Amazon  AWS Certified Machine Learning Engineer – Associate MLA-C01   Exam  Dumps and Practice Test Questions Set 1  Q 1 – 20 

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Question 1

A company wants to predict customer churn for their subscription service using machine learning. They have a large dataset of customer interactions, subscription history, and demographics. They want a solution that can handle missing data, categorical variables, and non-linear relationships. Which AWS service and algorithm would be most appropriate for this scenario?

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 learning algorithm designed for classification and regression. It works efficiently with linear relationships between features and target variables and supports sparse data. It can handle large feature spaces and categorical encoding. However, Linear Learner assumes linearity in the data, which makes it less effective for customer churn prediction where patterns are typically complex and non-linear. Real-world churn data involves interactions between features such as customer tenure, activity levels, demographics, and engagement, which linear models may fail to capture without extensive feature engineering. Handling categorical variables and missing data is possible but requires additional preprocessing effort. Therefore, while Linear Learner is powerful for linear relationships, it may underfit in scenarios with non-linear and interactive patterns, making it less suitable for this task.

B) Amazon SageMaker XGBoost is an optimized gradient boosting framework that sequentially builds decision trees to correct previous errors, making it ideal for capturing non-linear relationships and feature interactions. It has built-in support for handling missing values and can efficiently process categorical variables with proper encoding. For customer churn prediction, XGBoost can model the complex relationships between customer behavior, subscription history, and demographics effectively. It includes regularization parameters to prevent overfitting and provides feature importance metrics for interpretability. XGBoost’s flexibility, performance, and ability to handle real-world tabular datasets make it the most suitable choice for this scenario, providing robust predictive capability and operational efficiency.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm that groups similar data points but does not provide predictions for labeled outcomes like churn. While clustering can help identify patterns or segments among customers, it cannot produce a probability of churn for individual users. Its assumptions of roughly spherical, similar-sized clusters may not align with the complexity of churn behavior. K-Means is valuable for exploratory analysis but does not fulfill the requirement of supervised prediction, which is necessary in this case.

D) Amazon SageMaker Factorization Machines models feature interactions efficiently and is effective for sparse datasets, such as recommendation systems. However, customer churn datasets are usually dense, tabular, and involve complex non-linear interactions. Factorization Machines primarily capture pairwise feature interactions and may underperform in scenarios requiring deeper modeling of non-linear dependencies. Feature engineering is more involved, and performance may not match the flexibility and predictive accuracy provided by XGBoost for tabular churn data.

In XGBoost stands out due to its ability to handle missing data, categorical variables, and complex non-linear relationships, making it ideal for customer churn prediction. Linear models, clustering, or factorization approaches either fail to capture the necessary complexity or are not designed for supervised predictive tasks.

Question 2

A data scientist is building a model to predict the probability of equipment failure in a manufacturing plant. The dataset contains sensor readings with some missing values, highly correlated features, and imbalanced classes. Which approach should be prioritized to handle the imbalanced data effectively in AWS?

A) Oversampling the minority class using Amazon SageMaker Data Wrangler

B) Applying SMOTE (Synthetic Minority Oversampling Technique) within the SageMaker training script

C) Using class_weight parameter in XGBoost or Linear Learner

D) Ignoring imbalance because XGBoost handles it automatically

Answer

C) Using class_weight parameter in XGBoost or Linear Learner

Explanation

A) Oversampling using Amazon SageMaker Data Wrangler duplicates or resamples minority class instances to balance class representation. While this can improve exposure of rare events, it can lead to overfitting since the model sees identical samples multiple times. For large datasets, it also increases training time and resource usage. Oversampling is helpful but may not be the most efficient or scalable approach compared to algorithm-level solutions.

B) SMOTE generates synthetic minority class samples by interpolating existing instances. It introduces variation and may improve generalization. However, for sensor data, synthetic samples must be realistic; otherwise, they introduce noise. Implementing SMOTE within training scripts adds complexity and requires careful validation to ensure that synthetic data does not degrade model quality. Computational overhead can be significant for very large datasets.

C) Using class_weight or equivalent parameters directly in algorithms adjusts the importance of each class during training, allowing the model to prioritize correct prediction of rare failure events. XGBoost provides scale_pos_weight for this purpose, which balances gradient contributions for minority instances. Linear Learner has similar weighting options. This approach efficiently addresses imbalance without duplicating data or generating synthetic points, improving recall and precision for critical minority class events. It is scalable, computationally efficient, and integrates naturally into the learning process, making it ideal for imbalanced datasets with high stakes like equipment failure prediction.

D) Ignoring imbalance because XGBoost handles it automatically is incorrect. Although XGBoost is robust and handles noisy or complex data, severe imbalance will still bias predictions toward the majority class. This can result in high overall accuracy but poor detection of minority events, which is critical in predictive maintenance scenarios. Ignoring imbalance risks missed failures and operational losses.

In using class weights during model training is the most effective, scalable, and principled method to address class imbalance. Alternative approaches like oversampling or SMOTE carry risks or computational costs, and ignoring the problem can result in inadequate detection of rare events.

Question 3

An ML engineer wants to deploy a real-time recommendation engine on AWS that serves millions of users with low latency. The system must handle high read and write throughput, and continuously update models as new interactions occur. Which solution should be considered?

A) Amazon SageMaker Endpoint with real-time inference

B) Batch transform jobs in SageMaker for daily recommendations

C) Amazon Redshift for querying recommendations

D) Amazon S3 with Athena for recommendation queries

Answer

A) Amazon SageMaker Endpoint with real-time inference

Explanation

A) Amazon SageMaker real-time endpoints provide low-latency access to trained models for live inference. Endpoints can autoscale to handle millions of requests and integrate with APIs for immediate personalization. They support dynamic model updates through deployment variants and blue/green strategies. This makes them ideal for recommendation engines requiring real-time predictions as new interactions are captured.

B) Batch transform jobs are offline, periodic inference processes for large datasets. They are suitable for updating recommendations once per day or in scheduled intervals but cannot provide instantaneous suggestions to users. High-latency batch jobs cannot meet real-time requirements.

C) Amazon Redshift is a data warehouse for analytical queries over structured data. Querying recommendations from Redshift introduces latency and does not support model inference. While useful for batch analytics and reporting, it is unsuitable for low-latency recommendation serving.

D) Amazon S3 with Athena allows SQL-like queries over S3-stored data. This approach is inherently batch-oriented and does not provide real-time inference capability. Query latency is variable, and integration with ML models is cumbersome. It is not appropriate for live recommendation scenarios.

In SageMaker real-time endpoints provide the necessary scalability, low latency, and continuous update capabilities required for a high-performance recommendation engine. Other solutions are batch-oriented, slow, or not designed for live ML inference.

Question 4

A machine learning model deployed on SageMaker is showing degradation in accuracy over time. The dataset characteristics change frequently, and new features are added regularly. Which strategy should be implemented to maintain model performance?

A) Periodic retraining using the latest data

B) Increase model complexity without retraining

C) Ignore changes and monitor metrics only

D) Manually adjust predictions using business rules

Answer

A) Periodic retraining using the latest data

Explanation

A) Periodic retraining addresses concept drift, which occurs when input data distributions change over time, reducing model accuracy. By retraining with the latest data, the model learns current patterns, adapts to new features, and maintains predictive performance. SageMaker Pipelines and automated workflows facilitate scheduled retraining triggered by monitoring or drift detection. Retraining ensures models remain aligned with evolving business environments and data characteristics, maintaining accuracy and reliability.

B) Increasing model complexity without retraining does not solve performance degradation. Additional layers or parameters cannot learn new data patterns without exposure to updated data. Complexity may even worsen overfitting on outdated data, further reducing generalization.

C) Ignoring changes and monitoring metrics is reactive. While monitoring identifies degradation, it does not correct the model. Metrics alone cannot maintain performance; retraining or adaptation strategies are required to respond to drift effectively.

D) Manually adjusting predictions using business rules provides limited, temporary fixes. Rules cannot capture complex data interactions or evolving patterns, and scaling this approach is difficult. It is labor-intensive, prone to errors, and cannot replace data-driven adaptation.

In periodic retraining using the latest data is essential to counter concept drift, integrate new features, and sustain model performance. Other approaches are insufficient, either ignoring the root cause or introducing unsustainable manual processes.

Question 5

A data engineer is preparing a large dataset for training a deep learning model on AWS. The dataset contains millions of images stored in S3, and some images are corrupted. Which preprocessing approach is most suitable to ensure high-quality training data?

A) Use Amazon SageMaker Processing jobs to validate and clean images

B) Ignore corrupted images and proceed with training

C) Load all images in memory and manually filter corrupted files

D) Use AWS Glue ETL to transform images into numerical arrays

Answer

A) Use Amazon SageMaker Processing jobs to validate and clean images

Explanation

A) SageMaker Processing jobs allow scalable, automated validation, cleaning, and feature engineering. They can remove corrupted images, resize or normalize images, and store the cleaned dataset in S3 for training. Processing jobs support distributed computation for large datasets and integrate seamlessly with SageMaker training pipelines, ensuring high-quality inputs and reproducibility. Logging and monitoring track the number of corrupted files removed, improving data quality assurance.

B) Ignoring corrupted images risks runtime errors, wasted compute resources, and degraded model performance. Even a small number of corrupted files can disrupt deep learning training and introduce noise.

C) Loading all images into memory for manual filtering is impractical for millions of images. It is time-consuming, error-prone, and memory-intensive, making it unsuitable for large-scale pipelines.

D) AWS Glue ETL is designed for structured data transformations, not unstructured image data. Transforming images into arrays without validation risks propagating corrupted data into training, harming model quality. Glue lacks native image preprocessing capabilities such as validation, resizing, or normalization.

In  SageMaker Processing jobs provide a scalable, automated, and integrated solution for validating and cleaning large image datasets, ensuring high-quality inputs for deep learning. Alternative approaches are either impractical, risky, or unsuitable for image data.

Question 6

A company wants to classify customer support tickets into categories such as billing, technical issue, and account management using machine learning. They have a dataset of past tickets in text form. Which AWS service and approach would be 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 optimized algorithm for word embeddings and text classification tasks. It supports supervised and unsupervised modes and can learn word representations or classify text into categories. BlazingText works well for large datasets and can be integrated into a custom training workflow in SageMaker. However, it requires preprocessing, setting up training jobs, handling hyperparameter tuning, and deploying endpoints. For teams seeking a fully managed, turnkey solution, BlazingText demands more effort compared to pre-built natural language processing services. BlazingText is suitable when organizations want deep customization or need embeddings for downstream tasks like recommendation systems or similarity search. For simpler classification without building and maintaining models, BlazingText may introduce unnecessary overhead.

B) Amazon SageMaker Linear Learner can perform classification tasks on structured numerical data but is not optimized for unstructured text data. Text data needs to be converted into numerical feature vectors through embeddings, TF-IDF, or bag-of-words approaches before using Linear Learner. This preprocessing increases complexity and requires careful handling to avoid high-dimensional sparse features that can impact performance. Linear Learner lacks built-in capabilities for natural language understanding, semantic extraction, or sentiment analysis, making it less practical for direct text classification in comparison to specialized NLP services like Comprehend.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm that identifies groups of similar data points. While K-Means can be used for exploratory text clustering, it does not perform supervised classification of customer support tickets into predefined categories. Clustering may reveal natural groupings in ticket content, but it cannot directly assign a label such as billing or technical issue without additional mapping or manual labeling. Its assumption of spherical clusters may not align with textual data patterns, which are typically high-dimensional and sparse, further limiting its effectiveness for text classification tasks.

D) Amazon Comprehend is a fully managed NLP service that can analyze text and provide insights such as entity recognition, sentiment analysis, and text classification. It supports custom classification models that can be trained using labeled datasets with minimal setup. Comprehend handles preprocessing, tokenization, feature extraction, and model training internally, reducing the engineering burden. It is designed for scalability, supports integration with other AWS services, and provides monitoring and evaluation metrics for classification accuracy. For categorizing support tickets, Comprehend enables rapid deployment of a managed NLP solution without needing to build models from scratch or handle feature engineering manually. It can efficiently process thousands of tickets and continuously improve with updated labeled datasets, making it the most suitable choice for this scenario.

Question 7

An ML engineer needs to predict customer lifetime value (CLV) for an e-commerce platform. The dataset contains numerical, categorical, and transactional features. The engineer wants to handle non-linear relationships and interactions between variables efficiently. 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 well for linear regression and classification tasks. It handles structured datasets with large numbers of features and can efficiently process numerical and categorical data once encoded properly. However, it assumes linear relationships between input features and the target variable. CLV prediction often involves complex non-linear interactions between purchase frequency, transaction amounts, customer demographics, and behavior. Linear Learner may underfit these relationships unless extensive feature engineering, transformations, or interaction terms are introduced. Even with engineered features, capturing non-linear patterns may still be less effective compared to tree-based ensemble models, making Linear Learner less ideal for CLV prediction in complex datasets.

B) Amazon SageMaker XGBoost uses gradient boosting over decision trees, which allows it to model complex non-linear relationships and interactions between features. It handles missing values, categorical variables (after encoding), and high-dimensional datasets efficiently. For CLV prediction, XGBoost can capture relationships such as high-value customers with sporadic large transactions, seasonal behaviors, or interaction effects between categorical and numerical variables. Regularization parameters prevent overfitting, and feature importance metrics help identify key drivers of CLV. Its robustness, performance, and ability to handle heterogeneous datasets make XGBoost the most appropriate algorithm for this task, providing accurate predictions while minimizing engineering overhead for feature interaction modeling.

C) Amazon SageMaker Factorization Machines excel at modeling sparse data with pairwise feature interactions, commonly used for recommendation systems or click-through rate prediction. While Factorization Machines capture interactions efficiently, they primarily focus on pairwise relationships and are optimized for sparse, high-dimensional feature spaces. CLV prediction typically involves dense numerical and categorical features with non-linear and higher-order interactions beyond pairwise effects. Factorization Machines may underperform in this scenario due to these limitations and the need for more expressive modeling capabilities that can capture complex interactions and non-linear dependencies.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm, suitable for grouping similar customers or transactions but not for predicting numerical outcomes like CLV. K-Means cannot provide regression outputs or handle target variable predictions. While clustering could segment customers into groups with similar spending patterns, it does not yield individual CLV predictions and is not designed for supervised regression tasks.

XGBoost provides a comprehensive, flexible, and efficient solution for CLV prediction, capable of modeling complex relationships and interactions between numerical and categorical features. Other algorithms either assume linearity, focus on sparse interactions, or are unsupervised, making them less suitable for this type of regression problem.

Question 8

A company wants to detect fraudulent transactions in real time using AWS services. The transactions dataset is large, high-dimensional, and contains imbalanced classes. Which approach is best suited for this task?

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 an unsupervised algorithm used for anomaly detection. It identifies outliers in multidimensional datasets by constructing trees based on random cuts and scoring deviations from normal patterns. RCF works well when fraud manifests as anomalies distinct from typical transaction behavior. However, not all fraudulent transactions are outliers; some can resemble normal patterns and require supervised learning to distinguish them. RCF cannot leverage labeled fraud data directly and may miss subtle fraudulent patterns embedded in the majority class.

B) Amazon SageMaker Linear Learner supports binary classification and allows class weighting to address imbalanced datasets. Using class_weight ensures the model penalizes misclassification of minority fraud instances more heavily, improving recall for fraudulent transactions. Linear Learner efficiently handles large datasets and supports sparse or numerical features, making it scalable for high-dimensional transaction data. With proper feature engineering, it can capture relationships between transaction attributes such as amount, location, time, and user behavior. Supervised learning with class weighting is appropriate when historical labeled fraud data exists, providing precise detection of fraud events in real time.

C) Amazon SageMaker K-Means is an unsupervised clustering method. It groups similar transactions but does not directly classify them as fraudulent or legitimate. Clustering could highlight unusual patterns, but supervised learning is more effective when labeled fraud instances are available. K-Means assumes clusters of roughly equal size and may not effectively isolate rare fraud cases in high-dimensional space.

D) Batch transform jobs on historical transactions generate predictions for large datasets but are not suitable for real-time detection. Fraud prevention often requires immediate action to prevent loss or block suspicious transactions. Batch jobs have high latency, cannot scale for instantaneous decision-making, and do not allow continuous learning from streaming transactions.

Linear Learner with class weighting combines supervised learning, imbalance handling, and scalability for high-dimensional data, making it the most appropriate solution for real-time fraud detection. Other approaches either rely on unsupervised anomaly detection, cannot classify rare events effectively, or introduce latency that is unsuitable for immediate response.

Question 9

An ML engineer wants to build a model that predicts employee attrition based on historical HR data. The dataset contains mixed data types, including categorical, numerical, and ordinal features. Which preprocessing step is most critical for training a model in SageMaker?

A) Encoding categorical and ordinal features

B) Normalizing only numerical features

C) Removing outliers from numerical columns exclusively

D) Using raw data without preprocessing

Answer

A) Encoding categorical and ordinal features

Explanation

A) Encoding categorical and ordinal features transforms non-numeric variables into numeric representations that machine learning algorithms can process. Many SageMaker algorithms, such as Linear Learner and XGBoost, require numeric input. Categorical features like department, job role, or office location need one-hot encoding or integer mapping, while ordinal features such as performance ratings or education levels must preserve order relationships. Proper encoding ensures that the model interprets feature values meaningfully and can learn patterns associated with employee attrition. Failing to encode these features can prevent the model from training correctly or yield inaccurate predictions.

B) Normalizing numerical features is often useful, particularly for gradient-based algorithms. However, for tree-based algorithms like XGBoost, normalization is less critical because decision trees are invariant to monotonic transformations. While normalization helps in some cases, it does not address categorical or ordinal data, which are often more prevalent in HR datasets.

C) Removing outliers from numerical columns may improve robustness but does not guarantee model performance. Outliers might carry valuable signals, such as unusually high absenteeism or extreme salaries, which could correlate with attrition. Exclusive focus on outliers without addressing categorical encoding would leave the model unable to process key non-numerical features.

D) Using raw data without preprocessing is ineffective because machine learning algorithms require numeric input. Categorical and ordinal data cannot be interpreted as numbers without encoding, and models may fail or produce meaningless results. Preprocessing is essential to ensure model interpretability and predictive performance.

Encoding categorical and ordinal features is the most critical step, enabling the model to leverage all relevant information. While numerical normalization or outlier handling can further enhance performance, encoding directly impacts the ability to use the dataset effectively for model training.

Question 10

A data scientist wants to deploy a trained machine learning model for image classification with minimal latency on AWS. The model was trained using SageMaker and will be queried by a mobile application. Which deployment approach is best?

A) Deploy the model as a SageMaker real-time endpoint

B) Use SageMaker batch transform jobs

C) Store the model in S3 and load it in the mobile app

D) Query the model through Athena

Answer

A) Deploy the model as a SageMaker real-time endpoint

Explanation

A) SageMaker real-time endpoints provide low-latency, scalable access to trained models. Endpoints can autoscale to handle varying request volumes and respond in milliseconds, making them suitable for mobile applications that require instant predictions. They support containerized models, automatic logging, monitoring, and integration with other AWS services. Real-time endpoints allow updates to the deployed model without downtime, ensuring the mobile app always uses the latest version for predictions.

B) Batch transform jobs are designed for offline inference over large datasets. They cannot respond instantly to individual mobile application requests and introduce latency that is unacceptable for real-time image classification. Batch jobs are useful for analytics or periodic processing but are unsuitable for live applications.

C) Storing the model in S3 and loading it directly in the mobile app is impractical. Mobile devices typically lack the computational resources and memory to load large image classification models, particularly deep learning models. Model inference would be slow, energy-intensive, and difficult to maintain across app updates.

D) Querying the model through Athena is irrelevant. Athena queries structured data in S3 using SQL, not trained machine learning models. It does not provide inference capabilities for image classification, and using Athena in this context would be ineffective.

Deploying the model as a SageMaker real-time endpoint ensures low latency, scalability, and maintainability for mobile applications, allowing rapid and accurate predictions for image classification. Other approaches are either offline, resource-constrained, or do not support real-time inference.

Question 11

A company wants to forecast monthly sales for multiple retail stores using historical sales data. The dataset contains time series data with seasonal patterns, holidays, and promotions. Which AWS approach would be most suitable for accurate forecasting?

A) Amazon SageMaker Linear Learner

B) Amazon SageMaker DeepAR Forecasting

C) Amazon SageMaker K-Means

D) Amazon SageMaker Factorization Machines

Answer

B) Amazon SageMaker DeepAR Forecasting

Explanation

A) Amazon SageMaker Linear Learner is a supervised learning algorithm primarily used for regression and classification tasks. It efficiently handles linear relationships between features and target variables and can scale to large datasets. For time series forecasting, Linear Learner could be applied by creating lag features and trend variables. However, it cannot naturally capture temporal dependencies, seasonality, or complex sequential patterns without extensive feature engineering. Linear Learner assumes a static relationship between input features and outputs, which limits its effectiveness in forecasting scenarios with strong seasonal effects, promotions, or holiday spikes. While linear regression could provide baseline forecasts, it is not ideal for capturing non-linear trends or multi-step dependencies across multiple stores simultaneously.

B) Amazon SageMaker DeepAR is a supervised recurrent neural network-based algorithm specifically designed for probabilistic time series forecasting. It is optimized for datasets with multiple time series, such as sales across different stores, and can model complex patterns, including seasonality, trends, holidays, and special events. DeepAR automatically learns temporal dependencies, making it suitable for multi-step forecasting and providing predictive distributions rather than just point estimates. It can incorporate covariates like promotions, store locations, and holidays, enabling more accurate and actionable forecasts. DeepAR scales efficiently for large datasets, provides uncertainty estimates that help in risk-aware decision-making, and integrates seamlessly with SageMaker pipelines. It is the preferred choice for retail sales forecasting because it captures non-linear and temporal dependencies that are difficult to model using classical regression methods.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm that groups similar data points. While clustering could potentially segment stores with similar sales patterns or customer behavior, K-Means cannot perform time series forecasting because it does not predict numerical target values over time. Clustering alone cannot capture trends, seasonal variations, or temporal dependencies required for monthly sales forecasting, making it unsuitable for this task.

D) Amazon SageMaker Factorization Machines model interactions between features efficiently and are commonly used for sparse, high-dimensional data such as recommendation systems or click-through rate prediction. While Factorization Machines can capture pairwise interactions, they are not designed to handle sequential data or temporal dependencies inherent in time series forecasting. Using Factorization Machines for sales forecasting would require extensive feature engineering and would still be unable to naturally model seasonality or temporal patterns, making them less suitable than DeepAR.

DeepAR provides the specialized architecture to capture sequential patterns, seasonality, and covariates critical for accurate multi-store monthly sales forecasting. Other algorithms either assume linearity, lack temporal modeling capabilities, or are unsupervised, making them insufficient for this forecasting scenario.

Question 12

A bank wants to detect money laundering activities using machine learning. Transactions are high volume, and fraudulent activities are rare compared to normal transactions. Which AWS ML approach is most appropriate to identify anomalous transactions?

A) Amazon SageMaker Random Cut Forest (RCF)

B) Amazon SageMaker K-Means

C) Amazon SageMaker Linear Learner without class weighting

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. It identifies anomalies by constructing trees based on random cuts of multidimensional data and calculating anomaly scores for each data point. This approach is ideal for scenarios where anomalies are rare, as is the case with money laundering transactions. RCF does not require labeled fraud examples and can detect unusual transactions even when the specific patterns of fraud evolve over time. It is scalable for high-volume data streams and can be deployed in real-time or batch mode. RCF generates interpretable anomaly scores, allowing analysts to focus on the highest-risk transactions, which is critical for regulatory compliance and investigation efficiency. Its unsupervised nature is advantageous because fraudulent behavior constantly changes, and labeled examples may be limited or outdated.

B) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering can group similar transactions, it does not provide anomaly scores or directly detect rare fraudulent transactions. Clusters are determined based on density and proximity in feature space, which may fail to highlight subtle anomalous patterns characteristic of money laundering. Clustering is more appropriate for exploratory data analysis rather than supervised or unsupervised anomaly detection at scale.

C) Amazon SageMaker Linear Learner without class weighting performs supervised classification but is not suitable for detecting rare events in highly imbalanced datasets. Without class weighting, the model would be biased towards the majority class (legitimate transactions) and fail to identify fraudulent ones. Supervised classification requires labeled fraud instances, which are often limited, making it less practical for continuous, adaptive anomaly detection compared to RCF.

D) Amazon SageMaker Factorization Machines are optimized for sparse, high-dimensional data with pairwise feature interactions, such as recommendation systems. They are not designed for anomaly detection and cannot effectively identify rare or evolving fraudulent patterns in financial transactions. Using Factorization Machines for this purpose would require extensive feature engineering and labeling, and still may not detect unusual transactions reliably.

Random Cut Forest is best suited for detecting anomalies in high-volume, rare-event datasets like financial transactions. Other algorithms either cannot handle unsupervised anomaly detection effectively or are biased toward majority classes, reducing their utility in identifying suspicious activities.

Question 13

A company wants to build a personalized product recommendation system based on user behavior and past purchases. The dataset contains sparse interactions between users and products. Which AWS SageMaker algorithm is most appropriate for this use case?

A) Amazon SageMaker Factorization Machines

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker XGBoost

D) Amazon SageMaker K-Means

Answer

A) Amazon SageMaker Factorization Machines

Explanation

A) Amazon SageMaker Factorization Machines (FM) are designed for sparse datasets with pairwise feature interactions, making them ideal for recommendation systems. FM models can learn latent factors representing user preferences and item characteristics, allowing accurate prediction of user-item interactions. They handle sparse input efficiently, which is common in user-product matrices where most interactions are missing. Factorization Machines capture pairwise relationships between users and products without requiring dense matrices, improving computational efficiency and predictive accuracy. FM is widely used in collaborative filtering, personalized recommendations, and click-through rate prediction, making it highly suitable for this scenario.

B) Amazon SageMaker Linear Learner can perform supervised regression or classification. While it handles numerical and categorical features, Linear Learner does not naturally capture latent interactions in sparse datasets. For a recommendation system with sparse user-product interactions, it would require extensive feature engineering to create interaction terms, which could be computationally expensive and less effective than Factorization Machines.

C) Amazon SageMaker XGBoost is a tree-based ensemble algorithm effective for dense tabular data with structured features. It captures non-linear relationships and interactions but does not natively handle extremely sparse matrices efficiently. For recommendation systems with large numbers of users and products, XGBoost would require preprocessing to create dense input features, which increases memory requirements and training time. While XGBoost can be adapted for recommendation tasks, FM is more naturally suited for sparse interaction datasets.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. Clustering users or products could provide segmentation insights but does not generate personalized recommendations or predict specific user-product interactions. K-Means alone cannot perform the supervised prediction of interaction likelihood, which is essential for a recommendation engine.

Factorization Machines provide an efficient and accurate approach for modeling sparse user-item interactions, capturing latent factors and enabling personalized recommendations. Other algorithms either lack efficiency on sparse data or cannot provide direct predictions for recommendations.

Question 14

A data scientist wants to perform topic modeling on a large set of customer reviews to extract common themes. Which AWS service is most suitable for this task?

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 designed for text classification and word embeddings. While it can learn vector representations of words and be used for supervised text classification, it does not directly provide unsupervised topic modeling. Implementing topic extraction would require additional steps like clustering embeddings or LDA-like processing, increasing complexity. BlazingText is effective for classification tasks but less convenient for extracting latent topics from text corpora.

B) Amazon Comprehend is a fully managed natural language processing service with built-in capabilities for topic modeling and entity extraction. It can process large collections of documents, identify themes, and categorize text automatically. Comprehend uses unsupervised learning methods for topic extraction, providing interpretable topics without requiring manual labeling. It scales efficiently for large datasets, handles tokenization, preprocessing, and language nuances, and outputs topics with representative terms, enabling businesses to understand common themes in customer feedback quickly and accurately.

C) Amazon SageMaker Linear Learner is a supervised learning algorithm designed for numeric input data and labeled regression or classification tasks. It does not handle unstructured text or perform topic modeling. Using Linear Learner for topic extraction would require extensive preprocessing, vectorization, and additional unsupervised algorithms, making it unsuitable for this task.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While K-Means could be applied to document embeddings or TF-IDF vectors to group similar reviews, it lacks native support for generating interpretable topics. The clusters would require post-processing to extract meaningful themes, and K-Means assumptions about cluster shape may not align well with textual data distributions. This approach is more manual and less efficient compared to using a managed NLP service like Comprehend.

Amazon Comprehend provides a managed, scalable, and interpretable solution for topic modeling, handling preprocessing, tokenization, and document analysis efficiently. Other approaches either require significant manual engineering or are not directly suitable for unsupervised topic extraction.

Question 15

A retail company wants to identify customer segments for targeted marketing campaigns. The dataset contains demographics, purchase behavior, and website activity. Which AWS approach is best suited for segmenting customers?

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 feature similarity. For customer segmentation, K-Means can cluster customers according to demographics, purchase history, and website interactions, revealing patterns and distinct segments. Clustering helps marketers design targeted campaigns, personalize offers, and improve customer engagement. K-Means scales efficiently to large datasets, supports multiple initializations to optimize cluster quality, and integrates with SageMaker pipelines for operational workflows. Proper feature scaling and selection improve cluster quality and interpretability. K-Means is the most appropriate choice for segmenting customers without predefined labels, allowing data-driven identification of market segments.

B) Amazon SageMaker Linear Learner is a supervised algorithm for regression or classification tasks. It requires labeled outcomes and is not suitable for unsupervised segmentation. While it could predict customer churn or purchase probability, it cannot group customers into clusters without labels.

C) Amazon SageMaker XGBoost is a supervised, gradient-boosted decision tree algorithm. It predicts outcomes given features and labels but does not perform unsupervised clustering. XGBoost is suitable for regression or classification tasks but not for exploratory segmentation of unlabeled customers.

D) Amazon Comprehend is an NLP service for analyzing text data. While useful for sentiment analysis or topic extraction in textual content, it cannot cluster customers based on structured demographic or behavioral data.

K-Means provides a scalable, effective approach for unsupervised customer segmentation. Other algorithms are supervised or designed for text analysis, making them unsuitable for exploratory clustering of customer features.

Question 16

A company wants to predict the probability of customers clicking on online ads using historical clickstream data. The dataset is sparse and contains millions of users and ad impressions. Which AWS SageMaker algorithm is most appropriate for this task?

A) Amazon SageMaker Factorization Machines

B) Amazon SageMaker Linear Learner

C) Amazon SageMaker K-Means

D) Amazon SageMaker XGBoost

Answer

A) Amazon SageMaker Factorization Machines

Explanation

A) Amazon SageMaker Factorization Machines (FM) are particularly well-suited for datasets with high-dimensional and sparse features, such as clickstream data with millions of users and ad impressions. FM models can efficiently capture pairwise interactions between features, such as the interaction between a specific user and a specific ad, without creating an enormous number of parameters. This latent factor modeling allows the algorithm to generalize well even when most entries in the user-item matrix are missing. In the context of click prediction, FM can handle the sparsity inherent in large-scale clickstream data while providing accurate probability estimates for rare click events. It is computationally efficient for large-scale problems and integrates seamlessly with SageMaker training and deployment pipelines, making it ideal for real-world advertising prediction scenarios.

B) Amazon SageMaker Linear Learner is capable of performing binary classification and can scale to large datasets. However, linear models do not capture interactions between users and ads unless extensive feature engineering is performed. Sparse clickstream data would require preprocessing into dense representations, which increases computational complexity. Linear models may fail to capture non-linear dependencies between features, which are critical in predicting ad clicks where user preferences and ad attributes interact in complex ways. While Linear Learner could provide a baseline, it is less effective than Factorization Machines for high-dimensional, sparse datasets with latent interactions.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm that groups similar data points. Clustering users or ads could provide segmentation insights but does not directly predict the probability of clicks. K-Means lacks supervised learning capabilities and cannot model rare event probabilities accurately. Using K-Means for click prediction would require additional modeling steps, such as building a separate classifier for each cluster, making the approach cumbersome and less efficient for large-scale datasets.

D) Amazon SageMaker XGBoost is a powerful supervised algorithm capable of handling non-linear relationships. It works well with structured tabular data but is less optimized for extremely sparse user-item interactions common in clickstream datasets. XGBoost would require dense feature engineering to represent millions of user-ad combinations, significantly increasing memory and computational requirements. While it can achieve high accuracy on engineered features, Factorization Machines provide a more scalable and efficient solution specifically designed for sparse data and latent interactions.

Factorization Machines provide the most suitable framework for predicting clicks in sparse, high-dimensional datasets, capturing interactions efficiently without extensive preprocessing. Other algorithms either lack efficiency with sparse data, require heavy feature engineering, or do not provide probability predictions directly.

Question 17

A company wants to forecast hourly electricity demand for a city. The dataset contains historical electricity usage, weather data, and holiday indicators. Which AWS SageMaker algorithm is most suitable for this time series forecasting task?

A) Amazon SageMaker Linear Learner

B) Amazon SageMaker DeepAR Forecasting

C) Amazon SageMaker XGBoost

D) Amazon SageMaker K-Means

Answer

B) Amazon SageMaker DeepAR Forecasting

Explanation

A) Amazon SageMaker Linear Learner is a supervised learning algorithm suitable for regression tasks. While it can be adapted for time series forecasting by creating lag features, rolling averages, and external covariates, it does not inherently model temporal dependencies or sequential patterns. Hourly electricity demand exhibits complex patterns influenced by daily and weekly cycles, weather variations, and holidays. Linear Learner may struggle to capture these non-linear temporal dependencies, making forecasts less accurate without extensive feature engineering and model tuning. Linear regression is better suited for simpler, static relationships rather than highly seasonal and time-dependent datasets.

B) Amazon SageMaker DeepAR is a specialized recurrent neural network-based algorithm designed for probabilistic time series forecasting. It can model complex temporal dependencies, handle covariates like weather and holidays, and learn from multiple related time series simultaneously. DeepAR provides both point forecasts and prediction intervals, which are critical for electricity demand planning to manage risk and ensure grid stability. The algorithm scales well with large datasets and automatically learns sequential dependencies, seasonality, and trend patterns, making it highly effective for forecasting hourly electricity demand with high accuracy and robustness against sudden changes.

C) Amazon SageMaker XGBoost is a gradient-boosted decision tree algorithm suitable for regression and classification tasks with structured tabular data. While XGBoost can model non-linear relationships, it is not inherently designed for sequential data or probabilistic time series forecasting. To use XGBoost for electricity demand prediction, extensive lag features and rolling statistics would need to be engineered manually. It would not naturally capture the sequential correlations and temporal dependencies that DeepAR is optimized for, potentially leading to suboptimal forecasts.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering could group similar days or weather patterns for exploratory analysis, it does not provide direct numeric forecasts of electricity demand. K-Means does not model temporal trends or dependencies and is unsuitable for supervised forecasting tasks. Its application in this scenario is limited to segmentation rather than predictive modeling.

DeepAR is uniquely suited for multi-step time series forecasting, modeling seasonality, trends, and covariates efficiently. Other algorithms require significant feature engineering or do not support temporal modeling natively, reducing their effectiveness for high-resolution electricity demand forecasting.

Question 18

A bank wants to classify loan applications as approved or denied. The dataset contains numerical features like income, age, and loan amount, as well as categorical features such as occupation and education. Some features contain missing values. Which AWS SageMaker algorithm is most appropriate for this classification task?

A) Amazon SageMaker Linear Learner

B) Amazon SageMaker Factorization Machines

C) Amazon SageMaker K-Means

D) Amazon SageMaker DeepAR

Answer

A) Amazon SageMaker Linear Learner

Explanation

A) Amazon SageMaker Linear Learner is a supervised algorithm designed for binary classification and regression. It handles numerical and categorical features efficiently after proper encoding and can handle missing values natively. For loan application classification, Linear Learner provides a robust solution due to its scalability, interpretability, and support for large datasets. It allows the inclusion of regularization to prevent overfitting, supports sparse features, and can generate probability estimates for binary outcomes. The algorithm can produce explainable feature weights, which is important for financial institutions to meet regulatory requirements. Linear Learner is efficient for structured datasets, making it well-suited for classifying loan applications with mixed numeric and categorical features.

B) Amazon SageMaker Factorization Machines are optimized for modeling pairwise interactions in sparse datasets, such as recommendation systems. While they can handle sparse data and interactions, loan application datasets are typically dense, structured, and do not involve massive sparse interactions. Using Factorization Machines would be unnecessarily complex and less interpretable than Linear Learner. Additionally, they are not inherently designed for traditional tabular classification tasks with moderate feature dimensionality.

C) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While clustering could be used for exploratory analysis to identify patterns in applicant data, it does not provide supervised classification of approved versus denied applications. K-Means cannot generate probabilities or directly predict outcomes, making it unsuitable for binary classification in loan decisioning.

D) Amazon SageMaker DeepAR is specialized for probabilistic time series forecasting. Loan application classification is not a temporal prediction problem, and DeepAR does not support traditional binary classification tasks. Using DeepAR for this scenario would be inappropriate and would not yield meaningful results.

Linear Learner effectively handles structured, dense datasets with mixed feature types, manages missing values, and supports binary classification with interpretable output, making it the most appropriate choice for loan application approval prediction. Other algorithms are either specialized for sparse interactions, unsupervised clustering, or time series, making them less suitable.

Question 19

A company wants to analyze sentiment in customer reviews to improve product quality. The dataset contains thousands of text reviews. Which AWS service is best suited for this natural language processing task?

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 an NLP algorithm that can generate word embeddings or perform supervised text classification. It requires labeled data and preprocessing to train a model for sentiment classification. While effective for custom classification tasks, it requires more setup, hyperparameter tuning, and endpoint deployment compared to a fully managed service. For teams seeking rapid deployment and analysis, BlazingText may be unnecessarily complex for sentiment analysis of large volumes of text reviews.

B) Amazon Comprehend is a fully managed NLP service capable of analyzing sentiment in text data without extensive preprocessing or model training. It can process large datasets, detect positive, negative, neutral, and mixed sentiments, and extract entities or key phrases from text. Comprehend automatically handles tokenization, normalization, and language nuances, providing scalable and interpretable sentiment analysis results. For customer reviews, it provides actionable insights into product quality and customer experience, enabling rapid decision-making and continuous monitoring. Comprehend’s managed nature reduces engineering overhead and allows businesses to focus on deriving insights rather than building models from scratch.

C) Amazon SageMaker Linear Learner is a supervised regression or classification algorithm for structured numeric data. It cannot process raw text directly and requires extensive feature extraction, vectorization, and preprocessing to handle natural language. Linear Learner is unsuitable for direct sentiment analysis without significant engineering effort, and it does not provide native text-based NLP capabilities.

D) Amazon SageMaker K-Means is an unsupervised clustering algorithm. While K-Means could group similar reviews based on embeddings or feature vectors, it does not provide sentiment analysis or classify reviews as positive, negative, or neutral. Additional steps would be required to interpret clusters and map them to sentiment categories, making the process more complex and less efficient than using Comprehend.

Amazon Comprehend provides a managed, scalable, and accurate solution for sentiment analysis, automatically handling preprocessing, language nuances, and large datasets. Other options either require extensive custom preprocessing, are not suitable for NLP, or provide only exploratory clustering without sentiment interpretation.

Question 20

A healthcare provider wants to predict patient readmission risk using historical electronic health records (EHR). The dataset contains numerical lab results, categorical patient demographics, and sparse features representing diagnoses. Which AWS SageMaker algorithm is most appropriate?

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 supervised gradient-boosted decision tree algorithm well-suited for structured datasets with numerical, categorical, and sparse features. In predicting patient readmission risk, XGBoost can model non-linear relationships and interactions among lab results, demographics, and diagnosis codes. It handles missing values, provides feature importance metrics for interpretability, and supports large datasets efficiently. XGBoost can manage class imbalance through weighting, which is critical as readmission events are often rare compared to non-readmissions. Its robustness, scalability, and predictive accuracy make it ideal for healthcare risk prediction where multiple heterogeneous features interact in complex ways.

B) Amazon SageMaker Factorization Machines are optimized for sparse datasets with pairwise interactions. While they can handle sparse diagnosis features, they are less effective at modeling dense numerical features like lab results and continuous patient metrics. FM may miss higher-order interactions and non-linear dependencies critical for readmission prediction, reducing overall accuracy.

C) Amazon SageMaker Linear Learner performs well for regression or binary classification on structured datasets. It is interpretable and handles numeric and categorical features after preprocessing. However, it assumes linear relationships, which may be inadequate for the complex interactions present in patient EHR data, such as interactions between multiple lab results, comorbidities, and demographics. Linear models may underfit, leading to lower predictive performance compared to XGBoost.

D) Amazon SageMaker DeepAR is designed for probabilistic time series forecasting. Predicting patient readmission is a classification problem rather than a sequential time series prediction. DeepAR cannot directly model the mixed numerical, categorical, and sparse features required for readmission risk assessment, making it unsuitable for this healthcare scenario.

XGBoost efficiently models complex interactions, handles heterogeneous features, manages missing values, and provides interpretable predictions, making it the most appropriate algorithm for predicting patient readmission risk. Other algorithms either assume linearity, focus on sparse interactions only, or are designed for time series forecasting, making them less effective for this classification task.