Amazon  AWS Certified Machine Learning Engineer – Associate MLA-C01   Exam  Dumps and Practice Test Questions Set 4 Q 61- 80

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

Question 61

A retail company wants to forecast daily demand for multiple products across different stores, incorporating promotions, holidays, and seasonality. 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 can model temporal dependencies, trends, and seasonality in sequential data, making it ideal for forecasting daily product demand across multiple stores. DeepAR leverages data from multiple related time series, such as different products or store locations, enabling the model to learn patterns from sparse or limited historical data. Covariates like promotions, holidays, and seasonality can be included to enhance forecast accuracy. DeepAR provides both point forecasts and prediction intervals, which help businesses manage inventory, allocate resources, and make risk-aware operational decisions. Its scalability and ability to adapt to changing trends make it highly effective for large datasets involving multiple stores and products.

B) Amazon SageMaker Linear Learner is a regression algorithm that assumes linear relationships between features and the target variable. While it can incorporate lag features, promotions, and seasonal effects, it does not capture temporal dependencies or multi-step sequences naturally. Multi-step forecasting would require extensive feature engineering and still lack probabilistic uncertainty estimates, which are essential for risk management in demand planning.

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 and rolling statistics, it is not inherently sequential and does not naturally model multi-step forecasts or uncertainty. Feature engineering for multiple related time series and products would be complex, making it less efficient than DeepAR.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It may group stores or products with similar demand patterns but cannot provide numerical forecasts or prediction intervals. K-Means is exploratory and cannot be used for predictive multi-step forecasting.

DeepAR is the most suitable choice for forecasting daily demand across multiple products and stores because it captures sequential dependencies, incorporates covariates, provides probabilistic forecasts, and leverages patterns across multiple related time series. Other algorithms assume linearity, are not sequential, or are exploratory.

Question 62

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

A) Amazon SageMaker XGBoost

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker Factorization Machines

D) Amazon SageMaker DeepAR Forecasting

Answer

A) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm well-suited for structured, heterogeneous datasets such as electronic health records. It efficiently handles numerical, categorical, and sparse features while capturing non-linear interactions among lab results, demographics, and diagnoses. XGBoost can address class imbalance using weighting or sampling techniques, which is crucial because readmissions are relatively rare events. It provides feature importance metrics, helping healthcare providers understand the key factors driving readmission risk. XGBoost supports scalable training and deployment for both batch and real-time predictions, enabling timely intervention for at-risk patients. Its robustness and interpretability make it the most appropriate choice for predicting readmission outcomes.

B) Amazon SageMaker Linear Learner assumes linear relationships between features and outcomes. While interpretable, Linear Learner may underfit EHR data with complex, non-linear interactions between lab results, demographics, and diagnoses. Extensive feature engineering would be required to improve performance, which reduces efficiency.

C) Amazon SageMaker Factorization Machines are optimized for sparse, high-dimensional datasets, primarily used for recommendation systems. EHR datasets consist of dense numerical lab results and categorical features in addition to sparse diagnosis codes, which Factorization Machines may not capture effectively. Predictive accuracy is likely to be lower than XGBoost in this context.

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

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

Question 63

A company wants to detect anomalies in streaming IoT sensor data, including temperature, vibration, and pressure readings. 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 specifically designed for high-dimensional continuous data. For IoT sensor streams, RCF can detect unusual points that deviate from normal operating behavior, computing anomaly scores in real time. Its unsupervised nature is crucial because labeled anomalies are scarce and often unknown in advance. RCF can detect both point and contextual anomalies, handle correlated features like temperature and vibration, and scale efficiently to high-volume streaming data. Deploying RCF in real-time allows immediate identification of abnormal sensor behavior, enabling predictive maintenance, reducing downtime, and improving operational efficiency. Its interpretability and ability to adapt to evolving normal patterns make it ideal for industrial IoT anomaly detection.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled anomaly data, which is rare in IoT environments. Even with labeled data, Linear Learner may not capture the complex interactions and correlations between multiple continuous sensor features.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group normal operating states, it does not produce anomaly scores or detect rare deviations effectively. K-Means is exploratory rather than predictive and is unsuitable for real-time anomaly detection.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While it can be applied to structured data classification tasks, it requires labeled anomalies, which are scarce in IoT sensor streams. XGBoost also cannot natively operate on streaming data without retraining, making it impractical for real-time detection.

Random Cut Forest is the most appropriate choice for real-time anomaly detection in IoT sensor data due to its unsupervised design, scalability, ability to handle high-dimensional continuous features, and provision of interpretable anomaly scores. Other algorithms are either supervised or exploratory.

Question 64

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

A) Amazon SageMaker Factorization Machines

B) Amazon SageMaker XGBoost

C) Amazon SageMaker Linear Learner

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker Factorization Machines

Explanation

A) Amazon SageMaker Factorization Machines are specifically designed to handle sparse, high-dimensional datasets and learn pairwise interactions between users and items. This makes them ideal for recommendation systems where many user-item interactions are unobserved. Factorization Machines can incorporate side information such as user demographics or item attributes to improve prediction quality. They efficiently model latent factors for unobserved user-item pairs, enabling collaborative filtering. FM scales to millions of users and items, providing personalized recommendations even in large-scale production environments.

B) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm suitable for structured tabular data. While it can handle engineered interaction features, it does not naturally model latent factors in sparse user-item matrices, making it less effective for collaborative filtering and personalized recommendation tasks.

C) Amazon SageMaker Linear Learner is a supervised algorithm that assumes linear relationships. While it can process sparse inputs, it cannot capture complex interactions between users and items, limiting recommendation accuracy and personalization. Linear Learner may underfit high-dimensional sparse datasets.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It can group users or items with similar characteristics but cannot provide personalized recommendations or predict unobserved preferences. K-Means is exploratory and not suitable for collaborative filtering.

Factorization Machines are the most appropriate choice due to their ability to model sparse interactions, learn latent factors, incorporate side information, and scale efficiently. Other algorithms either focus on structured tabular data, linear relationships, or exploratory clustering.

Question 65

A telecom company wants to detect abnormal patterns in network metrics such as latency, throughput, and error rates. 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 designed for high-dimensional continuous data. For telecom network metrics like latency, throughput, and error rates, RCF can identify unusual deviations from normal patterns by computing anomaly scores. Labeled anomalies are often rare and difficult to obtain in network monitoring, making unsupervised detection crucial. RCF scales efficiently for streaming data, handles correlated metrics, and can detect both point and contextual anomalies. Real-time deployment enables immediate identification of potential network issues, allowing proactive maintenance, reduced downtime, and improved service reliability. RCF’s interpretability and ability to adapt to evolving patterns in network behavior make it ideal for real-time monitoring.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled anomalies, which are scarce in real-world network data. Linear Learner may also underfit complex relationships between correlated network metrics, reducing detection accuracy.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can cluster similar network states, it does not provide anomaly scores or detect rare deviations effectively. 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 for training and frequent retraining to detect evolving patterns. XGBoost is not inherently designed for unsupervised real-time anomaly detection.

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

Question 66

A manufacturing company wants to detect anomalies in sensor readings from industrial machines to prevent equipment failure. The data includes continuous metrics like temperature, pressure, and vibration. Which AWS SageMaker algorithm is most appropriate 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 designed to handle high-dimensional continuous data. In industrial settings, detecting anomalies in sensor readings such as temperature, pressure, and vibration is crucial for predictive maintenance and preventing equipment failures. RCF identifies data points that deviate from the normal pattern by assigning anomaly scores, making it possible to detect rare events without labeled anomalies. Its unsupervised nature is particularly useful because labeled failure data is often scarce. RCF can detect both point anomalies (sudden spikes or drops) and contextual anomalies (unusual sequences over time). Additionally, it scales efficiently to large datasets and can process real-time streaming data from multiple machines simultaneously. Deploying RCF in a real-time monitoring system allows proactive alerts and automated responses, reducing downtime and maintenance costs.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled anomaly data, which is difficult to obtain in industrial sensor streams. Even with labeled data, Linear Learner may not capture complex interactions between continuous metrics such as temperature and vibration. Its assumption of linear relationships may also underfit non-linear patterns present in sensor data.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group sensor readings into clusters of similar behavior, it does not provide anomaly scores or detect rare deviations effectively. K-Means is exploratory and not suitable for real-time anomaly detection in high-dimensional continuous data.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. It requires labeled anomalies and frequent retraining to adapt to evolving sensor behavior. XGBoost is not inherently designed for unsupervised anomaly detection in streaming data and would be less practical for real-time monitoring of industrial sensors.

Random Cut Forest is the most suitable solution for detecting anomalies in sensor data because it operates unsupervised, provides real-time detection, handles high-dimensional continuous metrics, and scales effectively. Other algorithms require supervision, labeled data, or are exploratory.

Question 67

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

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 multi-step probabilistic time series forecasting. It can learn temporal dependencies, trends, and seasonality across multiple related time series, making it ideal for forecasting daily sales for hundreds of products across numerous stores. DeepAR can incorporate covariates such as promotions, holidays, and special events to improve forecast accuracy. It provides both point predictions and prediction intervals, allowing businesses to plan inventory, staffing, and logistics while accounting for uncertainty. DeepAR leverages shared patterns across multiple stores and products, which is especially useful for products or locations with limited historical data. Its scalability and adaptability to changing patterns ensure accurate, reliable forecasts in complex retail environments.

B) Amazon SageMaker Linear Learner is a regression algorithm assuming linear relationships between features and targets. It can incorporate lag features and covariates but cannot naturally model sequential dependencies or multi-step forecasts. Probabilistic forecasting is also limited, which reduces effectiveness in demand planning.

C) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm suitable for structured tabular data. While it can be adapted for time series forecasting with engineered lag features, it does not inherently model sequential dependencies, multi-step forecasts, or uncertainty. Feature engineering for multiple products and stores would be complex and less efficient than DeepAR.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It may help identify products or stores with similar sales patterns but cannot generate numerical forecasts or prediction intervals. K-Means is exploratory and does not meet the requirements for accurate multi-step forecasting.

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

Question 68

A bank wants to detect unusual transactions in real time to prevent credit card fraud. The dataset contains numerical transaction amounts, timestamps, and categorical merchant codes. 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 ideal for detecting rare, unusual events in high-dimensional continuous data. In credit card fraud detection, anomalies can be identified based on unusual combinations of transaction amounts, timestamps, and merchant codes. RCF does not require labeled fraudulent transactions, which are scarce and constantly evolving. It assigns anomaly scores to each transaction, enabling real-time alerts and immediate investigation. RCF can handle both point anomalies (single unusual transactions) and contextual anomalies (transactions that are unusual given recent behavior), making it highly effective for fraud detection. Its scalability and ability to process streaming data make it practical for high-volume financial environments.

B) Amazon SageMaker Linear Learner is a supervised algorithm requiring labeled data. Without sufficient labeled fraudulent transactions, the model may fail to detect anomalies. Additionally, it assumes linear relationships, which may not capture complex interactions among transaction features.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group transactions with similar behavior, it does not provide anomaly scores or reliably detect rare deviations. 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 fraudulent transactions and frequent retraining to adapt to new fraud patterns, making it less suitable for real-time detection. It is also less effective in handling unseen anomalies.

Random Cut Forest is the most appropriate choice for real-time anomaly detection in credit card transactions due to its unsupervised design, scalability, ability to detect rare events, and real-time deployment capability. Other algorithms require supervision, labeled data, or are exploratory.

Question 69

A company wants to build a recommendation system for its e-commerce platform using sparse user-item interactions and additional 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 designed for sparse, high-dimensional datasets, making them ideal for recommendation systems with sparse user-item interactions. Factorization Machines model pairwise interactions between users and items and can incorporate side information such as demographics, item attributes, or contextual data to improve prediction quality. They learn latent factors for unobserved user-item pairs, enabling collaborative filtering and personalized recommendations. FM also scales efficiently to millions of users and items, which is crucial for large e-commerce platforms. Their ability to handle sparse data and latent interactions ensures accurate and scalable recommendation systems.

B) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm suitable for structured tabular data. While it can be adapted to recommendation tasks through feature engineering, it does not naturally handle sparse interactions or latent factors, making it less effective for collaborative filtering.

C) Amazon SageMaker Linear Learner is a supervised algorithm that assumes linear relationships. It can process sparse data but cannot capture complex interactions between users and items, limiting recommendation accuracy. Linear Learner may underfit in high-dimensional sparse datasets.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It can group users or items based on similarity but cannot provide personalized recommendations or predict unobserved preferences. K-Means is exploratory and unsuitable for collaborative filtering.

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

Question 70

A telecom company wants to detect abnormal network patterns in real time, including 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 for high-dimensional continuous data. In telecom networks, RCF can detect anomalies in metrics such as latency, throughput, and error rates by assigning anomaly scores to unusual data points. Its unsupervised nature is critical because labeled anomalies are rare and evolving. RCF scales efficiently for streaming data, handles correlated metrics, and detects both point anomalies and contextual anomalies. Real-time deployment enables immediate identification of potential network issues, proactive maintenance, and service quality assurance. Its interpretability and adaptability to changing network patterns make it highly suitable for operational monitoring.

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 interactions between correlated metrics, reducing detection accuracy.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group network states based on similarity, 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 monitoring. It is less effective for unsupervised detection of rare network anomalies.

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

Question 71

A logistics company wants to forecast weekly shipment volumes for multiple routes using historical shipment data, weather, and holiday schedules. 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 specifically designed for multi-step probabilistic time series forecasting. It uses recurrent neural networks to model temporal dependencies and sequential patterns, making it ideal for forecasting weekly shipment volumes across multiple routes. DeepAR can incorporate covariates such as weather conditions, holidays, and operational constraints to improve forecast accuracy. It leverages patterns from multiple related time series, such as shipments across different routes or products, which is especially beneficial when historical data for certain routes is limited. The algorithm generates probabilistic forecasts, providing both point predictions and uncertainty intervals. This is critical for logistics planning, resource allocation, and risk management, as it allows managers to prepare for variability in shipment volumes. DeepAR also scales efficiently to large datasets and can adapt to changing trends, ensuring reliable predictions in complex, dynamic logistics environments.

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 capture sequential dependencies naturally. Multi-step forecasting would require extensive feature engineering, and probabilistic forecasting is not directly available, which limits its effectiveness in logistics planning.

C) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm optimized for structured tabular data. While it can be adapted for time series forecasting using lag features, it is not inherently sequential and does not model multi-step forecasts or uncertainty. Capturing patterns across multiple routes and products would require complex feature engineering, making it less efficient than DeepAR.

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 does not satisfy the requirements for multi-step forecasting.

DeepAR is the most suitable solution for forecasting weekly shipment volumes across multiple routes due to its ability to model sequential dependencies, incorporate covariates, provide probabilistic forecasts, and leverage patterns from multiple related time series. Other algorithms assume linearity, are not sequential, or are exploratory.

Question 72

A bank wants to detect fraudulent transactions in real time. The dataset includes transaction amounts, merchant codes, timestamps, 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 to identify rare and unusual patterns in high-dimensional continuous data. In credit card fraud detection, anomalies can be defined as transactions that deviate significantly from normal behavior, taking into account transaction amounts, timestamps, merchant codes, and behavioral patterns. RCF does not require labeled fraudulent transactions, which are scarce and constantly evolving. It generates anomaly scores for each transaction, enabling real-time monitoring and alerting. RCF can detect both point anomalies (isolated unusual transactions) and contextual anomalies (transactions that are unusual in a sequence of behavior). Its scalability allows deployment on high-volume financial transaction streams, and its adaptability ensures detection of emerging fraud patterns without retraining on labeled data.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. It requires labeled fraudulent transactions, which are rare, and it assumes linear relationships between features. This limits its ability to detect complex non-linear anomalies in transactional data.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can segment transactions into similar groups, it does not provide anomaly scores and cannot reliably detect rare deviations or fraudulent activity. 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 highly effective for structured classification tasks, it requires labeled fraudulent transactions and frequent retraining to capture evolving fraud patterns, making it less practical for real-time anomaly detection.

Random Cut Forest is the most appropriate solution for real-time detection of fraudulent transactions because it operates unsupervised, assigns anomaly scores, scales efficiently, and adapts to changing patterns. Other algorithms require supervision, labeled anomalies, or are exploratory.

Question 73

A healthcare provider wants to predict patient readmissions within 30 days using EHR data containing 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 such as electronic health records. It can handle numerical lab results, categorical demographics, and sparse diagnosis codes while capturing complex non-linear interactions between features. XGBoost can address class imbalance using weighting or sampling techniques, which is crucial for predicting rare events like readmissions. It provides feature importance metrics that allow clinicians to identify key risk factors influencing readmission, such as comorbidities or abnormal lab results. XGBoost scales efficiently for large datasets and supports batch and real-time prediction deployment, enabling timely interventions for at-risk patients. Its robustness, accuracy, and interpretability make it the most suitable choice for patient readmission prediction.

B) Amazon SageMaker Linear Learner assumes linear relationships between features and outcomes. While interpretable, Linear Learner may underfit EHR data with complex, non-linear interactions among lab results, demographics, and diagnoses. Extensive feature engineering would be required to achieve similar accuracy to XGBoost, reducing efficiency.

C) Amazon SageMaker Factorization Machines are optimized for sparse high-dimensional datasets, mainly used in recommendation systems. While they can capture pairwise interactions, they may not effectively model dense numerical features like lab results combined with sparse diagnosis codes, resulting in lower predictive accuracy.

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

XGBoost is the most appropriate choice because it can handle heterogeneous data, model complex interactions, manage class imbalance, provide interpretability, and scale effectively. Other algorithms are either linear, sparse-focused, or time series-oriented.

Question 74

A retail company wants to build a recommendation system for users based on sparse interactions with products and user demographic data. Which AWS SageMaker algorithm is most appropriate?

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 designed to handle sparse, high-dimensional datasets. They capture latent interactions between users and items, making them ideal for collaborative filtering and recommendation systems. FM can incorporate side information, such as user demographics or product attributes, to improve predictions. It predicts preferences for unobserved user-item pairs and scales efficiently for large e-commerce datasets. By modeling pairwise interactions, FM provides accurate, personalized recommendations even when user interactions are sparse, enabling high-quality personalization at scale.

B) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm suitable for structured tabular data. While it can handle engineered interaction features, it does not naturally model 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. While it can process sparse inputs, 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 products, it cannot provide personalized recommendations or predict unobserved preferences. K-Means is exploratory and not suitable for recommendation systems.

Factorization Machines are the most suitable solution due to their ability to handle sparse data, learn latent factors, incorporate side information, and provide scalable personalized recommendations. Other algorithms are either linear, structured-data focused, or exploratory.

Question 75

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, RCF can detect abnormal deviations in metrics such as latency, throughput, and error rates by assigning anomaly scores to unusual data points. Labeled anomalies are rare and evolving in real-world networks, making unsupervised detection essential. RCF handles both point anomalies (sudden spikes) and contextual anomalies (abnormal sequences over time). Its scalability allows deployment for high-volume streaming network data, and real-time monitoring enables proactive maintenance, reduced downtime, and improved service reliability. RCF also provides interpretability, allowing network engineers to understand the contribution of each metric to anomaly scores.

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.

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

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

Question 76

A manufacturing company wants to detect anomalies in real-time sensor data from industrial equipment, including temperature, vibration, and pressure readings. 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 specifically designed for unsupervised anomaly detection in high-dimensional continuous data. In manufacturing, detecting anomalies in sensor readings such as temperature, vibration, and pressure is essential for predictive maintenance and preventing costly equipment failures. RCF computes anomaly scores for each data point by identifying deviations from normal patterns without requiring labeled examples, which is crucial because labeled anomaly data is often rare or unavailable. It can detect point anomalies (isolated abnormal readings) and contextual anomalies (patterns unusual relative to recent history). RCF scales efficiently to large datasets and supports real-time streaming data, allowing for proactive alerts and interventions. Deploying RCF in real-time ensures that unusual equipment behavior is identified early, preventing downtime and reducing maintenance costs. Its interpretability and adaptability to changing patterns further enhance its value in industrial monitoring.

B) Amazon SageMaker Linear Learner is a supervised regression or classification algorithm that requires labeled anomalies. In real-time sensor monitoring, obtaining labeled anomaly data is difficult, and Linear Learner may underfit complex interactions between continuous metrics like temperature and vibration. Its linear assumption limits effectiveness for non-linear sensor patterns, making it less suitable than RCF.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group sensor readings into clusters of similar behavior, it does not provide anomaly scores or detect rare deviations effectively. K-Means is exploratory rather than predictive and cannot reliably handle real-time anomaly detection in high-dimensional sensor data.

D) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. It requires labeled anomalies and frequent retraining to adapt to changing sensor behavior. XGBoost is not inherently designed for unsupervised anomaly detection in streaming sensor data, making it less practical for real-time industrial monitoring.

Random Cut Forest is the most appropriate algorithm for detecting anomalies in real-time industrial sensor data due to its unsupervised design, ability to handle high-dimensional continuous features, scalability, and interpretability. Other algorithms require labeled data, are linear, or are exploratory.

Question 77

A retail company wants to forecast weekly demand for multiple products across stores, considering holidays, promotions, and seasonal patterns. 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 highly suitable for predicting weekly demand for multiple products across stores, as it captures sequential patterns, trends, and seasonality in historical data. DeepAR can incorporate covariates such as holidays, promotions, and other external factors to enhance forecast accuracy. Its probabilistic approach provides both point predictions and prediction intervals, enabling effective inventory planning, staffing, and supply chain management while accounting for uncertainty. DeepAR leverages data from multiple related time series, which allows the model to share patterns across products and stores, improving accuracy for items or locations with limited historical data. Its scalability ensures efficient handling of large datasets and adaptation to changing trends, making it ideal for dynamic retail environments.

B) Amazon SageMaker Linear Learner is a supervised regression algorithm assuming linear relationships between features and targets. While it can use lag features and covariates, it does not naturally model sequential dependencies or multi-step forecasts, and it cannot generate probabilistic outputs. Extensive feature engineering is required to approximate multi-step forecasting, making it less practical for complex retail demand forecasting.

C) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm suitable for structured tabular data. While it can be adapted for time series forecasting using lag features, it does not natively handle sequential dependencies or multi-step forecasts. Feature engineering for multiple products and stores is complex, and uncertainty intervals are not naturally provided.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can identify products or stores with similar demand patterns, it cannot produce numerical forecasts or prediction intervals. K-Means is exploratory and cannot support operational planning for retail demand.

DeepAR is the best choice for multi-product, multi-store weekly demand forecasting due to its sequential modeling, covariate incorporation, probabilistic outputs, and ability to leverage shared patterns across time series. Other algorithms assume linearity, are not sequential, or are exploratory.

 

Question 78

A financial institution wants to detect unusual transactions to prevent fraud. The dataset contains transaction amounts, merchant codes, timestamps, and user behavior features. 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 data. In the context of financial fraud, RCF can detect unusual transactions based on amounts, timestamps, merchant codes, and user behavior. Its unsupervised nature is critical because labeled fraudulent transactions are scarce, constantly evolving, and difficult to obtain. RCF assigns anomaly scores to each transaction, supporting real-time monitoring and proactive alerts. It detects both point anomalies (isolated unusual transactions) and contextual anomalies (transactions unusual in a sequence of user behavior). RCF scales efficiently for high-volume transaction streams and adapts to emerging fraud patterns without retraining, making it highly suitable for real-time fraud detection.

B) Amazon SageMaker Linear Learner is a supervised classification or regression algorithm. Without labeled fraudulent transactions, it cannot reliably detect anomalies. Linear Learner also assumes linear relationships between features, limiting its ability to capture complex non-linear patterns inherent in fraud detection.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it can group similar transactions, 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. It requires labeled fraudulent transactions and frequent retraining to detect new fraud patterns. XGBoost is less practical for unsupervised real-time detection and cannot efficiently handle emerging anomalies.

Random Cut Forest is the most appropriate algorithm for real-time anomaly detection in financial transactions due to its unsupervised design, ability to detect rare deviations, scalability, and real-time deployment capability. Other algorithms require supervision, labeled anomalies, or are exploratory.

Question 79

A healthcare provider wants to predict patient readmissions within 30 days using EHR data, including lab results, demographics, and diagnoses. 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 structured datasets. It can handle numerical lab results, categorical demographics, and sparse diagnoses while capturing complex non-linear interactions among features. XGBoost is highly effective in addressing class imbalance, which is important for predicting rare readmission events. It provides feature importance metrics, allowing clinicians to understand risk factors such as abnormal lab results or comorbidities. XGBoost scales efficiently for large datasets and can be deployed for batch or real-time prediction to support timely interventions for at-risk patients. Its combination of accuracy, scalability, and interpretability makes it the ideal solution for patient readmission prediction.

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

C) Amazon SageMaker Factorization Machines are designed for sparse, high-dimensional datasets, often 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 may be suboptimal.

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

XGBoost is the most suitable choice due to its ability to handle heterogeneous structured data, capture complex interactions, address class imbalance, and scale for large healthcare datasets. Other algorithms are linear, sparse-focused, or designed for time series.

Question 80

A retail company wants to build a recommendation system for users based on sparse 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 specifically designed to handle sparse, high-dimensional datasets typical of recommendation systems. They model pairwise interactions between users and items, learning latent factors that enable accurate predictions for unobserved user-item pairs. Factorization Machines can incorporate side information such as user demographics or item attributes to improve recommendations. FM scales efficiently to millions of users and items, making it suitable for large e-commerce platforms. Its ability to learn latent factors in sparse interaction matrices ensures personalized recommendations even with limited observed interactions.

B) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm designed for structured tabular data. While it can handle engineered interaction features, it does not naturally capture latent factors or unobserved user-item interactions, limiting its effectiveness for collaborative filtering.

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

Factorization Machines are the most appropriate choice for building scalable and accurate recommendation systems because they handle sparse data, learn latent factors, incorporate side information, and support collaborative filtering. Other algorithms are either linear, structured-data focused, or exploratory.