Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 13 Q181-195 - Certbolt

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Question 181:

A global logistics company wants to implement a real-time package tracking and predictive delivery system. The system must ingest millions of events per second, track package locations and status updates, detect anomalies such as delayed or lost packages instantly, trigger alerts to operational teams, store historical package movement data for analysis, and support predictive modeling for delivery time optimization. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

A real-time package tracking system requires low-latency ingestion of extremely high-frequency events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A is the architecture that meets all these requirements.

Amazon Kinesis Data Streams ingests millions of package events per second, including location updates, status changes, and delivery confirmations. Its scalability, durability, and multi-consumer support allow simultaneous processing of real-time data streams to monitor delivery performance, detect anomalies, and feed predictive analytics. This ensures the system can handle surges in package volume during peak shipping periods or special campaigns.

AWS Lambda processes incoming events in real time. Lambda functions analyze package movement data to detect anomalies such as unexpected delays, incorrect routing, or potential lost packages. Alerts can be triggered immediately to operational teams for corrective action. Lambda’s serverless architecture allows automatic scaling to handle spikes in package tracking events without manual infrastructure management.

Amazon S3 provides durable storage for raw and processed package data, supporting historical trend analysis, compliance reporting, and model training. Lifecycle policies allow older datasets to be moved to Amazon Glacier to reduce storage costs while maintaining accessibility for long-term analysis and strategic decision-making.

Amazon SageMaker allows predictive models to be trained on historical package movement data. These models can forecast delivery times, optimize routing strategies, and identify patterns contributing to delays. Real-time inference applies models immediately to active package streams, enabling proactive interventions, improving delivery accuracy, and enhancing customer satisfaction.

Option B (S3 + Glue + Redshift) is batch-oriented. ETL jobs and Redshift analytics introduce latency that is incompatible with real-time anomaly detection, operational alerts, or predictive delivery optimizations. While Redshift supports historical analysis, it cannot provide actionable insights in real time.

Option C (RDS + QuickSight) cannot handle millions of events per second. QuickSight dashboards deliver delayed insights, and scaling RDS globally for high-frequency package event ingestion is operationally complex and costly.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR’s batch-processing nature introduces latency, preventing real-time detection, alerts, and predictive modeling. Additional orchestration increases system complexity and operational risk.

Option A provides a fully integrated, low-latency, scalable architecture capable of ingestion, real-time processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for global logistics package tracking and delivery optimization.

Question 182:

A global autonomous drone delivery service wants to implement a real-time fleet telemetry, safety monitoring, and predictive maintenance system. The system must ingest millions of telemetry events per second, detect anomalies such as collisions or mechanical failures instantly, trigger alerts to operational teams, store historical telemetry for trend analysis, and support predictive modeling for route optimization and battery usage. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time autonomous drone fleet management requires extremely low-latency ingestion of high-frequency telemetry events, immediate anomaly detection, operational alerts, durable storage, and predictive analytics. Option A satisfies all these requirements comprehensively.

Amazon Kinesis Data Streams ingests millions of telemetry events per second from drones, including GPS location, altitude, battery levels, and environmental metrics. Multiple consumers can process the stream simultaneously for anomaly detection, operational monitoring, and predictive analytics. Horizontal scalability ensures the system can handle surges during peak drone activity or adverse weather conditions.

AWS Lambda processes events in real time. Lambda functions detect anomalies such as collision risks, unexpected battery depletion, or mechanical failures and trigger alerts to operational teams. The serverless architecture scales automatically to handle peak event loads without manual intervention, ensuring continuous low-latency performance.

Amazon S3 stores raw and processed telemetry data for historical analysis, compliance, and predictive model training. Lifecycle policies allow cost-effective archival to Glacier while maintaining accessibility for research, trend analysis, and fleet optimization.

Amazon SageMaker trains predictive models on historical telemetry data to optimize drone routes, battery usage, and maintenance schedules. Real-time inference applies models immediately to active drone operations, enabling proactive maintenance, improved safety, and operational efficiency.

Option B (S3 + Glue + Redshift) is batch-oriented. ETL jobs and Redshift analytics introduce latency, making real-time anomaly detection, operational alerts, and predictive modeling impossible. Redshift supports historical analysis but cannot deliver actionable insights in real time.

Option C (RDS + QuickSight) cannot ingest millions of telemetry events per second. QuickSight dashboards provide delayed insights, and scaling RDS globally for high-frequency telemetry is operationally complex and costly.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR latency prevents real-time detection, alerts, and predictive modeling. Additional orchestration introduces operational complexity and risk.

Question 183:

A global telecommunications company wants to implement a real-time network traffic analytics system. The system must ingest millions of network events per second, detect anomalies such as suspicious traffic patterns or outages instantly, trigger alerts to network operation teams, store historical traffic data for analysis, and support predictive modeling for network optimization and capacity planning. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time network analytics requires extremely low-latency ingestion, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A meets all requirements.

Amazon Kinesis Data Streams ingests millions of network events per second, including packet data, connection logs, and routing information. Its durability and ability to support multiple consumers allow concurrent processing for real-time anomaly detection, capacity monitoring, and operational insights. Horizontal scalability ensures the system handles surges in network traffic during peak usage periods or cyberattacks.

AWS Lambda processes network events in real time. Lambda functions detect anomalies such as potential DDoS attacks, suspicious traffic patterns, or outages. Alerts are triggered immediately to operational teams for mitigation. The serverless architecture allows automatic scaling to handle peak traffic without manual infrastructure intervention, ensuring continuous low-latency monitoring.

Amazon S3 provides durable storage for raw and processed network data, supporting historical trend analysis, compliance, and predictive model training. Lifecycle policies optimize costs by moving older datasets to Glacier while maintaining accessibility for capacity planning and long-term analysis.

Amazon SageMaker trains predictive models using historical network traffic data to optimize routing, anticipate outages, and plan capacity expansions. Real-time inference applies models to active network traffic, enabling proactive network adjustments, improved reliability, and operational efficiency.

Option B (S3 + Glue + Redshift) is batch-oriented, unsuitable for real-time anomaly detection, alerts, or predictive network optimization. Redshift supports historical analysis but cannot deliver immediate operational insights.

Option C (RDS + QuickSight) cannot handle millions of network events per second. QuickSight dashboards provide delayed insights, and scaling RDS for global network traffic is operationally complex and expensive.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR latency prevents real-time detection, alerts, and predictive network optimization. Additional orchestration increases complexity and operational risk.

Question 184:

A global autonomous vehicle company wants to implement a real-time fleet monitoring and predictive maintenance system. The system must ingest millions of telemetry events per second, detect anomalies such as mechanical failures or unsafe driving instantly, trigger alerts to operational teams, store historical telemetry for analysis, and support predictive modeling for route optimization and vehicle efficiency. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Autonomous vehicle telemetry requires ingestion of high-frequency events, immediate anomaly detection, operational alerts, durable storage, and predictive analytics. Option A meets all these requirements.

Amazon Kinesis Data Streams ingests millions of telemetry events per second, including GPS data, engine metrics, and environmental readings. Its durability, fault tolerance, and multi-consumer support enable real-time analysis for anomaly detection, fleet monitoring, and predictive analytics. Horizontal scalability ensures the system can manage surges in data during peak operational periods or complex environments.

AWS Lambda processes incoming events instantly. Lambda functions detect anomalies such as mechanical failures, unsafe driving behavior, or route deviations. Alerts are triggered immediately to operational teams, allowing proactive intervention. The serverless architecture scales automatically to accommodate peaks without manual infrastructure management, ensuring continuous low-latency monitoring.

Amazon S3 stores raw and processed telemetry for historical trend analysis, compliance, and predictive model training. Lifecycle policies move older data to Glacier to optimize costs while maintaining accessibility for research, analysis, and operational improvement.

Amazon SageMaker trains predictive models on historical telemetry data to forecast failures, optimize routing, and improve vehicle efficiency. Real-time inference allows models to be applied to active telemetry streams, enabling proactive maintenance, operational optimization, and safety improvements.

Option B (S3 + Glue + Redshift) is batch-oriented. ETL jobs and Redshift analytics introduce latency incompatible with real-time anomaly detection, alerts, or predictive fleet optimization. Redshift supports historical analysis but not real-time operational insights.

Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight dashboards provide delayed insights, and scaling RDS globally for high-frequency telemetry is operationally complex and costly.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR latency prevents real-time detection, alerts, and predictive modeling. Additional orchestration increases complexity and operational risk.

Question 185:

A global smart agriculture company wants to implement a real-time crop and soil monitoring system. The system must ingest millions of IoT sensor events per second, detect anomalies such as soil moisture deficits or pest infestations instantly, trigger alerts to farmers, store historical crop data for analysis, and support predictive modeling for irrigation scheduling and yield optimization. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time crop and soil monitoring requires low-latency ingestion of high-frequency IoT sensor events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A fulfills all these requirements comprehensively.

Amazon Kinesis Data Streams ingests millions of sensor events per second, including soil moisture, temperature, nutrient levels, and pest activity. Multiple consumers can process the stream for real-time anomaly detection, alerting, and predictive modeling. Horizontal scalability ensures the system handles seasonal surges or large-scale farm operations.

AWS Lambda processes events in real time. Lambda functions detect anomalies such as soil moisture deficits, nutrient imbalance, or pest infestations and trigger alerts to farmers for immediate corrective action. The serverless architecture scales automatically to handle surges in sensor data without manual infrastructure management, ensuring continuous low-latency performance.

Amazon S3 stores raw and processed IoT sensor data for historical analysis, regulatory compliance, and predictive model training. Lifecycle policies move older data to Glacier to optimize storage costs while maintaining accessibility for long-term analysis, research, and planning.

Amazon SageMaker trains predictive models using historical sensor data to optimize irrigation schedules, forecast crop yields, and detect patterns affecting productivity. Real-time inference applies models to active sensor streams, enabling proactive interventions, resource optimization, and improved agricultural efficiency.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time anomaly detection, operational alerts, or predictive interventions. Redshift supports historical analysis but cannot deliver immediate actionable insights.

Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight dashboards provide delayed insights, and scaling RDS globally for high-frequency IoT telemetry is operationally complex and expensive.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR latency prevents real-time detection, alerts, and predictive modeling. Additional orchestration adds operational complexity and risk.

Question 186:

A global online gaming company wants to implement a real-time player activity monitoring and personalized recommendation system. The system must ingest millions of player events per second, detect unusual activity or cheating instantly, trigger alerts to the security and operations teams, store historical player behavior data for analysis, and support predictive modeling for in-game recommendations and content personalization. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time player activity monitoring requires ingestion of extremely high-frequency events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A satisfies all these requirements.

Amazon Kinesis Data Streams provides scalable, low-latency ingestion of millions of events per second, including player actions such as login, gameplay, purchases, and chat interactions. The durability and ability to support multiple consumers allow real-time analysis for anomaly detection, cheat prevention, and recommendation generation. Horizontal scalability ensures the system handles surges during game releases or tournaments.

AWS Lambda processes events in real time, detecting anomalies such as cheating, suspicious login patterns, or abnormal gameplay behavior. Alerts can be immediately triggered to security and operational teams for timely intervention. The serverless architecture scales automatically to accommodate peaks in event volume without manual infrastructure management.

Amazon S3 stores raw and processed player activity data, supporting historical trend analysis, compliance, and predictive model training. Lifecycle policies allow cost-efficient archival of older datasets to Glacier while maintaining accessibility for research and model improvements.

Amazon SageMaker allows predictive models to be trained using historical player behavior data to generate personalized recommendations, optimize in-game content delivery, and improve engagement. Real-time inference applies these models to active players, enabling adaptive gameplay experiences and dynamic recommendations.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time anomaly detection or predictive recommendations. Redshift supports historical analytics but cannot deliver immediate operational insights.

Option C (RDS + QuickSight) cannot handle millions of events per second. QuickSight dashboards provide delayed insights, and scaling RDS globally for high-frequency gaming telemetry is complex and costly.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR’s latency prevents real-time detection, alerts, and predictive modeling. Additional orchestration increases operational complexity and risk.

Option A is the only architecture that integrates real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for global online gaming activity monitoring and personalized recommendations.

Question 187:

A global e-commerce company wants to implement a real-time order tracking and fraud detection system. The system must ingest millions of order events per second, detect fraudulent or anomalous transactions instantly, trigger alerts to operations teams, store historical order data for trend analysis, and support predictive modeling for customer behavior and order processing optimization. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time order tracking and fraud detection requires ingestion of high-frequency transaction events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A fulfills all these requirements comprehensively.

Amazon Kinesis Data Streams ingests millions of order events per second, including customer activity, payments, and shipment updates. Its durability and multi-consumer support allow simultaneous real-time processing for fraud detection, operational monitoring, and predictive analytics. Horizontal scalability ensures the system handles surges during peak shopping seasons or promotions.

AWS Lambda processes events instantly, detecting anomalies such as fraudulent activity, unusual purchase patterns, or order processing errors. Alerts can be immediately triggered to operations and security teams for proactive intervention. Lambda’s serverless nature enables automatic scaling during peak loads without manual infrastructure management.

Amazon S3 provides durable storage for raw and processed order data, supporting historical trend analysis, compliance, and predictive model training. Lifecycle policies enable cost-efficient archival to Glacier while maintaining accessibility for research, customer behavior analysis, and strategy optimization.

Amazon SageMaker allows predictive models to be trained using historical order data to forecast customer behavior, optimize inventory, and detect potential fraud. Real-time inference applies these models to active orders, enabling dynamic risk assessment, fraud prevention, and operational optimization.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time fraud detection or predictive optimization. Redshift supports historical analysis but cannot provide immediate operational insights.

Option C (RDS + QuickSight) cannot ingest millions of transactions per second. QuickSight dashboards provide delayed insights, and scaling RDS globally for high-frequency e-commerce events is operationally complex and costly.

Question 188:

A global health monitoring company wants to implement a real-time patient telemetry and anomaly detection system. The system must ingest millions of medical sensor events per second, detect anomalies such as critical health events instantly, trigger alerts to medical teams, store historical patient data for analysis, and support predictive modeling for patient risk assessment and preventive care recommendations. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time patient telemetry requires ingestion of extremely high-frequency medical sensor events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A meets all these requirements.

Amazon Kinesis Data Streams ingests millions of telemetry events per second from wearable devices, medical sensors, and patient monitoring systems. Its durability and multi-consumer support allow real-time analysis for anomaly detection, operational monitoring, and predictive analytics. Horizontal scalability ensures the system handles surges during peak monitoring periods or emergencies.

AWS Lambda processes events instantly, detecting anomalies such as abnormal heart rates, blood pressure deviations, or other critical health indicators. Alerts are immediately triggered to medical teams for proactive intervention. Lambda’s serverless architecture allows automatic scaling to handle peaks in telemetry events without manual infrastructure management.

Amazon S3 stores raw and processed telemetry data for historical analysis, regulatory compliance, and predictive model training. Lifecycle policies optimize storage costs by archiving older datasets to Glacier while maintaining accessibility for longitudinal studies, research, and patient care optimization.

Amazon SageMaker allows predictive models to be trained on historical patient telemetry data to forecast health risks, optimize care interventions, and provide preventive recommendations. Real-time inference applies models immediately to active patient data streams, enabling timely intervention and improving patient outcomes.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time anomaly detection, operational alerts, and predictive health assessments. Redshift supports historical analytics but cannot deliver immediate operational insights.

Question 189:

A global airline wants to implement a real-time aircraft telemetry, safety monitoring, and predictive maintenance system. The system must ingest millions of telemetry events per second, detect anomalies such as engine malfunctions or abnormal flight patterns instantly, trigger alerts to operational teams, store historical aircraft telemetry for analysis, and support predictive modeling for flight safety and maintenance optimization. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time aircraft telemetry monitoring requires ingestion of high-frequency events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A fulfills all these requirements.

Amazon Kinesis Data Streams ingests millions of telemetry events per second from aircraft sensors, including engine performance, altitude, speed, and environmental conditions. Its durability and multi-consumer support allow concurrent processing for anomaly detection, operational monitoring, and predictive analytics. Horizontal scalability ensures the system can handle surges during peak flight schedules or extreme weather events.

AWS Lambda processes telemetry events in real time, detecting anomalies such as engine malfunctions, unsafe flight patterns, or sensor failures. Alerts are immediately triggered to operational teams for proactive intervention. Lambda’s serverless architecture allows automatic scaling to manage peak telemetry loads without manual intervention, ensuring low-latency performance.

Amazon S3 stores raw and processed telemetry data for historical trend analysis, regulatory compliance, and predictive model training. Lifecycle policies enable cost-efficient archival of older data to Glacier while maintaining accessibility for longitudinal studies, safety audits, and operational research.

Amazon SageMaker trains predictive models using historical telemetry to forecast maintenance needs, optimize flight safety protocols, and enhance operational efficiency. Real-time inference applies models immediately to active flight data streams, enabling proactive maintenance and risk mitigation.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time anomaly detection, operational alerts, and predictive modeling. Redshift supports historical analysis but cannot deliver immediate operational insights.

Option C (RDS + QuickSight) cannot ingest millions of telemetry events per second. QuickSight dashboards provide delayed insights, and scaling RDS for high-frequency aircraft telemetry is operationally complex and costly.

Option A is the only architecture that integrates real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for global aircraft telemetry monitoring and predictive maintenance.

Question 190:

A global smart city project wants to implement a real-time environmental monitoring system. The system must ingest millions of IoT sensor events per second, detect anomalies such as air quality spikes, water contamination, or noise pollution instantly, trigger alerts to city authorities, store historical sensor data for analysis, and support predictive modeling for urban planning and environmental interventions. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time environmental monitoring requires ingestion of high-frequency IoT sensor events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A satisfies all these requirements comprehensively.

Amazon Kinesis Data Streams ingests millions of sensor events per second from air quality monitors, water sensors, noise meters, and weather stations. Its durability and multi-consumer support allow real-time analysis for anomaly detection, operational alerts, and predictive modeling. Horizontal scalability ensures the system can handle surges during extreme environmental events or city-wide monitoring expansions.

AWS Lambda processes events instantly. Lambda functions detect anomalies such as sudden spikes in pollution levels or water contamination. Alerts can be triggered immediately to city authorities for intervention. Lambda’s serverless architecture allows automatic scaling during periods of high sensor activity without manual infrastructure management.

Amazon S3 stores raw and processed sensor data for historical trend analysis, regulatory compliance, and predictive model training. Lifecycle policies optimize storage costs by moving older data to Glacier while maintaining accessibility for long-term urban planning and research.

Amazon SageMaker trains predictive models using historical environmental data to forecast pollution events, optimize resource allocation, and plan interventions. Real-time inference applies models to active sensor streams, enabling proactive measures and evidence-based decision-making for urban management.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time anomaly detection or operational alerts. Redshift supports historical analysis but cannot provide immediate actionable insights.

Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight dashboards provide delayed insights, and scaling RDS for global IoT sensor telemetry is operationally complex and costly.

Question 191:

A global retail chain wants to implement a real-time inventory monitoring and dynamic pricing system. The system must ingest millions of inventory and sales events per second, detect anomalies such as stockouts or sudden demand spikes instantly, trigger alerts to store managers, store historical inventory and sales data for trend analysis, and support predictive modeling for demand forecasting and dynamic pricing optimization. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time inventory monitoring and dynamic pricing require ingestion of extremely high-frequency events from thousands of stores, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A satisfies all these requirements.

Amazon Kinesis Data Streams ingests millions of inventory and sales events per second, including point-of-sale transactions, stock updates, and pricing changes. Its durability and support for multiple consumers enable real-time processing for anomaly detection, operational monitoring, and predictive modeling. Horizontal scalability ensures the system handles surges during promotional campaigns, holiday seasons, or flash sales.

AWS Lambda processes events in real time. Lambda functions detect anomalies such as stockouts, unusual demand spikes, or errors in pricing updates. Alerts can be immediately sent to store managers or central operations teams for corrective action. Lambda’s serverless architecture automatically scales to accommodate peak event volumes without requiring manual intervention, ensuring consistent low-latency monitoring.

Amazon S3 stores raw and processed inventory and sales data, supporting historical trend analysis, compliance, and predictive model training. Lifecycle policies allow cost-efficient archival of older datasets to Glacier while maintaining accessibility for demand analysis, dynamic pricing strategy evaluation, and long-term planning.

Amazon SageMaker allows predictive models to be trained using historical sales and inventory data to forecast demand, optimize pricing, and improve stock management. Real-time inference applies these models to live data streams, enabling dynamic pricing adjustments and proactive inventory replenishment.

Option B (S3 + Glue + Redshift) is batch-oriented. ETL jobs and Redshift analytics introduce latency incompatible with real-time anomaly detection, alerts, or predictive dynamic pricing. While Redshift supports historical analytics, it cannot deliver immediate operational insights for proactive management.

Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight dashboards provide delayed insights, and scaling RDS globally for high-frequency retail telemetry is complex and costly.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR’s batch-processing latency prevents real-time detection, alerts, and predictive modeling. Additional orchestration increases operational complexity and operational risk.

Option A is the only architecture that integrates real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for a global retail chain’s inventory monitoring and dynamic pricing system.

Question 192:

A global transportation company wants to implement a real-time vehicle fleet telemetry and predictive maintenance system. The system must ingest millions of telemetry events per second, detect anomalies such as engine failures or unsafe driving instantly, trigger alerts to fleet management teams, store historical telemetry for analysis, and support predictive modeling for route optimization and vehicle maintenance scheduling. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time vehicle fleet monitoring requires ingestion of extremely high-frequency telemetry events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A meets all these requirements.

Amazon Kinesis Data Streams ingests millions of telemetry events per second from vehicles, including GPS location, engine metrics, speed, and environmental readings. Its durability, multi-consumer support, and horizontal scalability enable concurrent real-time processing for anomaly detection, operational monitoring, and predictive modeling, ensuring the system can handle surges during peak operations or adverse weather events.

AWS Lambda processes telemetry events instantly, detecting anomalies such as engine failures, unsafe driving behaviors, or deviations from expected routes. Alerts are immediately sent to fleet management teams, enabling proactive intervention. Lambda’s serverless architecture scales automatically to handle peak telemetry loads without manual infrastructure management.

Amazon S3 stores raw and processed telemetry data for historical analysis, regulatory compliance, and predictive model training. Lifecycle policies optimize costs by moving older datasets to Glacier while maintaining accessibility for trend analysis, maintenance planning, and operational improvement.

Amazon SageMaker allows predictive models to be trained using historical vehicle telemetry to forecast maintenance needs, optimize routes, and improve operational efficiency. Real-time inference applies models immediately to active telemetry streams, enabling proactive interventions and reducing downtime or accident risks.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time anomaly detection, operational alerts, or predictive fleet optimization. Redshift supports historical analysis but cannot deliver immediate operational insights.

Option A provides a fully integrated, low-latency, scalable architecture capable of ingestion, real-time processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for vehicle fleet telemetry and predictive maintenance.

Question 193:

A global financial services company wants to implement a real-time transaction monitoring and fraud detection system. The system must ingest millions of financial events per second, detect fraudulent or anomalous transactions instantly, trigger alerts to compliance teams, store historical transaction data for analysis, and support predictive modeling for risk scoring and fraud prevention. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time transaction monitoring and fraud detection require ingestion of extremely high-frequency financial events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A satisfies all these requirements.

Amazon Kinesis Data Streams ingests millions of financial events per second, including payments, transfers, and trading activities. Its durability and multi-consumer support allow real-time processing for fraud detection, operational monitoring, and predictive modeling. Horizontal scalability ensures the system can handle spikes during trading hours, high-volume transactions, or market fluctuations.

AWS Lambda processes events in real time, detecting anomalies such as unusual transaction patterns, high-value transfers, or potential fraud attempts. Alerts are triggered immediately to compliance and operational teams, enabling proactive intervention. Lambda’s serverless architecture scales automatically, ensuring low-latency monitoring even during transaction surges.

Amazon S3 stores raw and processed transaction data for historical analysis, compliance reporting, and predictive model training. Lifecycle policies optimize storage costs by moving older datasets to Glacier while maintaining accessibility for trend analysis, auditing, and risk evaluation.

Amazon SageMaker allows predictive models to be trained using historical transaction data to forecast risk, detect fraud patterns, and optimize compliance monitoring. Real-time inference applies models immediately to active transactions, enabling dynamic risk scoring and early fraud prevention.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time fraud detection. Redshift supports historical analytics but cannot provide immediate operational insights.

Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight dashboards provide delayed insights, and scaling RDS globally for high-frequency financial telemetry is complex and costly.

Option A is the only architecture that integrates real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for financial transaction monitoring and fraud detection.

Question 194:

A global logistics company wants to implement a real-time warehouse monitoring and predictive replenishment system. The system must ingest millions of sensor and inventory events per second, detect anomalies such as stock discrepancies or equipment failures instantly, trigger alerts to warehouse operations teams, store historical data for analysis, and support predictive modeling for inventory management and warehouse optimization. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time warehouse monitoring requires ingestion of extremely high-frequency sensor and inventory events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A fulfills all these requirements.

Amazon Kinesis Data Streams ingests millions of events per second, including inventory updates, sensor readings, and equipment status. Its durability and multi-consumer support enable concurrent real-time processing for anomaly detection, operational monitoring, and predictive modeling. Horizontal scalability ensures the system can manage surges during peak warehouse operations or logistics spikes.

AWS Lambda processes incoming events instantly. Lambda functions detect anomalies such as stock discrepancies, equipment malfunctions, or process deviations. Alerts can be immediately triggered to warehouse operations teams for corrective action. Lambda’s serverless architecture scales automatically to accommodate peaks without manual intervention, ensuring low-latency operational monitoring.

Amazon S3 stores raw and processed warehouse data for historical analysis, compliance, and predictive model training. Lifecycle policies optimize costs by moving older datasets to Glacier while maintaining accessibility for trend analysis, equipment maintenance planning, and operational optimization.

Amazon SageMaker trains predictive models using historical warehouse data to forecast inventory needs, optimize warehouse operations, and schedule preventive maintenance. Real-time inference applies models immediately to active warehouse streams, enabling proactive interventions, resource optimization, and reduced operational costs.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time anomaly detection, alerts, or predictive warehouse optimization. Redshift supports historical analysis but cannot provide immediate operational insights.

Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight dashboards provide delayed insights, and scaling RDS globally for high-frequency warehouse telemetry is operationally complex and costly.

Option A integrates real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for warehouse monitoring and predictive replenishment.

Question 195:

A global energy company wants to implement a real-time power grid monitoring and predictive maintenance system. The system must ingest millions of telemetry events per second, detect anomalies such as voltage fluctuations or equipment failures instantly, trigger alerts to operational teams, store historical telemetry for analysis, and support predictive modeling for grid optimization and preventive maintenance. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon SageMaker

Explanation:

Real-time power grid monitoring requires ingestion of extremely high-frequency telemetry events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A satisfies all these requirements.

Amazon Kinesis Data Streams ingests millions of telemetry events per second, including voltage levels, transformer status, and power flow data. Its durability and multi-consumer support allow real-time processing for anomaly detection, operational monitoring, and predictive modeling. Horizontal scalability ensures the system can handle surges in grid activity or during peak energy demand.

AWS Lambda processes telemetry events in real time. Lambda functions detect anomalies such as voltage fluctuations, transformer failures, or abnormal power flows. Alerts are immediately sent to operational teams for proactive intervention. Lambda’s serverless architecture scales automatically to manage peak telemetry loads without manual infrastructure management, ensuring continuous low-latency monitoring.

Amazon S3 stores raw and processed telemetry data for historical analysis, compliance, and predictive model training. Lifecycle policies optimize costs by archiving older datasets to Glacier while maintaining accessibility for long-term energy management analysis and regulatory reporting.

Amazon SageMaker allows predictive models to be trained on historical grid telemetry to forecast equipment failures, optimize power flow, and schedule preventive maintenance. Real-time inference applies models immediately to active telemetry streams, enabling proactive interventions, improved grid reliability, and operational efficiency.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time anomaly detection, operational alerts, or predictive grid optimization. Redshift supports historical analysis but cannot provide immediate actionable insights.

Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 13 Q181-195

Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 13 Q181-195

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