Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 11 Q151-165 - Certbolt

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Question 151:

A global streaming service needs a real-time recommendation engine for millions of users. The system must ingest user interactions, such as viewing, rating, and clicks, in real-time, provide immediate personalized recommendations, detect anomalies in user behaviour, store historical interactions for model training, and support predictive analytics for content optimisation. 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 global streaming service requires a real-time recommendation engine that can handle millions of events per second, provide immediate personalised recommendations, detect anomalies, and store historical data for predictive analytics. Option A offers a fully integrated solution. Amazon Kinesis Data Streams allows the ingestion of massive amounts of real-time user interaction data. Its scalability and durability support high-volume ingestion while enabling multiple consumers to process the same stream concurrently. This allows real-time analytics pipelines, anomaly detection, and immediate recommendation computation.

AWS Lambda processes the incoming events in real time. Lambda functions analyse user behaviour, detect anomalies, and generate recommendations instantly, ensuring the platform adapts to changing user preferences and patterns. The serverless nature of Lambda provides automatic scaling to handle peak traffic without manual infrastructure management, ensuring consistent low-latency performance.

Amazon S3 stores raw and processed user interaction data durably. This storage supports model training, trend analysis, and regulatory compliance. Lifecycle policies allow archival of older data to Glacier or Glacier Deep Archive, optimising storage costs while retaining valuable historical insights.

Amazon SageMaker can utilise the historical interaction data to train machine learning models, allowing the platform to continuously refine its recommendation engine. SageMaker also supports real-time inference for immediate personalisation, ensuring that recommendations remain accurate and relevant based on the latest user activity.

Option B (S3 + Glue + Redshift) is batch-oriented. Glue ETL jobs and Redshift analytics introduce latency, making them unsuitable for real-time personalised recommendations or anomaly detection. They are appropriate for historical analytics but cannot support immediate operational decisions.

Option C (RDS + QuickSight) is limited in scale and latency. RDS cannot ingest millions of events per second, and QuickSight provides delayed visual insights. Scaling RDS for global real-time processing introduces complexity and high cost.

Option D (DynamoDB + EMR) supports scalable storage and batch analytics. However, EMR’s batch processing introduces latency incompatible with real-time recommendations. Additional orchestration is required to implement low-latency anomaly detection and immediate recommendation delivery, increasing complexity and potential operational delays.

Option A delivers a fully integrated architecture that supports low-latency ingestion, real-time processing, anomaly detection, durable storage, and predictive analytics, making it ideal for a global streaming platform recommendation engine.

Question 152:

A global airline wants to implement a real-time flight tracking and operational monitoring system. The system must ingest flight telemetry data from aircraft sensors, detect anomalies in altitude, speed, or engine performance, trigger alerts to operations and safety teams, store historical flight data for trend analysis and regulatory compliance, and support predictive maintenance analytics. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon CloudWatch
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 CloudWatch

Explanation:

A real-time flight tracking system requires low-latency ingestion of high-frequency telemetry data, immediate anomaly detection, operational alerting, durable storage, and predictive maintenance support. Option A meets all requirements. Kinesis Data Streams provides scalable and durable ingestion of telemetry data from aircraft, handling millions of events per second across multiple geographic regions. Multiple consumers can process the same stream for real-time analytics, alert generation, and predictive modelling.

AWS Lambda processes incoming telemetry events in real time, detecting anomalies in altitude, speed, or engine performance. Lambda triggers alerts to operations and safety teams, enabling rapid interventions to prevent incidents or operational delays. Lambda’s serverless architecture automatically scales with traffic from aircraft, ensuring consistent low-latency performance without manual infrastructure management.

Amazon S3 stores raw and processed flight data durably for regulatory compliance, trend analysis, and predictive maintenance modelling. Lifecycle policies transition older data to Glacier or Glacier Deep Archive for cost-effective long-term storage while maintaining durability and compliance.

Amazon CloudWatch monitors system health, ingestion latency, and processing metrics. CloudWatch alarms alert operations teams to potential issues in the data pipeline or detected anomalies, providing proactive monitoring and operational reliability. Dashboards provide real-time visibility into aircraft performance and operational metrics.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with immediate anomaly detection and operational alerts. While Redshift supports historical analytics, it cannot meet real-time flight monitoring requirements.

Option C (RDS + QuickSight) is unsuitable for high-frequency telemetry ingestion. RDS cannot handle millions of events per second, and QuickSight dashboards are delayed, unsuitable for operational monitoring and real-time safety alerts.

Option D (DynamoDB + EMR) supports storage and batch analytics. EMR’s batch processing is incompatible with real-time monitoring and alerting. Additional orchestration would be required, increasing complexity and potential latency in operational decisions.

Option A provides an integrated architecture capable of ingestion, real-time processing, anomaly detection, alerting, durable storage, and predictive analytics, making it ideal for global flight tracking and operational monitoring.

Question 153:

A global healthcare provider wants to implement a real-time patient monitoring system for ICU devices. The system must ingest millions of events per second from heart monitors, ventilators, and other medical devices, detect anomalies instantly, trigger alerts to medical staff, store historical patient data for research and compliance, and support predictive analytics for patient outcome modelling. Which AWS architecture is most suitable?

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

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

Explanation:

ICU patient monitoring requires extremely low-latency ingestion, real-time anomaly detection, operational alerts, durable storage, and predictive analytics. Option A provides a fully integrated solution. Kinesis Data Streams ingests millions of events per second from various medical devices, ensuring durability, fault tolerance, and scalability. Multiple consumers can process the same stream concurrently for real-time analytics and anomaly detection.

Kinesis Data Analytics applies continuous real-time computations on the data stream, detecting deviations from expected patient vitals, sudden critical events, or abnormal device readings. Lambda triggers immediate alerts to medical staff for intervention, ensuring timely responses to critical events. Lambda’s serverless nature ensures the system scales automatically to accommodate peak ICU activity without manual management.

Amazon S3 stores raw and processed telemetry data for historical analysis, regulatory compliance, and research. Lifecycle policies move older data to Glacier for cost-effective long-term retention while maintaining accessibility for studies and model training.

Option B (S3 + Glue + Athena) is batch-oriented. Glue ETL jobs and Athena queries introduce latency, preventing real-time detection and operational alerts. While suitable for research analytics, this architecture cannot support immediate patient care interventions.

Option C (RDS + QuickSight) cannot handle millions of device events per second. QuickSight dashboards provide delayed insights, unsuitable for ICU monitoring. Scaling RDS for global high-frequency device telemetry introduces complexity and cost.

Option D (DynamoDB + EMR) provides storage and batch analytics, but EMR introduces latency incompatible with real-time monitoring and alerting. Additional orchestration is required, increasing operational complexity and reducing reliability.

Option A delivers a fully integrated, low-latency architecture capable of ingestion, real-time anomaly detection, alerting, durable storage, and predictive analytics, making it ideal for ICU patient monitoring systems.

Question 154:

A global financial institution wants to implement a real-time fraud detection system for credit card transactions. The system must ingest millions of transactions per second, detect fraudulent activity instantly, trigger alerts to fraud investigation teams, store historical transactions for regulatory compliance, and support machine learning-based predictive fraud modelling. 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 credit card fraud detection requires ingestion of high-frequency transaction data, low-latency processing, anomaly detection, operational alerts, durable storage, and predictive modelling. Option A is optimal. Kinesis Data Streams ingests millions of transactions per second with durability, fault tolerance, and scalability. Multiple consumers process the same stream for real-time detection and alerting.

AWS Lambda processes transactions in real time, applying fraud detection algorithms to identify suspicious activity. Lambda triggers alerts to investigation teams, enabling rapid intervention. Serverless scaling ensures performance during transaction surges without manual infrastructure management.

Amazon S3 stores raw and processed transactions for historical analysis, compliance, and training predictive fraud models. Lifecycle policies transition older data to Glacier for cost-effective long-term retention.

Amazon SageMaker uses historical data to train machine learning models for predictive fraud detection. Real-time inference allows proactive identification of fraudulent activity, reducing financial loss and risk exposure.

Option B (S3 + Glue + Redshift) is batch-oriented, unsuitable for low-latency fraud detection. Batch processing introduces delays incompatible with real-time operational requirements.

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

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

Option A delivers an integrated architecture capable of low-latency ingestion, real-time fraud detection, operational alerting, durable storage, and predictive modelling, making it ideal for credit card fraud monitoring.

Question 155:

A global e-commerce platform wants to implement a real-time recommendation and pricing optimisation system. The system must ingest millions of user interactions and purchase events per second, provide immediate personalised recommendations, detect anomalous pricing or buying patterns, store historical data for trend analysis, and support predictive analytics for 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:

Real-time recommendation and pricing optimisation requires ingestion of high-frequency events, immediate analysis, anomaly detection, operational alerts, durable storage, and predictive analytics. Option A provides an end-to-end solution. Kinesis Data Streams handles millions of interactions per second from user clicks, views, and purchases, ensuring durability and scalability. Multiple consumers can process the same stream for real-time analytics, anomaly detection, and pricing optimisation.

AWS Lambda processes events in real time, generating personalised recommendations, identifying unusual buying patterns, and adjusting pricing dynamically. Lambda’s serverless scaling ensures consistent low-latency performance during peak traffic, such as holiday sales.

Amazon S3 stores raw and processed event data for historical analysis, compliance, and training predictive models. Lifecycle policies enable cost-effective archival of older data while maintaining durability and accessibility.

Amazon SageMaker trains machine learning models using historical data to optimise recommendations and dynamic pricing strategies. Real-time inference allows immediate application of models to current events, ensuring personalised and competitive pricing while detecting anomalies.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time recommendations and pricing adjustments.

Option C (RDS + QuickSight) cannot handle millions of events per second, and QuickSight dashboards are delayed, unsuitable for operational decision-making.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR latency prevents real-time recommendations and pricing adjustments. Additional orchestration is required, increasing operational complexity.

Option A delivers a fully integrated architecture capable of ingestion, real-time processing, anomaly detection, alerting, durable storage, and predictive analytics, making it ideal for global e-commerce recommendation and pricing optimisation.

Question 156:

A global ride-hailing platform needs a real-time surge pricing system. The system must ingest millions of ride requests per second, analyse supply-demand patterns in real time, adjust pricing dynamically, trigger alerts for operational teams in case of anomalies, store historical ride and pricing data for trend analysis, and support predictive modelling for future demand. 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:

Dynamic surge pricing in ride-hailing requires ingestion of extremely high-frequency ride requests, real-time computation of supply-demand imbalances, immediate pricing adjustments, operational alerting, historical data storage, and predictive analytics support. Option A delivers a complete solution.

Amazon Kinesis Data Streams enables the ingestion of millions of ride requests per second globally. It provides durability, fault tolerance, and allows multiple consumers to simultaneously process streams, supporting real-time computation of dynamic pricing, detection of unusual demand spikes, and feeding downstream analytics pipelines.

AWS Lambda processes these incoming events instantly. Lambda functions compute real-time supply-demand ratios, adjust pricing dynamically, and trigger alerts for operational anomalies, such as extreme surges or system discrepancies. Lambda’s serverless scaling ensures low-latency computation regardless of peak traffic events, such as during major holidays or city events.

Amazon S3 stores raw and processed ride and pricing data durably for trend analysis, regulatory compliance, and predictive modelling. Lifecycle policies allow older data to move to Glacier, optimising cost while retaining accessibility for analytics or audits.

Amazon SageMaker uses historical ride, demand, and pricing data to train predictive models for future demand patterns. Real-time inference enables proactive surge pricing strategies, ensuring maximum revenue optimisation while maintaining competitive pricing and fairness for riders.

Option B (S3 + Glue + Redshift) is batch-oriented. Scheduled ETL and analytics jobs introduce latency, making real-time dynamic pricing and anomaly detection impossible. While Redshift supports analytical queries on historical data, it cannot compute immediate pricing adjustments.

Option C (RDS + QuickSight) is unsuitable due to ingestion limits. RDS cannot handle millions of events per second, and QuickSight dashboards provide delayed insights. Scaling RDS globally for low-latency operations is operationally complex and costly.

Option D (DynamoDB + EMR) supports scalable storage and batch processing, but EMR introduces latency incompatible with real-time surge pricing. Additional orchestration for event processing, anomaly detection, and alerts adds complexity and potential delays.

Option A delivers a low-latency, scalable architecture capable of ingestion, processing, anomaly detection, alerting, durable storage, and predictive modelling, making it ideal for global ride-hailing surge pricing.

Question 157:

A global e-commerce company wants to implement a real-time product recommendation and personalisation engine. The system must ingest millions of user interactions per second, analyse behaviour to deliver immediate personalised recommendations, detect anomalies in buying patterns, store historical interactions for trend analysis, and support predictive modelling for product optimisation. 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 product recommendation requires ingestion of extremely high-frequency user interaction events, immediate personalisation computations, anomaly detection, operational alerts, historical data retention, and predictive analytics. Option A meets these requirements.

Amazon Kinesis Data Streams ingests millions of events per second, ensuring durability, fault tolerance, and the ability for multiple consumers to simultaneously process data for real-time analytics. This allows immediate computation of recommendations based on user behaviour while detecting anomalies such as sudden spikes in product interest or unusual transaction patterns.

AWS Lambda provides serverless real-time processing of incoming events. Lambda functions compute personalised recommendations dynamically, trigger alerts when unusual patterns are detected, and ensure that recommendations remain relevant and updated. The serverless nature allows automatic scaling during peak shopping times without manual intervention.

Amazon S3 stores both raw and processed user interaction data durably. This storage supports trend analysis, compliance, and predictive modelling. Lifecycle policies enable older data to transition to Glacier or Glacier Deep Archive for cost-effective long-term retention while retaining accessibility for analytics or machine learning model training.

Amazon SageMaker uses historical data to train and refine predictive models that inform recommendation strategies. Real-time inference allows immediate application of the latest models to new user interactions, ensuring recommendations are accurate, personalised, and timely.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency that prevents real-time personalisation or anomaly detection. Redshift supports historical analytics but cannot compute immediate recommendations at high frequency.

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

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR introduces latency incompatible with real-time recommendation generation. Additional orchestration for alerts and personalised recommendations increases operational complexity and delay.

Option A provides an integrated architecture capable of ingestion, real-time processing, anomaly detection, alerting, durable storage, and predictive modelling, making it ideal for global e-commerce personalisation and recommendation engines.

Question 158:

A global financial trading platform wants to implement a real-time risk monitoring system. The system must ingest millions of trade events per second, detect anomalies or potential risk exposures instantly, trigger alerts to compliance and risk teams, store historical trade data for audit and analysis, and support predictive analytics for risk mitigation. 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 financial risk monitoring requires ingestion of extremely high-frequency trade events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics for risk management. Option A addresses these requirements comprehensively.

Amazon Kinesis Data Streams ingests millions of trade events per second globally, ensuring durability and fault tolerance. Multiple consumers process the same stream concurrently, enabling immediate detection of trading anomalies, exposure to risk, or potential compliance breaches. This low-latency ingestion is critical for timely operational intervention in financial markets.

AWS Lambda processes events in real time, applying rules and algorithms to detect trading anomalies, unusual patterns, or risk exposure. Lambda triggers alerts to compliance and risk teams, enabling rapid intervention to mitigate financial and regulatory risk. Its serverless scaling ensures consistent performance during market surges or volatility, without manual infrastructure management.

Amazon S3 stores raw and processed trade data for historical analysis, regulatory audits, trend detection, and predictive modelling. Lifecycle policies transition older data to Glacier, optimising storage costs while retaining durability and accessibility for analytics or compliance requirements.

Amazon SageMaker uses historical trade data to train models for predictive risk analytics, allowing the platform to anticipate future exposures, model stress scenarios, and optimise mitigation strategies. Real-time inference ensures proactive risk monitoring and intervention.

Option B (S3 + Glue + Redshift) is batch-oriented. Glue ETL jobs and Redshift analytics introduce latency, making it unsuitable for immediate detection of trade anomalies or operational alerts. While Redshift is effective for historical trend analysis, it cannot provide real-time risk monitoring.

Option C (RDS + QuickSight) cannot ingest millions of trades 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 anomaly detection and operational alerts. Additional orchestration is required for predictive risk analysis, increasing complexity.

Option A delivers a fully integrated, low-latency, and scalable architecture capable of real-time ingestion, processing, anomaly detection, alerting, durable storage, and predictive analytics, making it ideal for financial trading risk monitoring.

Question 159:

A global logistics company wants to implement a real-time fleet tracking and route optimisation system. The system must ingest millions of vehicle GPS updates per second, detect route deviations or delays instantly, trigger alerts for fleet managers, store historical GPS data for trend analysis, and support predictive analytics for route optimisation. 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 and route optimisation require ingestion of high-frequency GPS updates, immediate processing for route deviation detection, operational alerting, durable storage, and predictive analytics for route planning. Option A provides a comprehensive solution.

Amazon Kinesis Data Streams ingests millions of GPS updates per second, ensuring durability, fault tolerance, and scalability. Multiple consumers can process streams simultaneously, enabling real-time detection of delays, route deviations, and operational alerts.

AWS Lambda processes GPS events in real time, identifying route deviations, traffic issues, or delays, and triggering alerts for fleet managers. Lambda scales automatically, ensuring consistent low-latency performance regardless of fleet size or peak traffic periods.

Amazon S3 stores raw and processed GPS data for historical trend analysis, compliance, and predictive modelling. Lifecycle policies transition older data to Glacier, optimising storage costs while maintaining access for analytics and route optimisation modelling.

Amazon SageMaker uses historical GPS and operational data to train predictive models for route optimisation, traffic prediction, and fleet management. Real-time inference supports immediate operational decisions, improving efficiency and reducing delays.

Option B (S3 + Glue + Redshift) is batch-oriented. ETL jobs and Redshift analytics introduce latency incompatible with real-time fleet monitoring and alerting. While Redshift supports historical trend analysis, it cannot provide low-latency operational insights.

Option C (RDS + QuickSight) is unsuitable for high-frequency GPS ingestion. QuickSight dashboards provide delayed insights, and RDS scaling for global ingestion is complex and costly.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR latency prevents real-time detection, alerting, and predictive routing. Additional orchestration increases complexity and delays operational decisions.

Option A delivers a fully integrated, low-latency, scalable architecture for real-time fleet tracking, anomaly detection, operational alerts, durable storage, and predictive analytics.

Question 160:

A global healthcare research institute wants to implement a real-time genomics data processing pipeline. The system must ingest millions of DNA sequencing reads per second, process and analyse sequences for mutations or patterns instantly, trigger alerts for significant findings, store historical sequencing data for research, and support machine learning-based predictive modelling. 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 genomics data processing requires ingestion of extremely high-frequency sequencing reads, immediate analysis for critical mutations or patterns, operational alerting, durable storage, and predictive modelling for research purposes. Option A addresses these requirements.

Amazon Kinesis Data Streams ingests millions of DNA sequencing reads per second. Its durability and fault tolerance ensure reliable data capture, while multiple consumers can process streams simultaneously for real-time analysis. This allows rapid detection of significant genetic mutations or patterns in patient or population datasets.

AWS Lambda provides serverless, low-latency processing of sequence reads. Lambda functions can analyse reads, identify anomalies or medically significant patterns, and trigger alerts for researchers or clinical teams. Its automatic scaling ensures that processing capacity adjusts dynamically based on sequencing volume.

Amazon S3 stores raw and processed genomic data durably for long-term research, compliance, and model training. Lifecycle policies enable older data to move to Glacier, minimising storage costs while retaining accessibility for ongoing studies and analytics.

Amazon SageMaker supports machine learning model training on historical genomic data. Predictive models can identify disease susceptibility, evolutionary patterns, or population-level insights. Real-time inference allows immediate application of models to new sequencing data, providing actionable insights and accelerating research outcomes.

Option B (S3 + Glue + Redshift) is batch-oriented. ETL jobs and Redshift analytics introduce delays incompatible with real-time analysis of sequencing data. Redshift is suitable for large-scale historical analytics but cannot provide immediate processing and alerts.

Option C (RDS + QuickSight) cannot handle millions of sequencing reads per second. QuickSight dashboards provide delayed insights, and RDS scaling for high-throughput genomic data is operationally complex and costly.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR introduces latency incompatible with real-time genomic analysis and alerts. Additional orchestration increases complexity and slows operational response.

Option A provides a fully integrated, low-latency, scalable architecture capable of ingestion, real-time processing, anomaly detection, alerting, durable storage, and predictive analytics, making it ideal for genomics research pipelines.

Question 161:

A multinational retail chain wants to implement a real-time inventory tracking system across thousands of stores globally. The system must ingest millions of inventory update events per second, detect anomalies such as sudden stock depletion, trigger alerts to store managers and supply chain teams, store historical inventory data for trend analysis, and support predictive modelling for restocking optimisation. 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 inventory tracking system for a multinational retail chain requires low-latency ingestion of high-frequency inventory update events, immediate anomaly detection, operational alerts, durable storage, and predictive analytics for restocking. Option A addresses all these requirements comprehensively.

Amazon Kinesis Data Streams can ingest millions of inventory update events per second from thousands of stores globally. Its scalability, durability, and ability to allow multiple consumers to process the same stream in parallel are critical for real-time analytics and anomaly detection. This ensures that the system can immediately identify stock depletion, supply chain bottlenecks, or unusual sales patterns without latency.

AWS Lambda functions process incoming events in real time. Lambda can detect anomalies, such as unexpected stock reductions or replenishment failures, and trigger alerts to store managers and supply chain teams. Its serverless nature allows automatic scaling based on incoming data volume, ensuring consistent performance without manual intervention, especially during peak seasons or promotional campaigns.

Amazon S3 stores raw and processed inventory data for historical analysis, compliance, and predictive modelling. Lifecycle policies move older data to Glacier or Glacier Deep Archive to optimise storage costs while maintaining durability and accessibility for historical analysis or trend reporting.

Amazon SageMaker uses historical inventory and sales data to train predictive models that inform restocking strategies and optimise inventory levels. Real-time inference enables immediate application of models to current inventory levels, ensuring that restocking decisions are proactive and data-driven, minimising stockouts and overstock scenarios.

Option B (S3 + Glue + Redshift) is batch-oriented. Scheduled ETL and analytics jobs introduce latency, making immediate anomaly detection, operational alerts, or proactive restocking impossible. Redshift can provide historical analysis, but cannot support real-time operational decisions.

Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight dashboards provide delayed insights, and scaling RDS globally is complex and costly. This option is unsuitable for real-time inventory tracking and alerting.

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR’s batch-oriented processing introduces latency incompatible with real-time anomaly detection, operational alerts, and predictive modelling. Additional orchestration increases complexity and risk of delayed responses.

Option A provides a fully integrated, low-latency, scalable architecture capable of ingestion, real-time processing, anomaly detection, alerting, durable storage, and predictive modelling, making it ideal for global retail inventory management.

Question 162:

A global sports analytics company wants to implement a real-time player performance monitoring system for multiple ongoing games worldwide. The system must ingest millions of player telemetry events per second, analyse performance metrics instantly, trigger alerts for coaches in case of anomalies, store historical performance data for trend analysis, and support predictive modelling for strategy optimisation. Which AWS architecture is most suitable?

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

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

Explanation:

Real-time player performance monitoring requires low-latency ingestion of high-frequency telemetry events, immediate performance computation, anomaly detection, operational alerts, durable storage, and predictive analytics for strategy optimisation. Option A meets all these requirements comprehensively.

Amazon Kinesis Data Streams ingests millions of telemetry events per second from multiple games worldwide. Its scalability, durability, and ability for multiple consumers to process the same stream concurrently are essential for real-time analytics and anomaly detection. The system can immediately compute player performance metrics, detect unexpected deviations, and identify patterns indicative of injury risk, fatigue, or strategic opportunities.

AWS Lambda functions process incoming telemetry events in real time. Lambda can compute performance metrics, trigger alerts for coaches when anomalies are detected, and ensure that real-time decision-making is informed by the latest data. Its serverless architecture ensures automatic scaling to accommodate peak traffic during major tournaments or high-profile games without requiring manual infrastructure adjustments.

Amazon S3 stores raw and processed telemetry data for historical analysis, compliance, and predictive modelling. Lifecycle policies allow older data to move to Glacier, optimising storage costs while maintaining access for research, trend analysis, and model training.

Amazon SageMaker trains predictive models using historical performance data to optimise strategy, player rotation, and game plans. Real-time inference applies these models instantly to ongoing games, enabling coaches and analysts to make data-driven decisions for maximum competitive advantage.

Option B (S3 + Glue + Redshift) is batch-oriented. Batch ETL and analytics jobs introduce latency incompatible with real-time performance monitoring and alerts. Redshift is suitable for historical trend analysis but cannot support immediate operational decisions.

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

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR’s batch processing introduces latency incompatible with real-time monitoring, anomaly detection, and predictive modelling. Additional orchestration is required, increasing complexity.

Option A provides a low-latency, scalable architecture capable of ingestion, real-time processing, anomaly detection, alerting, durable storage, and predictive modelling, making it ideal for global sports performance analytics.

Question 163:

A multinational telecom provider wants to implement a real-time network monitoring and anomaly detection system. The system must ingest millions of network telemetry events per second from multiple regions, detect outages or anomalies instantly, trigger alerts to network operations teams, store historical network data for trend analysis, and support predictive modelling for capacity planning. Which AWS architecture is most suitable?

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

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

Explanation:

Real-time network monitoring requires ingestion of extremely high-frequency telemetry events, immediate anomaly detection, operational alerting, durable storage, and predictive analytics for capacity planning. Option A fulfils all these requirements.

Amazon Kinesis Data Streams ingests millions of telemetry events per second from multiple regions globally. Its scalability, durability, and multiple consumer support allow real-time processing for immediate detection of outages, unusual traffic patterns, or performance degradation. This low-latency ingestion is critical for maintaining network reliability and customer satisfaction.

AWS Lambda processes telemetry events in real time. Lambda functions analyse network metrics, detect anomalies or outages, and trigger alerts for network operations teams. The serverless nature of Lambda ensures automatic scaling to accommodate peak traffic or unexpected network activity without manual infrastructure adjustments.

Amazon S3 stores raw and processed telemetry data for historical analysis, regulatory compliance, and predictive modelling. Lifecycle policies move older data to Glacier or Glacier Deep Archive, reducing storage costs while retaining durability and accessibility for analysis and model training.

Amazon SageMaker trains predictive models on historical network data to anticipate capacity requirements, detect patterns leading to outages, and optimise resource allocation. Real-time inference allows proactive network adjustments and strategic planning for peak usage periods.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with immediate detection and operational alerting. Redshift is suitable for historical trend analysis but not for low-latency network monitoring.

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

Option D (DynamoDB + EMR) supports storage and batch analytics, but EMR latency prevents real-time anomaly detection, alerting, and predictive modelling. Additional orchestration is required, increasing complexity.

Option A delivers a fully integrated, low-latency, scalable architecture capable of ingestion, real-time processing, anomaly detection, alerting, durable storage, and predictive modelling, making it ideal for telecom network monitoring.

Question 164:

A global weather forecasting organisation wants to implement a real-time weather data processing system. The system must ingest millions of sensor and satellite telemetry events per second, detect severe weather anomalies instantly, trigger alerts to relevant authorities, store historical weather data for trend analysis, and support predictive modelling for long-term climate patterns. 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 weather data processing requires low-latency ingestion of high-frequency sensor and satellite telemetry, immediate anomaly detection, operational alerting, durable storage, and predictive analytics. Option A addresses these requirements comprehensively.

Amazon Kinesis Data Streams ingests millions of telemetry events per second globally, ensuring durability, fault tolerance, and multiple consumer support for concurrent real-time processing. Immediate detection of severe weather anomalies, such as storms or hurricanes, is critical for public safety and operational response.

AWS Lambda processes incoming telemetry events in real time. Lambda functions detect weather anomalies, trigger alerts to relevant authorities, and support decision-making for disaster response. Serverless scaling ensures consistent low-latency performance regardless of sensor network size or event frequency.

Amazon S3 stores raw and processed weather data for historical analysis, regulatory compliance, and predictive modelling. Lifecycle policies allow older data to move to Glacier, optimising storage costs while maintaining accessibility for trend analysis and research.

Amazon SageMaker trains predictive models using historical weather and sensor data, enabling long-term climate forecasting, trend detection, and anomaly prediction. Real-time inference applies models to current data, providing actionable insights for authorities and researchers.

Option B (S3 + Glue + Redshift) is batch-oriented, introducing latency incompatible with real-time anomaly detection and operational alerts. Redshift supports historical analytics but not low-latency forecasting.

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

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

Question 165:

A global autonomous vehicle company wants to implement a real-time vehicle telemetry and safety monitoring system. The system must ingest millions of telemetry events per second, detect anomalies in vehicle performance instantly, trigger alerts to operational teams, store historical vehicle telemetry for trend analysis, and support predictive modelling for safety and route optimisation. Which AWS architecture is most suitable?

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

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

Explanation:

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

Amazon Kinesis Data Streams ingests millions of telemetry events per second, ensuring durability, fault tolerance, and multiple consumer support. Real-time processing enables immediate detection of vehicle performance anomalies, safety risks, or operational issues.

AWS Lambda processes telemetry events in real time. Lambda detects anomalies in performance metrics, triggers alerts to operational teams, and ensures vehicles operate safely. Serverless scaling ensures consistent low-latency performance, even during peak autonomous vehicle traffic periods.

Amazon S3 stores raw and processed telemetry for historical analysis, regulatory compliance, trend analysis, and predictive modelling. Lifecycle policies move older data to Glacier, optimising cost while maintaining access for analysis and safety modelling.

Amazon SageMaker trains predictive models on historical telemetry to optimise vehicle safety, route planning, and maintenance. Real-time inference enables proactive safety monitoring and route optimisation, reducing incidents and improving operational efficiency.

Option B (S3 + Glue + Redshift) is batch-oriented, unsuitable for real-time anomaly detection and operational alerting. Redshift is effective for historical analysis but cannot support immediate operational decisions.

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

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

Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 11 Q151-165

Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 11 Q151-165

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