Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 7 Q91-105
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Question 91:
A global social media platform wants to implement a real-time content recommendation system. The system must handle millions of user interactions per second, provide dynamic personalised suggestions, and store historical data for trend analysis and machine learning model retraining. Which AWS architecture is most appropriate?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker + Amazon S3
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 SageMaker + Amazon S3
Explanation:
Real-time content recommendations require high-velocity data ingestion, immediate processing, dynamic model inference, and historical storage. Option A fulfils these requirements effectively. Kinesis Data Streams ingests millions of events per second, ensuring durability and scalability. AWS Lambda processes the stream in real-time, performing transformations and enrichment to make data usable for machine learning models. Amazon SageMaker hosts trained models that generate personalised recommendations dynamically, leveraging both live user interactions and historical patterns. Amazon S3 stores raw and processed events for trend analysis, audits, and retraining machine learning models, ensuring continuous improvement.
Option B, S3 + Glue + Redshift, is batch-oriented. While S3 stores large datasets and Glue performs ETL, the batch nature introduces latency, making real-time recommendations impossible. Redshift is suitable for analytics, but it cannot process millions of events per second efficiently for real-time recommendations.
Option C, RDS + QuickSight, cannot handle the ingestion rate of millions of interactions. QuickSight dashboards do not support real-time personalisation, and scaling RDS for this workload introduces operational complexity and high cost.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency incompatible with real-time recommendation updates, and orchestrating dashboards or alerts adds operational overhead.
Thus, Option A provides a low-latency, scalable, and fully integrated architecture for dynamic content recommendation and historical analysis.
Question 92:
A healthcare provider needs to implement a real-time monitoring system for patient IoT devices. The system must ingest telemetry continuously, detect anomalies instantly, trigger alerts, and store historical data for trend analysis and compliance reporting. Which AWS service combination is best suited?
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 monitoring of patient IoT devices requires ingestion of high-frequency telemetry, immediate anomaly detection, alerting, and durable storage. Option A fulfils these requirements comprehensively. Kinesis Data Streams ingests telemetry data from global IoT devices, providing scalability and durability. Kinesis Data Analytics performs continuous stream processing to detect anomalies such as abnormal heart rates or irregular oxygen levels. AWS Lambda triggers alerts in real-time, notifying medical personnel or automated workflows. Amazon S3 stores historical telemetry data for trend analysis, audits, and regulatory compliance.
Option B, S3 + Glue + Athena, is batch-oriented. Scheduled ETL jobs introduce latency, making real-time anomaly detection and alerting impossible. Athena allows historical analysis but cannot operate on streaming data in real-time.
Option C, RDS + QuickSight, is unsuitable due to throughput limitations. RDS cannot handle high-velocity telemetry, and QuickSight dashboards are not real-time. Scaling RDS to manage telemetry globally adds operational complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency, preventing immediate anomaly detection. Alerts and dashboards require additional orchestration, increasing complexity.
Thus, Option A offers a low-latency, scalable, and integrated architecture for real-time patient monitoring, alerting, and historical data analysis.
Question 93:
A financial institution must store decades of transactional data securely and cost-effectively. They need occasional queries for audits or regulatory compliance without restoring the entire dataset. Which AWS solution is most appropriate?
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 transactional data requires durability, cost efficiency, and selective query capability. Option A is ideal. Glacier Deep Archive offers extremely low-cost storage with eleven nines of durability, making it suitable for decades of data retention. Lifecycle policies automate moving data from S3 Standard to Glacier Deep Archive, reducing storage costs. Athena enables SQL-based queries on archived datasets without restoring the entire dataset, allowing audits, compliance checks, and regulatory reporting to be performed efficiently. This approach reduces operational overhead while ensuring compliance.
Option B, S3 Standard + Lambda, is expensive for long-term storage. Lambda cannot provide query capabilities on archived datasets.
Option C, RDS + Redshift, supports structured storage and analytics but is costly for decades-long archival. Redshift requires active clusters for queries, increasing complexity and expense.
Option D, DynamoDB + EMR, offers batch analytics but introduces latency and operational complexity. DynamoDB is costly for long-term storage, and EMR cannot perform efficient ad-hoc queries on archived datasets.
Thus, Option A delivers a cost-effective, durable, compliant, and queryable solution for long-term storage of transactional data.
Question 94:
An e-commerce company wants to perform clickstream analytics for millions of users per second. The solution must transform data 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, near real-time transformation, storage, and reporting. Option A meets these requirements. Kinesis Data Firehose ingests high-velocity events, scaling automatically to accommodate traffic. AWS Lambda performs real-time transformations such as enrichment, filtering, and aggregation. Amazon S3 stores raw and processed datasets cost-effectively for long-term analysis and auditing. Athena allows SQL-based queries directly on S3, supporting dashboards and business intelligence without moving data, minimising latency and operational complexity.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs run on schedules, introducing delays. Redshift supports structured analytics but cannot handle high-velocity streaming efficiently.
Option C, RDS + QuickSight, is unsuitable for high-frequency clickstream events. RDS cannot ingest millions of events per second, and QuickSight dashboards are not real-time. Scaling RDS adds complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency incompatible with near-real-time analytics, and orchestrating dashboards requires extra operational effort.
Thus, Option A provides a fully integrated, low-latency, scalable architecture for clickstream ingestion, transformation, storage, and business intelligence.
Question 95:
A financial services company needs a real-time fraud detection system capable of ingesting millions of transactions per second, detecting anomalies instantly, triggering alerts, and storing all transactions for auditing and compliance. Which AWS architecture is best suited?
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 scalable ingestion, immediate anomaly detection, alerting, and durable storage. Option A meets these requirements. Kinesis Data Streams ingests millions of transactions per second, offering durability and auto-scaling. AWS Lambda processes each transaction in real-time, applying fraud detection logic to flag suspicious activity instantly. CloudWatch monitors system metrics and triggers alerts for operational teams or automated workflows. Amazon S3 stores all transactions durably, supporting auditing, compliance, and historical analysis for model retraining.
Option B, S3 + Glue + Athena, is batch-oriented and cannot provide real-time detection or alerts. Queries are delayed, reducing operational effectiveness.
Option C, RDS + Redshift, supports structured storage and analytics but cannot handle high-frequency streaming ingestion. Scaling RDS or Redshift to handle millions of transactions per second adds complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency incompatible with real-time fraud detection, and orchestrating alerts and dashboards requires additional operational effort.
Thus, Option A delivers a fully integrated, low-latency, scalable architecture for real-time fraud detection, alerting, and compliance.
Understanding the Requirements for Real-Time Fraud Detection
Fraud detection in modern digital systems, particularly in financial services, e-commerce, or payment processing platforms, requires the ability to detect anomalous transactions as they occur. The system must satisfy multiple critical requirements simultaneously: the ingestion of high-volume data streams, real-time processing for anomaly detection, immediate alerting mechanisms, scalable infrastructure, and durable storage for auditing and regulatory compliance. High-frequency transaction environments generate millions of events per second, and a delay of even a few seconds in identifying suspicious activity can result in substantial financial losses and reputational damage. Therefore, the architecture must handle variable workloads efficiently, process events with minimal latency, and maintain operational reliability.
Option A: Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Option A provides a fully integrated approach that aligns perfectly with these requirements. Amazon Kinesis Data Streams is a high-throughput, fully managed streaming platform designed to capture and process real-time data streams. Its ability to automatically scale ensures that surges in transaction volume—such as those caused by seasonal spikes, promotional events, or coordinated fraudulent activity—can be handled without performance degradation. The service also provides durability and data replication across multiple availability zones, safeguarding against data loss during infrastructure failures.
AWS Lambda complements Kinesis by providing serverless, event-driven processing. Lambda allows the immediate execution of fraud detection logic on each incoming transaction. This could include rule-based checks, anomaly detection models, or machine learning inference to evaluate whether a transaction is suspicious. By processing events as they arrive, Lambda ensures that fraudulent activity is flagged almost instantly, allowing operational teams or automated systems to respond immediately. The serverless nature of Lambda eliminates the need for pre-provisioned servers, reducing management overhead while automatically scaling to accommodate high-volume workloads.
Amazon CloudWatch plays a pivotal role in monitoring and operational visibility. CloudWatch collects metrics from Kinesis, Lambda, and other integrated services, enabling teams to track the performance, throughput, and error rates of the system. CloudWatch alarms can trigger notifications, invoke additional Lambda functions, or integrate with automated workflows, ensuring that any detected anomaly is addressed without delay. This monitoring capability is essential for operational resilience, as it provides continuous insight into system health while supporting proactive incident response.
Amazon S3 offers durable and cost-effective storage for all transaction data, both raw and processed. By maintaining a complete historical record, S3 supports regulatory requirements and provides the foundation for subsequent analyses, such as model retraining or long-term trend evaluation. S3’s integration with services such as Athena, Redshift Spectrum, or SageMaker allows organisations to perform analytics and build machine learning models directly on stored datasets, facilitating continuous improvement of fraud detection capabilities.
The combined architecture of Kinesis, Lambda, CloudWatch, and S3 ensures low-latency processing, real-time alerting, operational monitoring, and long-term durability. It allows organisations to meet both operational and compliance requirements without sacrificing performance or scalability. The serverless components reduce infrastructure complexity, simplify scaling, and provide a cost-effective solution for handling fluctuating workloads.
Option B: Amazon S3 + AWS Glue + Amazon Athena
Option B is more suitable for batch-oriented processing rather than real-time operational detection. AWS Glue is designed for extract, transform, and load (ETL) operations, typically executed on scheduled intervals. While Glue can efficiently transform large datasets stored in S3, its batch nature introduces inherent latency. Fraud detection requires immediate evaluation of each transaction, and any delay in processing reduces the system’s ability to prevent losses or alert operational teams promptly.
Amazon Athena allows SQL-based querying on data stored in S3. While it is effective for historical analysis, reporting, and auditing, it does not provide the low-latency, event-driven processing required for operational fraud detection. Queries on Athena are executed on a dataset after ingestion, meaning that potential fraudulent activity may only be identified minutes or hours after it occurs, which is unsuitable for real-time protection.
Although S3, Glue, and Athena provide a powerful, scalable analytics platform, the batch-oriented nature of this architecture fails to address the immediacy required for detecting high-velocity fraudulent transactions. Organisations using this approach would likely need to supplement it with additional streaming and alerting solutions to achieve real-time capabilities, adding operational complexity.
Option C: Amazon RDS + Amazon Redshift
Option C focuses on structured data storage and analytical querying. Amazon RDS provides relational database capabilities for transactional workloads, while Redshift enables high-performance analytical queries on large datasets. However, neither service is optimised for ingesting and processing millions of events per second in real time. Scaling RDS to accommodate such high-throughput workloads is operationally challenging and expensive. RDS typically supports conventional transactional loads rather than continuous streaming data, and write contention or connection saturation can occur under extreme load.
Redshift excels at analysing structured historical datasets but is inherently batch-oriented, with queries executed on already ingested data. As such, it cannot provide real-time anomaly detection or immediate alerts. Organisations relying solely on RDS and Redshift would face significant delays in identifying fraudulent activity, limiting operational responsiveness. Moreover, maintaining and scaling Redshift clusters for real-time detection would increase complexity and operational costs, making it less suitable for the high-frequency requirements of fraud detection.
Option D: Amazon DynamoDB + Amazon EMR
Option D combines scalable NoSQL storage with distributed data processing. DynamoDB provides fast, low-latency access to large volumes of structured or semi-structured data and can scale horizontally to accommodate high-throughput workloads. Amazon EMR allows for distributed batch processing using frameworks such as Spark or Hadoop, making it suitable for large-scale analytics.
However, EMR is fundamentally a batch-processing platform. Data processed through EMR must first be ingested, then distributed across cluster nodes, and finally processed and aggregated before results are available. This workflow introduces latency that is incompatible with real-time fraud detection. In addition, orchestrating alerting, dashboards, or automated responses requires additional layers of integration, increasing operational complexity. While DynamoDB + EMR is valuable for historical analysis or large-scale model training, it cannot provide the immediate detection and alerting needed for operational fraud prevention.
Question 96:
A global video streaming service wants to implement a real-time recommendation engine. The system must ingest millions of user interactions per second, generate personalised recommendations dynamically, and store historical data for analytics and model retraining. Which AWS architecture is most appropriate?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker + Amazon S3
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 SageMaker + Amazon S3
Explanation:
Real-time recommendation engines require ingestion of high-velocity user events, low-latency processing, and durable historical storage. Option A fulfils these requirements optimally. Kinesis Data Streams ingests millions of events per second, providing durability, scalability, and fault tolerance. AWS Lambda processes events in real-time, performing necessary transformations and enrichment before sending the data to SageMaker models. Amazon SageMaker generates dynamic recommendations based on live user activity and historical interactions stored in S3. S3 retains raw and processed datasets, enabling trend analysis, model retraining, and audit compliance.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs run periodically, which introduces latency incompatible with real-time recommendations. Redshift supports analytics but cannot handle streaming events at high velocity.
Option C, RDS + QuickSight, cannot scale to millions of events per second. QuickSight dashboards are delayed and cannot provide real-time personalised suggestions. Scaling RDS for this workload increases cost and operational complexity.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics, but EMR introduces latency, making it unsuitable for real-time recommendations. Orchestrating dashboards or alerts requires additional operational effort.
Thus, Option A delivers a fully integrated, low-latency, scalable solution for real-time personalised recommendations and historical analysis.
Question 97:
A healthcare organisation wants to monitor patient IoT devices in real-time. The solution must ingest telemetry continuously, detect anomalies instantly, trigger alerts, and store historical data for trend analysis and regulatory compliance. Which AWS service combination is best suited?
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 requires high-throughput ingestion, continuous anomaly detection, immediate alerting, and long-term storage. Option A satisfies these requirements comprehensively. Kinesis Data Streams ingests telemetry from IoT devices globally, ensuring scalability, durability, and fault tolerance. Kinesis Data Analytics performs continuous computations on streams, detecting abnormal vitals such as irregular heart rate or oxygen levels. AWS Lambda triggers alerts immediately, notifying healthcare providers or initiating automated workflows. S3 stores historical telemetry for audits, trend analysis, and regulatory compliance, ensuring operational continuity.
Option B, S3 + Glue + Athena, is batch-oriented. Scheduled ETL jobs introduce delays, preventing real-time detection and alerting. Athena supports historical analysis but cannot process streaming telemetry.
Option C, RDS + QuickSight, cannot handle the volume of high-frequency telemetry events. QuickSight dashboards are delayed and cannot trigger real-time alerts. Scaling RDS globally is costly and operationally complex.
Option D, DynamoDB + EMR, offers scalable storage and batch processing. EMR introduces latency incompatible with real-time anomaly detection. Alert orchestration requires additional operational effort.
Thus, Option A offers a fully integrated, low-latency, scalable solution for real-time patient monitoring, anomaly detection, alerting, and historical analysis.
Question 98:
A financial company must store decades of transactional data securely and cost-effectively. They need occasional queries for audits or regulatory compliance without restoring the entire dataset. Which AWS solution is most appropriate?
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 transactional storage requires durability, cost efficiency, and selective query capability. Option A is optimal. Glacier Deep Archive provides highly durable, low-cost storage, ideal for decades-long retention. Lifecycle policies allow automated movement from S3 Standard to Glacier Deep Archive, reducing cost while maintaining accessibility. Athena enables SQL-based queries on archived datasets without restoring the full dataset, supporting audits, regulatory reporting, and compliance efficiently.
Option B, S3 Standard + Lambda, is expensive for long-term archival and does not provide efficient querying capabilities on archived datasets. Lambda cannot replace Athena for queries.
Option C, RDS + Redshift, provides structured storage and analytics but is expensive for long-term retention. Redshift requires active clusters for queries, increasing operational overhead and cost.
Option D, DynamoDB + EMR, allows batch analytics but introduces latency and complexity. DynamoDB is costly for long-term archival, and EMR cannot perform efficient ad-hoc queries on archived datasets.
Thus, Option A delivers a compliant, cost-effective, durable, and queryable solution for decades of financial transactional data.
Question 99:
An e-commerce company wants to perform clickstream analytics on millions of user interactions per second. The system must transform data 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, storage, and reporting. Option A is fully integrated. Kinesis Data Firehose ingests millions of events per second, scaling automatically with traffic. AWS Lambda transforms and enriches the data in real-time. S3 stores raw and processed datasets cost-effectively, allowing long-term analysis and trend evaluation. Athena enables SQL-based queries on S3, supporting dashboards and business intelligence reporting without moving data, minimising latency and operational complexity.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs run periodically, introducing delays incompatible with near-real-time analytics. Redshift handles structured analytics but cannot process streaming data at high velocity efficiently.
Option C, RDS + QuickSight, is unsuitable. RDS cannot ingest millions of events per second, and QuickSight dashboards are delayed. Scaling RDS for high-frequency events is costly and operationally complex.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency, making near-real-time processing difficult. Orchestrating dashboards and alerts requires additional operational overhead.
Thus, Option A provides a fully scalable, low-latency, and integrated architecture for clickstream ingestion, transformation, storage, and business intelligence reporting.
Question 100:
A financial institution needs 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 best suited?
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 scalable ingestion, immediate anomaly detection, alerting, and durable storage. Option A is optimal. Kinesis Data Streams ingests millions of transactions per second, offering durability and automatic scaling. AWS Lambda processes transactions in real-time, applying fraud detection logic to identify suspicious activity instantly. CloudWatch monitors system metrics and triggers alerts for operational teams or automated workflows. S3 stores all transactions durably, supporting auditing, regulatory compliance, and historical analysis for model retraining.
Option B, S3 + Glue + Athena, is batch-oriented and cannot provide real-time anomaly detection or alerts. Queries are delayed, reducing operational effectiveness.
Option C, RDS + Redshift, supports structured storage and analytics but cannot ingest high-frequency transactions efficiently. Scaling RDS or Redshift for millions of transactions per second adds complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency incompatible with real-time fraud detection, and orchestrating alerts and dashboards requires additional operational effort.
Thus, Option A delivers a fully integrated, low-latency, scalable architecture for real-time fraud detection, alerting, and regulatory compliance.
Core Requirements for Real-Time Fraud Detection
Fraud detection in high-volume transactional systems, such as banking platforms, e-commerce websites, or digital payment solutions, is a critical operational capability. The primary goal is to identify suspicious or anomalous transactions the moment they occur. Real-time fraud detection requires the architecture to support continuous ingestion of massive amounts of data, immediate analysis and decision-making, automated alerting for operational teams, and durable storage of all transactional records. Latency must be extremely low—often measured in milliseconds—because delayed detection can result in substantial financial losses, regulatory violations, and reputational damage. Additionally, the architecture must be scalable to handle spikes in transaction volume, resilient to failures, and cost-efficient to operate, while ensuring regulatory compliance through reliable storage and auditability of all data. Historical transaction data is also critical for refining fraud detection models, retraining machine learning algorithms, and understanding emerging threat patterns.
Option A: Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Option A represents the most suitable architecture for real-time fraud detection. Amazon Kinesis Data Streams provides highly scalable, durable ingestion for streaming data, capable of processing millions of transactions per second. Its ability to handle high-velocity data streams ensures that every transaction is captured and delivered to the processing pipeline without loss. Kinesis also allows automatic scaling, which is essential for systems where transaction volume fluctuates unpredictably, such as during sales events or financial market peaks.
AWS Lambda enables near-instantaneous processing of each transaction as it arrives in the stream. Lambda functions can apply predefined rules, complex logic, or machine learning-based risk scoring to detect anomalies in real-time. This instant processing capability ensures that potentially fraudulent transactions are flagged immediately, allowing rapid mitigation. Amazon CloudWatch provides monitoring of key system metrics, including processing latency, error rates, and throughput. Alerts triggered by CloudWatch can notify operational teams or trigger automated workflows to take immediate action on suspicious transactions.
Amazon S3 ensures that all transaction data—both raw and processed—is durably stored. This storage capability is critical for regulatory compliance, auditing, forensic analysis, and retraining of fraud detection models. By combining real-time processing with durable storage, Option A creates a system that not only detects fraud immediately but also retains historical data to continually improve detection accuracy. The integration of these services forms a seamless, low-latency, fully managed architecture that minimises operational complexity while meeting the stringent requirements of real-time fraud detection.
Option B: Amazon S3 + AWS Glue + Amazon Athena
Option B is primarily designed for batch analytics. While S3 provides durable storage, and Glue orchestrates ETL jobs to process and transform data, these operations are not real-time. Glue jobs are scheduled and can only process accumulated data at intervals, introducing delays that are incompatible with immediate fraud detection. Athena allows SQL-based queries on data stored in S3, but these queries operate on batch-processed data, meaning alerts and insights are delayed. This architecture is suitable for historical reporting, audit preparation, and offline analytics, but it cannot provide the low-latency responses required for proactive fraud prevention.
Option C: Amazon RDS + Amazon Redshift
Option C leverages relational database storage and data warehousing for analytics. Amazon RDS provides structured storage for transaction records, while Redshift enables complex queries on large datasets. However, neither RDS nor Redshift is designed for continuous ingestion at the scale of millions of transactions per second. Scaling these services to handle high-frequency streaming data requires significant operational effort, including sharding, replication, and cluster management. Additionally, these services introduce latency that prevents immediate fraud detection, making this option more appropriate for post-facto analysis rather than real-time anomaly detection.
Option D: Amazon DynamoDB + Amazon EMR
Option D provides scalable NoSQL storage with distributed batch processing. DynamoDB can ingest high volumes of data efficiently, offering low-latency access for stored transactions. However, Amazon EMR is designed for large-scale batch analytics and introduces delays unsuitable for real-time monitoring. Detecting fraud in milliseconds is impossible with this architecture, and additional orchestration would be needed to generate alerts or dashboards. While this setup is suitable for historical data analytics or large-scale batch reporting, it cannot meet the operational requirements for real-time fraud detection and mitigation.
Question 101:
A global online retail company wants to implement a real-time product recommendation system. The system must process millions of user interactions per second, generate dynamic personalised recommendations, and store historical interactions for analytics and model retraining. Which AWS architecture is most appropriate?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker + Amazon S3
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 SageMaker + Amazon S3
Explanation:
Implementing a real-time product recommendation system at a global scale requires handling extremely high volumes of data, low-latency processing, and durable historical storage. Option A addresses all these requirements efficiently. Amazon Kinesis Data Streams provides scalable ingestion for millions of user interaction events per second. It ensures data durability and real-time availability for downstream processing. AWS Lambda acts as a serverless compute layer to process streaming data immediately, performing transformations, enrichment, and aggregation. Amazon SageMaker serves machine learning models that generate personalised recommendations dynamically based on real-time input as well as historical patterns stored in S3. Amazon S3 stores both raw and processed interaction data, enabling trend analysis, auditing, and retraining of models to improve recommendation accuracy continuously.
Option B, Amazon S3 + AWS Glue + Amazon Redshift, is a batch-oriented approach. Glue ETL jobs process data on scheduled intervals, which introduces latency, making real-time recommendations impossible. Redshift supports structured analytics but is not optimised for real-time stream processing and cannot handle millions of events per second efficiently.
Option C, Amazon RDS + Amazon QuickSight, is unsuitable for such high-velocity workloads. RDS cannot sustain millions of updates per second. QuickSight provides visualisation but is not real-time, and scaling RDS for this level of throughput significantly increases operational complexity and cost.
Option D, Amazon DynamoDB + Amazon EMR, provides scalable storage and batch processing capabilities. EMR introduces latency unsuitable for real-time personalisation. Orchestrating dashboards or alerts adds further operational complexity.
Option A is fully integrated, providing low-latency, scalable real-time processing, dynamic personalisation, and long-term historical data storage for analytics and retraining, making it the best choice.
Question 102:
A healthcare provider needs a real-time patient monitoring system. The system must ingest telemetry from IoT devices continuously, detect anomalies instantly, trigger alerts, and store historical data for trend analysis, reporting, and 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 is mission-critical, requiring high-frequency ingestion, immediate anomaly detection, operational alerting, and durable storage. Option A addresses all requirements comprehensively. Amazon Kinesis Data Streams ingests telemetry from IoT devices globally, providing scalable, durable, and highly available data ingestion. Kinesis Data Analytics continuously processes these streams to detect anomalies, such as abnormal heart rates or oxygen saturation levels. AWS Lambda triggers alerts instantly, notifying healthcare personnel or initiating automated workflows, reducing response time and improving patient safety. Amazon S3 serves as cost-effective, durable storage for historical telemetry data, supporting audits, trend analysis, regulatory compliance, and retrospective research.
Option B, Amazon S3 + AWS Glue + Athena, is batch-oriented. ETL jobs scheduled in Glue introduce latency, preventing real-time anomaly detection. Athena allows historical queries but cannot provide live monitoring or alerts, making it insufficient for operational requirements.
Option C, Amazon RDS + QuickSight, is inadequate due to throughput limitations. RDS cannot handle millions of telemetry events per second, and QuickSight dashboards are delayed, preventing real-time response. Scaling RDS for global telemetry is operationally complex and costly.
Option D, Amazon DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces processing latency incompatible with real-time anomaly detection. Alerts require additional orchestration, increasing complexity.
Option A delivers a low-latency, scalable, integrated architecture for patient monitoring, anomaly detection, alerting, and historical analysis, making it the most suitable solution.
Question 103:
A financial institution must securely store decades of transactional data cost-effectively. Occasionally, the data must be queried for audits and regulatory compliance without restoring the entire dataset. Which AWS solution is best suited?
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 archival of transactional data requires durability, low cost, and selective query capability. Option A is ideal. Glacier Deep Archive offers extremely low-cost, highly durable storage suitable for decades-long retention. Lifecycle policies can automatically migrate data from S3 Standard to Glacier Deep Archive, optimising storage costs. Amazon Athena enables SQL queries directly on archived datasets without full restoration, facilitating audits, regulatory reporting, and compliance verification efficiently.
Option B, S3 Standard + Lambda, is expensive for decades-long storage. Lambda cannot provide interactive query capabilities on archival datasets.
Option C, RDS + Redshift, provides structured storage and analytics but is not cost-effective for long-term archival. Redshift requires active clusters for queries, increasing operational complexity and expense.
Option D, DynamoDB + EMR, allows batch analytics but introduces latency and operational complexity. DynamoDB is expensive for long-term archival, and EMR cannot perform ad-hoc queries efficiently on archived data.
Option A delivers a compliant, durable, cost-effective, and queryable solution for decades of financial transactional data.
Question 104:
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 requires ingestion of high-velocity data, real-time processing, storage, and reporting. Option A is fully integrated. Kinesis Data Firehose ingests millions of events per second, scaling automatically to accommodate spikes. AWS Lambda performs transformations and enrichment in real-time, making the data usable for analytics immediately. Amazon S3 stores raw and processed datasets cost-effectively, supporting long-term analysis, auditing, and trend evaluation. Athena enables SQL queries directly on S3 without data movement, reducing latency and operational complexity.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs run periodically, introducing delays incompatible with near-real-time analytics. Redshift handles structured analytics but cannot process high-velocity streaming efficiently.
Option C, RDS + QuickSight, cannot ingest millions of events per second. QuickSight dashboards are delayed, preventing timely insights. Scaling RDS introduces complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency incompatible with near-real-time analysis. Orchestrating dashboards and alerts adds further operational overhead.
Option A delivers a scalable, low-latency, and integrated architecture for clickstream ingestion, transformation, storage, and business intelligence reporting.
Question 105:
A financial institution 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 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 scalable ingestion, immediate anomaly detection, alerting, and durable storage. Option A addresses all requirements effectively. Kinesis Data Streams ingests millions of transactions per second, providing durability and automatic scaling. AWS Lambda processes each transaction in real-time, applying fraud detection logic to identify suspicious activity instantly. CloudWatch monitors system metrics and triggers alerts for operational teams or automated workflows. Amazon S3 stores all transactions durably, supporting auditing, regulatory compliance, and historical analysis for model retraining.
Option B, S3 + Glue + Athena, is batch-oriented. Queries on batch data are delayed, making real-time detection and alerting impossible.
Option C, RDS + Redshift, supports structured storage and analytics but cannot handle millions of transactions per second. Scaling RDS or Redshift introduces complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency incompatible with real-time fraud detection, and orchestrating alerts adds operational overhead.
Option A delivers a fully integrated, low-latency, scalable architecture for real-time fraud detection, alerting, auditing, and compliance.
Fraud detection in modern financial systems, e-commerce platforms, or digital payment solutions is a mission-critical capability. Fraudulent activity can occur within milliseconds, and delayed detection can result in significant financial losses, regulatory penalties, and reputational damage. Real-time fraud detection systems must meet multiple requirements: continuous ingestion of transactional data, immediate processing to detect anomalies, automated or human-alerting mechanisms, and durable storage for auditing and compliance purposes. These systems must scale seamlessly to accommodate fluctuating transaction volumes, often reaching millions of events per second. Additionally, maintaining historical transaction data is crucial for training machine learning models, refining rules, and supporting forensic investigations. Operational simplicity is also a key consideration because complex architectures can introduce failure points, latency, and maintenance challenges that undermine detection accuracy.
Option A: Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Option A represents a fully integrated architecture tailored for real-time fraud detection. Amazon Kinesis Data Streams is a fully managed, high-throughput data ingestion service that can handle millions of events per second. It ensures that each transaction is captured reliably and delivered to processing pipelines without loss, even during sudden traffic spikes. Kinesis Data Streams automatically scales, providing durability and fault tolerance, which is critical for high-stakes financial environments.
AWS Lambda adds the ability to process each transaction immediately as it arrives in the stream. Lambda functions can apply complex fraud detection logic, such as checking transaction patterns, comparing against historical behaviour, or applying machine learning-based risk scoring. This immediate processing allows suspicious activities to be flagged in near real-time, minimising potential losses.
Amazon CloudWatch provides robust monitoring and alerting capabilities. It can track throughput, latency, error rates, and other critical metrics across the Kinesis stream and Lambda functions. When thresholds are exceeded, CloudWatch triggers alerts, which can notify operational teams or initiate automated responses to mitigate potential fraud. This ensures that anomalies are not only detected but also acted upon immediately.
Amazon S3 serves as durable storage for all incoming transactions, both raw and processed. S3 ensures long-term retention, which is essential for regulatory compliance, audit trails, and retraining of fraud detection models. Historical data can be analysed to detect emerging patterns, refine detection algorithms, and improve system accuracy over time. Together, these components create a resilient, low-latency, and scalable solution for continuous fraud monitoring.
Option B: Amazon S3 + AWS Glue + Amazon Athena
Option B represents a batch-oriented architecture. Data is stored in S3, transformed with Glue ETL jobs, and queried using Athena. While this approach is suitable for historical analysis and ad hoc reporting, it is inherently unsuitable for real-time fraud detection. Glue jobs are scheduled, meaning transactions accumulate over time before processing. This delay introduces latency that prevents immediate detection and alerting. Athena, although efficient for SQL-based queries, operates on batch data stored in S3, further compounding delays. Consequently, this architecture is only effective for post-facto analysis or reporting but cannot prevent fraudulent transactions in real-time.
Option C: Amazon RDS + Amazon Redshift
Option C focuses on structured storage and analytical processing. RDS can store transactional data, while Redshift enables complex queries and analytics. While this setup works for reporting, it cannot handle the ingestion and processing of millions of events per second efficiently. Scaling RDS to meet such high throughput demands involves provisioning large database instances and managing replication and failover mechanisms, which increases operational complexity and costs. Redshift excels at analytical queries on large datasets but is not optimised for real-time event processing. As a result, Option C cannot meet the low-latency requirements necessary for proactive fraud detection.
Option D: Amazon DynamoDB + Amazon EMR
Option D combines scalable NoSQL storage with batch analytics. DynamoDB can handle high-frequency writes and provides low-latency access to stored transactions. However, Amazon EMR is designed primarily for distributed batch processing, such as large-scale ETL or analytics jobs. EMR introduces processing delays that are incompatible with the millisecond-level detection required for real-time fraud monitoring. Additionally, orchestrating alerts and notifications from EMR data requires additional workflow management, which increases operational complexity and reduces overall system responsiveness. While this option provides scalable storage and batch analytics, it fails to deliver the immediate detection and action capabilities essential for live fraud monitoring.
Real-time fraud detection requires a seamless integration of continuous data ingestion, immediate processing, real-time alerting, and durable storage. Option A—Kinesis Data Streams, AWS Lambda, CloudWatch, and S3—fulfils all these requirements. Kinesis ensures high-throughput, scalable ingestion, Lambda provides instant processing and anomaly detection, CloudWatch delivers monitoring and alerting, and S3 guarantees durable storage for auditing, compliance, and historical analysis. Options B, C, and D, while suitable for batch processing, historical analytics, or structured queries, cannot provide the low-latency detection and operational simplicity needed for effective real-time fraud prevention. Implementing Option A ensures proactive detection, rapid response, regulatory compliance, and long-term data availability, creating a robust and resilient fraud detection ecosystem.