Amazon  AWS Certified Machine Learning Engineer – Associate MLA-C01   Exam  Dumps and Practice Test Questions Set 6 Q 101- 120 

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

Question 101

A retail company wants to forecast hourly customer foot traffic in multiple stores using historical data, promotions, and holidays. Which AWS SageMaker algorithm is most suitable for multi-step time series forecasting?

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 a recurrent neural network-based algorithm specifically designed for multi-step probabilistic time series forecasting. Forecasting hourly customer foot traffic involves capturing temporal dependencies, daily and weekly patterns, seasonal trends, and the impact of promotions or holidays. DeepAR is particularly suited for this task because it can incorporate external covariates such as promotions and holidays, improving prediction accuracy. By leveraging multiple related time series across stores, DeepAR learns shared patterns that enhance forecasts for locations with limited historical data. Its probabilistic outputs provide prediction intervals in addition to point forecasts, which are essential for operational planning, staffing, and resource allocation. The scalability of DeepAR allows it to process large datasets efficiently, while its adaptability ensures it captures evolving patterns in customer behavior.

B) Amazon SageMaker Linear Learner is a supervised regression algorithm that assumes linear relationships between input features and the target. While it can incorporate lag features and external covariates, it does not naturally capture sequential dependencies, making it less suitable for multi-step forecasting. Producing probabilistic forecasts with Linear Learner requires additional modeling and feature engineering, reducing efficiency and accuracy.

C) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While XGBoost can be adapted for time series forecasting using engineered lag features, it does not inherently model temporal dependencies or multi-step predictions. It also does not natively produce probabilistic forecasts, limiting its usefulness for operational decision-making.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While K-Means can identify stores with similar foot traffic patterns, it cannot generate numerical forecasts or prediction intervals. K-Means is exploratory and not suitable for operational forecasting tasks.

DeepAR is the most appropriate algorithm for multi-store, hourly customer foot traffic forecasting due to its sequential modeling capabilities, ability to incorporate covariates, probabilistic forecasts, and the ability to learn shared patterns across multiple related time series. Other algorithms assume linearity, are not sequential, or are exploratory.

Question 102

A bank wants to detect unusual financial transactions in real time. The dataset includes transaction amounts, timestamps, merchant codes, and user behavior metrics. 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. It is particularly effective for detecting unusual financial transactions, as fraud often manifests as deviations from normal transaction patterns. RCF assigns an anomaly score to each transaction, enabling real-time detection without the need for labeled fraudulent examples, which are rare and constantly evolving. It identifies both point anomalies, such as unusually large transactions, and contextual anomalies, such as sequences of transactions that are atypical for a given user. RCF scales efficiently for high-volume streaming transaction data and adapts to emerging fraud patterns. Its real-time deployment allows banks to proactively respond to potential fraud, minimizing financial losses and protecting customer accounts.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled fraudulent transactions and assumes linear relationships between features and outcomes. These limitations reduce its effectiveness for detecting rare or evolving anomalies in real-time transaction data.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can segment transactions into clusters based on similarity, it does not provide anomaly scores or detect rare anomalies reliably. K-Means is exploratory, not predictive, and unsuitable for real-time fraud detection.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While XGBoost is powerful for structured classification tasks, it requires labeled examples of fraud and frequent retraining to adapt to new patterns, making it less practical for real-time anomaly detection.

RCF is the most suitable algorithm for real-time financial anomaly detection due to its unsupervised design, scalability, anomaly scoring, and adaptability. Other algorithms require labeled data, assume linearity, or are exploratory.

Question 103

A healthcare provider wants to predict patient readmissions within 30 days using EHR data that includes lab results, demographics, and 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 optimized for structured, heterogeneous datasets. It can handle numerical lab results, categorical demographics, and sparse diagnosis codes while capturing complex non-linear interactions between features. XGBoost is effective at handling class imbalance, which is critical for predicting rare events such as patient readmissions. It also provides feature importance metrics, helping healthcare providers identify the most influential factors contributing to readmissions, such as prior hospitalizations, comorbidities, and abnormal lab results. XGBoost supports both batch and real-time deployment, enabling timely interventions for at-risk patients. Its scalability, robustness, and interpretability make it the most suitable algorithm for readmission prediction.

B) Amazon SageMaker Linear Learner assumes linear relationships between features and the target. While interpretable, it may underfit complex interactions in healthcare data and requires extensive feature engineering, reducing predictive performance compared to XGBoost.

C) Amazon SageMaker Factorization Machines are designed for sparse, high-dimensional datasets commonly used in recommendation systems. While they capture pairwise interactions, they are not suitable for datasets with dense numerical lab results combined with sparse diagnosis codes, leading to suboptimal predictive performance.

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

XGBoost is the most appropriate algorithm due to its ability to handle heterogeneous structured data, model complex interactions, address class imbalance, and scale efficiently. Other algorithms are linear, sparse-focused, or time series-oriented.

Question 104

A retail company wants to build a recommendation system based on sparse product interactions and user demographic information. 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 optimized for sparse, high-dimensional datasets, making them ideal for recommendation systems. They learn latent interactions between users and items, allowing predictions for unobserved user-item pairs. Factorization Machines can incorporate side information such as demographics or product attributes to enhance recommendation quality. They scale efficiently to millions of users and items, providing personalized recommendations even when interaction data is sparse. By modeling latent factors in the user-item matrix, Factorization Machines enable collaborative filtering, helping businesses deliver targeted, relevant product recommendations that improve engagement and sales.

B) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While it can process engineered interaction features, it does not naturally capture latent factors in sparse data, limiting its effectiveness for collaborative filtering and personalized recommendations.

C) Amazon SageMaker Linear Learner is a supervised linear algorithm. It cannot capture complex latent interactions between users and items, which reduces recommendation accuracy.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group similar users or items, it cannot generate personalized recommendations or predict unobserved preferences. K-Means is exploratory, not predictive.

Factorization Machines are the most suitable algorithm for scalable and accurate recommendation systems due to their ability to handle sparse data, learn latent factors, incorporate side information, and support collaborative filtering.

Question 105

A telecom company wants to detect abnormal network patterns in real time using metrics such as latency, throughput, and error rates. 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 optimized for high-dimensional continuous data. It is particularly effective for detecting unusual network behavior, as anomalies manifest as deviations in latency, throughput, or error rates. RCF assigns anomaly scores to data points, capturing both point anomalies (isolated spikes) and contextual anomalies (unusual sequences over time). Labeled network anomalies are rare and constantly evolving, making unsupervised detection essential. RCF scales efficiently for high-volume streaming data and supports real-time deployment, allowing proactive monitoring and reducing downtime. Its interpretability allows engineers to understand which metrics contribute most to anomalies, facilitating rapid root cause analysis and remediation.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled anomalies and may underfit complex interactions among network metrics, limiting its suitability for real-time anomaly detection.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group similar network patterns, it does not provide anomaly scores or reliably detect rare deviations. K-Means is exploratory, not predictive.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. It requires labeled anomalies and frequent retraining, making it less effective for detecting emerging anomalies in real time.

Random Cut Forest is the most suitable algorithm for real-time network anomaly detection due to its unsupervised design, scalability, real-time deployment, and interpretability. Other algorithms require labeled data, assume linearity, or are exploratory.

Question 106

A retail company wants to forecast weekly sales for multiple product categories across different stores, considering promotions, holidays, and seasonal trends. Which AWS SageMaker algorithm is most suitable for multi-step time series forecasting?

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 a recurrent neural network-based algorithm optimized for multi-step probabilistic time series forecasting. In retail, forecasting weekly sales across multiple categories and stores involves capturing temporal dependencies, seasonal trends, and external influences like promotions and holidays. DeepAR is particularly suitable because it can incorporate covariates such as promotions, holidays, and special events, which significantly impact sales patterns. By leveraging multiple related time series across stores and categories, DeepAR learns shared patterns that improve accuracy for products or locations with limited historical data. Its probabilistic forecasts provide both point estimates and prediction intervals, which are crucial for inventory planning, supply chain optimization, and risk management. The algorithm scales efficiently for large datasets and adapts to evolving trends, ensuring operational reliability and improved business decisions.

B) Amazon SageMaker Linear Learner is a supervised regression algorithm that assumes linear relationships between features and the target variable. While it can incorporate lag features and covariates, it does not naturally capture sequential dependencies or produce probabilistic outputs. Multi-step forecasting would require significant feature engineering, making Linear Learner less practical for complex retail scenarios.

C) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm for structured tabular data. While it can be adapted for forecasting using engineered lag features, it does not inherently model temporal dependencies or multi-step forecasts. It also does not naturally provide uncertainty estimates, which limits its applicability in operational planning.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group stores or products with similar historical sales patterns, it cannot generate numerical forecasts or prediction intervals. K-Means is exploratory and not suitable for operational forecasting.

DeepAR is the most suitable choice for multi-category, multi-store weekly sales forecasting due to its sequential modeling, covariate incorporation, probabilistic outputs, and ability to leverage patterns across multiple related time series. Other algorithms assume linearity, are not sequential, or are exploratory.

Question 107

A bank wants to detect unusual transactions in real time. The dataset includes transaction amounts, timestamps, merchant codes, and user behavioral patterns. 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 optimized for high-dimensional continuous data. In banking, fraudulent transactions are anomalies that deviate from normal user behavior. RCF assigns an anomaly score to each transaction, enabling real-time detection without requiring labeled examples, which are often rare and constantly evolving. It can detect both point anomalies, such as unusually large transactions, and contextual anomalies, such as a series of atypical transactions. RCF scales efficiently for high-volume streaming data and adapts to emerging fraud patterns. Its real-time deployment capability allows banks to proactively respond to potential fraud, minimizing losses and protecting customer accounts.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm that requires labeled fraudulent transactions and assumes linear relationships between features and outcomes. These constraints reduce its effectiveness for real-time anomaly detection.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group transactions into clusters, it does not provide anomaly scores or detect rare fraudulent events reliably. K-Means is exploratory and unsuitable for real-time fraud detection.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. It requires labeled examples of fraud and frequent retraining to adapt to new patterns, making it less practical for real-time unsupervised detection.

Random Cut Forest is the most suitable algorithm for real-time detection of unusual banking transactions due to its unsupervised design, scalability, anomaly scoring, and adaptability. Other algorithms require labeled data, assume linearity, or are exploratory.

Question 108

A healthcare provider wants to predict patient readmissions within 30 days using EHR data that includes lab results, demographics, and 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 optimized for heterogeneous structured datasets. It can process numerical lab results, categorical demographics, and sparse diagnosis codes while capturing complex non-linear interactions. XGBoost is highly effective for predicting rare events like patient readmissions because it can handle class imbalance. It also provides feature importance metrics, helping healthcare professionals identify key risk factors such as comorbidities, abnormal lab results, or prior hospitalizations. XGBoost supports both batch and real-time deployment, allowing timely interventions for at-risk patients. Its scalability, robustness, and interpretability make it the most suitable algorithm for readmission prediction.

B) Amazon SageMaker Linear Learner assumes linear relationships between features and outcomes. While interpretable, it may underfit complex interactions in healthcare data and requires extensive feature engineering, reducing predictive accuracy.

C) Amazon SageMaker Factorization Machines are designed for sparse, high-dimensional datasets, typically used in recommendation systems. They are not well-suited for datasets with dense numerical lab results and sparse diagnosis codes, leading to suboptimal performance in predicting readmissions.

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

XGBoost is the most appropriate algorithm due to its ability to handle heterogeneous structured data, model complex interactions, address class imbalance, and scale efficiently. Other algorithms are linear, sparse-focused, or time series-oriented.

Question 109

A retail company wants to build a recommendation system based on sparse user-product interactions and user demographic information. 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 optimized for sparse, high-dimensional datasets, making them ideal for recommendation systems. They capture latent interactions between users and items, enabling accurate predictions for unobserved user-item pairs. Factorization Machines can incorporate side information such as demographics or product attributes to improve recommendation quality. They scale efficiently to millions of users and items, providing personalized recommendations even when interaction data is sparse. By modeling latent factors in the user-item matrix, Factorization Machines support collaborative filtering, enabling the system to recommend relevant products to individual users.

B) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While it can process engineered interaction features, it does not naturally capture latent factors in sparse user-item matrices, limiting its effectiveness for collaborative filtering and personalized recommendations.

C) Amazon SageMaker Linear Learner is a supervised linear algorithm. It cannot model complex latent interactions between users and items, reducing recommendation accuracy.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group users or items with similar characteristics, it does not provide personalized recommendations or predict unobserved interactions. K-Means is exploratory and not predictive.

Factorization Machines are the most suitable algorithm for building scalable and accurate recommendation systems due to their ability to handle sparse data, learn latent factors, incorporate side information, and support collaborative filtering.

Question 110

A telecom company wants to detect abnormal network behavior in real time using metrics such as latency, throughput, and error rates. 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 optimized for high-dimensional continuous data. In telecom networks, abnormal behavior manifests as unusual deviations in latency, throughput, or error rates. RCF assigns anomaly scores to each metric, detecting both point anomalies, such as isolated spikes, and contextual anomalies, such as unusual sequences over time. Labeled network anomalies are rare and evolving, making unsupervised detection essential. RCF scales efficiently for high-volume streaming data and supports real-time deployment, enabling proactive monitoring, reduced downtime, and improved network reliability. Its interpretability helps engineers identify which metrics contribute most to anomalies, facilitating root cause analysis and rapid remediation.

B) Amazon SageMaker Linear Learner is a supervised algorithm requiring labeled anomalies. It may underfit complex correlations among network metrics, limiting its effectiveness for real-time anomaly detection.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group similar network patterns, it does not produce anomaly scores or reliably detect rare deviations. K-Means is exploratory and not suitable for predictive anomaly detection.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. It requires labeled anomalies and frequent retraining, making it less practical for real-time anomaly detection in evolving network environments.

Random Cut Forest is the most suitable algorithm for real-time network anomaly detection due to its unsupervised design, scalability, real-time deployment capability, and interpretability. Other algorithms require labeled data, assume linearity, or are exploratory.

Question 111

A retail company wants to forecast daily inventory requirements for multiple products across several stores, considering seasonal trends, promotions, and holidays. Which AWS SageMaker algorithm is most suitable for multi-step time series forecasting?

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 a recurrent neural network-based algorithm specifically designed for probabilistic multi-step time series forecasting. Forecasting daily inventory across multiple products and stores involves capturing temporal dependencies, trends, seasonal patterns, and external factors such as promotions and holidays. DeepAR excels in this context because it can incorporate covariates such as holiday effects, promotional campaigns, and other external influences, significantly improving prediction accuracy. By leveraging multiple related time series across products and stores, DeepAR learns shared patterns, which improves forecast accuracy for products with limited historical data. Its probabilistic outputs provide both point predictions and prediction intervals, essential for inventory planning, demand forecasting, and reducing stockouts or overstock scenarios. DeepAR is scalable, efficient, and adaptable, allowing it to capture evolving sales and inventory patterns.

B) Amazon SageMaker Linear Learner is a supervised regression algorithm assuming linear relationships between input features and the target variable. While it can incorporate lag features and covariates, it does not naturally model sequential dependencies or produce probabilistic forecasts. Multi-step forecasting requires extensive feature engineering, reducing practical efficiency for complex retail inventory forecasting.

C) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm suitable for structured tabular data. While it can be adapted for time series using lag features, it does not inherently capture temporal dependencies or multi-step forecasts. It also does not provide prediction intervals, limiting risk-aware inventory planning.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can identify products or stores with similar inventory patterns, it cannot generate numerical forecasts or prediction intervals. K-Means is exploratory, not predictive, and unsuitable for operational inventory forecasting.

DeepAR is the most suitable algorithm for multi-product, multi-store daily inventory forecasting due to its sequential modeling, covariate incorporation, probabilistic outputs, and ability to learn patterns across related time series. Other algorithms assume linearity, are not sequential, or are exploratory.

Question 112

A bank wants to identify unusual transactions in real time. The dataset includes transaction amounts, timestamps, merchant codes, and user behavioral patterns. 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. In banking, fraudulent transactions typically appear as anomalies deviating from normal user behavior. RCF assigns an anomaly score to each transaction, enabling real-time detection without requiring labeled examples, which are rare and evolving. RCF detects both point anomalies, such as unusually large transactions, and contextual anomalies, such as sequences of transactions that are atypical for a specific user. It scales efficiently to handle high-volume streaming data and adapts to emerging fraud patterns. Real-time deployment allows banks to respond proactively, reducing financial loss and protecting customer accounts.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled fraudulent transactions and assumes linear relationships between features and outcomes. These limitations make it less effective for real-time anomaly detection, particularly for rare or evolving fraud scenarios.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group transactions based on similarity, it does not produce anomaly scores or reliably detect rare anomalies. K-Means is exploratory and unsuitable for real-time fraud detection.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While powerful for structured classification, it requires labeled fraud examples and frequent retraining to adapt to evolving fraud patterns, limiting its practicality for real-time unsupervised anomaly detection.

Random Cut Forest is the most suitable algorithm for real-time detection of unusual banking transactions due to its unsupervised design, scalability, anomaly scoring, and adaptability. Other algorithms require labeled data, assume linearity, or are exploratory.

Question 113

A healthcare provider wants to predict 30-day patient readmissions using electronic health record (EHR) data, including lab results, demographics, and 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 optimized for heterogeneous structured datasets. It can handle numerical lab results, categorical demographics, and sparse diagnosis codes while capturing complex non-linear interactions among features. XGBoost effectively addresses class imbalance, which is crucial for predicting rare events such as patient readmissions. Additionally, it provides feature importance metrics, allowing healthcare professionals to identify contributing factors such as comorbidities, abnormal lab results, and prior hospitalizations. XGBoost supports batch and real-time prediction deployment, enabling timely interventions for at-risk patients. Its scalability, interpretability, and robust predictive power make it the most suitable algorithm for readmission prediction.

B) Amazon SageMaker Linear Learner assumes linear relationships between features and the target. While interpretable, it may underfit complex interactions in healthcare data, requiring extensive feature engineering, which reduces predictive accuracy compared to XGBoost.

C) Amazon SageMaker Factorization Machines are designed for sparse, high-dimensional datasets, primarily for recommendation systems. They are not optimized for structured EHR data and would perform suboptimally for patient readmission prediction.

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

XGBoost is the most appropriate algorithm due to its ability to handle heterogeneous structured data, model complex interactions, address class imbalance, and scale efficiently. Other algorithms are linear, sparse-focused, or time series-oriented.

Question 114

A retail company wants to develop a recommendation system for users based on sparse product interactions and user demographic data. 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 optimized for sparse, high-dimensional datasets, making them ideal for recommendation systems. They model latent interactions between users and items, enabling accurate predictions for unobserved user-item pairs. Factorization Machines can incorporate side information such as demographics or product attributes to enhance recommendation quality. They scale efficiently to millions of users and items, providing personalized recommendations even with sparse interaction data. By modeling latent factors, Factorization Machines support collaborative filtering, allowing the system to recommend relevant products to individual users and improving engagement and sales.

B) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While it can handle engineered interaction features, it does not naturally capture latent factors in sparse user-item matrices, limiting its effectiveness for collaborative filtering and personalized recommendations.

C) Amazon SageMaker Linear Learner is a supervised linear algorithm. It cannot capture complex latent interactions between users and items, reducing recommendation accuracy.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group similar users or items, it cannot generate personalized recommendations or predict unobserved preferences. K-Means is exploratory and not predictive.

Factorization Machines are the most suitable choice for scalable and accurate recommendation systems due to their ability to handle sparse data, learn latent factors, incorporate side information, and support collaborative filtering.

Question 115

A telecom company wants to detect abnormal network behavior in real time using metrics such as latency, throughput, and error rates. 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. In telecom networks, abnormal behavior manifests as unusual deviations in latency, throughput, or error rates. RCF assigns anomaly scores to each metric, detecting both point anomalies, such as isolated spikes, and contextual anomalies, such as unusual sequences over time. Labeled network anomalies are rare and constantly evolving, making unsupervised detection essential. RCF scales efficiently for high-volume streaming data and supports real-time deployment, enabling proactive monitoring, reducing downtime, and improving network reliability. Its interpretability allows engineers to identify which metrics contribute most to anomalies, facilitating rapid troubleshooting and remediation.

B) Amazon SageMaker Linear Learner is a supervised algorithm requiring labeled anomalies. It may underfit complex correlations among network metrics, limiting its effectiveness for real-time anomaly detection.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group similar network patterns, it does not produce anomaly scores or reliably detect rare deviations. K-Means is exploratory and not suitable for predictive anomaly detection.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. It requires labeled anomalies and frequent retraining, making it less practical for real-time anomaly detection in evolving network environments.

Random Cut Forest is the most suitable algorithm for real-time network anomaly detection due to its unsupervised design, scalability, real-time deployment capability, and interpretability. Other algorithms require labeled data, assume linearity, or are exploratory.

Question 116

A financial analytics company wants to classify whether a customer will default on a loan using structured inputs such as income, credit score, loan amount, debt-to-income ratio, and historical payment behavior. The dataset includes both numerical and categorical fields. Which AWS SageMaker algorithm is most suitable for this binary classification problem?

A) Amazon SageMaker XGBoost
B) Amazon SageMaker K-Means
C) Amazon SageMaker PCA
D) Amazon SageMaker Seq2Seq

Answer

A) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker XGBoost is the most suitable algorithm for this specific scenario because the problem is a classic structured-data, tabular binary classification task. Predicting loan default involves relationships among numerical and categorical variables—income levels, credit history, employment duration, loan-to-income ratios, previous delinquencies, and more. XGBoost is one of the most powerful algorithms for structured data, particularly when complex interactions exist among features. It uses gradient-boosted decision trees, which naturally handle non-linear relationships, feature interactions, missing values, and outliers. With high predictive performance, strong generalization capability, interpretability tools such as feature importance, and scalability through SageMaker’s distributed training support, XGBoost is ideal for financial risk classification.

XGBoost excels in imbalanced datasets, a common issue in loan default modeling where the number of defaulting customers is significantly smaller than non-defaulting ones. The algorithm includes built-in hyperparameters such as scale_pos_weight to correct for imbalances. This means the model can be calibrated to avoid overpredicting the majority class and to tune for maximum recall, precision, or F1 score, depending on the institution’s risk appetite and regulatory requirements.

In financial risk analytics, interpretability is foundational. XGBoost offers multiple tools, including SHAP (SHapley Additive exPlanations), gain-based feature importance, and split-based importance. These allow credit analysts to see which factors influence predictions the most. For example, if debt-to-income ratio, credit utilization, or repeated late payments strongly affect risk, stakeholders can integrate the insights into underwriting, pricing decisions, or risk management strategies.

Moreover, XGBoost handles categorical variables after being one-hot encoded or target-encoded, and handles numerical fields effectively without requiring strict normalization. Its ability to work well with thousands of training samples or millions makes it appropriate for institutions with large historical loan datasets.

B) Amazon SageMaker K-Means is not appropriate for this task because it is an unsupervised clustering algorithm. Loan default classification is a supervised problem where the goal is to predict a target variable (default or no default). K-Means does not predict labels; it only groups data into clusters based on similarity. Using K-Means, one might cluster customers into risky and non-risky segments based on patterns, but it cannot provide precise predictions of default probability or classification outputs. It also lacks interpretability in a supervised sense and cannot optimize for classification metrics.

C) Amazon SageMaker PCA (Principal Component Analysis) is a dimensionality reduction technique, not a classification algorithm. PCA reduces the number of variables by projecting them into principal components that capture the variance structure. While PCA is sometimes used in preprocessing for high-dimensional datasets, it cannot classify loan default on its own. It is unsuitable for a supervised classification task because it does not learn from labeled outcomes. PCA also risks distorting interpretability—critical in finance—since transformed features have no intuitive meaning.

D) Amazon SageMaker Seq2Seq is designed for sequence-to-sequence tasks such as machine translation, text summarization, or speech-to-text mapping. Loan default prediction is a tabular classification problem, not a sequential modeling or text generation problem. Seq2Seq models (typically based on RNNs, LSTMs, or Transformers) are unnecessary and inefficient for structured financial variables, and their complexity would increase training time and reduce interpretability. Seq2Seq models also require paired sequences as input and output, which the dataset does not provide.

Thus, XGBoost is the optimal algorithm because it is specifically designed for structured tabular classification tasks, handles non-linearity, manages imbalanced datasets effectively, scales well, and provides interpretability—all essential for a financial default prediction model.

Question 117

A logistics company wants to optimize delivery times by predicting estimated time of arrival (ETA) for shipments using structured numerical variables (distance, traffic density indices, vehicle type, historical delays) and categorical variables (route IDs, driver IDs, weather categories). Which algorithm should be used?

A) Amazon SageMaker XGBoost
B) Amazon SageMaker K-Means
C) Amazon SageMaker BlazingText
D) Amazon SageMaker NTM (Neural Topic Modeling)

Answer

A) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker XGBoost is the most suitable algorithm because the ETA prediction problem is a supervised regression task using structured numerical and categorical data. XGBoost is particularly strong in cases like this, where there are numerous features with non-linear interactions. ETA predictions are inherently complex due to the interplay of dynamic factors: traffic, driver behavior, road conditions, weather, stop durations, and logistical constraints. Gradient-boosted decision trees capture such interactions elegantly.

XGBoost can model non-linear relationships—for example, how distance interacts with weather, or how certain drivers perform consistently differently on specific routes. XGBoost also handles missing data internally and supports fast training and inference when deployed on SageMaker endpoints. It handles categorical variables once encoded, and the natural structure of decision trees makes it robust against outliers and skewed distributions. ETA prediction requires minimizing error metrics such as MAE or RMSE, both of which XGBoost optimizes effectively.

Additionally, logistic companies often generate large datasets, with millions of historical shipments. XGBoost on SageMaker supports distributed training, enabling fast training even with large-scale numerical datasets. The interpretability of XGBoost is also beneficial: analysts can determine which factors—route congestion, driver behavior, weather patterns—contribute most to delays, improving operational planning.

B) Amazon SageMaker K-Means would not work for ETA prediction because it is unsupervised and does not generate numeric predictions. It only clusters shipments into groups, and while clusters might represent fast vs. slow routes, it would not provide minute-level ETA predictions.

C) Amazon SageMaker BlazingText is specialized for NLP tasks such as word embedding generation (Word2Vec) and text classification. ETA prediction has nothing to do with natural language. Although route descriptions might be text-based, the core prediction uses structured numeric variables, not text embeddings. Thus BlazingText is irrelevant.

D) Amazon SageMaker NTM (Neural Topic Modeling) is also an NLP algorithm designed for unsupervised discovery of latent topics in large text corpora. It cannot perform regression, nor does it work with structured numeric inputs. ETA prediction is unrelated to topic extraction.

Therefore, XGBoost is the correct algorithm because it is explicitly optimized for structured numerical regression tasks with high complexity, large datasets, and critical accuracy requirements.

Question 118

A social media analytics firm wants to classify user comments as positive, negative, or neutral. The dataset contains millions of text samples. Which AWS SageMaker algorithm is best suited for this sentiment analysis task?

A) Amazon SageMaker BlazingText
B) Amazon SageMaker K-Means
C) Amazon SageMaker Random Cut Forest
D) Amazon SageMaker PCA

Answer

A) Amazon SageMaker BlazingText

Explanation

A) Amazon SageMaker BlazingText is designed specifically for large-scale natural language processing tasks such as text classification and word embedding generation. It includes fast, scalable implementations of algorithms like Word2Vec as well as supervised text classification architectures. Sentiment analysis is a supervised multi-class classification problem using raw text data, which BlazingText handles efficiently. Unlike traditional models requiring heavy preprocessing, BlazingText can process raw sentences directly, tokenize them, learn vector embeddings, and generate high-performing sentiment classifications.

BlazingText excels for large datasets, even with tens of millions of samples, due to its multi-threaded, distributed training capabilities. Sentiment analysis models must identify nuanced expressions of positivity, negativity, or neutrality, which natural language embeddings capture effectively. Moreover, BlazingText supports subword embeddings, essential for handling misspellings, slang, abbreviations, and emerging internet language variations.

Feature engineering requirements are minimal because the model learns semantic representations internally. After training, the model can be deployed on a SageMaker endpoint for real-time sentiment classification of social media comments. This makes it extremely efficient and scalable for organizations that analyze high-volume social sentiment in real time.

B) Amazon SageMaker K-Means is not suitable because sentiment analysis is supervised, and clustering cannot classify sentiments into predefined labels. It might group comments by similarity, but not classify them into positive, negative, or neutral categories.

C) Amazon SageMaker Random Cut Forest is an anomaly detection algorithm, completely unsuitable for text classification. It does not accept raw text; it requires numerical vector inputs and is not a supervised classifier.

D) Amazon SageMaker PCA is a dimensionality reduction technique, not a classifier. It cannot analyze sentiment, nor can it operate directly on text data.

Thus, BlazingText is the correct choice because it is optimized for high-performance NLP classification and embeddings at scale.

 

Question 119

A cybersecurity operations center wants to detect unusual login behavior across thousands of servers. The data includes login timestamps, user IDs, IP addresses, failed attempts, device signatures, and geographic location fields. The dataset has no labels. What is the best AWS SageMaker algorithm?

A) Amazon SageMaker Random Cut Forest
B) Amazon SageMaker Seq2Seq
C) Amazon SageMaker Linear Learner
D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker Random Cut Forest

Explanation

A) Amazon SageMaker Random Cut Forest is ideal because login anomaly detection is an unsupervised problem involving multidimensional numerical and categorical features converted into numeric representations. Cybersecurity datasets rarely have labeled attack samples due to the evolving nature of threats. RCF operates by identifying data points that deviate significantly from normal behavior patterns. It can detect unusual login times, abnormal IP address changes, improbable geographic jumps, excessive failed attempts, or unusual device signatures that indicate credential compromise or brute-force attacks.

RCF measures how isolated a data point is from the rest of the dataset. Unusual login activity produces high anomaly scores, which can be used for real-time alerting. RCF is designed specifically for streaming or batch anomaly detection at scale. Cybersecurity environments require fast, real-time inference to catch attacks as they occur; RCF supports deployment on SageMaker endpoints for immediate detection.

B) Amazon SageMaker Seq2Seq is a text-sequence model, and cybersecurity login logs do not require sequence-to-sequence structures. This algorithm would be computationally expensive, ineffective, and unsuitable for anomaly detection.

C) Amazon SageMaker Linear Learner requires labeled outcomes for supervised learning; cybersecurity anomalies are unlabeled. It also cannot capture complex nonlinear login behaviors.

D) Amazon SageMaker K-Means might cluster login behaviors but cannot produce anomaly scores or detect rare deviations reliably. It also struggles in high-dimensional behavioral datasets.

Therefore, RCF is the correct unsupervised, scalable anomaly detection algorithm for cybersecurity environments.

Question 120

A movie streaming service wants to predict the rating a user is likely to give to a movie based on sparse user–movie interaction data and side metadata such as user age group, movie genres, and viewing history. Which AWS SageMaker algorithm should be used?

A) Amazon SageMaker Factorization Machines
B) Amazon SageMaker PCA
C) Amazon SageMaker Random Cut Forest
D) Amazon SageMaker Linear Learner

Answer

A) Amazon SageMaker Factorization Machines

Explanation

A) Amazon SageMaker Factorization Machines are specifically designed for sparse, high-dimensional datasets where interactions between variables (user × movie pairs) drive predictions. Movie recommendation systems are the classic use case. Predicting user rating is a supervised regression task requiring the model to learn latent factors representing user preferences and movie characteristics. FM models excel at learning these latent interactions using matrix factorization principles combined with linear modeling.

User–movie interactions are sparse because each user watches only a tiny fraction of all available movies. Factorization Machines efficiently model second-order feature interactions without requiring explicit polynomial feature expansion. They also integrate side metadata such as age groups, genres, release years, or viewing history, improving prediction accuracy.

B) Amazon SageMaker PCA is not a predictive algorithm. It reduces dimensionality but cannot generate rating predictions.

C) Amazon SageMaker Random Cut Forest is for anomaly detection, not rating prediction.

D) Amazon SageMaker Linear Learner cannot model latent interactions effectively, greatly limiting accuracy for recommendation systems.

Thus, Factorization Machines are the most suitable choice because they handle sparse user–item interactions, incorporate side metadata, and deliver strong predictive performance for recommendation systems.