Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 9 Q121-135
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Question 121:
A global online retail company wants to implement a real-time inventory management system. The system must ingest millions of inventory updates per second, detect low-stock situations immediately, trigger replenishment alerts, and store historical inventory data for analytics and forecasting. 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:
Managing global inventory in real-time requires the ability to ingest massive volumes of events, process them instantly, trigger alerts for operational actions, and maintain durable historical storage. Option A is optimal because Amazon Kinesis Data Streams can handle millions of updates per second while maintaining durability and allowing multiple consumers to process the same stream. AWS Lambda provides serverless compute to process events as they arrive, applying logic to detect low-stock items and trigger alerts or replenishment workflows. Amazon S3 provides long-term, durable storage for all inventory data, enabling analytics, trend forecasting, and historical reporting. CloudWatch monitors the system, triggering notifications or automated actions for operational teams when anomalies or low-stock situations are detected.
Option B, S3 + Glue + Redshift, is primarily batch-oriented. Glue ETL jobs are scheduled, which introduces latency incompatible with immediate alerting and real-time inventory tracking. Redshift supports analytics but is unsuitable for low-latency event processing at high frequency.
Option C, RDS + QuickSight, cannot handle millions of events per second. QuickSight dashboards provide delayed visualisation, which is insufficient for operational alerts and immediate inventory replenishment. Scaling RDS globally is costly and operationally complex.
Option D, DynamoDB + EMR, provides scalable storage and batch processing, but EMR is not optimised for real-time event processing. Integrating alerting mechanisms would require additional operational orchestration, adding complexity.
Option A delivers an end-to-end, low-latency, scalable architecture that integrates real-time processing, operational alerting, durable storage, and analytics, making it the best solution for a global real-time inventory system.
Question 122:
A healthcare organisation needs a system to monitor patient telemetry in real-time. The system must ingest high-frequency IoT device data, detect anomalies instantly, trigger alerts to medical staff, and store historical data for trend analysis and regulatory compliance. Which AWS architecture is most appropriate?
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:
Real-time patient monitoring demands continuous high-frequency ingestion, immediate anomaly detection, alerting, and secure long-term storage. Option A provides a fully integrated solution. Kinesis Data Streams can handle millions of IoT telemetry events per second, ensuring high throughput and durability. Kinesis Data Analytics performs streaming computations to detect anomalies such as irregular heart rates, oxygen saturation, or blood pressure deviations in near real-time. AWS Lambda triggers alerts to medical personnel or automated systems, enabling rapid response. Amazon S3 provides cost-effective, highly durable storage for raw and processed telemetry, supporting historical trend analysis, auditing, and compliance reporting.
Option B, S3 + Glue + Athena, is batch-oriented. ETL jobs introduce latency that prevents immediate anomaly detection and alerting. Athena enables queries on historical data but cannot operate in real-time on streaming telemetry.
Option C, RDS + QuickSight, cannot handle high-velocity telemetry. QuickSight dashboards are delayed, preventing immediate intervention. Scaling RDS for real-time ingestion globally is operationally complex and expensive.
Option D, DynamoDB + EMR, offers scalable storage and batch analytics. EMR introduces latency incompatible with real-time monitoring. Alerting would require additional orchestration, increasing operational overhead.
Option A ensures low-latency processing, real-time anomaly detection, alerting, and historical analytics, delivering a comprehensive solution for patient telemetry monitoring.
Question 123:
A financial institution must store decades of transactional data in a cost-effective, durable, and secure manner. Auditors require the ability to query historical data without restoring the entire dataset. Which AWS solution is optimal?
A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena
Explanation:
Storing decades of financial transactions requires durability, cost-effectiveness, and selective query capabilities. Option A is ideal. Glacier Deep Archive offers the lowest-cost storage with eleven nines of durability, suitable for multi-decade retention. Lifecycle policies automatically transition less-frequently accessed data from S3 Standard to Glacier Deep Archive, optimising storage cost. Athena allows auditors to query archived data directly without full restoration, enabling efficient compliance checks and reporting.
Option B, S3 Standard + Lambda, is cost-prohibitive for decades-long storage. Lambda does not provide the ad-hoc query capabilities required for auditing and compliance.
Option C, RDS + Redshift, provides structured storage and analytics but is not cost-effective for long-term archival. Redshift requires active clusters to perform queries, increasing operational complexity and cost.
Option D, DynamoDB + EMR, introduces latency and operational complexity. DynamoDB is expensive for long-term archival, and EMR cannot efficiently perform ad-hoc queries on archived datasets.
Option A provides a secure, durable, cost-effective solution with query capabilities for auditing and regulatory compliance over decades, making it the optimal choice.
Question 124:
An e-commerce platform wants to perform clickstream analytics on millions of user interactions per second. The system must transform data in near real-time, store raw and processed datasets, and provide dashboards for business intelligence. Which AWS architecture is most suitable?
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
Explanation:
Clickstream analytics requires ingestion of high-velocity events, near real-time transformation, durable storage, and reporting. Option A is fully optimised for these needs. Kinesis Data Firehose ingests millions of events per second and scales automatically. AWS Lambda transforms and enriches the data in near real-time, making it immediately usable for analytics. Amazon S3 stores raw and processed datasets cost-effectively, supporting long-term analysis, auditing, and reporting. Athena provides SQL-based queries directly on S3, enabling dashboards and business intelligence without moving data, reducing latency and operational complexity.
Option B, S3 + Glue + Redshift, is batch-oriented. ETL jobs run on a schedule, introducing latency that prevents near-real-time insights. Redshift is suitable for structured analytics but cannot handle high-velocity streaming efficiently.
Option C, RDS + QuickSight, cannot ingest millions of events per second. QuickSight dashboards introduce delays, limiting actionable insights. Scaling RDS adds cost and operational complexity.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency, making near-real-time analysis impossible. Additional orchestration is required for dashboards and alerts.
Option A provides a low-latency, scalable, and fully integrated architecture for clickstream ingestion, transformation, storage, and reporting.
Question 125:
A financial services company requires a real-time fraud detection system capable of ingesting millions of transactions per second, detecting anomalies instantly, triggering operational alerts, and storing all transactions for auditing and compliance. Which AWS architecture is most appropriate?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Explanation:
Real-time fraud detection requires high-throughput ingestion, immediate anomaly detection, alerting, and durable storage. Option A provides a fully integrated solution. Kinesis Data Streams ingests millions of transactions per second, offering scalability and durability. AWS Lambda processes transactions in real-time, applying fraud detection rules and identifying anomalies instantly. CloudWatch monitors system metrics and triggers alerts to operational teams or automated workflows, ensuring rapid response to potential fraud. Amazon S3 stores all transactions durably, enabling auditing, regulatory compliance, and historical analysis for model retraining.
Option B, S3 + Glue + Athena, is batch-oriented, introducing delays incompatible with real-time anomaly detection and alerting. Query latency reduces operational effectiveness.
Option C, RDS + Redshift, provides structured analytics but cannot handle high-frequency transaction ingestion efficiently. Scaling clusters for millions of transactions per second increases operational complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency incompatible with real-time fraud detection. Additional orchestration is required for alerting, increasing operational complexity.
Option A delivers low-latency, fully integrated, scalable real-time fraud detection with alerting, auditing, and compliance, making it the best solution for financial services organisations.
Question 126:
A global logistics company needs a real-time fleet tracking system that can process millions of GPS events per second, detect anomalies such as route deviations, trigger operational alerts, and store historical data for analytics and route optimisation. 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:
Real-time fleet tracking involves continuous ingestion of high-frequency GPS data, immediate detection of anomalies, alerting operational teams, and durable storage for analysis and optimisation. Option A is ideal because Amazon Kinesis Data Streams can scale to millions of events per second and provide multiple consumers for simultaneous processing, ensuring low-latency and high-throughput ingestion. AWS Lambda offers serverless compute to process incoming events in real-time, applying anomaly detection algorithms to identify deviations from planned routes or schedules. This processing layer allows immediate notifications to operations teams to take corrective action.
Amazon S3 offers highly durable storage for raw and processed GPS data, enabling trend analysis, predictive route optimisation, and historical performance reporting. CloudWatch provides system monitoring and automated alerts, ensuring operational visibility and timely responses to anomalies.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs are scheduled and introduce latency that prevents real-time anomaly detection. Redshift is suitable for analytics, but it cannot ingest and process millions of GPS events per second in real-time.
Option C, RDS + QuickSight, is unsuitable due to RDS’s limitations in handling high-velocity data streams. QuickSight dashboards are delayed and cannot provide immediate operational alerts. Scaling RDS globally is complex and costly.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency, making near-real-time anomaly detection impossible. Orchestration of alerts adds operational complexity.
Option A delivers a low-latency, fully integrated solution for fleet tracking, anomaly detection, alerting, and historical analytics, making it the optimal choice.
Question 127:
A healthcare organisation needs a system for real-time monitoring of medical device telemetry. The system must ingest data from thousands of devices per second, detect anomalies instantly, trigger alerts to medical staff, and store historical data for research and regulatory compliance. 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:
Real-time telemetry monitoring requires high-throughput ingestion, immediate anomaly detection, operational alerting, and secure long-term storage. Option A provides a fully integrated solution. Kinesis Data Streams handles large volumes of device telemetry, ensuring durability and high throughput. Kinesis Data Analytics performs continuous real-time computations to detect anomalies, such as abnormal heart rates or blood pressure deviations. AWS Lambda triggers alerts to medical personnel or automated systems in near real-time, enabling rapid interventions. Amazon S3 provides durable, cost-effective storage for both raw and processed telemetry, supporting long-term research, audits, and regulatory compliance.
Option B, S3 + Glue + Athena, is batch-oriented. ETL jobs run periodically, introducing latency that prevents real-time anomaly detection. Athena enables queries on historical data but cannot operate on high-frequency streams.
Option C, RDS + QuickSight, cannot handle the ingestion rate from thousands of devices. QuickSight dashboards are delayed, limiting timely interventions. Scaling RDS for high-velocity ingestion is operationally complex and costly.
Option D, DynamoDB + EMR, supports scalable storage and batch analytics. EMR introduces latency incompatible with real-time monitoring. Alert orchestration would require additional operational layers, increasing complexity.
Option A ensures low-latency processing, anomaly detection, alerting, and long-term historical analytics, making it the ideal solution.
Question 128:
A financial institution must securely store decades of transaction records and provide auditors the ability to query data without restoring the entire dataset. Which AWS solution is optimal?
A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena
Explanation:
Long-term storage of financial transactions requires durability, cost-effectiveness, and selective query capabilities. Option A provides a secure and optimised solution. Glacier Deep Archive offers extremely low-cost storage with eleven nines of durability, suitable for decades-long retention. Lifecycle policies can automatically transition less-frequently accessed data from S3 Standard to Glacier Deep Archive, optimising storage cost. Athena allows auditors to perform queries directly on archived data without restoring the entire dataset, enabling efficient compliance checks and reporting.
Option B, S3 Standard + Lambda, is expensive for multi-decade storage. Lambda does not provide ad-hoc query capabilities required for auditing.
Option C, RDS + Redshift, is suitable for structured storage and analytics but not for long-term archival. Redshift requires active clusters for queries, increasing cost and operational complexity.
Option D, DynamoDB + EMR, is costly for long-term storage, and EMR introduces latency, making ad-hoc auditing inefficient.
Option A ensures secure, durable, cost-effective storage and query capabilities for auditing and regulatory compliance, making it the optimal choice.
Question 129:
An e-commerce company wants to perform clickstream analytics on millions of user interactions per second. The system must perform near real-time transformations, store raw and processed datasets, and provide dashboards for business intelligence. Which AWS architecture is most suitable?
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
Explanation:
Clickstream analytics involves ingestion of high-frequency events, near real-time transformation, storage, and reporting. Option A provides a fully integrated solution. Kinesis Data Firehose ingests millions of events per second and scales automatically. AWS Lambda transforms and enriches data in near real-time, making it immediately usable for analytics. Amazon S3 stores raw and processed datasets cost-effectively, supporting long-term trend analysis, auditing, and reporting. Athena enables SQL-based queries directly on S3, facilitating business intelligence dashboards without moving data, reducing latency and operational complexity.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs introduce latency incompatible with near-real-time analytics. Redshift supports structured analytics but cannot handle high-velocity streaming efficiently.
Option C, RDS + QuickSight, cannot handle millions of events per second. QuickSight dashboards are delayed, limiting actionable insights. Scaling RDS globally increases complexity and cost.
Option D, DynamoDB + EMR, supports scalable storage and batch analytics, but EMR introduces latency incompatible with near-real-time analysis. Additional orchestration is required for dashboards and alerts.
Option A provides a low-latency, scalable, fully integrated architecture for clickstream ingestion, transformation, storage, and reporting.
Clickstream analytics is critical for businesses seeking to understand user behaviour on websites, mobile applications, or other digital platforms. Every user interaction—clicks, page views, searches, or navigation events—generates high-velocity data that needs to be ingested, transformed, analysed, and reported. Organisations require architectures that can handle millions of events per second while providing near real-time insights to drive business decisions, optimise user experience, and detect anomalies such as unexpected traffic spikes, potential bot activity, or drop-off points in user journeys. The key challenges include managing high-volume event streams, processing data in near real-time, storing it cost-effectively, and providing accessible reporting capabilities for business intelligence teams.
High-Velocity Data Ingestion with Kinesis Data Firehose
Option A leverages Amazon Kinesis Data Firehose for streaming ingestion of clickstream events. Firehose is designed to automatically scale to accommodate millions of events per second, making it ideal for high-traffic websites or applications with unpredictable usage patterns. It provides durability by replicating data across multiple availability zones, ensuring no loss of critical user interaction data. Additionally, Firehose can batch, compress, and encrypt data before delivering it to the destination, optimising storage efficiency and security. Unlike traditional batch pipelines, Firehose enables near real-time delivery to downstream systems, ensuring that analytics and reporting can keep pace with live user activity.
Near Real-Time Data Transformation with AWS Lambda
AWS Lambda complements Firehose by enabling transformation and enrichment of data in near real-time. For example, Lambda can add metadata, standardise formats, remove sensitive information, or calculate derived metrics on each event before it is stored in S3. This processing ensures that datasets are immediately ready for analytics, eliminating the need for lengthy batch processing jobs. By performing transformations in real-time, Lambda allows organisations to gain timely insights, monitor user engagement patterns, and react to behavioural trends quickly. The serverless nature of Lambda also reduces operational overhead, automatically scaling to meet fluctuating volumes without requiring manual intervention or infrastructure management.
Durable and Cost-Effective Storage in Amazon S3
Amazon S3 serves as the storage layer for both raw and processed clickstream data. S3 provides virtually unlimited storage capacity and ensures durability across multiple availability zones. Storing raw data allows organisations to maintain a complete historical record of user interactions, which is essential for auditing, compliance, and detailed trend analysis. Processed datasets in S3 can be organised and partitioned to optimise query performance and reduce cost when analysing large volumes of data. The separation of raw and processed data also enables repeated experimentation with different analytics models or transformations without re-ingesting the entire dataset.
Ad-Hoc and Business Intelligence Queries with Amazon Athena
Amazon Athena allows analysts to perform SQL-based queries directly on data stored in S3. This serverless approach eliminates the need to move data into a separate analytics warehouse, reducing latency and operational complexity. Business intelligence dashboards can be updated in near real-time, enabling teams to monitor metrics such as page views, user engagement rates, conversion rates, or traffic sources almost immediately after events occur. The ability to query large volumes of data without provisioning infrastructure or managing clusters allows organisations to maintain flexibility while providing actionable insights across departments.
Limitations of Batch-Oriented Architectures
Option B (S3 + Glue + Redshift) relies on batch-oriented ETL processes. While Glue and Redshift are suitable for historical analytics and structured queries, batch ETL introduces delays that are incompatible with near-real-time analytics requirements. Users’ interactions may not be visible in dashboards for hours or even days, reducing the ability to respond promptly to trends, optimise experiences, or identify anomalies. Scaling Redshift clusters to handle high-velocity clickstream data also adds operational complexity and cost, making it less practical for dynamic or unpredictable workloads.
Constraints of Relational Databases and BI Tools
Option C (RDS + QuickSight) cannot handle millions of events per second. Relational databases are optimised for transactional consistency and structured queries, but struggle with high-frequency streaming data. QuickSight dashboards are inherently delayed because they rely on periodic data ingestion or scheduled refreshes. As a result, insights are not actionable in real-time, limiting their usefulness for user engagement optimisation, anomaly detection, or targeted interventions. Additionally, scaling RDS globally to meet high ingestion volumes increases operational overhead and cost, further reducing system efficiency.
Challenges with NoSQL and Batch Big Data Analytics
Option D (DynamoDB + EMR) supports scalable storage and large-scale batch analytics. While DynamoDB can store high volumes of transactional or event data efficiently, EMR is designed for batch-oriented processing and introduces latency that prevents near real-time analysis. Dashboards and alerts require additional orchestration, which complicates operations and increases response time. Although this architecture can support historical trend analysis or model training, it does not meet the immediacy required for actionable clickstream analytics, particularly when monitoring user behaviour or detecting anomalies as they occur.
Advantages of Option A’s Fully Integrated Architecture
Option A provides a cohesive solution that addresses every aspect of clickstream analytics. High-frequency events are ingested reliably through Kinesis Data Firehose, enriched in near real-time by Lambda, stored durably in S3, and analysed immediately with Athena. This integration eliminates the delays associated with batch ETL, ensures data is available for reporting and trend analysis in near real-time, and reduces operational complexity by leveraging serverless managed services. It also supports cost efficiency by allowing organisations to pay only for the data processed and stored, rather than provisioning large clusters or infrastructure in advance.
Operational and Strategic Benefits
With Option A, organisations gain the ability to monitor and react to user behaviour almost instantaneously. Marketing teams can optimise campaigns based on real-time engagement metrics, product teams can improve user experience by identifying drop-off points immediately, and security teams can detect abnormal patterns suggestive of bot activity or malicious behaviour. Historical datasets stored in S3 allow continuous refinement of analytics models and support auditing or compliance reporting. The architecture also scales dynamically, accommodating unpredictable traffic spikes without manual intervention or service disruption.
Question 130:
A financial services company requires a real-time fraud detection system that can ingest millions of transactions per second, detect anomalies instantly, trigger operational alerts, and store all transactions for auditing and compliance. Which AWS architecture is most suitable?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Explanation:
Real-time fraud detection requires high-throughput ingestion, immediate anomaly detection, alerting, and durable storage. Option A is fully integrated to meet these requirements. Kinesis Data Streams can ingest millions of transactions per second with auto-scaling and durability. AWS Lambda processes transactions in real-time, applying fraud detection logic to detect anomalies instantly. CloudWatch monitors system metrics and triggers alerts to operational teams or automated workflows, enabling rapid response to potential fraud. Amazon S3 stores all transactions durably, enabling auditing, regulatory compliance, and historical analysis for model retraining.
Option B, S3 + Glue + Athena, is batch-oriented, introducing latency that prevents real-time anomaly detection and alerting. Query delays reduce operational effectiveness.
Option C, RDS + Redshift, provides structured analytics but cannot efficiently ingest high-frequency transactions. Scaling clusters for millions of transactions per second adds operational complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency incompatible with real-time fraud detection, and alert orchestration adds operational overhead.
Option A ensures low-latency, fully integrated, scalable real-time fraud detection with alerting, auditing, and compliance, making it the optimal solution.
Question 131:
A global logistics company wants to implement a real-time package tracking system. The system must ingest GPS data from millions of packages per second, detect delays instantly, trigger operational alerts, and store historical data for reporting and route optimisation. 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:
Real-time package tracking is a classic scenario that involves high-velocity data ingestion, immediate processing for operational alerts, durable storage for historical analysis, and robust monitoring. Amazon Kinesis Data Streams is designed for scenarios requiring ingestion of millions of events per second, providing high throughput and durability. It allows multiple consumers to read the same data stream simultaneously, enabling real-time analytics, operational alerting, and downstream processing without data loss. AWS Lambda acts as a serverless compute layer, processing each event as it arrives. It can apply logic to detect delays in delivery, abnormal route deviations, or other operational anomalies. Immediate processing ensures that operational teams can respond promptly to avoid customer dissatisfaction or logistical bottlenecks.
Amazon S3 provides long-term storage of raw and processed data, enabling historical reporting, trend analysis, and route optimisation. With S3, data durability is virtually guaranteed, and cost-effective storage allows the retention of massive datasets for years, which is essential for both operational auditing and predictive analytics. CloudWatch provides monitoring and alerting capabilities. It can track the health of the data ingestion and processing pipeline, trigger alarms in response to anomalies, and automate notifications to operational teams. This ensures end-to-end visibility into system performance and allows rapid intervention when issues arise.
Option B (S3 + Glue + Redshift) is primarily batch-oriented. While Redshift supports complex analytics, Glue ETL jobs are scheduled and cannot process high-frequency streaming data in real-time. This introduces latency, making the system unable to detect delays or trigger alerts instantly. Therefore, it is unsuitable for operational real-time package tracking.
Option C (RDS + QuickSight) cannot handle millions of concurrent events per second. RDS is a transactional database suitable for OLTP workloads but not for high-frequency streaming data ingestion. QuickSight dashboards are delayed by design and cannot provide immediate alerting. Scaling RDS globally for millions of events per second would also introduce operational complexity and cost overhead.
Option D (DynamoDB + EMR) supports scalable storage and batch processing. However, EMR operates primarily on batch workloads, introducing significant latency. While DynamoDB is suitable for high-velocity storage, the architecture lacks real-time processing and immediate alerting capabilities, making it unsuitable for operational package tracking.
Option A provides an end-to-end solution that seamlessly integrates real-time ingestion, processing, monitoring, and durable storage. It ensures low latency, scalability, reliability, and cost-effectiveness for a global logistics tracking system.
Question 132:
A healthcare organisation needs a real-time patient monitoring system. The system must ingest telemetry from thousands of IoT devices per second, detect anomalies instantly, trigger alerts to medical staff, and store historical data for research and regulatory compliance. 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:
Patient telemetry monitoring requires real-time ingestion of large volumes of data, immediate anomaly detection, operational alerting, and secure long-term storage. Option A provides a fully integrated solution to meet these requirements. Kinesis Data Streams supports the ingestion of millions of telemetry events per second from IoT medical devices, ensuring durability, fault tolerance, and the ability to handle spikes in device activity. Kinesis Data Analytics allows continuous streaming computations to identify anomalies such as abnormal heart rates, oxygen saturation fluctuations, or unexpected medication events.
AWS Lambda serves as a serverless compute layer that triggers alerts in real-time to medical personnel or automated intervention systems, enabling rapid response and potentially saving lives. Amazon S3 provides durable and cost-effective storage of both raw and processed telemetry data. This allows for historical trend analysis, longitudinal studies, research, and regulatory compliance audits.
Option B (S3 + Glue + Athena) is batch-oriented. Glue ETL jobs run on schedules, which introduces delays, making real-time anomaly detection impossible. Athena is suitable for historical queries but cannot process high-frequency streaming data for immediate alerting.
Option C (RDS + QuickSight) is unsuitable because RDS cannot ingest high-frequency device telemetry efficiently. QuickSight dashboards have delayed visualisation, limiting real-time operational effectiveness. Scaling RDS to accommodate thousands of devices per second is costly and operationally complex.
Option D (DynamoDB + EMR) provides scalable storage and batch processing, but EMR introduces latency that is incompatible with real-time anomaly detection and alerting. Additional orchestration would be required to trigger real-time alerts, adding complexity.
Option A ensures low-latency ingestion, anomaly detection, alerting, and long-term analytics. It delivers a comprehensive, scalable, and reliable architecture for critical healthcare monitoring.
Question 133:
A financial institution must store decades of transactional data securely while allowing auditors to query the data without restoring the entire dataset. Which AWS solution is optimal?
A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena
Explanation:
Decades-long retention of financial transactions requires durable, cost-effective, and queryable storage. Option A fulfils all requirements. Glacier Deep Archive provides extremely low-cost storage with eleven nines of durability, making it suitable for multi-decade retention. Lifecycle policies can automatically transition data from S3 Standard to Glacier Deep Archive to optimise storage costs. Athena enables auditors to run SQL-based queries directly on archived data without restoring the entire dataset, facilitating efficient regulatory audits and compliance reporting.
Option B (S3 Standard + Lambda) is expensive for multi-decade storage. Lambda cannot perform ad-hoc queries over massive historical datasets efficiently.
Option C (RDS + Redshift) is unsuitable for long-term archival due to high operational costs and complexity. Redshift requires active clusters for querying, increasing both cost and operational overhead.
Option D (DynamoDB + EMR) is costly for decades-long archival. EMR introduces latency and cannot efficiently provide ad-hoc queries for auditing purposes.
Option A delivers secure, durable, cost-effective storage with efficient query capabilities, meeting both long-term storage and auditing requirements.
Question 134:
An e-commerce company wants to perform near-real-time clickstream analytics on millions of user interactions per second. The system must transform data quickly, store raw and processed datasets, and provide dashboards for business intelligence. Which AWS architecture is most suitable?
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
Explanation:
Clickstream analytics requires ingestion of high-frequency events, near real-time transformation, durable storage, and business intelligence reporting. Option A is ideal. Kinesis Data Firehose can ingest millions of events per second and automatically scale. AWS Lambda transforms and enriches the data in near real-time, preparing it for analytics. Amazon S3 provides durable storage for both raw and processed datasets, supporting long-term trend analysis, auditing, and business intelligence. Athena allows SQL-based queries directly on S3, enabling dashboards and reporting without moving data, minimising latency and operational overhead.
Option B (S3 + Glue + Redshift) is batch-oriented. Glue ETL jobs are scheduled, introducing latency incompatible with near-real-time analytics. Redshift is optimised for analytics but cannot handle high-velocity streaming efficiently.
Option C (RDS + QuickSight) cannot ingest millions of events per second. QuickSight dashboards are delayed, limiting timely actionable insights. Scaling RDS globally adds cost and complexity.
Option D (DynamoDB + EMR) offers scalable storage and batch analytics, but EMR introduces latency that prevents near real-time analysis. Additional orchestration is required for dashboards and alerts.
Option A provides an end-to-end, low-latency, scalable architecture for clickstream ingestion, processing, storage, and reporting.
Question 135:
A financial services company requires a real-time fraud detection system capable of ingesting millions of transactions per second, detecting anomalies instantly, triggering operational alerts, and storing all transactions for auditing and compliance. Which AWS architecture is most suitable?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Explanation:
Real-time fraud detection requires high-throughput ingestion, immediate anomaly detection, alerting, and durable storage. Option A provides a fully integrated architecture. Kinesis Data Streams ingests millions of transactions per second, providing scalability, durability, and fault tolerance. AWS Lambda processes transactions in real-time, applying fraud detection logic to identify anomalies instantly. CloudWatch monitors metrics and triggers operational alerts or automated workflows, enabling rapid response to potential fraud. Amazon S3 stores all transactions durably, supporting auditing, regulatory compliance, and historical analysis for refining fraud detection models.
Option B (S3 + Glue + Athena) is batch-oriented. ETL jobs introduce latency incompatible with real-time fraud detection, and query delays reduce operational effectiveness.
Option C (RDS + Redshift) cannot efficiently handle millions of transactions per second. Scaling Redshift clusters introduces operational complexity and cost.
Option D (DynamoDB + EMR) provides scalable storage and batch analytics. EMR introduces latency incompatible with real-time detection, and alert orchestration requires additional complexity.
Option A delivers low-latency, scalable, real-time fraud detection with alerting, auditing, and compliance, making it the optimal solution for financial services organisations.
Importance of Real-Time Fraud Detection
In today’s digital economy, financial services, e-commerce, and online payment platforms face increasingly sophisticated fraud threats. Fraudsters exploit even the smallest delays in detection, meaning that real-time monitoring and response are crucial. A robust fraud detection system must process incoming transactions immediately, identify suspicious behaviour as it occurs, and trigger alerts or preventive actions without delay. Moreover, the architecture must handle high transaction volumes, maintain operational reliability, and ensure durability for historical data, regulatory compliance, and model improvement. Real-time detection is not merely about identifying fraud; it is about minimising financial losses, protecting customer trust, and providing actionable insights to operational teams instantaneously.
Scalable High-Velocity Data Ingestion
Option A leverages Amazon Kinesis Data Streams to handle the massive throughput of modern transactional systems. Kinesis is designed to ingest millions of events per second while maintaining order and ensuring durability. This is critical because transactions must be processed in sequence to accurately detect anomalies, such as sudden spikes in activity or repeated failed login attempts. Kinesis provides automatic scaling, enabling the system to adjust to fluctuating workloads during high-traffic periods, such as shopping holidays, promotional campaigns, or unexpected surges in online activity. Unlike batch-oriented ingestion systems, Kinesis ensures that no transactions are lost, even during peak volumes, making it an ideal foundation for real-time fraud detection pipelines.
Immediate Processing and Anomaly Detection
AWS Lambda plays a central role in enabling real-time evaluation of transactions. Each transaction streamed through Kinesis is processed instantly by Lambda functions, which can implement rules-based logic, statistical checks, or integrate machine learning-based anomaly detection models. This real-time processing allows the system to flag suspicious transactions immediately, such as unusually large purchases, repeated failed login attempts, or activity from unusual geographic locations. By processing events in parallel, Lambda ensures low latency and high throughput, so even during peak periods, every transaction is evaluated without delay. This capability is critical for preventing fraudulent transactions before they are completed, limiting financial risk, and maintaining operational security.
Monitoring, Alerts, and Operational Response
Amazon CloudWatch provides continuous observability into the performance and health of the fraud detection system. It tracks metrics such as transaction throughput, latency, error rates, and the frequency of flagged transactions. CloudWatch can also trigger automated alerts or workflows in response to anomalies. For example, if a sudden surge in potentially fraudulent transactions is detected, CloudWatch can notify operational teams via email or SMS, or automatically initiate workflows to temporarily block accounts or transactions. This proactive monitoring and alerting framework ensures that potential fraud is addressed in real time, enhancing both operational efficiency and security.
Durable Storage and Compliance
Amazon S3 serves as the durable, long-term storage layer for all transactional data. All incoming transactions, including those flagged as suspicious, are stored securely and reliably. This ensures that organisations maintain comprehensive audit trails, which are critical for regulatory compliance, forensic investigations, and reporting requirements. Additionally, historical transaction data in S3 can be used to retrain machine learning models or improve rule-based detection systems. By analysing trends over time, organisations can adapt to evolving fraud patterns and strengthen their fraud prevention strategies. S3 also provides cost-effective scalability, making it suitable for storing vast amounts of transactional data over extended periods without impacting operational performance.
Limitations of Batch-Oriented Alternatives
Option B (S3 + Glue + Athena) is inherently batch-oriented. While it is useful for historical analysis and reporting, the latency introduced by ETL processing and query execution makes it unsuitable for real-time fraud detection. By the time queries return results, fraudulent activity could already have caused significant financial or reputational damage. Furthermore, this architecture lacks native mechanisms for automated alerting or operational intervention, requiring additional integration efforts to achieve even partial real-time functionality.
Constraints of Structured Analytics Systems
Option C (RDS + Redshift) provides structured data storage and powerful analytics capabilities. However, it is not optimised for high-frequency, real-time transaction ingestion. Scaling RDS or Redshift clusters to handle millions of transactions per second increases operational complexity and cost. Additionally, Redshift primarily supports batch-oriented queries, which introduces latency and prevents timely fraud detection. Operational alerting in this architecture requires custom solutions, further adding to complexity and limiting responsiveness.
Challenges with Batch-Oriented NoSQL and Big Data Systems
Option D (DynamoDB + EMR) combines scalable NoSQL storage with batch analytics. While DynamoDB can store large volumes of transactional data efficiently, EMR is designed for big data batch processing and introduces latency incompatible with real-time detection. Alerts must be orchestrated manually or through additional tooling, increasing operational overhead. This architecture is better suited for historical analysis, model training, or trend detection rather than preventing fraudulent activity in real time.
Comprehensive Advantages of Option A
Option A offers a fully integrated, low-latency solution tailored to real-time fraud detection requirements. It ensures immediate processing of high-velocity data streams, automatic scaling during peak periods, real-time monitoring and alerts, and durable storage for compliance and analysis. The architecture reduces operational complexity by leveraging managed services, allowing organisations to focus on refining fraud detection logic and improving response strategies. It also enables a continuous feedback loop, where historical data informs model retraining and anomaly detection improvements, enhancing overall system effectiveness over time.