Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 12 Q166-180 - Certbolt

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Question 166:

A global online education platform wants to implement a real-time student activity analytics system. The system must ingest millions of events per second from user interactions across multiple courses, detect unusual activity patterns, trigger alerts for instructors, store historical activity data for analysis, and support predictive modeling for personalized course 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:

For a real-time student activity analytics system, low-latency ingestion, immediate anomaly detection, operational alerting, durable storage, and predictive modeling are critical. Option A provides a fully integrated solution.

Amazon Kinesis Data Streams ingests millions of student activity events per second, including course access, quiz submissions, and video interactions. Its durability and fault tolerance ensure reliable data capture, while multiple consumers can process the same stream concurrently for real-time analytics. This is essential for detecting unusual patterns, such as potential academic dishonesty or sudden drops in engagement, in real time.

AWS Lambda processes incoming events immediately. Lambda functions can compute engagement metrics, detect anomalies such as unusual activity bursts or inactivity, and trigger alerts for instructors or platform administrators. Its serverless nature ensures automatic scaling during peak usage periods, such as exam weeks, without infrastructure management overhead.

Amazon S3 stores raw and processed activity data for historical analysis, research, compliance, and model training. Lifecycle policies allow older data to move to Glacier, optimizing storage costs while maintaining accessibility for ongoing analytics.

Amazon SageMaker trains predictive models using historical activity data to personalize course recommendations and improve student outcomes. Real-time inference allows the latest models to influence course recommendations immediately as students interact with content, ensuring a highly personalized learning experience.

Option B (S3 + Glue + Redshift) is batch-oriented. ETL jobs and Redshift analytics introduce latency incompatible with real-time detection, alerts, or recommendations. While Redshift provides historical trend analysis, it cannot support immediate operational insights.

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

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

Option A provides a low-latency, scalable architecture for ingestion, real-time processing, anomaly detection, alerts, durable storage, and predictive analytics, making it ideal for a global online education analytics system.

Question 167:

A global online gaming company wants to implement a real-time leaderboard and player analytics system. The system must ingest millions of game events per second, calculate scores and rankings in real time, trigger alerts for unusual player behavior, store historical game data for analysis, and support predictive modeling for matchmaking and retention 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 leaderboard and player analytics require ingestion of millions of events per second, immediate score computation, anomaly detection, operational alerting, durable storage, and predictive analytics. Option A fulfills all requirements.

Amazon Kinesis Data Streams ingests high-frequency game events, ensuring durability and fault tolerance. Multiple consumers can process streams simultaneously, allowing real-time calculation of scores, rankings, and detection of unusual player behavior. This low-latency processing is critical to maintain fairness and engagement.

AWS Lambda processes these events in real time. Lambda functions compute scores, update leaderboards, detect anomalies such as cheating or irregular patterns, and trigger alerts to operational or moderation teams. Its serverless design ensures automatic scaling during global peak gaming hours without manual intervention.

Amazon S3 stores raw and processed game event data for historical analysis, compliance, and model training. Lifecycle policies allow cost-effective long-term storage while maintaining accessibility for research and analytics.

Amazon SageMaker trains predictive models using historical player behavior data to optimize matchmaking, predict churn, and enhance retention strategies. Real-time inference applies models instantly to active games, improving player experience and engagement.

Option B (S3 + Glue + Redshift) is batch-oriented. ETL jobs and Redshift analytics introduce delays, making real-time score computation, alerts, or matchmaking predictions impossible. Historical analysis is supported, but operational responsiveness is limited.

Option C (RDS + QuickSight) cannot handle millions of events per second. QuickSight provides delayed insights, and scaling RDS for global real-time ingestion is costly and operationally complex.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR latency prevents real-time processing, alerts, and leaderboard updates. Additional orchestration adds complexity and risk.

Option A provides a fully integrated, low-latency, scalable architecture capable of ingestion, real-time processing, anomaly detection, alerting, durable storage, and predictive modeling for global gaming analytics.

Question 168:

A global logistics and delivery company wants to implement a real-time package tracking and predictive delivery system. The system must ingest millions of GPS and scan events per second, detect anomalies such as misroutes or delays instantly, trigger alerts to operational teams, store historical delivery data for analysis, and support predictive modeling for optimized routing and delivery times. 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 package tracking and predictive delivery require ingestion of high-frequency GPS and scan events, immediate anomaly detection, operational alerts, durable storage, and predictive analytics. Option A fulfills all these requirements.

Amazon Kinesis Data Streams ingests millions of GPS and scan events per second globally. Multiple consumers can process the same stream concurrently, enabling real-time tracking, misroute detection, and delivery anomaly identification. This low-latency ingestion ensures that operational teams are immediately aware of delays or exceptions.

AWS Lambda processes incoming events in real time. Lambda functions detect misroutes, delays, or deviations from optimal paths and trigger alerts to operational teams. Lambda’s serverless nature ensures automatic scaling during peak delivery periods, such as holidays, without infrastructure management.

Amazon S3 stores raw and processed delivery data for historical trend analysis, compliance, and model training. Lifecycle policies allow cost-efficient long-term storage in Glacier while maintaining accessibility for trend and performance analytics.

Amazon SageMaker trains predictive models using historical delivery data to optimize routing, predict delays, and improve operational efficiency. Real-time inference allows the system to proactively adjust delivery routes, predict arrival times, and optimize fleet utilization.

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 operational insights.

Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight provides delayed dashboards, and RDS scaling for global high-frequency ingestion is complex and costly.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR latency prevents real-time tracking, alerts, and predictive route optimization. Additional orchestration adds complexity.

Option A delivers a low-latency, scalable architecture capable of ingestion, real-time processing, anomaly detection, alerting, durable storage, and predictive modeling, making it ideal for global logistics and delivery optimization.

Question 169:

A global healthcare monitoring company wants to implement a real-time patient vitals and anomaly detection system. The system must ingest millions of patient telemetry events per second, detect critical health anomalies instantly, trigger alerts to medical teams, store historical telemetry data for trend analysis, and support predictive modeling for early disease detection and intervention. 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 vitals monitoring requires low-latency ingestion of high-frequency telemetry, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A fulfills all requirements.

Amazon Kinesis Data Streams ingests millions of patient telemetry events per second from medical devices, wearables, and monitoring systems. Multiple consumers can process the same stream concurrently, enabling real-time detection of abnormal vitals such as arrhythmias, hypoxia, or sudden blood pressure changes.

AWS Lambda processes telemetry events in real time. Lambda functions detect critical anomalies, trigger alerts to medical staff, and ensure that patients receive timely interventions. Serverless scaling ensures performance consistency across peaks in patient monitoring demand.

Amazon S3 stores raw and processed telemetry for historical analysis, compliance, and predictive modeling. Lifecycle policies move older data to Glacier for cost optimization while retaining accessibility for longitudinal health studies and model training.

Amazon SageMaker trains predictive models using historical telemetry to detect early signs of disease, predict patient deterioration, and optimize interventions. Real-time inference allows models to apply to live data, enabling proactive healthcare delivery.

Option B (S3 + Glue + Redshift) is batch-oriented and introduces latency incompatible with critical anomaly detection or operational alerts. 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 RDS scaling for global high-frequency ingestion is complex and costly.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR latency prevents real-time anomaly detection, alerting, and predictive healthcare interventions. Additional orchestration adds complexity.

Option A provides a fully integrated, low-latency, scalable architecture capable of ingestion, real-time processing, anomaly detection, alerts, durable storage, and predictive analytics, making it ideal for global patient monitoring systems.

Question 170:

A global autonomous drone delivery company wants to implement a real-time drone telemetry and route monitoring system. The system must ingest millions of telemetry events per second, detect anomalies such as deviations or collisions instantly, trigger alerts to operational teams, store historical telemetry for trend analysis, and support predictive modeling for route optimization and safety. 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 drone delivery requires extremely low-latency ingestion of high-frequency telemetry events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A meets all requirements.

Amazon Kinesis Data Streams ingests millions of drone telemetry events per second, providing durability, fault tolerance, and multiple consumer support. Real-time processing allows immediate detection of deviations, potential collisions, or safety hazards.

AWS Lambda processes incoming events instantly. Lambda functions detect anomalies, trigger alerts to operational teams, and ensure drones operate safely and efficiently. Serverless scaling ensures low-latency performance regardless of drone fleet size or peak operation periods.

Amazon S3 stores raw and processed telemetry data for historical analysis, compliance, trend analysis, and model training. Lifecycle policies move older data to Glacier for cost optimization while maintaining accessibility.

Amazon SageMaker trains predictive models using historical telemetry to optimize routes, ensure safety, and improve operational efficiency. Real-time inference applies models immediately to live telemetry, allowing proactive route adjustments and hazard mitigation.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time anomaly detection, operational alerts, or safety interventions. Redshift supports historical analysis but not real-time decision-making.

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

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

Question 171:

A global e-commerce company wants to implement a real-time customer behavior analytics system to track website clicks, product views, and purchase events. The system must ingest millions of events per second, detect unusual patterns such as potential fraud, trigger alerts to fraud detection teams, store historical customer interaction data for analysis, and support predictive modeling for personalized recommendations and dynamic pricing. 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 behavior analytics system requires extremely low-latency ingestion, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A is the only architecture that comprehensively meets these requirements.

Amazon Kinesis Data Streams ingests millions of events per second from website clicks, product views, and purchase events. Its scalability and durability ensure reliable data ingestion, even during peak sales periods such as promotions or holiday seasons. The ability to have multiple consumers process the same stream allows real-time fraud detection, customer behavior analysis, and operational alerts concurrently.

AWS Lambda processes events as they arrive. Lambda functions can detect unusual patterns in real time, such as suspicious purchase behaviors indicative of potential fraud. Alerts can be triggered to fraud detection teams instantly, allowing them to respond proactively. Lambda’s serverless design automatically scales to handle surges in traffic without requiring manual infrastructure intervention.

Amazon S3 provides durable storage for raw and processed data, supporting historical analytics, regulatory compliance, and model training. Lifecycle policies enable cost-effective archival of older data into Glacier while keeping it accessible for trend analysis and predictive modeling.

Amazon SageMaker allows predictive models to be trained on historical customer behavior data. These models can personalize recommendations, optimize dynamic pricing, and predict customer churn. Real-time inference applies these models immediately to active customer sessions, enhancing engagement, conversion rates, and fraud prevention strategies.

Option B (S3 + Glue + Redshift) relies on batch ETL and analytics, introducing latency incompatible with real-time fraud detection, anomaly alerts, or personalized recommendations. While Redshift can support historical analysis, it cannot provide 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 ingestion is operationally complex and expensive.

Option D (DynamoDB + EMR) allows storage and batch analytics, but EMR’s batch-oriented processing introduces latency that prevents real-time anomaly detection, alerts, or predictive modeling. Additional orchestration adds complexity and operational risk.

Option A is 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 e-commerce behavior analytics.

Question 172:

A global financial services company wants to implement a real-time market data analytics system. The system must ingest millions of stock trades and quote events per second, detect anomalies such as potential market manipulation, trigger alerts to trading compliance teams, store historical market data for analysis, and support predictive modeling for algorithmic 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 market data analytics requires ingestion of extremely high-frequency trade and quote events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A fulfills these requirements comprehensively.

Amazon Kinesis Data Streams ingests millions of events per second from global financial exchanges, ensuring durability, fault tolerance, and multiple consumer support for parallel processing. Real-time processing allows immediate detection of unusual trading patterns, liquidity issues, or potential market manipulation, which is critical for compliance and risk management.

AWS Lambda processes these events instantly. Lambda functions detect anomalies, trigger alerts to trading compliance teams, and provide insights for risk mitigation. Its serverless architecture scales automatically to handle surges during high market volatility without infrastructure management overhead.

Amazon S3 stores raw and processed market data for historical trend analysis, regulatory compliance, and model training. Lifecycle policies move older data to Glacier to optimize storage costs while maintaining accessibility for long-term analysis, audits, and compliance.

Amazon SageMaker trains predictive models using historical market data to optimize algorithmic trading strategies, predict market trends, and support portfolio management. Real-time inference allows immediate application of these models to active trading streams, ensuring faster decision-making and competitive advantage.

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

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

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

Question 173:

A global ride-sharing company wants to implement a real-time driver and rider analytics system. The system must ingest millions of events per second, detect unusual patterns such as fraud or unsafe driving instantly, trigger alerts to operational teams, store historical ride data for analysis, and support predictive modeling for surge pricing and driver assignment. 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 ride-sharing analytics requires ingestion of high-frequency ride events, immediate anomaly detection, operational alerts, durable storage, and predictive modeling. Option A fulfills all requirements.

Amazon Kinesis Data Streams ingests millions of events per second, including driver locations, ride requests, and payment events. Its durability and ability to support multiple consumers enable real-time computation of metrics such as driver availability, ride matching, and anomaly detection. Detecting fraud or unsafe driving patterns in real time is critical for customer safety and operational efficiency.

AWS Lambda processes incoming events in real time. Lambda functions analyze driver and rider behavior, detect anomalies, and trigger alerts to operational teams. Its serverless nature allows automatic scaling to handle peak traffic during rush hours, promotional periods, or city-wide events.

Amazon S3 stores raw and processed ride data for historical trend analysis, compliance, and model training. Lifecycle policies enable cost-effective archival of older data into Glacier while maintaining accessibility for trend analysis, business intelligence, and compliance reporting.

Amazon SageMaker trains predictive models using historical ride and driver behavior data to optimize surge pricing, route assignments, and driver retention strategies. Real-time inference applies models instantly to active rides, enhancing operational efficiency and customer satisfaction.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency that prevents immediate detection of unsafe driving or fraud, and cannot provide real-time operational alerts or predictive ride assignment. 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 for high-frequency, global ride-sharing operations is operationally complex and expensive.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR’s latency prevents real-time anomaly detection, alerts, and predictive modeling for ride assignment or surge pricing. Additional orchestration adds complexity.

Question 174:

A global IoT manufacturing company wants to implement a real-time equipment monitoring and predictive maintenance system. The system must ingest millions of sensor telemetry events per second, detect anomalies such as potential failures instantly, trigger alerts to maintenance teams, store historical telemetry for trend analysis, and support predictive modeling for 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 equipment monitoring requires low-latency ingestion, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A meets all these requirements comprehensively.

Amazon Kinesis Data Streams ingests millions of sensor telemetry events per second from global manufacturing plants. Multiple consumers can process the stream concurrently, enabling real-time detection of equipment anomalies, performance degradation, or potential failures. This low-latency processing is critical for minimizing downtime and reducing maintenance costs.

AWS Lambda processes telemetry events as they arrive. Lambda functions detect anomalies, trigger alerts to maintenance teams, and support proactive intervention. Its serverless architecture ensures automatic scaling, handling surges in telemetry data without manual infrastructure management.

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

Amazon SageMaker trains predictive models using historical telemetry to forecast equipment failures, optimize maintenance schedules, and reduce operational downtime. Real-time inference applies models to current telemetry, enabling proactive maintenance and efficiency improvements.

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

Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight dashboards provide delayed insights, and scaling RDS globally 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 adds complexity and operational risk.

Question 175:

A global autonomous shipping company wants to implement a real-time fleet telemetry and route optimization system. The system must ingest millions of telemetry events per second, detect anomalies such as route deviations or mechanical issues instantly, trigger alerts to operational teams, store historical telemetry for trend analysis, and support predictive modeling for route efficiency and fleet 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:

Autonomous shipping telemetry requires extremely low-latency ingestion of high-frequency telemetry events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A meets all requirements.

Amazon Kinesis Data Streams ingests millions of telemetry events per second, providing durability, fault tolerance, and multiple consumer support. Real-time processing allows detection of deviations, mechanical anomalies, or potential collisions. This is critical for fleet safety, operational efficiency, and regulatory compliance.

AWS Lambda processes events in real time. Lambda functions detect anomalies, trigger alerts to operational teams, and ensure safe and efficient fleet operations. Serverless scaling guarantees low-latency performance across global shipping operations without manual intervention.

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

Amazon SageMaker trains predictive models using historical fleet telemetry to optimize routes, schedule maintenance, and enhance fleet efficiency. Real-time inference applies models immediately to active operations, enabling proactive adjustments and improved safety.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time anomaly detection, alerts, or predictive fleet 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 RDS scaling globally 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 for route optimization or maintenance. Additional orchestration introduces complexity.

Question 176:

A global streaming video service wants to implement a real-time content recommendation and analytics system. The system must ingest millions of events per second, track user interactions like play, pause, and skip, detect unusual activity patterns, trigger alerts to operational teams for anomalies, store historical user interaction data for analysis, and support predictive modeling for personalized content recommendations and trending content insights. 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 content recommendation system for a global streaming video service requires the ingestion of extremely high-frequency user interaction events, immediate anomaly detection, operational alerts, durable storage, and predictive analytics. Option A is the only architecture that meets all these requirements comprehensively.

Amazon Kinesis Data Streams provides low-latency ingestion of millions of events per second from user interactions such as play, pause, skip, and viewing duration. Kinesis ensures durability and fault tolerance, allowing multiple consumers to process the stream simultaneously for real-time analytics, anomaly detection, and recommendation computations. Its ability to scale horizontally ensures it can handle spikes in viewership during popular show releases or global events.

AWS Lambda processes incoming events in real time. Lambda functions analyze the data stream to detect unusual patterns, such as potential fraudulent activity, bots, or unexpected spikes in content access. Alerts can be triggered immediately to operational teams for timely intervention. Lambda’s serverless nature ensures automatic scaling to handle peak event loads without infrastructure management, allowing the system to respond dynamically to real-time traffic fluctuations.

Amazon S3 provides durable storage for raw and processed data, supporting historical analysis, regulatory compliance, and model training. Lifecycle management allows older datasets to be moved to Amazon Glacier for cost-efficient storage while retaining accessibility for trend analysis, long-term research, and content performance evaluation.

Amazon SageMaker allows predictive models to be trained on historical user interactions to generate personalized recommendations, predict content popularity, and optimize the streaming experience. Real-time inference applies these models to current user sessions, enhancing engagement and providing actionable insights for content strategy.

Option B (S3 + Glue + Redshift) is batch-oriented. ETL jobs and Redshift analytics introduce delays, making real-time anomaly detection, operational alerts, or personalized recommendations infeasible. Redshift can support historical analytics, but cannot provide immediate actionable insights for active users.

Option D (DynamoDB + EMR) allows storage and batch analytics, but EMR’s latency prevents real-time anomaly detection, alerts, and content recommendation computations. 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 a global streaming content recommendation system.

Question 177:

A global smart city initiative wants to implement a real-time traffic monitoring and predictive congestion management system. The system must ingest millions of sensor and vehicle telemetry events per second, detect anomalies such as accidents or unusual congestion instantly, trigger alerts to traffic management teams, store historical traffic data for analysis, and support predictive modeling for dynamic traffic signal adjustments and route 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 smart city traffic monitoring system requires ingestion of high-frequency telemetry from sensors, vehicles, and cameras, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A fulfills all these requirements.

Amazon Kinesis Data Streams ingests millions of traffic and vehicle telemetry events per second. Its durability and ability to support multiple consumers allow concurrent real-time analysis of traffic patterns, anomaly detection for accidents, and congestion identification. Horizontal scalability ensures the system can handle fluctuations during rush hours, city events, or emergencies.

AWS Lambda processes events instantly. Lambda functions analyze traffic patterns to detect anomalies such as collisions, abnormal congestion, or traffic signal malfunctions. Alerts can be triggered immediately to traffic management teams for proactive intervention. The serverless architecture allows automatic scaling during peak event loads without manual infrastructure management, ensuring continuous low-latency performance.

Amazon S3 stores raw and processed traffic data for historical trend analysis, urban planning, and compliance. Lifecycle policies allow cost-efficient archival of older data to Glacier while maintaining accessibility for research, predictive modeling, and policy decision-making.

Amazon SageMaker trains predictive models using historical traffic patterns to optimize traffic signal timing, predict congestion hotspots, and guide autonomous vehicle routing. Real-time inference applies models instantly to active traffic conditions, enabling dynamic adjustments that improve traffic flow, reduce accidents, and enhance urban mobility.

Option B (S3 + Glue + Redshift) is batch-oriented. ETL jobs and Redshift analytics introduce latency incompatible with real-time anomaly detection, operational alerts, or predictive traffic management. Redshift can provide historical insights, but cannot deliver immediate, actionable decisions.

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

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

Question 178:

A global renewable energy company wants to implement a real-time wind turbine monitoring and predictive maintenance system. The system must ingest millions of sensor telemetry events per second, detect anomalies such as potential turbine failures instantly, trigger alerts to maintenance teams, store historical telemetry for trend analysis, and support predictive modeling for efficiency 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 wind turbine monitoring requires ingestion of high-frequency sensor telemetry, immediate anomaly detection, operational alerts, durable storage, and predictive analytics. Option A fulfills all these requirements.

Amazon Kinesis Data Streams ingests millions of telemetry events per second from global wind turbines, ensuring durability, fault tolerance, and multiple consumer support for real-time analysis. Real-time processing allows immediate detection of turbine anomalies, operational inefficiencies, or potential failures. Horizontal scalability ensures the system can handle global sensor data surges due to environmental conditions or operational events.

AWS Lambda processes telemetry events in real time. Lambda functions detect anomalies such as blade stress, vibration deviations, or temperature spikes and trigger alerts to maintenance teams. Its serverless architecture allows automatic scaling to handle periods of high turbine activity without manual intervention, ensuring low-latency anomaly detection.

Amazon S3 provides durable storage for raw and processed telemetry, supporting historical trend analysis, regulatory compliance, and predictive model training. Lifecycle management moves older data to Glacier to optimize costs while retaining accessibility for analysis and research.

Amazon SageMaker trains predictive models using historical telemetry data to forecast turbine performance, predict failures, and optimize maintenance schedules. Real-time inference applies these models immediately, allowing proactive interventions that reduce downtime and operational costs while maximizing energy output.

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

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’s latency prevents real-time detection, alerts, or predictive modeling for turbine optimization. Additional orchestration adds complexity and operational risk.

Question 179:

A global autonomous trucking company wants to implement a real-time fleet telemetry 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 trend analysis, and support predictive modeling for route efficiency and vehicle 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 fleet telemetry monitoring requires ingestion of high-frequency telemetry 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 telemetry events per second from the autonomous trucks, including GPS location, engine metrics, and sensor readings. Its durability, fault tolerance, and multi-consumer support allow concurrent real-time analysis for anomaly detection, operational alerts, and predictive analytics. Horizontal scalability ensures the system handles surges in telemetry events during peak fleet activity.

AWS Lambda processes incoming events instantly. Lambda functions detect mechanical anomalies, unsafe driving behavior, and route deviations. Alerts are triggered immediately to operational teams for proactive intervention. The serverless architecture scales automatically to handle peaks without manual infrastructure management.

Amazon S3 stores raw and processed telemetry for historical trend analysis, compliance, and model training. Lifecycle policies optimize costs by archiving older data to Glacier while maintaining accessibility for research, analysis, and fleet optimization.

Amazon SageMaker trains predictive models on historical telemetry to optimize routes, schedule preventive maintenance, and improve fleet efficiency. Real-time inference allows application of models to active telemetry, enabling proactive maintenance, improved safety, and operational optimization.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time anomaly detection, operational alerts, or predictive fleet maintenance. 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 high-frequency global 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 system complexity and operational risk.

Question 180:

A global autonomous rail network company wants to implement a real-time train telemetry, safety monitoring, and predictive maintenance system. The system must ingest millions of telemetry events per second, detect anomalies such as derailments or mechanical failures instantly, trigger alerts to operational teams, store historical telemetry for trend analysis, and support predictive modeling for route optimization and safety assurance. 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 rail telemetry monitoring requires extremely low-latency ingestion of high-frequency telemetry 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 telemetry events per second from trains, including speed, engine health, and track condition metrics. Its durability, fault tolerance, and multi-consumer support enable real-time detection of anomalies such as derailments, mechanical failures, or unsafe speed variations. Horizontal scalability ensures the system handles surges in data due to operational peaks, extreme weather, or maintenance events.

AWS Lambda processes telemetry events instantly. Lambda functions detect anomalies, trigger alerts to operational teams, and support proactive interventions. Serverless architecture ensures automatic scaling during periods of high telemetry activity without manual infrastructure management.

Amazon S3 stores raw and processed telemetry data for historical trend analysis, regulatory compliance, and predictive model training. Lifecycle management allows cost-efficient archival to Glacier while maintaining accessibility for research, long-term safety studies, and operational analysis.

Amazon SageMaker trains predictive models using historical telemetry data to forecast mechanical failures, optimize route schedules, and enhance safety. Real-time inference applies models immediately, enabling proactive maintenance and route optimization.

Option C (RDS + QuickSight) cannot ingest millions of telemetry events per second. QuickSight dashboards provide delayed insights, and scaling RDS for global telemetry ingestion is operationally 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 adds complexity and operational risk.

Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 12 Q166-180

Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 12 Q166-180

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