Amazon  AWS Certified Machine Learning Engineer – Associate MLA-C01   Exam  Dumps and Practice Test Questions Set 2  Q 21- 40

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

Question 21

A financial institution wants to detect anomalous credit card transactions in real time to prevent fraud. The dataset contains high-dimensional transaction features, and fraudulent transactions are rare. Which AWS SageMaker approach is most suitable for this scenario?

A) Amazon SageMaker Random Cut Forest (RCF)

B) Amazon SageMaker Linear Learner without class weighting

C) Amazon SageMaker K-Means

D) Amazon SageMaker Factorization Machines

Answer

A) Amazon SageMaker Random Cut Forest (RCF)

Explanation

A) Amazon SageMaker Random Cut Forest is an unsupervised anomaly detection algorithm designed to identify outliers in high-dimensional datasets. It constructs an ensemble of trees based on random cuts in the feature space and assigns anomaly scores to data points that deviate from expected patterns. RCF is particularly effective for real-time fraud detection because it does not require labeled fraud data, which is often scarce and constantly evolving. It can handle continuous streams of transaction data, automatically scoring each new transaction for anomaly likelihood, and is scalable to high-dimensional inputs. By generating anomaly scores instead of binary predictions, RCF enables financial institutions to prioritize high-risk transactions for further investigation, improving both efficiency and compliance with regulatory standards.

B) Amazon SageMaker Linear Learner without class weighting performs supervised classification. While it can predict fraud if labeled data is available, ignoring class imbalance in highly skewed datasets leads to poor detection of the rare fraudulent transactions. Without class weighting or oversampling techniques, Linear Learner will bias predictions toward the majority (legitimate) class, making it unreliable for real-time fraud detection.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering can reveal patterns in transaction data, it does not provide anomaly detection capabilities. Rare fraudulent transactions may be grouped into clusters that are not easily distinguishable from legitimate transactions, making K-Means ineffective for direct fraud detection. K-Means is better suited for segmentation rather than high-precision outlier detection.

D) Amazon SageMaker Factorization Machines are optimized for sparse data with pairwise feature interactions, often used in recommendation systems. They are not inherently designed for anomaly detection and cannot efficiently identify rare deviations in high-dimensional continuous data. Applying Factorization Machines for fraud detection would require significant preprocessing and additional post-processing steps, reducing efficiency and real-time applicability.

Random Cut Forest provides a robust, scalable, and automated approach to detect anomalies in high-dimensional, real-time transaction data, making it the most suitable method for detecting credit card fraud. Other algorithms either fail to handle imbalance, lack anomaly detection capabilities, or are designed for entirely different problem domains.

Question 22

A company wants to classify customer support tickets into categories such as billing, technical issues, and account management. The dataset contains text data from past tickets. Which AWS service is most suitable for this task?

A) Amazon SageMaker BlazingText

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker K-Means

D) Amazon Comprehend

Answer

D) Amazon Comprehend

Explanation

A) Amazon SageMaker BlazingText is an NLP algorithm for text classification and word embeddings. It can classify text given a labeled dataset, but it requires preprocessing, training jobs setup, hyperparameter tuning, and endpoint deployment. BlazingText is best suited when a fully customized model is required, or embeddings are needed for downstream tasks. While capable, it demands more development effort and infrastructure management than fully managed NLP services.

B) Amazon SageMaker Linear Learner is designed for structured numeric data and supervised regression or classification tasks. Raw text cannot be processed directly, and extensive preprocessing such as tokenization, vectorization, or embedding creation would be required. Linear Learner is therefore impractical for direct text classification without significant engineering work.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm that groups similar data points. Clustering text embeddings could potentially reveal topics, but it does not assign predefined labels such as billing or technical issues. Clustering may help explore the data, but it cannot directly provide supervised classification, which is essential for categorizing tickets.

D) Amazon Comprehend is a fully managed NLP service that provides text classification, entity recognition, and sentiment analysis. Comprehend supports custom classification models that can be trained using labeled datasets, handling preprocessing and feature extraction automatically. It scales easily, integrates with other AWS services, and provides metrics for model evaluation. Comprehend enables rapid deployment and management of NLP tasks without the engineering overhead of building custom models from scratch. Its managed nature and text-focused capabilities make it the most suitable service for categorizing customer support tickets.

Question 23

An e-commerce company wants to predict customer lifetime value (CLV) using historical purchase and behavioral data. The dataset contains numerical, categorical, and transactional features. The model must capture non-linear interactions and relationships. Which AWS SageMaker algorithm should be used?

A) Amazon SageMaker Linear Learner

B) Amazon SageMaker XGBoost

C) Amazon SageMaker Factorization Machines

D) Amazon SageMaker K-Means

Answer

B) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker Linear Learner performs regression and classification on structured data but assumes linear relationships between features and the target. CLV prediction often involves complex, non-linear interactions between purchase frequency, transaction amounts, customer demographics, and behavior patterns. Linear Learner may underfit in this scenario, even with feature engineering to introduce interactions or polynomial terms.

B) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm capable of capturing non-linear relationships and complex feature interactions. It handles missing values, categorical features (after encoding), and dense datasets efficiently. For CLV prediction, XGBoost can model multi-factor dependencies such as purchase timing, product mix, seasonality, and customer demographics. Regularization controls overfitting, and feature importance metrics provide interpretability for business stakeholders. Its scalability and flexibility make it ideal for predicting CLV in heterogeneous datasets.

C) Amazon SageMaker Factorization Machines are optimized for sparse data with pairwise feature interactions, commonly used for recommendation systems or click-through prediction. CLV datasets are typically dense and involve higher-order interactions, making Factorization Machines less suitable. They may capture pairwise relationships but fail to model non-linear dependencies effectively.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering can segment customers based on purchasing behavior, it cannot predict individual CLV values. K-Means is exploratory in nature and does not provide regression outputs, making it unsuitable for this task.

XGBoost is best suited to handle dense, heterogeneous data with non-linear interactions and provides accurate, interpretable predictions for CLV. Other algorithms either assume linearity, focus on sparse interactions, or cannot perform supervised regression.

Question 24

A bank wants to detect fraudulent transactions in real time. The dataset contains high-volume, high-dimensional transaction features with a severe class imbalance. Which AWS SageMaker approach is most appropriate?

A) Amazon SageMaker Random Cut Forest (RCF)

B) Amazon SageMaker Linear Learner with class weighting

C) Amazon SageMaker K-Means

D) Batch transform jobs on historical transactions

Answer

B) Amazon SageMaker Linear Learner with class weighting

Explanation

A) Amazon SageMaker Random Cut Forest is effective for unsupervised anomaly detection and identifying unusual transactions. While useful when fraud is rare and unlabeled, RCF may miss subtle patterns that supervised learning can detect if labeled fraud data exists. Real-time detection with labeled examples benefits from supervised algorithms that optimize for class imbalance.

B) Amazon SageMaker Linear Learner with class weighting allows supervised classification of imbalanced datasets. Assigning higher weight to minority (fraudulent) transactions ensures the model prioritizes detecting fraud without being overwhelmed by the majority class. Linear Learner scales efficiently, handles high-dimensional structured data, and supports real-time endpoint deployment for instant predictions. Supervised learning with class weighting maximizes detection accuracy while controlling false positives, making it ideal for high-volume, imbalanced transaction datasets.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. Clustering may help segment transactions but does not provide direct classification or probability estimates for fraud. It cannot handle class imbalance inherently and is unsuitable for real-time supervised detection.

D) Batch transform jobs process data offline and cannot provide immediate predictions for live transactions. Real-time fraud detection requires low-latency inference, making batch processing impractical in this context.

Linear Learner with class weighting provides a scalable, supervised, and balanced approach to detect fraudulent transactions in real time. Other options either lack supervision, cannot handle class imbalance, or are not suitable for low-latency inference.

Question 25

A healthcare provider wants to predict patient readmission risk using electronic health records (EHR) data, which contains numerical lab results, categorical patient demographics, and sparse diagnosis codes. Which AWS SageMaker algorithm is most suitable for this task?

A) Amazon SageMaker XGBoost

B) Amazon SageMaker Factorization Machines

C) Amazon SageMaker Linear Learner

D) Amazon SageMaker DeepAR

Answer

A) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm suitable for structured datasets containing numerical, categorical, and sparse features. In predicting patient readmission, XGBoost can model non-linear interactions between lab results, demographics, and diagnoses. It handles missing values gracefully, supports feature importance analysis for interpretability, and scales efficiently for large EHR datasets. Class weighting can address rare readmissions, improving recall and precision. XGBoost’s robustness, flexibility, and predictive performance make it ideal for healthcare applications where accurate risk prediction is critical.

B) Amazon SageMaker Factorization Machines handle sparse, high-dimensional datasets with pairwise interactions, commonly used in recommendation systems. While they can model diagnosis code interactions, they are less effective for dense numerical lab results and higher-order interactions. Factorization Machines may underfit complex EHR data with multiple heterogeneous feature types.

C) Amazon SageMaker Linear Learner performs supervised regression or classification on structured data and handles categorical and numerical features. However, it assumes linear relationships, which may not capture complex dependencies among lab results, diagnoses, and demographics in readmission prediction. Linear models risk underfitting in this scenario.

D) Amazon SageMaker DeepAR is designed for time series forecasting, generating probabilistic predictions. Predicting readmission is a supervised classification problem with heterogeneous features rather than a temporal sequence prediction. DeepAR is therefore inappropriate for this task.

XGBoost efficiently handles heterogeneous, structured, and high-dimensional data, capturing non-linear interactions and rare events, making it the most suitable choice for patient readmission risk prediction. Other algorithms either assume linearity, focus on sparse interactions, or are designed for time series forecasting.

Question 26

A retail company wants to forecast daily demand for multiple products across hundreds of stores. The dataset contains historical sales data, promotions, holidays, and store-specific features. Which AWS SageMaker algorithm is most suitable for accurate multi-step forecasting?

A) Amazon SageMaker Linear Learner

B) Amazon SageMaker DeepAR Forecasting

C) Amazon SageMaker XGBoost

D) Amazon SageMaker K-Means

Answer

B) Amazon SageMaker DeepAR Forecasting

Explanation

A) Amazon SageMaker Linear Learner is a supervised learning algorithm suitable for regression or classification. It works well for linear relationships but cannot naturally model sequential dependencies, trends, or seasonality in time series data. While lag features, rolling averages, and external covariates can be created for Linear Learner, it still assumes a static linear relationship between features and the target. Multi-store, multi-product sales data involves complex temporal patterns, non-linear trends, and dependencies across products and stores, which Linear Learner may fail to capture accurately. Feature engineering would be required extensively, but even then, forecasting precision may be limited for multi-step predictions.

B) Amazon SageMaker DeepAR is a recurrent neural network-based algorithm designed for probabilistic time series forecasting. It handles multiple related time series, incorporates covariates such as promotions and holidays, and captures complex seasonality and trend patterns. DeepAR provides both point forecasts and prediction intervals, which are crucial for inventory planning and risk assessment. Its RNN architecture learns sequential dependencies and can forecast multiple steps ahead effectively. For hundreds of stores and products, DeepAR scales efficiently, learns from historical patterns, and adapts to new data, making it the ideal solution for multi-step sales forecasting.

C) Amazon SageMaker XGBoost is optimized for structured tabular data and is powerful for regression and classification. While XGBoost can be adapted for time series forecasting by creating lag features and rolling statistics, it is not inherently designed for sequential data. It does not model temporal dependencies naturally, and creating features for hundreds of stores and products would be computationally intensive and complex. Non-linear interactions may be captured, but sequential patterns and multi-step forecasting are less robust compared to DeepAR.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. Clustering may reveal groups of stores or products with similar demand patterns, but it does not produce numerical forecasts. It cannot provide multi-step predictions or account for covariates like promotions and holidays. K-Means is useful for exploratory segmentation, not predictive time series forecasting.

DeepAR is specifically designed for multi-step forecasting across multiple related time series with covariates, making it the most suitable algorithm for daily demand prediction. Other options either cannot handle sequential dependencies effectively, require extensive manual feature engineering, or are unsupervised and exploratory rather than predictive.

Question 27

A marketing team wants to segment customers based on demographics, purchase behavior, and website activity for targeted campaigns. The dataset is unlabeled. Which AWS SageMaker approach is best suited for this unsupervised task?

A) Amazon SageMaker K-Means

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker XGBoost

D) Amazon Comprehend

Answer

A) Amazon SageMaker K-Means

Explanation

A) Amazon SageMaker K-Means is an unsupervised clustering algorithm ideal for grouping similar data points based on their features. In customer segmentation, K-Means can identify clusters of customers who exhibit similar purchase patterns, website interactions, and demographic attributes. This allows marketers to tailor campaigns and offers to each segment, improving engagement and conversion rates. K-Means scales efficiently for large datasets, provides interpretability through cluster centroids, and can integrate seamlessly with SageMaker pipelines for automated segmentation. Feature scaling and selection are important to ensure meaningful clusters, and multiple initialization runs help avoid local minima, enhancing clustering quality.

B) Amazon SageMaker Linear Learner is a supervised algorithm for regression or classification tasks. It requires labeled data for training, which is not available in this unsupervised segmentation scenario. Linear Learner cannot naturally group customers into clusters without outcome labels, making it unsuitable for this task.

C) Amazon SageMaker XGBoost is a supervised gradient-boosted decision tree algorithm. While highly effective for classification and regression with labeled outcomes, it cannot perform unsupervised clustering. Using XGBoost would require constructing labels artificially, which defeats the purpose of unsupervised segmentation.

D) Amazon Comprehend is a managed NLP service for text analysis. It provides sentiment analysis, entity extraction, and topic modeling, but it is designed for unstructured text rather than structured numeric and categorical customer data. Comprehend cannot segment customers based on demographics or behavioral metrics.

K-Means is the most appropriate solution for unsupervised customer segmentation, providing interpretable, scalable clusters that marketers can use for targeted campaigns. Other options are either supervised or text-focused and cannot perform clustering on structured customer data effectively.

 

Question 28

A telecommunications company wants to predict customer churn using historical customer usage data. The dataset contains numerical usage statistics, categorical subscription features, and sparse features representing service interactions. Which AWS SageMaker algorithm is most suitable for this classification task?

A) Amazon SageMaker XGBoost

B) Amazon SageMaker Factorization Machines

C) Amazon SageMaker Linear Learner

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm well-suited for structured, heterogeneous datasets. It captures non-linear relationships and complex feature interactions, handles missing values efficiently, and supports class weighting to address churn class imbalance. For customer churn prediction, XGBoost can model the effects of numerical usage patterns, categorical subscription types, and sparse service interactions. It provides high predictive accuracy and feature importance metrics for interpretability, which is valuable for understanding churn drivers and informing retention strategies. XGBoost also scales to large datasets and integrates with SageMaker endpoints for real-time or batch inference.

B) Amazon SageMaker Factorization Machines are optimized for sparse, high-dimensional datasets, particularly where pairwise feature interactions are critical, such as recommendation systems. While Factorization Machines could model sparse interaction features, they are less effective at capturing dense numerical features or complex non-linear dependencies present in churn prediction datasets. Factorization Machines may underperform compared to XGBoost in terms of accuracy and interpretability for this use case.

C) Amazon SageMaker Linear Learner performs linear regression or classification and assumes linear relationships between features and the target. While interpretable and scalable, Linear Learner may underfit customer churn data because churn depends on complex interactions and non-linear patterns in usage, subscription, and interaction features. Extensive feature engineering would be required to approximate non-linear relationships, which adds complexity.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering could help explore patterns in customer behavior, it cannot directly predict churn labels. K-Means does not provide probability estimates for classification and is unsuitable for supervised predictive tasks like churn prediction.

XGBoost provides the best balance of predictive power, interpretability, scalability, and ability to handle mixed feature types for customer churn prediction. Other algorithms either underfit, focus on sparse interactions, or are unsupervised and exploratory.

Question 29

A company wants to perform sentiment analysis on customer reviews to understand product satisfaction trends. The dataset contains thousands of text reviews. Which AWS service is most suitable?

A) Amazon SageMaker BlazingText

B) Amazon Comprehend

C) Amazon SageMaker Linear Learner

D) Amazon SageMaker K-Means

Answer

B) Amazon Comprehend

Explanation

A) Amazon SageMaker BlazingText is a supervised NLP algorithm that can perform text classification and generate word embeddings. It requires labeled datasets for training and involves preprocessing, hyperparameter tuning, and endpoint deployment. While effective for custom sentiment models, it demands significant engineering effort and is not fully managed. For straightforward sentiment analysis on customer reviews, BlazingText may be unnecessarily complex.

B) Amazon Comprehend is a fully managed natural language processing service capable of sentiment analysis, entity extraction, and key phrase detection. It automatically preprocesses text, handles language nuances, and scales to large datasets. Comprehend provides sentiment categories (positive, negative, neutral, mixed) and supports custom classification models if needed. It is ideal for understanding trends in customer feedback, providing actionable insights without the overhead of custom model development. Its integration with AWS analytics pipelines allows for continuous monitoring and reporting.

C) Amazon SageMaker Linear Learner is designed for numeric structured data and supervised classification. It cannot directly process raw text and requires extensive preprocessing, such as tokenization and vectorization, to work with NLP data. This makes it impractical for large-scale sentiment analysis of reviews.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering could group similar reviews based on embeddings, it does not provide sentiment labels. It is more suitable for exploratory analysis rather than actionable sentiment classification.

Amazon Comprehend is the most suitable service for sentiment analysis on textual customer reviews due to its managed nature, scalability, and NLP-specific capabilities. Other options require extensive preprocessing, manual engineering, or are not focused on sentiment.

Question 30

A bank wants to detect suspicious transactions in real time to prevent money laundering. The dataset contains high-dimensional features, with fraudulent activities being extremely rare. Which AWS approach is most effective for anomaly detection in this scenario?

A) Amazon SageMaker Random Cut Forest (RCF)

B) Amazon SageMaker Linear Learner without class weighting

C) Amazon SageMaker K-Means

D) Amazon SageMaker Factorization Machines

Answer

A) Amazon SageMaker Random Cut Forest (RCF)

Explanation

A) Amazon SageMaker Random Cut Forest is an unsupervised algorithm designed for anomaly detection in high-dimensional data. It assigns anomaly scores to each transaction based on deviations from normal patterns. RCF is particularly effective for rare-event detection, such as money laundering, because it does not require labeled examples, which are scarce and constantly evolving. The algorithm can be deployed in real time to score transactions as they occur, providing immediate alerts for further investigation. RCF scales to high-volume, high-dimensional datasets and provides interpretable anomaly scores, allowing banks to prioritize high-risk transactions. Its ability to adapt to evolving patterns makes it ideal for continuous monitoring in financial compliance.

B) Amazon SageMaker Linear Learner without class weighting is a supervised classifier. Ignoring class imbalance in datasets with rare fraudulent events results in biased predictions toward the majority class (legitimate transactions). It would fail to detect rare anomalies effectively. Supervised learning without proper weighting is insufficient for real-time anomaly detection in imbalanced scenarios.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering can reveal patterns or segment transactions, it does not assign anomaly scores or detect rare fraudulent events reliably. K-Means is exploratory and cannot perform supervised or unsupervised anomaly detection at the required precision and scale.

D) Amazon SageMaker Factorization Machines are optimized for sparse feature interactions, commonly used in recommendation or click-through prediction systems. They are not designed for anomaly detection and cannot reliably identify rare deviations in high-dimensional continuous data.

Random Cut Forest provides a scalable, unsupervised, and robust approach to detect anomalies in real-time transaction streams, making it the most effective solution for detecting suspicious transactions. Other algorithms either ignore class imbalance, are unsupervised but exploratory, or are unsuitable for rare-event detection.

Question 31

A telecommunications company wants to predict network equipment failure to perform preventive maintenance. The dataset contains time-stamped sensor readings, device metadata, and usage statistics. Which AWS SageMaker algorithm is most suitable for this predictive maintenance task?

A) Amazon SageMaker DeepAR Forecasting

B) Amazon SageMaker XGBoost

C) Amazon SageMaker K-Means

D) Amazon SageMaker Factorization Machines

Answer

B) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker DeepAR Forecasting is a recurrent neural network-based time series forecasting algorithm. It is designed for predicting sequential data trends and probabilistic forecasting of multiple related time series. While DeepAR can capture temporal patterns, predictive maintenance often involves classification or regression based on multivariate sensor data, not purely sequential forecasting. Equipment failures depend on a combination of factors including usage patterns, device metadata, and anomalies in sensor readings rather than only time-series trends. DeepAR may not efficiently leverage non-temporal features such as categorical device attributes or operational metrics, limiting its applicability for complex predictive maintenance datasets that combine both temporal and non-temporal information.

B) Amazon SageMaker XGBoost is highly suitable for predictive maintenance using structured datasets that include both numerical sensor readings and categorical metadata. XGBoost can model non-linear relationships and interactions between features such as sensor thresholds, operating conditions, and historical failure events. It can handle missing data, imbalanced datasets, and outliers, which are common in industrial sensor data. XGBoost also provides feature importance metrics, allowing engineers to understand the critical factors contributing to equipment failure. Additionally, XGBoost can be deployed through real-time SageMaker endpoints for continuous monitoring, enabling timely alerts for preventive maintenance actions. This combination of predictive accuracy, interpretability, and scalability makes XGBoost the most appropriate choice for predictive maintenance.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While it could cluster equipment by usage patterns or sensor similarity, it does not provide supervised predictions of failures. K-Means may help explore the data and identify abnormal patterns, but it cannot generate actionable failure predictions. Using K-Means would require a separate classifier for each cluster, adding complexity and reducing predictive reliability.

D) Amazon SageMaker Factorization Machines are optimized for sparse data with pairwise interactions, often used in recommendation systems. Predictive maintenance datasets typically contain dense numerical sensor readings and categorical metadata, not sparse user-item-like interactions. Factorization Machines may underfit or fail to capture higher-order interactions in multivariate industrial datasets, making them less effective than XGBoost for predictive maintenance.

XGBoost effectively models multivariate interactions, handles mixed feature types, accommodates missing values, and provides interpretability, making it ideal for predictive maintenance. Other algorithms either focus on sequential forecasting, clustering, or sparse interactions and are less suitable for actionable equipment failure prediction.

Question 32

A retail company wants to recommend products to users based on historical purchase data and sparse interaction features. Which AWS SageMaker algorithm is most suitable for building a recommendation system?

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 supervised learning on sparse datasets with high-dimensional feature spaces. They excel at modeling pairwise feature interactions, which is crucial in recommendation systems where interactions between users and products must be captured. Factorization Machines learn latent representations for users and items, enabling the prediction of preferences even for user-item combinations that have not been observed. This makes FM ideal for collaborative filtering tasks, handling millions of users and items efficiently. Additionally, FM can incorporate side information such as user demographics or product attributes to improve recommendation quality. Its scalability and ability to work with sparse interactions directly make it the optimal choice for product recommendation systems.

B) Amazon SageMaker XGBoost is powerful for structured data with non-linear relationships but is not specifically optimized for sparse user-item interaction matrices. While XGBoost could be used with engineered features representing interactions, it would require significant preprocessing and feature expansion, increasing computational complexity. It cannot naturally learn latent factors for unseen user-item pairs, which is critical in collaborative filtering scenarios.

C) Amazon SageMaker Linear Learner is effective for classification and regression on structured tabular data. It can handle dense or sparse features but assumes linear relationships, which may not capture complex user-item interactions for recommendation tasks. Linear Learner is less effective than FM in modeling latent interactions and predicting preferences for previously unseen combinations.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering could segment users or products, it does not generate personalized recommendations or predict user preferences directly. Clustering alone cannot provide the predictive output required for a recommendation engine.

Factorization Machines are uniquely suited for recommendation systems because they capture latent user-item interactions, handle high-dimensional sparse data efficiently, and can incorporate side information. Other algorithms either require extensive preprocessing, assume linearity, or are not designed for personalized predictions.

Question 33

A company wants to predict the probability of loan default using historical loan application data containing numerical features, categorical features, and missing values. Which AWS SageMaker algorithm is most suitable?

A) Amazon SageMaker XGBoost

B) Amazon SageMaker Factorization Machines

C) Amazon SageMaker Linear Learner

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm suitable for predicting loan default. It handles numerical and categorical features (after encoding), captures non-linear relationships and feature interactions, and efficiently manages missing values. For binary classification of loan default, XGBoost can handle class imbalance through weighting or subsampling, providing accurate probability estimates for default risk. Feature importance metrics allow financial institutions to understand critical risk factors such as income, debt-to-income ratio, and credit history. XGBoost’s scalability and high predictive accuracy make it ideal for large loan datasets where regulatory interpretability and performance are crucial.

B) Amazon SageMaker Factorization Machines are designed for sparse, high-dimensional datasets, typically in recommendation or click-through prediction scenarios. While FM could model interactions between categorical variables, it may underperform for dense numerical features common in loan data. It is less suitable for predicting default risk where complex non-linear dependencies exist among multiple dense features.

C) Amazon SageMaker Linear Learner is a supervised classification algorithm that assumes linear relationships between input features and the target. While scalable and interpretable, linear assumptions may fail to capture non-linear interactions between financial variables affecting loan default. Extensive feature engineering would be required to approximate non-linearities, increasing model complexity and risk of underfitting.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. Clustering can identify groups of similar loan applicants but cannot predict loan default probabilities. K-Means provides exploratory insights but is unsuitable for supervised risk prediction.

XGBoost provides the best balance of predictive power, interpretability, handling of missing values, and scalability for loan default prediction. Other algorithms either assume linearity, focus on sparse interactions, or are exploratory and unsupervised.

Question 34

A healthcare organization wants to forecast hospital admissions over the next month using historical daily admission data and external factors like weather, holidays, and local events. 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 specifically designed for probabilistic time series forecasting. It uses recurrent neural networks to capture sequential dependencies, seasonality, and trends. DeepAR can incorporate covariates such as weather, holidays, and local events, which influence hospital admissions. It supports multi-step forecasting and provides both point predictions and uncertainty intervals. This probabilistic output is crucial in healthcare for planning resources, managing staff, and optimizing bed allocation. DeepAR can also learn from multiple related time series, such as admissions across different departments or hospitals, improving prediction accuracy and robustness. Its scalability and ability to adapt to changing patterns make it ideal for hospital admissions forecasting.

B) Amazon SageMaker Linear Learner can perform regression but assumes linear relationships between input features and targets. While possible to create lag features and external covariates, Linear Learner does not naturally capture temporal dependencies, seasonality, or multi-step forecasts. This can limit its predictive performance for time-dependent, multi-factor healthcare datasets.

C) Amazon SageMaker XGBoost can be adapted for time series prediction using lag features and engineered covariates. However, it is not inherently designed for sequential data and may struggle to model temporal trends or multi-step forecasts without extensive preprocessing. DeepAR is superior for sequential forecasting tasks.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. Clustering may group days with similar admission patterns but cannot provide numerical forecasts for future admissions. K-Means is exploratory, not predictive.

DeepAR is the most suitable solution for probabilistic, multi-step forecasting of hospital admissions, capturing temporal dependencies and incorporating covariates. Other algorithms either assume linearity, require feature engineering, or are unsupervised and exploratory.

Question 35

A company wants to detect unusual spikes in server metrics to proactively prevent downtime. The dataset contains continuous metrics such as CPU usage, memory utilization, and network traffic. Which AWS SageMaker approach 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 algorithm for detecting anomalies in high-dimensional continuous data. It constructs an ensemble of trees using random cuts and assigns anomaly scores to new data points that deviate from normal patterns. For server metrics like CPU, memory, and network traffic, RCF can detect unusual spikes in real time, enabling proactive alerts and preventive action. RCF is scalable to high-volume streaming data, handles correlated metrics efficiently, and does not require labeled anomaly data, which is often rare or unavailable. Its interpretability through anomaly scores allows operators to prioritize alerts effectively and investigate the most critical anomalies. Real-time deployment via SageMaker endpoints ensures continuous monitoring of server health and operational reliability.

B) Amazon SageMaker Linear Learner is a supervised algorithm for classification or regression. It cannot detect anomalies in unlabeled continuous streams without prior labeling of anomalous events. Using supervised methods is impractical for real-time anomaly detection when anomalies are rare and evolving.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering could reveal patterns or typical operating states, it cannot directly provide anomaly scores or detect rare events in real time. K-Means is exploratory and insufficient for live anomaly detection tasks.

D) Amazon SageMaker XGBoost is a supervised classification/regression algorithm. Detecting anomalies with XGBoost requires labeled anomaly data, which is often unavailable in real-time monitoring scenarios. It is not naturally suited for streaming anomaly detection in high-dimensional continuous metrics.

Random Cut Forest is the most appropriate solution for real-time anomaly detection in continuous, high-dimensional server metrics, providing automated, scalable, and interpretable anomaly scores. Other algorithms either require labels, assume supervision, or are exploratory and cannot provide actionable real-time detection.

Question 36

A financial company wants to predict whether a customer will open a new account based on demographic data, transaction history, and marketing interactions. The dataset contains missing values, categorical variables, and non-linear interactions. Which AWS SageMaker algorithm is most suitable for this supervised classification task?

A) Amazon SageMaker Linear Learner

B) Amazon SageMaker XGBoost

C) Amazon SageMaker K-Means

D) Amazon SageMaker Factorization Machines

Answer

B) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker Linear Learner is a supervised classification algorithm that assumes linear relationships between features and the target variable. While it can handle missing values and categorical features after preprocessing, Linear Learner may underfit in scenarios with complex non-linear interactions, such as predicting account opening behavior influenced by demographic patterns, past transaction behavior, and marketing engagement. Capturing higher-order interactions requires extensive feature engineering, increasing complexity and the risk of introducing errors or overfitting. Therefore, although Linear Learner can produce interpretable models, it is less suitable for datasets with non-linear dependencies.

B) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm that excels at capturing non-linear relationships and feature interactions. It naturally handles missing values, supports categorical encoding, and can work effectively with heterogeneous datasets. For predicting account openings, XGBoost can model complex dependencies between customer demographics, transactional patterns, and marketing interactions, providing highly accurate predictions. Its scalability allows processing large datasets, while its feature importance metrics provide interpretability for business decisions. XGBoost also allows handling class imbalance through parameter tuning, making it ideal when the target class is rare.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm that groups similar data points based on feature similarity. While clustering could help identify segments of potential customers, it does not perform supervised classification and cannot produce probability estimates for account opening. K-Means is exploratory and cannot directly fulfill the predictive requirements of this task.

D) Amazon SageMaker Factorization Machines are optimized for sparse, high-dimensional datasets, focusing on pairwise feature interactions, commonly used in recommendation systems. Predicting account openings involves dense numeric and categorical features with complex non-linear interactions, which Factorization Machines may fail to capture effectively. FM models may underfit or fail to generalize in datasets with heterogeneous feature types.

XGBoost is the most appropriate solution due to its ability to handle missing values, non-linear feature interactions, heterogeneous datasets, and class imbalance while providing interpretability and scalability. Other algorithms either assume linearity, focus on sparse interactions, or are unsupervised and exploratory.

Question 37

A company wants to forecast monthly energy consumption for multiple buildings using historical consumption data, weather, and occupancy information. The dataset includes multiple related time series with seasonal patterns. Which AWS SageMaker algorithm is most suitable for this forecasting task?

A) Amazon SageMaker DeepAR Forecasting

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker XGBoost

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker DeepAR Forecasting

Explanation

A) Amazon SageMaker DeepAR Forecasting is a recurrent neural network-based time series forecasting algorithm designed to handle multiple related time series. It can learn temporal dependencies, trends, and seasonality from historical data. DeepAR can incorporate covariates such as weather patterns, building occupancy, and holidays, which influence energy consumption. It provides both point forecasts and probabilistic prediction intervals, enabling risk-aware planning and decision-making. For multi-building, multi-product time series, DeepAR leverages shared patterns across series, improving forecast accuracy even for buildings with limited historical data. Its ability to scale, adapt to changing patterns, and provide probabilistic outputs makes it highly suitable for energy consumption forecasting.

B) Amazon SageMaker Linear Learner is a regression algorithm that assumes linear relationships between features and the target variable. While it could incorporate lag features or external covariates, it does not naturally capture sequential dependencies, trends, or seasonality inherent in time series. Multi-step, probabilistic forecasting would be limited in accuracy with Linear Learner.

C) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm designed for structured data. It can be adapted for time series prediction using lag features and rolling statistics, but it is not inherently sequential. Modeling multiple related time series across buildings with XGBoost would require extensive preprocessing, and capturing seasonality or temporal correlations would be more complex than using DeepAR.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It may group buildings with similar energy consumption patterns but cannot provide numerical forecasts. K-Means is exploratory and cannot perform time-dependent predictions, making it unsuitable for energy forecasting.

DeepAR’s ability to model multiple related time series, handle covariates, learn trends and seasonality, and provide probabilistic forecasts makes it the most suitable solution for energy consumption prediction. Other algorithms either assume linearity, are not sequential, or are unsupervised.

Question 38

A company wants to classify images of handwritten digits for a digit recognition system. The dataset contains labeled images of varying sizes and resolutions. Which AWS SageMaker algorithm is most suitable?

A) Amazon SageMaker Linear Learner

B) Amazon SageMaker XGBoost

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

D) Amazon SageMaker K-Means

Answer

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

Explanation

A) Amazon SageMaker Linear Learner is a supervised classification algorithm for tabular structured data. It cannot process raw image pixels directly. To apply Linear Learner to image recognition, one would need to flatten and preprocess images extensively, losing spatial information crucial for accurate classification. Consequently, Linear Learner is unsuitable for image recognition tasks.

B) Amazon SageMaker XGBoost is designed for structured tabular data and cannot process raw image data without significant feature engineering. While features could be extracted manually (e.g., pixel intensities or embeddings), this approach is inefficient and likely to underperform compared to convolutional neural networks that can learn hierarchical features automatically.

C) Amazon SageMaker Image Classification uses convolutional neural networks (CNNs) optimized for image recognition tasks. It can automatically preprocess images, handle varying resolutions and sizes, and learn spatial hierarchies such as edges, textures, and shapes. CNNs are well-suited for recognizing handwritten digits, which require capturing local patterns and spatial dependencies in pixel arrangements. SageMaker provides built-in CNNs with flexible architectures, allowing for high accuracy and scalability for large image datasets. Data augmentation, transfer learning, and GPU acceleration further enhance performance and reduce training time.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. It may group similar images but cannot classify them into predefined digit categories. K-Means is exploratory and cannot provide label predictions for digit recognition.

The built-in CNN Image Classification algorithm is the most appropriate solution for handwritten digit recognition due to its ability to process raw images, learn spatial features, scale efficiently, and produce high-accuracy predictions. Other algorithms are either designed for tabular data or unsupervised clustering.

Question 39

A bank wants to predict the likelihood of a customer defaulting on a loan using historical loan data with numerical features, categorical features, and missing values. Which AWS SageMaker algorithm is most suitable?

A) Amazon SageMaker Linear Learner

B) Amazon SageMaker XGBoost

C) Amazon SageMaker Factorization Machines

D) Amazon SageMaker K-Means

Answer

B) Amazon SageMaker XGBoost

Explanation

A) Amazon SageMaker Linear Learner can perform binary classification and handle missing values and categorical features after encoding. However, it assumes linear relationships between features and the target. Loan default is influenced by complex, non-linear interactions among income, debt ratios, credit history, and other financial factors. Linear Learner may underfit without extensive feature engineering, reducing predictive accuracy.

B) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm capable of capturing non-linear relationships and complex interactions among features. It handles missing values natively, supports categorical features with proper encoding, and can scale to large datasets. For loan default prediction, XGBoost can provide probability estimates, feature importance metrics, and support class weighting to address imbalanced datasets. Its flexibility, high accuracy, interpretability, and scalability make it the most suitable choice for predicting customer default risk.

C) Amazon SageMaker Factorization Machines excel with sparse, high-dimensional datasets for pairwise interactions, commonly used in recommendation systems. Loan datasets are usually dense with numerical and categorical variables, not sparse high-dimensional interactions. Factorization Machines may underperform due to their inability to capture complex, multi-feature non-linear dependencies effectively.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering can segment loan applicants based on similarity, it cannot provide direct probability estimates for default, which is essential for risk assessment. K-Means is exploratory, not predictive, and unsuitable for supervised classification tasks like loan default prediction.

XGBoost provides the most robust solution due to its ability to handle heterogeneous, dense datasets, non-linear interactions, missing values, and class imbalance while offering interpretability and scalability. Other algorithms are either linear, sparse-focused, or unsupervised.

Question 40

A company wants to detect unusual patterns in web server logs to identify potential security threats. The dataset contains continuous features such as request rate, latency, and error counts. Which AWS SageMaker approach is most appropriate?

A) Amazon SageMaker Random Cut Forest (RCF)

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker K-Means

D) Amazon SageMaker XGBoost

Answer

A) Amazon SageMaker Random Cut Forest (RCF)

Explanation

A) Amazon SageMaker Random Cut Forest is an unsupervised algorithm designed for anomaly detection in high-dimensional continuous data. It identifies points that deviate from expected patterns by assigning anomaly scores. For web server logs, RCF can detect unusual spikes in request rates, latency, or error counts that may indicate security threats such as DDoS attacks or unauthorized access attempts. RCF does not require labeled anomalies, which are rare and evolving in security contexts. It scales efficiently to high-volume streams, handles correlated metrics, and provides interpretable scores for prioritizing investigation. Real-time deployment via SageMaker endpoints ensures continuous monitoring and immediate detection of anomalies.

B) Amazon SageMaker Linear Learner is a supervised algorithm. Detecting anomalies without labeled threat data would require generating labels, which is impractical for real-time security monitoring. Supervised methods are unsuitable when anomalies are rare or undefined.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering may identify groups of typical log patterns, it does not provide anomaly scores or detect rare deviations in real time. K-Means is exploratory rather than predictive for security anomalies.

D) Amazon SageMaker XGBoost is a supervised classification/regression algorithm. It requires labeled anomalies, which are scarce in security contexts. It is not naturally suited for streaming anomaly detection in continuous server metrics.

Random Cut Forest is the most appropriate choice for real-time detection of unusual patterns in web server logs due to its unsupervised nature, scalability, ability to handle continuous high-dimensional data, and interpretability. Other algorithms either require supervision, assume linearity, or are exploratory.