Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 14 Q196-210 - Certbolt

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Question 196:

A multinational retail company wants to implement a real-time customer behavior tracking and recommendation system across all its online platforms. The system must ingest millions of events per second, detect abnormal patterns instantly, trigger operational alerts, store historical clickstream and purchase data, and support predictive modeling for personalized product 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 customer behavior tracking and recommendation requires the ingestion of extremely high-frequency events from web, mobile, and in-app platforms. This includes clicks, page views, product interactions, cart updates, and purchases. Amazon Kinesis Data Streams provides the scalable infrastructure necessary to ingest millions of events per second, offering durability, multiple consumers, and horizontal scaling to handle spikes in traffic, such as during sales events or holidays.

AWS Lambda processes these events in real time, detecting anomalies such as unusual spikes in product views or suspicious account activity. Operational alerts can be triggered immediately, allowing customer support and security teams to intervene proactively. Lambda’s serverless nature automatically scales to meet demand without manual infrastructure management, ensuring low-latency processing and operational resilience.

Amazon S3 stores both raw and processed data, providing a durable, cost-effective repository for historical analysis. Lifecycle policies allow older datasets to be moved to Glacier for long-term storage while remaining accessible for model retraining or trend analysis. Historical data stored in S3 serves as the foundation for predictive modeling, supporting advanced analytics and machine learning.

Amazon SageMaker trains predictive models using historical and real-time data to generate personalized recommendations for customers. Real-time inference allows the system to deliver relevant product suggestions as users interact with the platform, improving engagement and conversion rates.

Option B (S3 + Glue + Redshift) is batch-oriented, which introduces latency incompatible with real-time detection and recommendation. Redshift supports analytical queries and historical analysis but cannot deliver the immediate insights required for operational alerting or dynamic personalization.

Option C (RDS + QuickSight) is not suitable for high-volume event ingestion. RDS cannot scale to millions of events per second, and QuickSight provides delayed visualization rather than real-time operational feedback or predictive inference.

Option D (DynamoDB + EMR) supports storage and batch processing, but EMR’s batch-oriented processing introduces latency that prevents real-time anomaly detection, alerts, and recommendations. Additionally, the operational complexity of orchestrating EMR clusters adds unnecessary risk.

Option A provides a fully integrated, scalable solution for real-time ingestion, processing, anomaly detection, operational alerts, durable storage, and predictive analytics, making it ideal for a global retail company’s customer behavior tracking and recommendation system.

Question 197:

A global media streaming company wants to implement a real-time content recommendation and personalization engine. The system must ingest millions of streaming events per second, detect unusual viewing patterns instantly, trigger alerts to content teams, store historical viewing data for trend analysis, and support predictive modeling for personalized 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 content personalization requires ingestion of extremely high-frequency streaming events from millions of concurrent viewers, including play, pause, rewind, skip, and rating interactions. Amazon Kinesis Data Streams provides a fully managed, scalable platform to ingest millions of events per second while ensuring data durability and support for multiple concurrent consumers, enabling parallel processing for analytics and recommendation models.

AWS Lambda processes events in real time to detect anomalies such as sudden spikes in content access, potential streaming issues, or suspicious account behavior. Operational alerts can be triggered immediately to content management and technical teams to maintain the quality of service. Lambda’s serverless design automatically scales to handle traffic spikes, such as during popular show releases, ensuring consistent, low-latency processing.

Amazon S3 stores raw and processed streaming events, creating a durable, cost-effective repository for historical analysis. This data is essential for trend analysis, compliance, and training predictive models. Lifecycle policies allow older datasets to be archived to Glacier while maintaining accessibility for research, content performance evaluation, and recommender system training.

Amazon SageMaker trains predictive models using historical viewing patterns to forecast user preferences and generate personalized recommendations. Real-time inference allows the platform to serve tailored content immediately, enhancing user engagement and satisfaction.

Option B (S3 + Glue + Redshift) is batch-oriented, unsuitable for real-time anomaly detection or dynamic recommendation. While Redshift is excellent for historical analytics, it cannot provide the low-latency insights required for operational alerting or real-time personalization.

Option C (RDS + QuickSight) is limited in scale. RDS cannot handle millions of events per second, and QuickSight dashboards provide delayed insights rather than real-time operational intelligence.

Option D (DynamoDB + EMR) provides storage and batch processing, but EMR latency prevents real-time detection, alerts, and personalized recommendations. Additional orchestration complexity also increases operational risk.

Option A is the only architecture capable of integrating real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for a global media streaming company’s content recommendation system.

Question 198:

A global IoT company wants to implement a real-time smart home device monitoring system. The system must ingest millions of device telemetry events per second, detect anomalies such as device failures or unusual usage instantly, trigger alerts to support teams, store historical telemetry for analysis, and support predictive modeling for device maintenance and energy 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 smart home device monitoring requires ingestion of extremely high-frequency telemetry events from millions of devices worldwide, including temperature, power consumption, motion, and status updates. Amazon Kinesis Data Streams provides a scalable ingestion platform capable of processing millions of events per second with durability and multi-consumer support. This ensures real-time processing for anomaly detection, operational monitoring, and predictive analytics.

AWS Lambda processes telemetry events in real time, detecting anomalies such as device failures, abnormal power consumption, or unusual usage patterns. Alerts are triggered immediately to support teams for timely intervention. Lambda’s serverless architecture scales automatically to meet peak telemetry loads, ensuring continuous low-latency monitoring without manual management.

Amazon S3 stores both raw and processed telemetry data for historical analysis, regulatory compliance, and predictive model training. Lifecycle policies optimize storage costs by moving older datasets to Glacier while retaining accessibility for trend analysis, device performance evaluation, and machine learning model retraining.

Amazon SageMaker trains predictive models using historical device telemetry to forecast maintenance needs, optimize energy consumption, and detect potential failures before they occur. Real-time inference applies these models immediately to active telemetry streams, enabling proactive interventions and improved device reliability.

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 deliver immediate actionable insights.

Option C (RDS + QuickSight) cannot ingest millions of telemetry events per second. QuickSight dashboards provide delayed insights rather than real-time operational intelligence, and scaling RDS for high-frequency global telemetry is operationally complex and costly.

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

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

Question 199:

A global logistics and supply chain company wants to implement a real-time shipment tracking and predictive delivery system. The system must ingest millions of telemetry and location events per second, detect anomalies such as route deviations instantly, trigger alerts to operational teams, store historical shipment data for analysis, and support predictive modeling for delivery 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 shipment tracking requires ingestion of extremely high-frequency telemetry events from GPS devices, IoT sensors, and logistics systems. Amazon Kinesis Data Streams provides a fully managed, scalable platform capable of ingesting millions of events per second, offering durability, multiple consumers, and horizontal scalability. This supports real-time anomaly detection, operational monitoring, and predictive analytics for shipment tracking and delivery optimization.

AWS Lambda processes telemetry events in real time, detecting anomalies such as route deviations, delays, or unexpected stops. Alerts are immediately triggered to operations teams, enabling proactive intervention and corrective action. Lambda’s serverless architecture automatically scales to meet peak event loads, ensuring low-latency monitoring across the global fleet.

Amazon S3 stores raw and processed shipment telemetry for historical analysis, compliance, and predictive model training. Lifecycle policies optimize storage costs by moving older datasets to Glacier while maintaining accessibility for trend analysis, performance evaluation, and model retraining.

Amazon SageMaker trains predictive models using historical shipment data to forecast delays, optimize routing, and enhance delivery performance. Real-time inference applies models to active telemetry streams, enabling dynamic routing adjustments and proactive delivery management.

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

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

Option A provides a fully integrated architecture for real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for global shipment tracking and predictive delivery.

Question 200:

A global healthcare provider wants to implement a real-time patient monitoring and predictive intervention system. The system must ingest millions of patient telemetry 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 care 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 patient monitoring requires ingestion of extremely high-frequency events from wearable devices, medical sensors, and patient monitoring systems. Amazon Kinesis Data Streams provides a scalable platform capable of ingesting millions of events per second while ensuring durability, multiple consumers, and horizontal scalability. This enables concurrent real-time processing for anomaly detection, operational monitoring, and predictive analytics.

AWS Lambda processes events in real time, detecting anomalies such as abnormal heart rates, blood pressure spikes, or other critical health indicators. Alerts are immediately sent to medical teams for timely intervention. Lambda’s serverless architecture allows automatic scaling to accommodate peaks in telemetry events without manual infrastructure management, ensuring consistent low-latency monitoring and response.

Amazon S3 stores both 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 research, long-term trend analysis, and model retraining.

Amazon SageMaker trains predictive models using historical patient telemetry to forecast health risks, optimize interventions, and provide preventive care recommendations. Real-time inference applies these models immediately to active patient streams, enabling proactive care and improved outcomes.

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

Option C (RDS + QuickSight) cannot ingest millions of telemetry events per second. QuickSight dashboards provide delayed insights, and scaling RDS for high-frequency medical 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 global patient monitoring and predictive healthcare intervention.

Question 201:

A global e-commerce company wants to implement a real-time customer support chat analytics system. The system must ingest millions of chat events per second from multiple platforms, detect sentiment anomalies instantly, trigger alerts to support supervisors, store historical chat and sentiment data, and support predictive modeling for proactive customer engagement. 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 customer support chat analytics system requires ingestion of high-frequency events from multiple platforms, such as web chat, mobile apps, social media messaging, and email integrations. These events include chat messages, timestamps, agent responses, and user engagement metrics. Amazon Kinesis Data Streams is capable of ingesting millions of events per second, providing durability and multi-consumer support to allow multiple processing pipelines for anomaly detection, sentiment analysis, and operational monitoring. Its horizontal scalability ensures it can handle spikes in chat volume, such as during promotional events or outages that drive increased customer inquiries.

AWS Lambda processes chat events in real time to detect anomalies in sentiment, unusual spikes in chat volume, or deviations in agent response patterns. Operational alerts are triggered immediately to supervisors or managers to enable proactive interventions. Lambda’s serverless nature provides automatic scaling without manual intervention, ensuring consistent low-latency processing.

Amazon S3 stores both raw and processed chat and sentiment data. Historical storage allows the company to analyze long-term trends in customer interactions, evaluate agent performance, and maintain compliance with regulatory requirements. Lifecycle policies can move older datasets to Glacier for cost optimization while maintaining accessibility for trend analysis and predictive model retraining.

Amazon SageMaker allows predictive models to be trained on historical chat and sentiment data to forecast potential issues, recommend proactive engagement strategies, and optimize resource allocation for support teams. Real-time inference enables the system to provide immediate recommendations or escalate issues dynamically, improving customer satisfaction and operational efficiency.

Option B (S3 + Glue + Redshift) is primarily batch-oriented, introducing latency that is incompatible with real-time anomaly detection, operational alerts, and predictive engagement. Redshift can support historical analytics, but cannot deliver immediate insights for live chat monitoring.

Option C (RDS + QuickSight) cannot scale to handle millions of chat events per second. QuickSight dashboards provide delayed insights and do not support real-time operational intervention. Scaling RDS to meet high-frequency event requirements is complex and costly.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR introduces latency, preventing real-time detection, alerts, and predictive modeling. Orchestration complexity also increases operational risk.

Option A integrates real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for global customer support chat analytics.

Question 202:

A global retail chain wants to implement a real-time checkout analytics and dynamic discounting system. The system must ingest millions of point-of-sale events per second, detect anomalies such as pricing errors instantly, trigger alerts to store managers, store historical checkout data, and support predictive modeling for dynamic discounts and inventory 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 checkout analytics and dynamic discounting require ingestion of extremely high-frequency point-of-sale events from thousands of store locations and online platforms. Amazon Kinesis Data Streams provides a fully managed, scalable solution for ingesting millions of events per second while ensuring durability and supporting multiple consumers for concurrent processing of real-time analytics, anomaly detection, and predictive modeling. Its horizontal scalability ensures the system can handle high-volume periods, such as sales events or holidays.

AWS Lambda processes events in real time, detecting anomalies like pricing errors, unusual transaction patterns, or potential fraud. Alerts are triggered immediately to store managers or operational teams, enabling fast corrective action. Lambda’s serverless architecture automatically scales to accommodate peak loads without manual intervention, maintaining low-latency monitoring and response.

Amazon S3 stores raw and processed checkout data for historical analysis, regulatory compliance, and predictive model training. Lifecycle policies allow older datasets to be archived to Glacier while remaining accessible for trend analysis, inventory management, and dynamic discounting model retraining.

Amazon SageMaker trains predictive models using historical checkout data to forecast demand, optimize pricing, and generate dynamic discounts. Real-time inference applies these models immediately to current transactions, enabling optimized pricing and stock management.

Option B (S3 + Glue + Redshift) is batch-oriented and cannot provide real-time detection, alerting, or dynamic discounting. Redshift supports historical analysis but lacks low-latency operational insight.

Option C (RDS + QuickSight) cannot scale to millions of transactions per second. QuickSight provides delayed insights rather than real-time intervention, and scaling RDS globally for high-frequency point-of-sale events is complex and costly.

Option D (DynamoDB + EMR) supports storage and batch processing, but EMR’s latency prevents real-time detection, alerts, and dynamic pricing. Orchestration complexity increases operational risk.

Option A integrates real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for a global retail checkout and dynamic discounting system.

Question 203:

A multinational airline wants to implement a real-time flight operations monitoring and predictive maintenance system. The system must ingest millions of telemetry and sensor events per second, detect anomalies such as mechanical failures instantly, trigger alerts to maintenance teams, store historical flight and sensor data, and support predictive modeling for flight optimization and 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 flight operations monitoring requires ingestion of extremely high-frequency telemetry events from aircraft systems, including engines, navigation, fuel, and environmental sensors. Amazon Kinesis Data Streams provides a scalable solution for ingesting millions of events per second with durability and support for multiple concurrent consumers, enabling real-time processing, anomaly detection, and predictive modeling for operational and maintenance insights.

AWS Lambda processes telemetry events in real time, detecting anomalies such as mechanical failures, deviations in flight parameters, or environmental risks. Alerts are triggered immediately to maintenance and operations teams to enable proactive interventions. Lambda’s serverless architecture scales automatically to handle peak telemetry, maintaining low-latency monitoring and intervention.

Amazon S3 stores raw and processed telemetry and operational data for historical analysis, regulatory compliance, and predictive model training. Lifecycle policies optimize storage costs by moving older datasets to Glacier while ensuring accessibility for trend analysis, fleet optimization, and predictive model retraining.

Amazon SageMaker allows predictive models to be trained using historical flight telemetry to forecast maintenance needs, optimize flight operations, and improve fuel efficiency. Real-time inference applies models immediately to current telemetry streams, enabling proactive interventions and operational optimization.

Option B (S3 + Glue + Redshift) is batch-oriented and introduces latency incompatible with real-time detection, alerts, and predictive operations. Redshift supports historical analytics but cannot provide immediate actionable insights.

Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight dashboards provide delayed insights rather than real-time monitoring, and scaling RDS for global aircraft 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 maintenance. Additional orchestration increases operational complexity and risk.

Option A provides integrated real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for airline flight operations monitoring and predictive maintenance.

Question 204:

A global banking institution wants to implement a real-time transaction analytics and risk scoring 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, and support predictive modeling for risk assessment 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 analytics requires the ingestion of high-frequency financial events, including payments, transfers, account updates, and trading activities. Amazon Kinesis Data Streams ingests millions of events per second with durability and multi-consumer support, enabling real-time processing, anomaly detection, and predictive modeling for compliance and risk assessment.

AWS Lambda processes transactions instantly, detecting fraudulent patterns, suspicious activity, or anomalies. Alerts are immediately triggered to compliance teams to enable timely investigation. Lambda’s serverless design ensures automatic scaling, maintaining consistent low-latency detection.

Amazon S3 stores raw and processed transaction data for historical analysis, regulatory compliance, and predictive model training. Lifecycle policies optimize storage costs by archiving older datasets while retaining access for long-term risk assessment and model retraining.

Amazon SageMaker trains predictive models on historical transaction data to forecast potential fraud, assess risk, and improve compliance monitoring. Real-time inference applies models to active transaction streams, enabling dynamic risk scoring and fraud prevention.

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

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

Option A provides real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for global banking transaction monitoring and risk scoring.

Question 205:

A global manufacturing company wants to implement a real-time production line monitoring and predictive quality control system. The system must ingest millions of sensor events per second, detect anomalies such as defects or equipment malfunctions instantly, trigger alerts to production supervisors, store historical sensor data, and support predictive modeling for quality 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 production line monitoring requires ingestion of extremely high-frequency sensor events, including temperature, pressure, vibration, and operational metrics from manufacturing equipment. Amazon Kinesis Data Streams ingests millions of events per second with durability and multi-consumer support, enabling real-time processing for anomaly detection, operational monitoring, and predictive modeling. Horizontal scalability ensures the handling of peak production loads.

AWS Lambda processes sensor events instantly, detecting anomalies such as defects, equipment malfunctions, or unusual operational deviations. Alerts are triggered immediately to production supervisors for intervention. Lambda’s serverless architecture scales automatically to accommodate peaks, ensuring low-latency monitoring and intervention.

Amazon S3 stores raw and processed sensor data for historical analysis, compliance, and predictive model training. Lifecycle policies optimize storage costs by archiving older datasets while maintaining accessibility for trend analysis, quality improvement, and model retraining.

Amazon SageMaker trains predictive models using historical sensor data to forecast defects, optimize quality control processes, and improve production efficiency. Real-time inference applies models immediately to active sensor streams, enabling proactive quality control and operational optimization.

Option B (S3 + Glue + Redshift) is batch-oriented, unsuitable for real-time detection, alerts, or predictive quality control. Redshift supports historical analytics but cannot deliver immediate, actionable insights.

Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight dashboards provide delayed insights rather than real-time operational intelligence, and scaling RDS globally for high-frequency manufacturing telemetry is complex and costly.

Option A integrates real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for global manufacturing production line monitoring and predictive quality control.

Question 206:

A global logistics company wants to implement a real-time fleet tracking and predictive delivery optimization system. The system must ingest millions of GPS and sensor events per second from thousands of vehicles, detect anomalies such as route deviations instantly, trigger alerts to operations teams, store historical fleet data, and support predictive modeling for optimal routing and delivery 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 fleet tracking requires ingestion of high-frequency telemetry and GPS data from a large number of vehicles. Amazon Kinesis Data Streams offers a scalable ingestion platform capable of handling millions of events per second. Its multi-consumer support allows parallel real-time processing for anomaly detection, operational monitoring, and predictive modeling. Kinesis ensures durability, low-latency processing, and horizontal scalability, making it ideal for global fleet operations with fluctuating traffic patterns.

AWS Lambda processes telemetry events immediately, detecting anomalies such as route deviations, unexpected stops, or potential accidents. Alerts are sent to operations teams instantly, enabling timely interventions. Lambda’s serverless architecture automatically scales to handle peak traffic, ensuring low-latency response and consistent operational monitoring without manual infrastructure management.

Amazon S3 stores both raw and processed fleet telemetry for historical analysis, regulatory compliance, trend identification, and predictive model training. Lifecycle policies allow older datasets to be archived to Glacier for cost optimization while keeping them accessible for retraining and fleet performance evaluation.

Amazon SageMaker trains predictive models using historical fleet and delivery data to forecast delays, optimize routing, and improve delivery schedules. Real-time inference applies these models to active telemetry streams, allowing dynamic routing adjustments and proactive operational management.

Option B (S3 + Glue + Redshift) is batch-oriented and introduces latency incompatible with real-time anomaly detection or predictive routing. Redshift supports historical analytics but cannot deliver actionable insights for live fleet management.

Option C (RDS + QuickSight) cannot scale to millions of telemetry events per second. QuickSight dashboards provide delayed insights rather than real-time operational monitoring. Scaling RDS to meet global fleet telemetry demands is complex and costly.

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

Option A is the only architecture capable of supporting real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics for a global fleet tracking and delivery optimization system.

Question 207:

A global telecommunications provider wants to implement a real-time network traffic monitoring and predictive fault detection system. The system must ingest millions of network events per second from multiple regions, detect anomalies such as outages instantly, trigger alerts to operations teams, store historical network traffic data, and support predictive modeling for proactive 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 network traffic monitoring requires ingestion of extremely high-frequency network events, including packet flows, latency measurements, error logs, and device telemetry. Amazon Kinesis Data Streams provides a scalable and durable platform capable of ingesting millions of events per second, enabling real-time processing for anomaly detection, operational monitoring, and predictive modeling. Its multi-consumer support allows simultaneous pipelines for operational dashboards, automated alerting, and predictive maintenance analytics.

AWS Lambda processes network events immediately, detecting anomalies such as outages, performance degradation, or unusual traffic patterns. Alerts are triggered instantly to operations teams for rapid intervention. Lambda’s serverless nature allows automatic scaling, ensuring low-latency monitoring and operational consistency across global regions without manual intervention.

Amazon S3 stores raw and processed network traffic for historical analysis, regulatory compliance, trend identification, and predictive model training. Lifecycle policies move older datasets to Glacier while retaining accessibility for model retraining, capacity planning, and network performance evaluation.

Amazon SageMaker trains predictive models using historical network data to forecast potential outages, optimize capacity planning, and proactively identify faults. Real-time inference applies these models to active traffic streams, enabling preemptive intervention and improved network reliability.

Option B (S3 + Glue + Redshift) is batch-oriented, unsuitable for real-time anomaly detection, operational alerting, and predictive maintenance. Redshift supports historical analysis but cannot provide immediate actionable insights.

Option C (RDS + QuickSight) cannot handle millions of network events per second. QuickSight dashboards provide delayed insights rather than real-time operational intelligence. Scaling RDS to meet high-frequency telemetry demands is complex and costly.

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

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

Question 208:

A global e-learning platform wants to implement a real-time student engagement monitoring and personalized content recommendation system. The system must ingest millions of activity events per second, detect abnormal engagement patterns instantly, trigger alerts to educators, store historical learning data, and support predictive modeling for personalized 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 student engagement monitoring requires ingestion of high-frequency events such as quiz submissions, video interactions, discussion forum activity, and resource access. Amazon Kinesis Data Streams ingests millions of events per second, providing durability and multi-consumer support for real-time analytics, anomaly detection, and predictive modeling. Horizontal scalability ensures the system can handle peak traffic during exams, live sessions, or special learning events.

AWS Lambda processes student activity events immediately, detecting abnormal engagement such as inactivity, sudden performance drops, or potential disengagement. Alerts are triggered to educators or academic support teams, enabling timely interventions. Lambda’s serverless architecture automatically scales to accommodate peaks, ensuring low-latency monitoring and intervention.

Amazon S3 stores raw and processed activity data for historical analysis, regulatory compliance, trend identification, and model training. Lifecycle policies archive older datasets to Glacier while maintaining accessibility for predictive modeling, engagement trend analysis, and personalized learning optimization.

Amazon SageMaker trains predictive models using historical student data to forecast learning outcomes, identify students at risk, and recommend personalized content. Real-time inference applies these models to active learning streams, enabling personalized recommendations and improved engagement.

Option B (S3 + Glue + Redshift) is batch-oriented, unsuitable for real-time anomaly detection, alerting, or personalized recommendations. Redshift supports historical analysis but cannot provide immediate actionable insights.

Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight provides delayed dashboards rather than real-time operational monitoring, and scaling RDS for high-frequency learning events is complex and costly.

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

Option A integrates real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for student engagement monitoring and personalized content recommendation.

Question 209:

A global financial services firm wants to implement a real-time stock market monitoring and predictive trading alert system. The system must ingest millions of trading events per second, detect anomalous market movements instantly, trigger alerts to traders, store historical trading data, and support predictive modeling for trading strategies. 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 stock market monitoring requires ingestion of high-frequency trading events, including bid/ask updates, transaction volumes, order book changes, and price fluctuations. Amazon Kinesis Data Streams can ingest millions of events per second with durability and multi-consumer support, enabling parallel real-time processing pipelines for anomaly detection, trading alerts, and predictive modeling. Its horizontal scalability allows the system to handle spikes during market opening, closing, or major financial news events.

AWS Lambda processes trading events immediately, detecting abnormal price movements, unusual volumes, or potential market manipulation. Alerts are triggered instantly to traders and analysts, enabling timely decisions. Lambda’s serverless architecture ensures automatic scaling to maintain low-latency processing across global trading sessions.

Amazon S3 stores raw and processed trading data for historical analysis, regulatory compliance, trend evaluation, and model training. Lifecycle policies move older datasets to Glacier while retaining accessibility for retrospective analysis and model retraining.

Amazon SageMaker trains predictive models using historical trading data to forecast market trends, optimize trading strategies, and generate predictive alerts. Real-time inference applies models to live market streams, enabling actionable trading insights.

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

Option C (RDS + QuickSight) cannot ingest millions of trading events per second. QuickSight dashboards provide delayed insights rather than real-time monitoring, and scaling RDS to meet high-frequency trading demands is complex and costly.

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

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

Question 210:

A global energy company wants to implement a real-time smart grid monitoring and predictive energy optimization system. The system must ingest millions of sensor events per second from smart meters and grid infrastructure, detect anomalies such as outages or unusual consumption instantly, trigger alerts to operations teams, store historical energy data, and support predictive modeling for demand forecasting and grid 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:

Smart grid monitoring requires ingestion of high-frequency sensor events from smart meters, transformers, substations, and IoT devices across the energy network. Amazon Kinesis Data Streams ingests millions of events per second with durability and multi-consumer support, enabling real-time processing for anomaly detection, operational monitoring, and predictive modeling. Horizontal scalability ensures the system can handle sudden spikes in telemetry during peak usage periods or emergencies.

AWS Lambda processes sensor events instantly, detecting anomalies such as outages, unusual consumption patterns, or equipment faults. Alerts are triggered immediately to operations teams for rapid intervention. Lambda’s serverless architecture automatically scales to accommodate peak loads, ensuring low-latency monitoring and response without manual infrastructure management.

Amazon S3 stores raw and processed sensor data for historical analysis, regulatory compliance, trend evaluation, and predictive model training. Lifecycle policies archive older datasets to Glacier while maintaining accessibility for energy demand forecasting, grid optimization, and model retraining.

Amazon SageMaker trains predictive models using historical sensor and consumption data to forecast energy demand, optimize grid operations, and enhance efficiency. Real-time inference applies models to active sensor streams, enabling proactive interventions, load balancing, and predictive maintenance.

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

Option C (RDS + QuickSight) cannot handle millions of telemetry events per second. QuickSight provides delayed dashboards rather than real-time operational monitoring, and scaling RDS globally for high-frequency smart grid events is complex and costly.

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

Option A integrates real-time ingestion, processing, anomaly detection, operational alerting, durable storage, and predictive analytics, making it ideal for global smart grid monitoring and energy optimization.

Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 14 Q196-210

Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 14 Q196-210

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