Amazon AWS Certified Machine Learning Engineer — Associate MLA-C01 Exam Dumps and Practice Test Questions Set 15 Q211-225
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Question 211
A healthcare company is training a machine learning model to detect anomalies in ECG (electrocardiogram) signals. The dataset is small, and the model is overfitting. Which approach should the data scientist apply to improve generalization?
A) Apply data augmentation and dropout
B) Increase the number of epochs
C) Use raw unnormalized signals
D) Remove early stopping
Answer: A
Explanation:
Overfitting is a common challenge when training models on small datasets, such as ECG signals. The first approach, applying data augmentation and dropout, is specifically designed to improve model generalization. Data augmentation artificially increases the size and diversity of the dataset by applying transformations that do not alter the underlying class labels. For ECG signals, augmentation can include adding noise, scaling amplitudes, shifting signals in time, or applying minor distortions. These transformations enable the model to learn more robust features rather than memorizing idiosyncratic patterns in the small dataset. Dropout, on the other hand, is a regularization technique in which randomly selected neurons are ignored during training, forcing the network to develop distributed representations. This prevents over-reliance on specific neurons or patterns and reduces the risk of overfitting. Together, data augmentation and dropout ensure that the model generalizes better to unseen data, which is critical in healthcare applications where accuracy on new patient data can have serious consequences. Implementing these techniques allows the model to leverage limited data efficiently while learning meaningful, predictive representations that apply across different patients and recording conditions.
Increasing the number of epochs is counterproductive. Longer training allows the network to fit noise and memorizes idiosyncrasies in the small dataset, further worsening overfitting. Instead of improving performance, extending training leads to a divergence between training accuracy and validation accuracy, which indicates poor generalization.
Using raw unnormalized signals does not address overfitting. While normalization or standardization can help with model convergence and stability, the primary issue is memorization of limited training examples. Feeding raw signals into the model without augmentation or regularization leaves it susceptible to learning non-generalizable patterns and noise, which does not solve the overfitting problem.
Removing early stopping is also harmful. Early stopping monitors validation performance and halts training when performance no longer improves. Disabling it allows the model to continue learning patterns specific to the training dataset, exacerbating overfitting. Maintaining early stopping in combination with augmentation and dropout ensures that the model stops at an optimal point for generalization.
The correct reasoning is that data augmentation and dropout directly address overfitting by increasing training diversity and preventing the network from memorizing small datasets. Increasing epochs, using raw signals, or disabling early stopping either worsen overfitting or fail to mitigate it. In healthcare contexts like ECG anomaly detection, these techniques improve model reliability and performance on new patient data, ensuring safe and accurate predictions. By augmenting data and applying dropout, engineers can create models that are both accurate and generalizable despite limited available data, ultimately improving patient safety and diagnostic quality.
Question 212
A company wants to detect unusual activity in server logs that could indicate security breaches. They need a managed solution that automatically identifies anomalies in time-series metrics without requiring custom model development. Which AWS service should they choose?
A) Amazon Lookout for Metrics
B) Amazon Athena
C) Amazon S3
D) AWS Glue
Answer: A
Explanation:
Detecting anomalies in server logs is crucial for cybersecurity and operational monitoring. Amazon Lookout for Metrics is specifically designed for automated anomaly detection in time-series metrics. It uses machine learning to learn normal behavior patterns in operational data and identifies deviations that indicate potential problems, such as security breaches, service failures, or abnormal traffic spikes. The service automatically handles feature selection, trend and seasonality modeling, and anomaly scoring without requiring custom model training, making it accessible to teams without deep ML expertise. Lookout for Metrics can ingest metrics from sources like Amazon S3, Amazon Redshift, or RDS, and it continuously monitors these metrics to detect unusual behavior in real time. Alerts can be sent via Amazon SNS or Lambda triggers to facilitate immediate operational responses, such as blocking suspicious traffic or notifying security teams. Lookout for Metrics also provides detailed explanations, highlighting which metrics and dimensions contributed most to the detected anomalies. This interpretability is essential for understanding the context of anomalies and taking corrective action. Using Lookout for Metrics allows organizations to implement robust monitoring pipelines without investing time and effort in building custom anomaly detection models from scratch, ensuring proactive security and operational efficiency.
Amazon Athena is a serverless SQL query engine designed for ad hoc queries on structured data in Amazon S3. While it is useful for analyzing historical logs and performing batch reporting, it does not perform automated anomaly detection or real-time monitoring. Athena lacks built-in capabilities to detect deviations in trends or trigger alerts automatically.
Amazon S3 provides scalable object storage for server logs and historical datasets. While it is necessary for storing logs and can serve as a data source for analytics, S3 does not analyze or detect anomalies independently. Organizations would need to pair S3 with additional analytics or ML tools to implement anomaly detection.
AWS Glue is a managed ETL service for transforming, cleaning, and preparing data for analysis. It is essential for preprocessing large volumes of server logs, but it does not provide anomaly detection or monitoring capabilities. Using Glue alone does not meet the operational requirement of detecting unusual activity automatically.
The correct reasoning is that Amazon Lookout for Metrics is purpose-built for automated anomaly detection in time-series data. Athena, S3, and Glue support data storage, batch queries, and preprocessing, but none provide real-time anomaly detection with automatic alerts. Lookout for Metrics enables proactive identification of security threats, operational issues, and unusual server activity without custom model development. By leveraging this service, organizations can detect and respond to potential security incidents quickly, maintaining system integrity and minimizing risk.
Question 213
A retail company wants to run large-scale batch inference nightly to update personalized product recommendations for millions of users. They require a fully managed, cost-effective solution that scales automatically. Which AWS service should they use?
A) SageMaker Batch Transform
B) SageMaker Real-time Inference Endpoint
C) Amazon Comprehend
D) AWS Lambda
Answer: A
Explanation:
Batch inference is ideal for scenarios where predictions do not need to be served instantly but must be updated regularly, such as nightly recommendation updates for millions of users. SageMaker Batch Transform is designed for exactly this purpose. It allows users to perform inference on large datasets stored in Amazon S3 using a trained model, without maintaining persistent endpoints. Batch Transform automatically provisions the required instances, distributes the computation, scales according to workload, and deallocates resources upon completion. This ensures cost efficiency, as compute resources are used only during processing. The service supports multiple instance types and distributed processing, allowing efficient handling of millions of predictions simultaneously. Logging and monitoring capabilities provide visibility into job status and performance metrics, which is critical for ensuring batch inference accuracy and reliability. Batch Transform is fully managed, eliminating the operational burden of managing servers, scaling, or load balancing. This makes it ideal for e-commerce personalization pipelines where daily updates must be processed for a large user base, ensuring recommendations are current and accurate without unnecessary infrastructure costs.
SageMaker Real-time Inference Endpoints are designed for low-latency online predictions. While suitable for interactive applications such as chatbots or fraud detection, maintaining real-time endpoints for nightly batch predictions is inefficient and expensive because endpoints run continuously, incurring cost even when idle.
Amazon Comprehend is a managed NLP service for sentiment analysis, entity recognition, and text analytics. It does not support arbitrary model inference or batch scoring for personalized recommendations. Comprehend is unrelated to serving large-scale prediction workloads for personalization.
AWS Lambda is a serverless compute service with runtime and memory limitations. Running batch inference for millions of users is impractical with Lambda due to payload size constraints, execution time limits, and lack of distributed processing. Lambda can be used for small-scale triggers or orchestration but cannot replace a managed batch inference service.
The correct reasoning is that SageMaker Batch Transform provides fully managed, scalable, and cost-effective batch inference for large datasets. Real-time endpoints, Comprehend, and Lambda either incur unnecessary costs, lack relevance, or cannot handle large-scale processing efficiently. By using Batch Transform, the company can update personalized recommendations reliably, cost-effectively, and at scale, ensuring timely delivery of accurate predictions to millions of users without manual infrastructure management.
Question 214
A company wants to build a model that predicts customer churn using historical transaction data. The dataset contains many categorical variables with high cardinality. Which preprocessing technique should the data scientist use to prepare these features for a machine learning model?
A) Apply one-hot encoding for all categorical variables
B) Apply target encoding or embeddings for high-cardinality categorical variables
C) Drop all categorical variables
D) Use raw categorical strings as input
Answer: B
Explanation:
Predicting customer churn involves identifying customers likely to stop using a service based on historical behavior and attributes. When datasets contain categorical variables with high cardinality, such as product IDs, ZIP codes, or customer segments, proper preprocessing is critical. One-hot encoding is a common method for converting categorical variables into numerical representations by creating binary columns for each category. While this works well for low-cardinality variables, applying one-hot encoding to high-cardinality variables leads to extremely sparse matrices and a very high number of features, which increases memory requirements, slows training, and can cause overfitting due to the curse of dimensionality.
Target encoding, also known as mean encoding, is a technique where categorical values are replaced with a statistic derived from the target variable, such as the average churn rate for each category. This reduces dimensionality while providing informative features for the model. Another approach, embedding representations, is widely used in deep learning. Embeddings map high-cardinality categorical variables to dense vectors of continuous values, capturing similarities between categories while reducing feature space. Embeddings are particularly effective for neural network models and can help capture latent relationships among categorical levels that would be difficult to represent with traditional encoding.
Dropping all categorical variables is not recommended because it discards potentially highly predictive features, weakening the model and reducing accuracy. Many categorical variables, such as customer type or product preference, often contain essential information about churn behavior.
Using raw categorical strings as input is also ineffective, as most machine learning algorithms require numerical input. Strings cannot be processed directly by models like XGBoost, random forests, or neural networks, and using them directly would result in errors or unpredictable behavior.
The correct reasoning is that target encoding or embeddings provide a scalable, informative method for handling high-cardinality categorical variables. They reduce dimensionality, maintain predictive information, and prevent overfitting, making them suitable for building accurate churn prediction models. One-hot encoding is feasible only for low-cardinality variables, while dropping or using raw strings is counterproductive. By applying target encoding or embeddings, data scientists can prepare categorical features efficiently, improving model performance and interpretability.
Question 215
A company wants to detect fraudulent credit card transactions in real time. The system must handle high throughput with minimal latency while providing accurate predictions. Which approach should the team use?
A) Deploy the trained model using a SageMaker real-time endpoint
B) Perform batch inference nightly using SageMaker Batch Transform
C) Store transactions in Amazon S3 and analyze later
D) Use AWS Glue to preprocess transactions only
Answer: A
Explanation:
Fraud detection requires immediate response, as any delay may allow fraudulent transactions to succeed. Real-time detection is essential to prevent financial loss and protect customers. Deploying a trained machine learning model on a SageMaker real-time endpoint is the appropriate solution. Real-time endpoints allow the system to accept transaction data via HTTPS API calls and return predictions with low latency, typically within milliseconds. SageMaker endpoints automatically scale to handle fluctuating transaction volume, ensuring that peak loads during busy periods do not compromise performance. Monitoring capabilities with Amazon CloudWatch provide visibility into latency, errors, and throughput, enabling proactive system management. By integrating with AWS Lambda, teams can trigger automated actions such as transaction blocking, alerting, or logging for investigation. This setup ensures real-time decision-making with high accuracy and operational reliability, which is critical for fraud prevention in financial systems.
Batch inference using SageMaker Batch Transform is suitable for scenarios where predictions can be delayed, such as nightly updates or recommendations. For real-time fraud detection, batch processing is insufficient because transactions must be assessed instantly to prevent financial losses.
Storing transactions in Amazon S3 and analyzing later does not provide immediate fraud detection. This approach introduces significant delays and prevents timely intervention. While S3 can serve as a source for historical model training or analysis, it cannot deliver real-time predictions.
Using AWS Glue only preprocesses transaction data. Glue is valuable for cleaning, transforming, and preparing datasets but does not provide inference or prediction capabilities. Without integration with an inference service, Glue alone cannot enable real-time fraud detection.
The correct reasoning is that SageMaker real-time endpoints provide low-latency, fully managed inference for high-throughput transactional data. Batch Transform, S3, and Glue support batch processing, storage, or preprocessing but do not meet real-time requirements. By deploying the model on a real-time endpoint, the company ensures timely and accurate detection of fraudulent transactions, minimizes financial risk, and provides operational scalability to handle variable workloads.
Question 216
A company wants to automatically detect defects in images of manufactured products. The dataset contains thousands of labeled images. The team prefers a managed computer vision service that reduces model training complexity and accelerates deployment. Which AWS service should they use?
A) Amazon Rekognition Custom Labels
B) SageMaker real-time endpoints
C) Amazon Comprehend
D) AWS Glue
Answer: A
Explanation:
Defect detection in manufacturing is a classic computer vision problem, often framed as image classification or object detection. Amazon Rekognition Custom Labels provides a managed solution for this use case. It enables users to train custom image classification models without requiring deep expertise in machine learning. The service automates much of the model training process, including data ingestion, preprocessing, augmentation, and iterative model optimization. Users provide labeled images of defective and non-defective products, and Rekognition Custom Labels handles feature extraction, model training, and evaluation. Once trained, the model can be deployed for inference with low latency through the Rekognition API. The managed workflow significantly reduces the complexity of building a custom computer vision pipeline and accelerates time-to-production. Rekognition Custom Labels also supports versioning, monitoring, and continuous model updates, enabling the system to adapt to new defect types or changes in production processes over time. This approach allows manufacturing teams to detect defects at scale without maintaining custom GPU infrastructure or manually implementing complex neural network architectures.
SageMaker real-time endpoints are useful for deploying models trained outside of managed services, including custom computer vision models. However, they require the engineer to manage preprocessing pipelines, neural network architecture, and model training, which increases complexity. For teams seeking a fully managed computer vision workflow, Rekognition Custom Labels reduces operational overhead.
Amazon Comprehend is a natural language processing service that analyzes text for sentiment, entities, or key phrases. It is unrelated to image analysis and cannot perform defect detection.
AWS Glue is a managed ETL service for cleaning and transforming data. While it can be used to preprocess metadata associated with images or integrate data sources, it does not provide machine learning or computer vision capabilities.
The correct reasoning is that Amazon Rekognition Custom Labels provides a managed, end-to-end solution for image-based defect detection. SageMaker endpoints, Comprehend, and Glue either require custom model management or do not address the image classification task. By leveraging Rekognition Custom Labels, the company can automate defect detection, reduce time-to-deployment, scale inference efficiently, and avoid the operational complexity associated with building custom computer vision pipelines.
Question 217
A company wants to build a predictive maintenance system for its industrial equipment. Sensors generate time-series data continuously, and the goal is to detect early signs of potential failures. The team wants a managed solution that requires minimal machine learning expertise. Which AWS service should they use?
A) Amazon Lookout for Equipment
B) Amazon Athena
C) AWS Glue
D) Amazon Comprehend
Answer: A
Explanation:
Predictive maintenance relies on detecting patterns in sensor data that indicate potential equipment failures. Industrial sensors generate time-series data, which often contains noise, trends, seasonality, and complex interactions between multiple signals. Building custom machine learning models to analyze such data typically requires expertise in signal processing, feature engineering, model selection, and hyperparameter tuning. Amazon Lookout for Equipment provides a fully managed service designed to simplify predictive maintenance by automating these processes. Lookout for Equipment ingests sensor data, automatically extracts features, identifies patterns, and trains machine learning models capable of detecting anomalies indicative of impending equipment failures. This reduces the need for data scientists to manually engineer features or build models from scratch.
The service can handle multiple sensor streams, learning normal operating behavior and recognizing deviations that could indicate failure. Lookout for Equipment uses advanced anomaly detection algorithms tailored for time-series data, enabling early warning alerts. The system can send notifications through Amazon SNS, Lambda, or other integrated services, allowing maintenance teams to take proactive action before catastrophic failures occur. Lookout for Equipment also provides detailed insights into which sensors and signals contributed most to detected anomalies, which is valuable for diagnosing root causes.
Amazon Athena is a serverless query engine that allows SQL-based analysis of structured data in Amazon S3. While useful for historical data exploration or ad hoc analysis, Athena cannot perform real-time anomaly detection or predictive modeling. It is not designed to automatically identify potential equipment failures from streaming sensor data.
AWS Glue is a managed ETL service for data cleaning, transformation, and integration. Glue can preprocess sensor data, aggregate metrics, or restructure datasets, but it does not perform predictive maintenance or anomaly detection. Glue supports upstream preparation for machine learning but does not provide built-in predictive modeling capabilities.
Amazon Comprehend is a natural language processing service for analyzing text, extracting sentiment, and identifying entities. It does not process numerical sensor data and is therefore unsuitable for predictive maintenance.
The correct reasoning is that Amazon Lookout for Equipment is purpose-built for predictive maintenance on time-series sensor data. It automates feature engineering, model training, and anomaly detection, providing actionable insights with minimal ML expertise. Athena, Glue, and Comprehend support data analysis or processing in other contexts but cannot provide the specialized predictive maintenance capabilities required in this scenario. By using Lookout for Equipment, companies can reduce unplanned downtime, extend equipment life, and optimize maintenance scheduling efficiently.
Question 218
A company wants to classify customer support emails into categories automatically. They have a large volume of historical emails with labels. Which AWS service provides an end-to-end managed solution for this task?
A) Amazon Comprehend
B) Amazon SageMaker Batch Transform
C) Amazon Textract
D) AWS Glue
Answer: A
Explanation:
Automatically classifying customer support emails involves natural language processing (NLP) to extract meaning from unstructured text and assign predefined labels. Amazon Comprehend provides a managed NLP solution capable of text classification, entity recognition, sentiment analysis, and topic modeling. For this scenario, Comprehend Custom Classification allows users to train models on labeled historical emails, enabling accurate categorization of new incoming emails without building and maintaining custom ML pipelines. The service automatically handles preprocessing, tokenization, model selection, training, and evaluation, significantly reducing the required expertise and operational overhead. Once trained, Comprehend provides APIs to classify emails in real time, enabling automated routing to the appropriate support team. The managed workflow includes built-in monitoring and model versioning, ensuring consistent performance and the ability to update models as new categories or language patterns emerge. This makes it ideal for organizations dealing with large volumes of customer communications.
Amazon SageMaker Batch Transform is designed for batch inference of custom ML models on structured or unstructured data. While it can process large datasets, it does not provide an end-to-end managed NLP classification workflow out of the box. Users would need to build, train, and deploy their own models before leveraging batch transform, which increases operational complexity.
Amazon Textract extracts text and structured data from documents, such as forms, PDFs, or scanned files. Although useful for converting email attachments or scanned correspondence into machine-readable text, Textract does not provide classification or NLP-based labeling capabilities.
AWS Glue is an ETL service that transforms and prepares data for downstream analytics. Glue can preprocess email datasets by cleaning text or merging sources, but it does not perform automated text classification or deliver predictions.
The correct reasoning is that Amazon Comprehend offers a fully managed, end-to-end NLP solution for classifying emails. SageMaker Batch Transform requires custom ML model management, Textract extracts text but does not classify it, and Glue only preprocesses data. Comprehend automates training, prediction, and deployment, enabling efficient, accurate classification of large volumes of customer support emails with minimal manual intervention.
Question 219
A retail company wants to recommend products to customers based on their browsing and purchase history. The solution must handle millions of users and scale automatically. Which AWS service should they use?
A) Amazon Personalize
B) Amazon SageMaker real-time endpoint
C) Amazon Comprehend
D) AWS Glue
Answer: A
Explanation:
Personalized product recommendations rely on analyzing user behavior, preferences, and historical interactions to suggest relevant products. Amazon Personalize provides a fully managed recommendation service that allows companies to generate personalized recommendations without building complex machine learning pipelines. Personalize supports multiple recommendation scenarios, including user-personalized ranking, related item suggestions, and personalized product recommendations for millions of users. The service automatically handles feature engineering, model training, hyperparameter tuning, and model deployment, enabling scalable inference with minimal ML expertise. Personalize also continuously learns from new interactions, allowing recommendations to adapt to evolving user behavior. It integrates with data sources such as Amazon S3, streaming events, and transactional databases to provide real-time recommendations via API endpoints. By using Personalize, companies can deliver highly relevant suggestions, increase engagement, and drive conversions at scale.
Amazon SageMaker real-time endpoints provide low-latency inference for custom models. While suitable for real-time predictions, building a recommendation system from scratch on SageMaker requires extensive ML expertise, data preprocessing, model design, and ongoing maintenance. For many companies, this approach is more complex and time-consuming compared to a fully managed service like Personalize.
Amazon Comprehend is a natural language processing service for text analysis. It does not provide product recommendation capabilities and cannot handle user behavior data for personalized suggestions.
AWS Glue is a managed ETL service that can clean, transform, and consolidate datasets. While essential for preparing user data for modeling, it does not perform recommendations or inference.
The correct reasoning is that Amazon Personalize is purpose-built for generating scalable, personalized recommendations. SageMaker endpoints require custom model development, Comprehend focuses on NLP tasks, and Glue is limited to data preparation. By leveraging Personalize, the retail company can deliver accurate, adaptive product recommendations for millions of users without managing infrastructure or model training.
Question 220
A company wants to predict customer lifetime value (CLV) using historical transaction data. The dataset includes many numerical and categorical features, some of which are correlated. Which approach should the data scientist take to improve model performance and prevent overfitting?
A) Apply feature selection and regularization
B) Train for more epochs without preprocessing
C) Use raw features without scaling or encoding
D) Remove cross-validation
Answer: A
Explanation:
Predicting customer lifetime value involves modeling complex relationships in historical data that includes multiple numerical and categorical variables. Overfitting is a concern because models can memorize noise or redundant features, leading to poor generalization on unseen customers. The first approach, applying feature selection and regularization, directly addresses these issues. Feature selection identifies the most informative predictors while removing highly correlated or redundant features, reducing dimensionality and minimizing the risk of overfitting. Regularization techniques such as L1 (Lasso) and L2 (Ridge) penalize large coefficients in linear or tree-based models, forcing the model to rely on generalizable patterns instead of memorizing training data. For neural networks, techniques like dropout similarly help reduce overfitting by randomly ignoring neurons during training. Combining feature selection with regularization ensures that the model emphasizes meaningful relationships and remains robust when applied to new data. Proper handling of both numerical and categorical variables allows the model to achieve high predictive accuracy and reliable CLV estimates.
Training for more epochs without preprocessing is counterproductive. Extended training amplifies overfitting, especially when features are noisy or highly correlated. The model may memorize the training set instead of learning patterns that generalize to future customers.
Using raw features without scaling or encoding can negatively impact performance. Many algorithms, particularly gradient-based methods and neural networks, require standardized or normalized numerical inputs for stable convergence. Categorical features must be encoded appropriately to provide meaningful representations for the model. Without preprocessing, features may introduce biases, cause convergence issues, and reduce generalization.
Removing cross-validation undermines the ability to evaluate model performance reliably. Cross-validation provides an estimate of the model’s predictive accuracy on unseen data. Without it, the model may appear to perform well on training data but fail in production, particularly when predicting CLV for new customers.
The correct reasoning is that feature selection and regularization enable the model to focus on meaningful variables, reduce complexity, and prevent overfitting. Other approaches like training longer, using raw features, or omitting cross-validation either exacerbate overfitting or compromise evaluation reliability. Applying these techniques ensures the model provides accurate, generalizable predictions of customer lifetime value, which is critical for informed business decisions, targeted marketing, and financial planning.
Question 221
A manufacturing company wants to detect anomalies in sensor data from assembly lines to prevent equipment failure. The team has large volumes of time-series data but lacks machine learning expertise. Which AWS service should they use?
A) Amazon Lookout for Equipment
B) Amazon Athena
C) AWS Glue
D) Amazon Comprehend
Answer: A
Explanation:
Anomaly detection in industrial environments is essential for predictive maintenance. Sensors produce large volumes of time-series data that may include multiple correlated variables, noise, trends, and seasonality. Detecting anomalies manually is impractical, and building custom ML models requires expertise in time-series analysis, feature engineering, and model deployment. Amazon Lookout for Equipment is a managed service specifically designed to address these challenges. It automatically ingests time-series sensor data, extracts features, identifies normal operational patterns, and detects deviations that may indicate impending equipment failures. The service uses advanced machine learning algorithms tailored for time-series anomaly detection, eliminating the need for domain-specific ML expertise.
Lookout for Equipment can handle multiple sensors simultaneously and continuously monitors streams to detect anomalies in real time. It provides actionable alerts through Amazon SNS or Lambda triggers, enabling maintenance teams to take proactive corrective actions before failures occur. The service also offers interpretability by highlighting which sensors or signals contributed most to detected anomalies, assisting root cause analysis. By automating model creation, feature selection, and anomaly detection, Lookout for Equipment allows companies to implement predictive maintenance solutions efficiently and reliably.
Amazon Athena is a serverless query engine optimized for structured data in S3. While it is useful for ad hoc analysis and batch reporting, Athena does not provide anomaly detection, automated monitoring, or real-time predictive capabilities.
AWS Glue is a managed ETL service for cleaning, transforming, and consolidating data. Glue can preprocess sensor data for downstream analysis but does not perform anomaly detection or provide predictive capabilities.
Amazon Comprehend is a natural language processing service for text analysis, such as sentiment detection and entity extraction. It does not process numerical sensor data and cannot detect equipment anomalies.
The correct reasoning is that Amazon Lookout for Equipment is purpose-built for anomaly detection in industrial sensor data. It automates preprocessing, feature extraction, model training, and monitoring, providing actionable insights without requiring ML expertise. Athena, Glue, and Comprehend support queries, preprocessing, or text analysis, but cannot detect anomalies in time-series sensor data. By using Lookout for Equipment, the company can proactively identify potential equipment failures, reduce downtime, and optimize maintenance operations efficiently and reliably.
Question 222
A company wants to classify millions of images of defective and non-defective products to improve quality control. They prefer a managed solution that minimizes ML development effort. Which AWS service should they use?
A) Amazon Rekognition Custom Labels
B) SageMaker real-time endpoint
C) Amazon Comprehend
D) AWS Glue
Answer: A
Explanation:
Classifying images for quality control involves building models capable of distinguishing defective from non-defective products. This is a computer vision task, which typically requires large labeled datasets and expertise in neural network design, preprocessing, and deployment. Amazon Rekognition Custom Labels provides a fully managed service that simplifies this process. It allows users to upload labeled images, train custom image classification models, and deploy them for inference without needing deep machine learning expertise. The service automates feature extraction, model selection, training, and evaluation. Users can quickly generate predictions for new images via API calls, facilitating integration into quality control workflows. Rekognition Custom Labels also supports continuous model improvement by allowing retraining with additional labeled images, which helps the system adapt to new defect types or changing product designs. This managed approach reduces operational complexity, accelerates deployment, and ensures scalability for millions of images.
SageMaker real-time endpoints provide low-latency inference for models developed outside of managed services. While effective for custom ML pipelines, they require significant effort in model training, feature engineering, and deployment. For teams seeking a managed, simplified approach, Rekognition Custom Labels is more efficient and reduces operational overhead.
Amazon Comprehend is a natural language processing service for text analysis. It is not applicable for image classification or defect detection.
AWS Glue is an ETL service for data preprocessing and transformation. While useful for preparing metadata or organizing image datasets, Glue does not provide machine learning or inference capabilities for computer vision tasks.
The correct reasoning is that Amazon Rekognition Custom Labels provides an end-to-end managed solution for image-based defect classification. SageMaker endpoints, Comprehend, and Glue either require custom ML development or do not address the image classification task. By using Rekognition Custom Labels, the company can quickly classify images, improve quality control, scale efficiently, and reduce operational effort while maintaining high accuracy.
Question 223
A company wants to detect fraudulent transactions in real time for its e-commerce platform. The system must provide low-latency predictions and automatically scale with traffic. Which AWS service should they use?
A) Amazon SageMaker real-time endpoint
B) SageMaker Batch Transform
C) Amazon S3
D) AWS Glue
Answer: A
Explanation:
Fraud detection requires immediate action because any delay can result in financial losses, compromised customer accounts, or reputational damage. Real-time detection is essential to prevent fraudulent transactions from being approved. Amazon SageMaker real-time endpoints are specifically designed for low-latency inference in scenarios like fraud detection. These endpoints host trained machine learning models and allow predictions to be made on individual transactions as they occur, typically within milliseconds. Users send transaction data to the endpoint via HTTPS API requests, and the model returns fraud probability or classification. This enables instant blocking, alerting, or flagging of suspicious activity. SageMaker endpoints also support autoscaling, ensuring that the system can handle spikes in transaction volume during peak shopping periods without degrading performance. Monitoring through Amazon CloudWatch provides visibility into throughput, latency, and error rates, enabling proactive system management. By leveraging a managed real-time endpoint, teams avoid the operational complexity of manually provisioning servers, load balancers, and auto-scaling logic while maintaining high availability and reliability for fraud detection.
SageMaker Batch Transform is designed for batch inference, where predictions are performed on large datasets asynchronously. While useful for nightly or periodic scoring, it is unsuitable for fraud detection, which requires immediate evaluation of individual transactions. Batch processing introduces delays that can result in fraudulent transactions being approved before detection.
Amazon S3 provides object storage for historical transactions or model artifacts but does not perform inference. Using S3 alone would require additional compute resources and custom logic to analyze transactions, which is inefficient and does not meet real-time requirements.
AWS Glue is a managed ETL service for data cleaning, transformation, and preparation. While valuable for preprocessing transaction data, it does not provide real-time prediction capabilities. Glue cannot detect fraud or provide low-latency inference without integration with additional ML services.
The correct reasoning is that SageMaker real-time endpoints offer a fully managed, low-latency, scalable solution for fraud detection. Batch Transform, S3, and Glue either perform delayed processing, storage, or preprocessing but do not provide immediate predictions. By deploying a trained fraud detection model on a real-time endpoint, the company can detect and prevent fraudulent transactions instantaneously, scale efficiently during high traffic, and maintain system reliability and security.
Question 224
A healthcare company wants to classify medical reports into categories such as cardiology, neurology, and orthopedics. The team has a large labeled dataset of text reports and wants an end-to-end managed solution. Which AWS service is most appropriate?
A) Amazon Comprehend
B) SageMaker Batch Transform
C) Amazon Textract
D) AWS Glue
Answer: A
Explanation:
Classifying medical reports requires natural language processing to interpret unstructured text and assign predefined categories. Amazon Comprehend provides a managed NLP solution that supports custom classification, allowing teams to train models using labeled datasets without requiring extensive ML expertise. Users can upload the labeled medical reports, and Comprehend automatically handles preprocessing, tokenization, model selection, training, and evaluation. Once trained, the model can classify new medical reports in real time or in batch, enabling efficient categorization and routing to appropriate medical departments. Comprehend also provides interpretability, allowing users to understand which words or phrases influenced the classification decisions. The managed service includes built-in monitoring, versioning, and integration with AWS APIs for automated workflows, making it ideal for organizations dealing with high volumes of medical reports.
SageMaker Batch Transform is suitable for performing batch inference on pre-trained models. While it can process large datasets, it does not provide an end-to-end managed NLP solution. Teams would need to build, train, and deploy custom models before leveraging Batch Transform, increasing operational complexity.
Amazon Textract extracts text from scanned documents or PDFs but does not perform classification or NLP-based labeling. It is useful for converting reports into machine-readable text but cannot assign categories without additional processing and modeling.
AWS Glue is a managed ETL service that cleans, transforms, and integrates data. While Glue can preprocess the text for downstream analysis, it does not provide classification or prediction capabilities.
The correct reasoning is that Amazon Comprehend offers a fully managed, end-to-end NLP solution for text classification. Batch Transform requires custom model management, Textract only extracts text, and Glue only prepares data. Comprehend automates training, inference, and deployment, enabling efficient and accurate classification of medical reports into appropriate categories, reducing manual effort and improving operational efficiency in healthcare workflows.
Question 225
A retail company wants to update personalized product recommendations for millions of users every night. Predictions must be generated efficiently and at scale. Which AWS service should they use?
A) SageMaker Batch Transform
B) SageMaker real-time endpoint
C) Amazon Comprehend
D) AWS Glue
Answer: A
Explanation:
Updating personalized product recommendations nightly involves large-scale batch inference, where predictions are generated on millions of users simultaneously. SageMaker Batch Transform is designed for this use case. It allows users to perform inference on large datasets stored in Amazon S3 using pre-trained models without maintaining persistent endpoints. Batch Transform automatically provisions compute instances, distributes the workload, and deallocates resources after processing, ensuring cost efficiency. The service supports distributed processing across multiple instances, enabling efficient handling of millions of predictions. Batch Transform also integrates with monitoring tools such as CloudWatch to track job progress, performance, and errors, ensuring reliability for large-scale batch workloads. This managed solution reduces operational complexity compared to maintaining real-time endpoints for batch tasks, minimizes cost by using compute resources only when needed, and delivers timely updated recommendations for e-commerce personalization.
SageMaker real-time endpoints are intended for low-latency, online predictions. Using them for nightly batch inference is inefficient because endpoints run continuously, incurring cost even when not processing requests. This approach is overkill for batch updates where real-time responsiveness is unnecessary.
Amazon Comprehend is a managed NLP service for text analysis and classification. It does not support batch inference for arbitrary models or personalized recommendation generation.
AWS Glue is a managed ETL service for preprocessing and transforming data. While useful for preparing datasets before inference, Glue does not perform model scoring or prediction.
The correct reasoning is that SageMaker Batch Transform provides a fully managed, scalable, and cost-effective solution for nightly batch inference on millions of users. Real-time endpoints are better suited for online predictions, Comprehend is irrelevant for recommendation scoring, and Glue only handles data preparation. By using Batch Transform, the retail company can update personalized recommendations efficiently and reliably while controlling infrastructure costs.