Amazon  AWS Certified Machine Learning Engineer – Associate MLA-C01   Exam  Dumps and Practice Test Questions Set 5 Q  81- 100

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Question 81

A retail company wants to forecast daily product demand across multiple stores, considering historical sales, 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 designed specifically for probabilistic multi-step time series forecasting. In the context of daily product demand across multiple stores, DeepAR can effectively model temporal dependencies, trends, and seasonality in historical sales data. It supports covariates such as promotions, holidays, or special events, which can have a significant impact on product demand. DeepAR leverages data from multiple related time series, such as different products and stores, allowing the model to learn shared patterns that improve predictions, especially for products or stores with limited historical data. The probabilistic nature of DeepAR provides both point forecasts and prediction intervals, helping businesses manage inventory, allocate resources, and plan logistics while accounting for uncertainty. Its scalability ensures it can handle large datasets across many products and locations, and it can adapt to evolving patterns over time.

B) Amazon SageMaker Linear Learner is a supervised regression algorithm that assumes linear relationships between input features and the target variable. While it can incorporate lag features and covariates, it does not naturally capture sequential dependencies or multi-step forecast dynamics. Producing probabilistic forecasts with Linear Learner would require additional modeling and feature engineering, making it less practical for complex retail forecasting scenarios.

C) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While XGBoost can be adapted for time series forecasting through lag features and engineered covariates, it is not inherently sequential and does not model temporal dependencies directly. Multi-step forecasting requires substantial feature engineering, and the algorithm does not naturally provide probabilistic prediction intervals.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It can group similar stores or products based on historical demand patterns, but it cannot generate numerical forecasts or prediction intervals. K-Means is exploratory and does not satisfy the requirements for multi-step time series forecasting.

DeepAR is the most suitable choice because it models sequential dependencies, incorporates covariates, generates probabilistic forecasts, and leverages patterns across multiple related time series. Other algorithms either assume linearity, are not sequential, or are exploratory.

Question 82

A financial institution wants to detect fraudulent transactions in real time. The dataset contains transaction amounts, timestamps, merchant codes, and user behavior features. 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 that identifies rare deviations in high-dimensional continuous datasets. In real-time fraud detection, RCF is particularly effective because fraudulent transactions are often scarce and constantly evolving, making supervised labeling impractical. RCF assigns anomaly scores to transactions based on how much they deviate from normal patterns, taking into account features like transaction amount, timestamp, merchant code, and user behavior. It detects both point anomalies (isolated unusual transactions) and contextual anomalies (sequences of transactions unusual for a specific user). RCF scales efficiently to high-volume transaction streams and can operate in real time, providing timely alerts to prevent fraud. Its adaptability allows it to detect emerging fraud patterns without frequent retraining.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm that assumes linear relationships between features and the target. It requires labeled examples of fraudulent transactions, which are often rare and evolving. Linear Learner may also underfit complex non-linear relationships in transaction patterns, limiting its effectiveness for fraud detection.

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

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. It is highly effective for structured classification tasks but requires labeled fraudulent examples and frequent retraining to adapt to evolving fraud patterns. This makes it less suitable for real-time, unsupervised anomaly detection.

RCF is the most appropriate algorithm because it is unsupervised, scalable, provides real-time anomaly scores, and adapts to emerging fraud patterns. Other algorithms require labeled data, assume linearity, or are exploratory.

Question 83

A healthcare provider wants to predict patient readmissions within 30 days using electronic health records, including 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 handle numerical lab results, categorical demographics, and sparse diagnosis codes while capturing non-linear interactions between features. XGBoost is highly effective in dealing with class imbalance, which is crucial for predicting rare events such as patient readmissions. It provides feature importance metrics, enabling healthcare providers to understand which factors contribute most to readmission risk. XGBoost supports both batch and real-time deployment, allowing for timely intervention and risk mitigation. Its robustness, scalability, and interpretability make it the most suitable choice for predicting patient readmissions.

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

C) Amazon SageMaker Factorization Machines are designed for sparse, high-dimensional datasets and are commonly used in recommendation systems. While they capture pairwise interactions, they are not well-suited for datasets containing both dense numerical lab results and sparse diagnosis codes. Predictive performance for readmission prediction would be suboptimal.

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

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

Question 84

A retail company wants to build a recommendation system for users 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 capture latent interactions between users and items, enabling accurate predictions for unobserved user-item pairs. Factorization Machines can incorporate side information such as user demographics and product attributes to improve recommendation quality. They scale efficiently to millions of users and items, making them suitable for large e-commerce platforms. By modeling latent factors in sparse interaction matrices, Factorization Machines ensure personalized recommendations even when observed interactions are limited.

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 ability to perform collaborative filtering and personalized recommendations.

C) Amazon SageMaker Linear Learner is a supervised algorithm assuming linear relationships between features and outcomes. While it can process sparse inputs, 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 similar users or items, it cannot provide personalized recommendations or predict unobserved preferences. K-Means is exploratory and unsuitable for recommendation systems.

Factorization Machines are the most suitable choice for building accurate and scalable recommendation systems due to their ability to handle sparse data, learn latent factors, incorporate side information, and support collaborative filtering. Other algorithms are linear, structured-data focused, or exploratory.

Question 85

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 suitable for high-dimensional continuous data. In telecom networks, RCF detects unusual deviations in metrics such as latency, throughput, and error rates by assigning anomaly scores to each data point. Labeled anomalies are rare and constantly evolving, making unsupervised detection essential. RCF handles both point anomalies (isolated spikes) and contextual anomalies (unusual sequences over time). Its scalability allows real-time processing of high-volume streaming network data. Deploying RCF in real-time enables proactive network monitoring, reducing downtime and maintaining service quality. RCF also provides interpretability, allowing network engineers to understand which metrics contribute most to the anomaly score.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm that requires labeled anomalies. Labeled network anomalies are scarce, and Linear Learner may underfit complex correlations among network metrics, making it less effective for 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 and unsuitable for real-time anomaly detection.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. It requires labeled anomalies and frequent retraining to capture evolving patterns, making it impractical for real-time anomaly detection. It is less effective in detecting unseen anomalies in streaming data.

Random Cut Forest is the most appropriate choice for detecting abnormal network patterns due to its unsupervised design, ability to handle high-dimensional continuous data, scalability, real-time deployment, and interpretability. Other algorithms require labeled data, assume linearity, or are exploratory.

Question 86

A retail company wants to forecast weekly sales for multiple products across different stores, considering historical sales, 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. In the context of retail, forecasting weekly sales for multiple products across different stores requires capturing sequential dependencies, seasonal patterns, and trends. DeepAR can incorporate covariates such as promotions, holidays, and other external factors, which influence sales. By leveraging multiple related time series, the model can learn shared patterns across products and stores, improving accuracy for items with limited historical data. The probabilistic nature of DeepAR provides both point forecasts and prediction intervals, allowing businesses to make informed inventory, staffing, and supply chain decisions while accounting for uncertainty. Its scalability ensures it can efficiently process large datasets spanning multiple stores and product categories, and it adapts to changing trends over time.

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

C) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While it can be adapted for forecasting through lag features, it does not inherently model sequential dependencies or multi-step forecasts. XGBoost also does not naturally provide probabilistic prediction intervals, which are valuable for inventory planning and risk management.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group products or stores with similar sales patterns, it cannot generate numerical forecasts or prediction intervals. K-Means is exploratory and cannot provide actionable multi-step forecasts for retail operations.

DeepAR is the most appropriate choice for multi-product, multi-store weekly sales forecasting due to its ability to model sequential dependencies, incorporate covariates, generate probabilistic forecasts, and leverage patterns across multiple related time series. Other algorithms assume linearity, are not sequential, or are exploratory.

Question 87

A financial institution wants to detect unusual transactions to prevent fraud. The dataset includes transaction amounts, timestamps, merchant codes, and user behavioral patterns. 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 anomaly detection algorithm that identifies rare deviations in high-dimensional continuous datasets. In fraud detection, anomalies correspond to unusual transactions that deviate from typical user behavior patterns. RCF does not require labeled fraudulent transactions, which are often scarce and constantly changing. It assigns an anomaly score to each transaction based on its deviation from normal patterns, accounting for features such as transaction amount, timestamp, merchant code, and user behavior. RCF detects both point anomalies (isolated unusual transactions) and contextual anomalies (transactions unusual relative to prior behavior). Its scalability allows it to process high-volume streaming data in real time, providing timely alerts and enabling rapid responses to fraudulent activity. The unsupervised approach ensures adaptability to emerging fraud patterns without retraining.

B) Amazon SageMaker Linear Learner is a supervised algorithm requiring labeled examples of fraud. Without sufficient labeled data, it may underperform in detecting anomalies. Additionally, it assumes linear relationships between features and targets, limiting its ability to capture complex patterns in transaction data.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can segment transactions into groups based on similarity, it does not produce anomaly scores or detect rare deviations effectively. 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 classification tasks, it requires labeled examples of fraudulent transactions and frequent retraining to adapt to evolving fraud patterns, making it less practical for real-time unsupervised detection.

Random Cut Forest is the most suitable choice for real-time fraud detection due to its unsupervised design, scalability, ability to assign anomaly scores, and adaptability to emerging patterns. Other algorithms require supervision, labeled data, or are exploratory.

 

Question 88

A healthcare provider wants to predict patient readmissions within 30 days using electronic health records (EHR) that include 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 handle numerical lab results, categorical demographics, and sparse diagnosis codes, while capturing complex non-linear interactions between features. XGBoost is effective in managing class imbalance, which is critical when predicting rare events such as patient readmissions. It provides feature importance metrics, allowing healthcare professionals to understand key risk factors such as comorbidities, abnormal lab results, and prior hospitalizations. XGBoost scales efficiently for large healthcare datasets and supports both batch and real-time deployment, enabling timely interventions for at-risk patients. Its robustness, accuracy, and interpretability make it the most suitable algorithm for predicting patient readmissions.

B) Amazon SageMaker Linear Learner assumes linear relationships between features and the target. While it is interpretable, it may underfit complex interactions present in EHR data and require extensive feature engineering, resulting in lower predictive performance compared to XGBoost.

C) Amazon SageMaker Factorization Machines are designed for sparse, high-dimensional datasets, often used in recommendation systems. While they can capture pairwise interactions, they are not well-suited for datasets with dense numerical features and sparse diagnosis codes, leading to suboptimal predictions in patient readmission tasks.

D) Amazon SageMaker DeepAR Forecasting is designed for sequential time series prediction. Predicting patient readmissions is a supervised classification problem with heterogeneous features, not a sequential 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 for healthcare prediction tasks. Other algorithms are linear, sparse-focused, or designed for time series.

Question 89

A retail company wants to build a recommendation system for users based on sparse 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 capture latent interactions between users and items, enabling accurate predictions for unobserved user-item pairs. Factorization Machines can incorporate side information such as user demographics and product attributes to improve recommendation quality. They scale efficiently to millions of users and items, providing personalized recommendations even when observed interactions are limited. By modeling latent factors in sparse matrices, Factorization Machines ensure scalable and effective collaborative filtering.

B) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While it can handle engineered features representing user-item interactions, it does not naturally capture latent factors in sparse data, limiting its effectiveness for recommendation systems.

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

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group similar users or items, it does not provide personalized recommendations or predict unobserved preferences. K-Means is exploratory and not suitable for collaborative filtering.

Factorization Machines are the most appropriate solution for building accurate and scalable recommendation systems due to their ability to handle sparse data, learn latent factors, incorporate side information, and support collaborative filtering. Other algorithms are linear, structured-data focused, or exploratory.

Question 90

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 designed for high-dimensional continuous data. In telecom networks, RCF detects unusual deviations in metrics such as latency, throughput, and error rates by assigning anomaly scores to each data point. Labeled network anomalies are rare and constantly evolving, making unsupervised detection essential. RCF can identify both point anomalies (isolated spikes) and contextual anomalies (unusual sequences over time). Its scalability allows real-time processing of high-volume streaming data. Deploying RCF ensures proactive monitoring, reduces downtime, and maintains service reliability. RCF also provides interpretability, helping network engineers understand which metrics contribute most to anomalies.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled anomalies and may underfit complex correlations among network metrics, making it less effective for 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 and unsuitable for real-time anomaly detection.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. It requires labeled anomalies and frequent retraining to detect evolving patterns, making it impractical for real-time anomaly detection. It is less effective at identifying unseen anomalies.

Random Cut Forest is the most suitable algorithm due to its unsupervised design, ability to handle high-dimensional continuous metrics, scalability, real-time deployment, and interpretability. Other algorithms require labeled data, assume linearity, or are exploratory.

Question 91

A logistics company wants to forecast daily shipment volumes for multiple routes using historical shipment data, weather, 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 optimized for probabilistic multi-step time series forecasting. It is particularly suitable for forecasting daily shipment volumes for multiple routes, as it captures temporal dependencies, trends, and seasonal patterns in historical shipment data. DeepAR can incorporate covariates such as weather conditions, holidays, and other external factors that influence shipment volumes. By leveraging multiple related time series, such as shipments across different routes, it learns shared patterns that improve prediction accuracy, especially for routes with limited historical data. The algorithm produces probabilistic forecasts, providing point predictions and prediction intervals, which is crucial for logistics planning, inventory management, and capacity allocation. DeepAR scales efficiently to large datasets and adapts to evolving trends, making it highly suitable for complex logistics forecasting.

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

C) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm suitable for structured tabular data. While it can be adapted for time series forecasting using engineered lag features, it does not natively capture sequential dependencies or multi-step forecasts. It also does not provide uncertainty intervals naturally, limiting its utility for planning and risk management in logistics.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group routes with similar shipment patterns, it cannot generate numerical forecasts or prediction intervals. K-Means is exploratory and cannot provide actionable multi-step forecasts for logistics operations.

DeepAR is the most suitable choice for multi-route, daily shipment forecasting due to its sequential modeling, probabilistic forecasting, covariate incorporation, and ability to leverage patterns across multiple related time series. Other algorithms are linear, not sequential, or exploratory.

Question 92

A bank wants to detect fraudulent transactions in real time. The dataset includes transaction amounts, timestamps, merchant codes, and user behavior 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 the context of banking, fraudulent transactions are anomalies that deviate from typical user behavior. RCF assigns an anomaly score to each transaction, enabling real-time detection of unusual activity without requiring labeled fraudulent examples, which are rare and constantly evolving. It captures both point anomalies, such as an unusually high transaction, and contextual anomalies, such as a pattern of transactions unusual relative to a user’s historical behavior. RCF scales efficiently for large streaming datasets and adapts to emerging fraud patterns, providing proactive monitoring. Its real-time deployment capability allows banks to respond quickly, minimizing financial losses and protecting customer accounts.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled examples of fraudulent transactions and assumes linear relationships between features and the target. These limitations make it less effective for real-time fraud detection, where anomalies are rare, evolving, and often non-linear.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group transactions with similar characteristics, it does not provide anomaly scores or detect rare fraudulent activities 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 powerful for structured classification, it requires labeled fraud examples and frequent retraining to adapt to new fraud patterns. This makes it less practical for real-time anomaly detection.

Random Cut Forest is the most suitable algorithm for real-time detection of fraudulent transactions due to its unsupervised design, scalability, ability to provide anomaly scores, and adaptability to evolving patterns. Other algorithms require supervision, labeled anomalies, or are exploratory.

Question 93

A healthcare provider wants to predict patient readmissions within 30 days using electronic health record (EHR) data, including 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 and heterogeneous datasets. It can handle numerical lab results, categorical demographics, and sparse diagnosis codes while capturing complex non-linear interactions between features. XGBoost is highly effective in managing class imbalance, which is important for predicting rare events such as patient readmissions. It provides feature importance metrics, allowing healthcare providers to identify key factors contributing to readmission risk, such as abnormal lab results, comorbidities, or prior hospitalization history. XGBoost supports both batch and real-time prediction deployment, enabling timely interventions for at-risk patients. Its scalability, accuracy, and interpretability make it the most suitable choice for readmission prediction.

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

C) Amazon SageMaker Factorization Machines are designed for sparse, high-dimensional datasets and are commonly used in recommendation systems. While they capture pairwise interactions, they are not well-suited for mixed dense and sparse features such as lab results and diagnosis codes. Their predictive performance in patient readmission prediction would be suboptimal.

D) Amazon SageMaker DeepAR Forecasting is designed for sequential time series prediction. Patient readmissions are a supervised classification problem rather than 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 94

A retail company wants to build 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 and are 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 user demographics or product attributes, enhancing recommendation quality. They scale efficiently to millions of users and items, making them suitable for large e-commerce platforms. By modeling latent factors in sparse interaction matrices, Factorization Machines provide personalized recommendations even with limited observed interactions.

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 algorithm assuming linear relationships between features and outcomes. 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 similar users or items, it cannot provide personalized recommendations or predict unobserved preferences. K-Means is exploratory and unsuitable for recommendation systems.

Factorization Machines are the most appropriate 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 95

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 can manifest as unusual deviations in latency, throughput, or error rates. RCF assigns anomaly scores to data points, detecting both point anomalies (isolated spikes) and contextual anomalies (unusual sequences over time). Labeled network anomalies are rare, making unsupervised detection essential. RCF scales efficiently for high-volume streaming data and supports real-time deployment, enabling proactive monitoring and reducing network downtime. Its interpretability allows network engineers to understand which metrics contribute most to anomalies, facilitating root cause analysis and faster remediation.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled anomalies and may underfit complex correlations among network metrics, making it less suitable for 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 detect rare deviations. K-Means is exploratory and not suitable for real-time anomaly detection.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. It requires labeled anomalies and frequent retraining, which limits its effectiveness in detecting emerging network anomalies in real time.

Random Cut Forest is the most appropriate choice 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 96

A retail company wants to forecast monthly 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 designed for probabilistic multi-step time series forecasting. It is particularly suitable for forecasting monthly sales across multiple stores and product categories because it captures sequential dependencies, trends, and seasonal variations in historical sales data. DeepAR can incorporate covariates such as promotions, holidays, and other external factors that influence sales patterns. By leveraging multiple related time series, DeepAR learns shared patterns across stores and product categories, improving forecast accuracy for items or locations with limited historical data. The probabilistic forecasts produced by DeepAR include point predictions and prediction intervals, which are critical for inventory planning, resource allocation, and supply chain optimization while managing risk. Its scalability ensures that it can handle large datasets efficiently, and its adaptability allows it to account for changing market dynamics.

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 generate probabilistic outputs. Multi-step forecasting using Linear Learner would require extensive feature engineering, making it less practical for complex retail forecasting.

C) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm suitable for structured tabular data. While it can be adapted for forecasting using engineered lag features, it does not natively capture temporal dependencies or multi-step forecasts. XGBoost also does not provide prediction intervals, which are useful for risk assessment and inventory planning.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can identify groups of stores or products with similar historical sales patterns, it cannot generate numerical forecasts or prediction intervals. K-Means is exploratory and does not satisfy the requirements for multi-step time series forecasting.

DeepAR is the most suitable algorithm for multi-category, multi-store monthly sales forecasting due to its sequential modeling, ability to incorporate covariates, probabilistic forecasting, and capability to leverage patterns across related time series. Other algorithms are linear, not sequential, or exploratory.

Question 97

A bank wants to detect unusual 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 optimized for high-dimensional continuous data. It is particularly effective for detecting unusual banking transactions because fraudulent activities often represent anomalies that deviate from normal user behavior. RCF assigns an anomaly score to each transaction, which enables real-time detection without requiring labeled examples of fraud. It captures both point anomalies, such as an unusually high transaction, and contextual anomalies, where sequences of transactions are atypical relative to a user’s historical patterns. Its scalability allows it to process high-volume streaming transaction data efficiently. Real-time deployment of RCF allows banks to proactively detect and respond to potential fraud, reducing financial loss and improving customer trust.

B) Amazon SageMaker Linear Learner is a supervised algorithm that requires labeled examples of fraudulent transactions. It also assumes linear relationships between features and the target variable. These limitations reduce its effectiveness in 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 reliably detect rare fraudulent events. K-Means is exploratory and unsuitable for real-time fraud detection.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. Although it can handle structured classification tasks effectively, 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 fraudulent banking transactions due to its unsupervised design, ability to assign anomaly scores, scalability, and adaptability to evolving patterns. Other algorithms require supervision, labeled anomalies, or are exploratory.

Question 98

A healthcare provider wants to predict patient readmissions within 30 days using 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 data. It can process numerical lab results, categorical demographics, and sparse diagnosis codes while capturing complex non-linear interactions among features. XGBoost is particularly effective for predicting rare events such as patient readmissions, as it can handle class imbalance through specialized parameters. It also provides feature importance metrics, which help healthcare providers understand contributing factors such as comorbidities, abnormal lab results, or prior hospitalizations. XGBoost supports both batch and real-time predictions, enabling timely interventions for patients at high risk of readmission. 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. Although interpretable, it may underfit complex interactions in EHR datasets, requiring extensive feature engineering and resulting in lower predictive accuracy 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 ideal for datasets with dense numerical lab results combined with sparse diagnosis codes, leading to suboptimal performance in predicting readmissions.

D) Amazon SageMaker DeepAR Forecasting is designed for sequential time series prediction. Patient readmission prediction is a supervised classification problem with heterogeneous structured data rather than a time series forecasting problem, making DeepAR unsuitable.

XGBoost is the most appropriate choice 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 99

A retail company wants to build a recommendation system for users based on sparse interactions 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 optimized for sparse, high-dimensional datasets, making them ideal for recommendation systems. They learn latent interactions between users and items, enabling predictions for unobserved user-item pairs. Factorization Machines can incorporate side information such as demographics or product attributes, enhancing the accuracy of recommendations. They scale efficiently to millions of users and items, providing personalized recommendations even with limited observed interactions. By modeling latent factors, Factorization Machines support collaborative filtering and enable the system to recommend relevant products to each user, improving engagement and sales.

B) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. Although it can process engineered interaction features, it does not naturally capture latent factors in sparse user-item matrices. This limits its ability to provide personalized recommendations in collaborative filtering scenarios.

C) Amazon SageMaker Linear Learner is a supervised linear algorithm. While it can process sparse inputs, it cannot model complex latent interactions between users and items, reducing recommendation quality.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group users or items with similar characteristics, it cannot generate personalized recommendations or predict unobserved user-item interactions. K-Means is exploratory, 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 100

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 detect 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 to process high-volume streaming data and supports real-time deployment, allowing proactive monitoring, reduced downtime, and improved network reliability. Its interpretability helps engineers identify which metrics contribute most to anomalies, supporting rapid troubleshooting and resolution.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm that requires labeled anomalies and may underfit complex interactions among network metrics, limiting its suitability for 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 and not suitable for real-time anomaly detection.

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

Random Cut Forest is the most appropriate 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.