patterns, it does not provide anomaly scores or detect rare deviations effectively. K-Means is exploratory and unsuitable for real-time fraud detection.

Random Cut Forest is the most suitable algorithm for real-time anomaly detection in ATM transactions due to its unsupervised design, ability to handle high-dimensional continuous data, scalability, and real-time deployment capabilities. Other algorithms require supervision, labeled anomalies, or are exploratory.

Question 54

A company wants to predict customer churn using transaction history, demographics, and engagement metrics. The dataset contains numerical, categorical, and sparse features with class imbalance. Which AWS SageMaker algorithm is most suitable?

A) Amazon SageMaker XGBoost

B) Amazon SageMaker Factorization Machines

C) Amazon SageMaker Linear Learner

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm well-suited for structured data with numerical, categorical, and sparse features. It can handle non-linear interactions, manage missing values, and deal with class imbalance through weighting or sampling. For customer churn prediction, XGBoost can learn complex relationships between demographics, transaction behavior, and engagement metrics to accurately predict which customers are likely to leave. It provides probability scores and feature importance, enabling targeted retention strategies and actionable business insights. XGBoost’s scalability allows handling large datasets efficiently, supporting batch and real-time predictions.

B) Amazon SageMaker Factorization Machines are optimized for sparse, high-dimensional datasets and pairwise interactions, often used in recommendation systems. While FM can model sparse engagement metrics, it may not capture complex non-linear interactions across numerical and categorical features. Predictive performance for churn is likely to be lower than XGBoost.

C) Amazon SageMaker Linear Learner is a supervised classification algorithm assuming linear relationships between features and outcomes. While interpretable, it may underfit data with non-linear relationships, reducing predictive accuracy. Feature engineering could mitigate this but would increase complexity.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It can segment customers based on behavior but cannot provide supervised churn predictions or probability estimates. K-Means is exploratory and unsuitable for predictive classification.

XGBoost is the best choice for churn prediction due to its ability to handle heterogeneous features, non-linear interactions, class imbalance, interpretability, and scalability. Other algorithms either assume linearity, focus on sparse interactions, or are unsupervised.

Question 55

A healthcare provider wants to predict patient readmission within 30 days using EHR data with numerical lab results, categorical demographics, and sparse diagnosis codes. Which AWS SageMaker algorithm is most appropriate?

A) Amazon SageMaker XGBoost

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker Factorization Machines

D) Amazon SageMaker DeepAR Forecasting

Answer

A) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm designed for structured, heterogeneous datasets like EHRs. It handles numerical, categorical, and sparse features efficiently and captures non-linear interactions between lab results, demographics, and diagnoses. XGBoost can address class imbalance using weighting, which is critical because patient readmissions are relatively rare. Feature importance metrics enhance interpretability, helping healthcare providers identify key risk factors and prioritize interventions. XGBoost supports scalable training on large datasets and can be deployed for real-time or batch predictions, enabling proactive management of readmission risks and improved resource allocation.

B) Amazon SageMaker Linear Learner assumes linear relationships and may underfit datasets with complex interactions between lab results, demographics, and diagnosis codes. While simple and interpretable, extensive feature engineering would be required to achieve comparable predictive performance, reducing efficiency.

C) Amazon SageMaker Factorization Machines are optimized for sparse, high-dimensional datasets with pairwise interactions, primarily used for recommendation systems. EHR datasets contain both dense and sparse features with complex higher-order interactions, which FM may not capture effectively, leading to lower predictive accuracy.

D) Amazon SageMaker DeepAR Forecasting is designed for sequential time series prediction. Predicting patient readmission is a supervised classification problem with heterogeneous features, not sequential forecasting, making DeepAR unsuitable.

XGBoost is the most suitable solution for patient readmission prediction due to its ability to handle heterogeneous, dense and sparse features, non-linear interactions, class imbalance, interpretability, and scalability. Other algorithms either assume linearity, are sparse-focused, or designed for time series forecasting.

Question 56

A company wants to forecast monthly demand for multiple products across several stores using historical sales, promotions, and seasonal trends. Which AWS SageMaker algorithm is most suitable for this task?

A) Amazon SageMaker DeepAR Forecasting

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker XGBoost

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker DeepAR Forecasting

Explanation

A) Amazon SageMaker DeepAR Forecasting is designed for probabilistic multi-step time series forecasting. It uses recurrent neural networks to model sequential patterns, trends, and seasonality across multiple related time series, making it ideal for forecasting monthly demand across products and stores. DeepAR can incorporate covariates such as promotions, holidays, and seasonal events to improve forecast accuracy. It provides both point forecasts and prediction intervals, allowing businesses to plan inventory, allocate resources, and manage supply chain risks effectively. DeepAR leverages patterns shared across multiple time series, ensuring better predictions for products or stores with limited historical data. Its scalability and adaptability make it suitable for large datasets with multiple product-store combinations.

B) Amazon SageMaker Linear Learner is a supervised regression algorithm that assumes linear relationships between features and the target. While it could incorporate lag features and covariates, it cannot naturally model temporal dependencies or multi-step sequential forecasts. It also lacks probabilistic forecasting, which is critical for inventory planning and managing uncertainty in demand.

C) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm optimized for structured tabular data. It can be adapted for time series forecasting using lag features and rolling statistics, but it does not inherently capture sequential patterns, seasonality, or correlations across multiple related time series. Feature engineering requirements are extensive, and multi-step forecasting becomes complex and less accurate compared to DeepAR.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It can identify clusters of products or stores with similar demand patterns but cannot generate numerical forecasts or prediction intervals. K-Means is exploratory and does not provide actionable insights for demand planning.

DeepAR is the most suitable solution for multi-product, multi-store monthly demand forecasting due to its ability to model sequential dependencies, incorporate covariates, provide probabilistic forecasts, and leverage shared patterns across time series. Other algorithms either assume linearity, are not sequential, or are exploratory.

 

Question 57

A bank wants to predict credit card fraud using transaction data with numerical, categorical, and sparse features. Fraudulent transactions are rare. Which AWS SageMaker algorithm is most appropriate?

A) Amazon SageMaker Random Cut Forest (RCF)

B) Amazon SageMaker Linear Learner without class weighting

C) Amazon SageMaker XGBoost

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker Random Cut Forest (RCF)

Explanation

A) Amazon SageMaker Random Cut Forest is an unsupervised anomaly detection algorithm ideal for detecting rare, unusual patterns in high-dimensional continuous datasets. Credit card fraud detection requires identifying outliers in numerical, categorical, and sparse features such as transaction amounts, merchant types, locations, and time intervals. Since fraudulent transactions are rare and continuously evolving, obtaining labeled examples for supervised training is challenging. RCF does not require labeled anomalies; it computes anomaly scores for each transaction, allowing real-time monitoring and immediate investigation of suspicious activity. Its ability to handle high-dimensional correlated features, scale efficiently, and deploy in real-time makes it highly suitable for financial fraud detection systems.

B) Amazon SageMaker Linear Learner without class weighting is a supervised classification algorithm. Without class weighting, it would fail to properly handle class imbalance, predicting the majority class most of the time and missing rare fraudulent transactions. Supervised methods also require labeled examples, which are limited for fraud detection.

C) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While effective for structured classification tasks, it requires labeled fraudulent transactions. Acquiring comprehensive labeled data in real-time is challenging, and the model must be retrained frequently to adapt to evolving fraud patterns, reducing practicality for real-time detection.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. Although it can group similar transaction behaviors, it does not provide anomaly scores or detect rare deviations reliably. K-Means is exploratory and not suitable for real-time credit card fraud detection.

Random Cut Forest is the most appropriate approach for real-time credit card fraud detection due to its unsupervised nature, ability to handle high-dimensional data, detect rare anomalies, and scale efficiently. Other algorithms require supervision, labeled anomalies, or are exploratory.

Question 58

A healthcare provider wants to predict patient readmission within 30 days using EHR data with numerical lab results, categorical demographics, and sparse diagnosis codes. Which AWS SageMaker algorithm is most suitable?

A) Amazon SageMaker XGBoost

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker Factorization Machines

D) Amazon SageMaker DeepAR Forecasting

Answer

A) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm designed for heterogeneous structured datasets like electronic health records. It can efficiently handle numerical, categorical, and sparse features, capturing complex non-linear interactions between lab results, demographics, and diagnoses. XGBoost can address class imbalance using weighting, which is critical for predicting readmissions since such events are relatively rare. Its feature importance metrics provide interpretability, allowing healthcare providers to identify key risk factors such as comorbidities or abnormal lab results. XGBoost also supports scalable training and deployment for real-time or batch predictions, enabling proactive interventions and optimized resource allocation for at-risk patients.

B) Amazon SageMaker Linear Learner assumes linear relationships between features and outcomes. While simple and interpretable, Linear Learner may underfit EHR data due to complex interactions among lab results, demographics, and diagnoses. Extensive feature engineering would be required to improve performance, making it less efficient than XGBoost.

C) Amazon SageMaker Factorization Machines are optimized for sparse, high-dimensional datasets and pairwise interactions, often used in recommendation systems. EHR datasets include both dense and sparse features with higher-order interactions, which FM may fail to capture effectively, resulting in lower predictive accuracy.

D) Amazon SageMaker DeepAR Forecasting is designed for sequential time series prediction. Patient readmission prediction is a supervised classification problem, not sequential forecasting, so DeepAR is not appropriate.

XGBoost is the most suitable choice due to its ability to handle heterogeneous data, model complex non-linear interactions, manage class imbalance, provide interpretability, and scale for large datasets. Other algorithms are either linear, sparse-focused, or designed for time series.

Question 59

A retail company wants to recommend products to users based on sparse interaction data and user demographics. Which AWS SageMaker algorithm is most suitable?

A) Amazon SageMaker Factorization Machines

B) Amazon SageMaker XGBoost

C) Amazon SageMaker Linear Learner

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker Factorization Machines

Explanation

A) Amazon SageMaker Factorization Machines are specifically designed to handle sparse, high-dimensional datasets. They capture pairwise interactions between users and items, making them ideal for recommendation systems where interactions are often sparse. FM can incorporate side information, such as user demographics and item attributes, to improve prediction accuracy. It can predict preferences for unobserved user-item pairs, which is essential for collaborative filtering. FM also scales efficiently for large numbers of users and items, making it suitable for building personalized recommendation engines at scale.

B) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm designed for structured tabular data. While XGBoost can handle engineered interaction features, it is not optimized for sparse high-dimensional user-item interactions and cannot efficiently learn latent factors for unseen pairs, making it less effective for large-scale recommendation systems.

C) Amazon SageMaker Linear Learner is a supervised algorithm assuming linear relationships. While it can work with sparse features, it cannot capture complex latent interactions between users and items, limiting recommendation quality. Linear Learner may underfit high-dimensional sparse datasets.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can segment users or products, it cannot generate individual recommendations or predict preferences. K-Means is exploratory and not suitable for personalized recommendation systems.

Factorization Machines are the best choice for recommendation engines due to their ability to handle sparse data, model latent factors, incorporate side information, and scale efficiently. Other algorithms either focus on structured tabular data, linear relationships, or clustering.

Question 60

A telecom company wants to detect abnormal patterns in network metrics such as latency, throughput, and error rates to identify potential network issues. Which AWS SageMaker algorithm is most suitable?

A) Amazon SageMaker Random Cut Forest (RCF)

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker K-Means

D) Amazon SageMaker XGBoost

Answer

A) Amazon SageMaker Random Cut Forest (RCF)

Explanation

A) Amazon SageMaker Random Cut Forest is an unsupervised anomaly detection algorithm designed for high-dimensional continuous data. For telecom network metrics such as latency, throughput, and error rates, RCF can detect unusual deviations from normal behavior by computing anomaly scores for each observation. Its unsupervised nature is critical since labeled anomalies are rare, evolving, and difficult to obtain. RCF scales efficiently for large volumes of streaming data, handles correlated features, and can detect both point and contextual anomalies. Real-time deployment allows immediate identification and response to potential network issues, enabling proactive maintenance and service quality assurance.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled anomalies for training, which are rare in real-world network environments. Even with labels, Linear Learner may underfit complex interactions among network metrics, limiting its effectiveness for anomaly detection.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It can segment normal network patterns but does not provide anomaly scores or detect rare deviations. K-Means is exploratory and unsuitable for real-time anomaly detection in dynamic network environments.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While powerful for structured data, it requires labeled anomalies for training and frequent retraining to adapt to evolving patterns, making it impractical for real-time network monitoring.

Random Cut Forest is the most suitable algorithm for detecting abnormal network patterns due to its unsupervised nature, ability to handle high-dimensional continuous metrics, real-time deployment, scalability, and interpretability. Other algorithms either require supervision, labeled anomalies, or are exploratory.