Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 4 Q46-60
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Question 46:
A company wants to build a high-throughput real-time analytics platform for monitoring customer interactions on its mobile application. The system must ingest millions of events per second, perform streaming transformations, provide dashboards for operational insights, and store historical data for auditing and machine learning purposes. Which AWS architecture is most suitable?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon QuickSight
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 QuickSight
Explanation:
A high-throughput real-time analytics platform requires ingesting millions of events per second, performing immediate transformations, providing dashboards, and storing historical data. Option A, Kinesis Data Streams + Lambda + S3 + QuickSight, is designed for these requirements. Kinesis Data Streams ensures near-real-time ingestion of high-volume events and automatically scales to handle spikes. Lambda processes events in real-time, applying transformations or filtering before storing data. S3 provides durable, cost-effective storage for historical data, enabling compliance, auditing, and machine learning model training. QuickSight delivers visualisation dashboards for operational monitoring, combining streaming and historical data insights.
Option B, S3 + Glue + Redshift, is batch-oriented. While S3 stores data, Glue performs ETL jobs, and Redshift provides analytics, the batch nature introduces latency. This prevents real-time dashboards and streaming transformations, making it unsuitable for high-velocity mobile app events.
Option C, RDS + QuickSight, is optimised for structured relational data. RDS is not built to handle millions of events per second, and QuickSight cannot provide real-time insights from high-velocity streams. Scaling RDS for unpredictable spikes is complex and costly.
Option D, DynamoDB + EMR, provides scalable NoSQL storage and distributed batch processing. EMR introduces latency, making real-time transformations unfeasible. While DynamoDB handles fast writes, integrating real-time analytics and dashboards is operationally complex compared to Option A.
Thus, Option A provides the most scalable, real-time, and fully integrated solution for mobile application event monitoring, analytics, and historical storage.
Question 47:
A logistics company wants to implement a near-real-time predictive maintenance system for its fleet of vehicles. The system must ingest telemetry data, detect anomalies, trigger alerts for potential failures, and store historical data for trend analysis and machine learning. Which AWS services best fulfil these requirements?
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:
Predictive maintenance requires near-real-time ingestion of telemetry data, anomaly detection, alerting, and historical storage. Option A, Kinesis Data Streams + Kinesis Data Analytics + Lambda + S3, is the most suitable. Kinesis Data Streams ingests telemetry data from vehicles at high velocity, providing durable and scalable streaming capabilities. Kinesis Data Analytics allows continuous streaming processing and anomaly detection using SQL or custom logic. Lambda enables serverless, event-driven alerting based on anomaly results, allowing immediate operational responses. S3 stores historical telemetry data cost-efficiently, supporting trend analysis, machine learning model retraining, and compliance.
Option B, S3 + Glue + Athena, is batch-oriented. S3 provides storage, Glue performs ETL, and Athena queries data. While suitable for historical analysis, this architecture cannot detect anomalies in near-real time or trigger alerts immediately.
Option C, RDS + QuickSight, handles structured storage and visualisation. However, RDS cannot scale to handle high-frequency telemetry ingestion, and QuickSight cannot process streaming data for real-time anomaly detection.
Option D, DynamoDB + EMR, provides scalable NoSQL storage and batch processing. EMR introduces latency, making it unsuitable for real-time anomaly detection. Orchestrating alerts and integrating with dashboards is complex compared to Option A.
Thus, Option A delivers a complete, low-latency, scalable solution for predictive maintenance, anomaly detection, alerting, and historical analytics.
Question 48:
A healthcare organisation needs to store patient records for long-term retention due to regulatory requirements. The system must be cost-efficient, highly durable, and allow occasional querying for audits or research without restoring the entire dataset. Which AWS service combination 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:
Healthcare data retention requires cost-efficient, highly durable storage that allows selective queries for auditing or research. Option A, S3 Glacier Deep Archive + Athena, meets these requirements. Glacier Deep Archive provides eleven nines of durability and extremely low-cost storage for rarely accessed data. Lifecycle policies can automatically move data from other S3 classes to Glacier Deep Archive over time. Athena allows querying specific records using SQL without restoring entire datasets, reducing retrieval costs and enabling regulatory compliance. This approach minimises operational overhead while ensuring durability, accessibility, and query capabilities.
Option B, S3 Standard + Lambda, offers low-latency storage and event-driven processing. S3 Standard is costly for long-term archival, and Lambda cannot perform complex queries on archived data, making it unsuitable for compliance-driven retention.
Option C, RDS + Redshift, provides structured storage and analytics. RDS is expensive for long-term storage, and Redshift requires active clusters to query historical data, increasing operational complexity. This combination is not optimised for rarely accessed archival data.
Option D, DynamoDB + EMR, offers scalable storage and batch processing. DynamoDB is cost-prohibitive for long-term archival, and EMR’s batch-oriented analytics introduce latency and complexity for occasional queries.
Thus, Option A provides the most cost-efficient, durable, and queryable long-term storage solution for healthcare records.
Question 49:
A retail company wants to implement a clickstream analytics solution that ingests high-volume user interactions from its website, performs near-real-time transformations, stores raw and processed data, and allows reporting and analytics. Which AWS architecture is best suited for this scenario?
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 high-volume ingestion, near-real-time processing, and historical data storage. Option A, Kinesis Data Firehose + S3 + Lambda + Athena, provides a scalable, real-time solution. Firehose ingests user interactions and scales automatically. Lambda performs near-real-time transformations, such as aggregation or enrichment. S3 stores raw and transformed data for long-term analytics and reporting. Athena enables direct querying of data on S3 without moving it, supporting analytics and dashboarding efficiently. This architecture supports both operational and analytical use cases with minimal operational overhead.
Option B, S3 + Glue + Redshift, is batch-oriented. ETL jobs run on scheduled intervals, introducing latency that prevents near-real-time analysis. Redshift is suitable for batch analytics but not for immediate transformation and reporting of streaming data.
Option C, RDS + QuickSight, supports structured data storage and dashboards but cannot handle high-frequency streaming events or real-time transformations. Scaling RDS for unpredictable traffic spikes is operationally complex.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency, making real-time transformations and reporting unfeasible. Operational integration for dashboards adds complexity.
Thus, Option A offers a fully integrated, scalable, real-time solution for clickstream ingestion, transformation, and analytics.
Question 50:
A financial platform wants to implement a fraud detection system that can ingest high-volume transactions, detect anomalies in near-real-time, trigger alerts for suspicious activity, and store all transaction data durably for auditing. Which AWS services combination 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:
Fraud detection systems must handle high-volume transactions with low latency, detect anomalies, trigger immediate alerts, and store data for auditing. Option A, Kinesis Data Streams + Lambda + CloudWatch + S3, fulfills these requirements. Kinesis Data Streams provides scalable, durable ingestion of millions of transactions per second. Lambda processes events in near-real time, applying anomaly detection logic or scoring models to identify potential fraud. CloudWatch monitors key metrics and triggers alerts when anomalies are detected. S3 ensures durable storage of all transaction data for auditing, compliance, and historical analysis. This architecture scales automatically, provides operational monitoring, and enables low-latency responses to fraudulent activity.
Option B, S3 + Glue + Athena, is suitable for batch analysis but cannot perform near-real-time anomaly detection or trigger immediate alerts, making it unsuitable for operational fraud prevention.
Option C, RDS + Redshift, supports structured storage and batch analytics. RDS cannot handle high-frequency streaming events efficiently, and Redshift is batch-oriented, preventing real-time detection and alerting. Scaling RDS for bursts of traffic is operationally complex.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR’s batch nature introduces latency that prevents near-real-time detection. Operational complexity increases when integrating alerting and monitoring systems.
Therefore, Option A delivers a fully integrated, scalable, low-latency architecture for real-time fraud detection with immediate alerting and durable storage.
Question 51:
A global logistics company wants to implement a predictive maintenance system for its fleet of trucks. The system must ingest high-volume telemetry data in real-time, perform anomaly detection to predict potential failures, trigger alerts for immediate maintenance action, and store all historical data for compliance and analytics. Which AWS architecture is best suited for this requirement?
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:
Predictive maintenance for a global fleet requires continuous, high-volume ingestion of telemetry data, near-real-time anomaly detection, immediate operational alerts, and durable historical storage for compliance and analytics. Option A is the most suitable architecture. Kinesis Data Streams provides a robust, scalable platform for ingesting streaming data from thousands of vehicles simultaneously. Its automatic scaling ensures that sudden spikes in telemetry events do not overwhelm the system, maintaining low-latency, high-throughput processing. Kinesis Data Analytics allows real-time analysis of incoming streams, applying statistical models or SQL-based logic to detect anomalies that may indicate potential equipment failures. AWS Lambda functions are triggered upon detecting anomalies, sending immediate alerts to maintenance teams or initiating automated workflows for preventive action. Amazon S3 ensures cost-efficient, highly durable storage of historical telemetry and processed data, which is critical for auditing, compliance with transportation regulations, and future analytics or model retraining.
Option B, S3 + Glue + Athena, is batch-oriented. S3 provides scalable storage, Glue performs scheduled ETL transformations, and Athena allows SQL-based queries. While suitable for historical data analytics, this architecture cannot support real-time anomaly detection or immediate alerting. Any detection of potential failures would be delayed, reducing the operational effectiveness of predictive maintenance.
Option C, RDS + QuickSight, offers structured storage and visualization capabilities. RDS is optimised for relational transactional workloads but is not designed for high-frequency streaming data. QuickSight provides dashboards but cannot process live data in real-time. Scaling RDS to handle bursts of telemetry data from a global fleet is costly and operationally complex. Consequently, this architecture fails to meet the real-time requirements of predictive maintenance.
Option D, DynamoDB + EMR, provides scalable NoSQL storage and distributed batch analytics. DynamoDB can efficiently store telemetry data but does not natively support streaming analytics. EMR processes large datasets in batch mode, introducing latency that prevents real-time detection of anomalies. Integrating alerts and operational dashboards requires additional orchestration and increases system complexity.
Thus, Option A delivers a fully integrated, low-latency, scalable, and durable architecture for real-time predictive maintenance, anomaly detection, alerting, and historical data storage, fulfilling both operational and compliance requirements comprehensively.
Question 52:
A financial institution wants to build a fraud detection system capable of ingesting millions of payment transactions per second, detecting anomalies in near-real time, triggering alerts for suspicious activity, and storing transaction data durably for auditing and regulatory compliance. Which AWS services combination is the 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:
Fraud detection at a high transactional volume requires low-latency ingestion, immediate anomaly detection, operational alerting, and durable storage for regulatory compliance. Option A is the optimal architecture. Kinesis Data Streams ingests millions of transactions per second, offering durability and auto-scaling to handle surges during peak hours. AWS Lambda processes each transaction in real-time, applying scoring models or anomaly detection algorithms to flag potential fraudulent activity immediately. Amazon CloudWatch monitors key metrics, triggers alerts, and integrates with other AWS services or notification systems to notify operational teams of suspicious activity. Amazon S3 ensures durable, cost-efficient storage of all transaction data for auditing, compliance, historical analysis, and future machine learning model retraining.
Option B, S3 + Glue + Athena, is primarily batch-oriented. While S3 stores data, Glue performs scheduled ETL transformations, and Athena allows querying for analysis. Batch processing introduces delays that prevent real-time fraud detection, making this architecture inadequate for operational fraud prevention.
Option C, RDS + Redshift, offers structured storage and analytical capabilities. RDS is not optimised for high-velocity streams, and Redshift is batch-focused, limiting real-time anomaly detection and alerting. Scaling RDS for millions of transactions per second is complex, expensive, and operationally challenging.
Option D, DynamoDB + EMR, provides scalable storage and distributed batch processing. EMR processes data in batches, which introduces latency incompatible with near-real-time detection. Integrating alerting and monitoring mechanisms requires additional orchestration, increasing system complexity.
Therefore, Option A is the only architecture that ensures scalable ingestion, real-time anomaly detection, operational alerting, and durable storage for compliance and auditing, meeting the rigorous requirements of high-volume financial fraud detection.
Question 53:
A healthcare organization must archive patient records for long-term retention to comply with regulatory requirements. The solution must be highly durable, cost-efficient, and allow occasional querying for audits or research without restoring entire datasets. Which AWS architecture is most suitable?
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:
Healthcare records require extremely durable, cost-efficient storage for long-term retention. Option A, S3 Glacier Deep Archive + Athena, meets these requirements. Glacier Deep Archive provides eleven nines of durability, ensuring data remains intact for decades with minimal cost, suitable for regulatory-compliant long-term archival. Lifecycle policies allow automatic movement of older patient records from other S3 storage classes to Glacier Deep Archive, optimizing cost efficiency. Athena enables SQL-based querying on archived data subsets without restoring the entire dataset, facilitating audits and research while minimizing retrieval costs. This architecture simplifies operational overhead, supports compliance with healthcare regulations, and provides selective data access efficiently.
Option B, S3 Standard + Lambda, offers low-latency storage and event-driven compute. While suitable for frequently accessed data, S3 Standard is cost-prohibitive for long-term archival of rarely accessed healthcare records. Lambda cannot query archived datasets, rendering it unsuitable for compliance-driven audit requirements.
Option C, RDS + Redshift, provides structured storage and analytical querying. RDS is costly for long-term archival, and Redshift requires active clusters for queries, which increases operational complexity. The architecture is not optimised for rarely accessed, long-term data.
Option D, DynamoDB + EMR, offers scalable NoSQL storage and batch analytics. DynamoDB is expensive for archival storage, and EMR introduces latency and operational complexity for occasional queries on historical data.
Thus, Option A delivers a fully compliant, durable, cost-efficient, and queryable archival solution for long-term healthcare records.
Question 54:
An e-commerce platform wants to implement a clickstream analytics system that ingests millions of user interactions per second, performs near-real-time transformations, stores raw and processed data for historical analysis, and supports reporting and business intelligence. Which AWS architecture is best suited?
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 high-volume ingestion, real-time processing, and historical storage for business intelligence. Option A is the most appropriate solution. Kinesis Data Firehose ingests millions of clickstream events per second, automatically scaling to accommodate fluctuations in user activity. AWS Lambda performs near-real-time transformations, such as filtering, enrichment, or aggregation of clickstream data. Amazon S3 stores both raw and processed data cost-effectively, enabling long-term analysis, reporting, and historical auditing. Athena allows users to run SQL-based queries directly on S3 data without moving it, supporting business intelligence, operational reporting, and analytics efficiently. This architecture combines real-time and batch analytics capabilities, minimising operational overhead while supporting multiple use cases.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs run on schedules, which introduces latency and prevents near-real-time transformations. Redshift is suited for large-scale queries on structured data but does not support real-time ingestion or processing, making it less suitable for high-velocity clickstream data.
Option C, RDS + QuickSight, provides structured relational storage and dashboards. However, RDS cannot scale to handle millions of events per second, and QuickSight cannot process streaming data in real-time. Scaling RDS for high-velocity clickstreams is operationally complex and costly.
Option D, DynamoDB + EMR, offers scalable storage and distributed batch analytics. EMR processes data in batch mode, introducing latency that prevents real-time transformations. Integrating dashboards with EMR results in operational complexity, making it less efficient than Option A.
Thus, Option A provides the fully integrated, scalable, low-latency architecture required for near-real-time clickstream analytics, transformation, and historical analysis.
Question 55:
A financial platform requires a real-time fraud detection system that ingests high-volume transactions, detects anomalies immediately, triggers operational alerts, and stores all transactions for auditing and compliance purposes. Which AWS service combination 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:
A real-time fraud detection system must provide low-latency ingestion, immediate anomaly detection, alerting, and durable storage for auditing and compliance. Option A is the most appropriate architecture. Kinesis Data Streams ingests millions of transactions per second, providing high throughput, durability, and automatic scaling to handle surges in transaction volumes. AWS Lambda processes events in real-time, applying anomaly detection logic or machine learning models to identify potentially fraudulent transactions immediately. Amazon CloudWatch monitors metrics, triggers alerts, and integrates with notification systems to notify operational teams of suspicious activity. Amazon S3 stores all transaction data durably and cost-efficiently, ensuring compliance, auditability, and historical analysis. This architecture is scalable, low-latency, fully managed, and operationally efficient, meeting both real-time operational and compliance requirements.
Option B, S3 + Glue + Athena, is batch-oriented. It supports historical analysis but cannot detect fraud in real-time or trigger immediate alerts, making it unsuitable for operational fraud prevention.
Option C, RDS + Redshift, supports structured storage and analytical queries but is not designed for high-velocity streaming ingestion. Redshift is batch-oriented, preventing immediate detection and alerting, and scaling RDS for large transaction volumes is operationally complex.
Option D, DynamoDB + EMR, provides scalable storage and distributed batch processing. EMR introduces latency that is incompatible with real-time anomaly detection. Integrating alerts and monitoring requires additional orchestration, increasing complexity.
Therefore, Option A provides the complete, scalable, low-latency, and durable architecture necessary for real-time fraud detection with alerting and compliance support.
Question 56:
A media company wants to build a real-time recommendation system for video streaming. The system must ingest user behaviour events at high velocity, generate personalised recommendations dynamically, support real-time analytics, and store all historical events for model retraining and reporting. Which AWS architecture is most suitable?
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:
A recommendation system for video streaming requires real-time ingestion of high-velocity user interactions, dynamic personalisation, real-time analytics, and long-term storage for historical analysis. Option A, Kinesis Data Streams + Lambda + SageMaker + S3, is the most appropriate architecture. Kinesis Data Streams ingests streaming events from user interactions, ensuring scalability and low-latency processing even during peak traffic periods. Lambda provides serverless, near-real-time event processing, applying transformations and enrichment needed for recommendation generation. Amazon SageMaker hosts the machine learning models that generate personalised recommendations based on real-time inputs and historical patterns. S3 stores historical user events and model output, enabling cost-efficient storage for auditing, reporting, and retraining machine learning models to improve recommendation quality.
Option B, S3 + Glue + Redshift, is batch-oriented. While S3 stores raw data, Glue performs scheduled ETL jobs, and Redshift provides analytics on structured data. This approach cannot generate recommendations in real-time or respond dynamically to user behaviour, which diminishes personalisation effectiveness.
Option C, RDS + QuickSight, provides structured storage and dashboards but cannot handle high-velocity streaming data or perform real-time recommendations. Scaling RDS for large-scale streaming ingestion is operationally complex and expensive. QuickSight cannot process live data for immediate personalised recommendations.
Option D, DynamoDB + EMR, offers scalable NoSQL storage and batch processing. EMR’s batch nature introduces latency, preventing near-real-time recommendation generation. Integrating real-time analytics and dashboards with EMR increases operational complexity and reduces efficiency compared to Option A.
Therefore, Option A provides a fully integrated solution for scalable, real-time recommendations, dynamic analytics, and long-term storage for model retraining and reporting, meeting both operational and strategic business requirements.
Comparison with Option B: S3 + Glue + Redshift
Option B is primarily batch-oriented. While S3 can store raw events, AWS Glue performs scheduled ETL jobs, and Redshift provides structured analytics. This architecture cannot respond to user behaviour in real time, resulting in delayed recommendations that may no longer align with current preferences. Batch processing introduces latency that reduces personalisation effectiveness and user satisfaction, making it unsuitable for dynamic, high-engagement streaming platforms.
Comparison with Option C: RDS + QuickSight
RDS provides structured relational storage, and QuickSight offers visualisation dashboards. While this combination is suitable for reporting and analytics on static or slowly changing datasets, it cannot handle high-velocity streaming interactions. Generating real-time recommendations would require complex polling and frequent database writes, creating operational overhead and increasing latency. QuickSight dashboards cannot serve the immediate personalisation needs of a streaming platform where every interaction counts toward recommendation accuracy.
Comparison with Option D: DynamoDB + EMR
DynamoDB can scale for large read/write operations, and EMR provides distributed data processing. However, EMR operates in batch mode, introducing significant delays between event ingestion and model-driven recommendations. Real-time analytics and dynamic personalisation would require additional orchestration layers, increasing system complexity and operational risk. Compared to Option A, this approach cannot deliver low-latency recommendations at scale, limiting its effectiveness for a responsive streaming experience.
Option A integrates ingestion, real-time processing, machine learning inference, and historical storage into a seamless architecture. Each component complements the others to deliver low-latency recommendations, accurate personalisation, and scalable performance. Kinesis ensures reliable data flow, Lambda enables immediate processing, SageMaker provides intelligent predictions, and S3 guarantees durable storage for historical analysis and model retraining. This combination optimises user engagement while minimising operational complexity, providing a robust and scalable solution for a modern video streaming recommendation system.
Question 57:
A transportation company wants to implement a near-real-time traffic monitoring system using IoT sensors installed on vehicles. The system must ingest telemetry data continuously, detect anomalies, trigger alerts for unusual traffic patterns, and store historical data for trend analysis and regulatory reporting. Which AWS service combination is best suited for this scenario?
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:
Near real-time traffic monitoring with IoT telemetry requires scalable ingestion of high-velocity sensor data, continuous analytics, alerting mechanisms, and durable historical storage. Option A fulfils all these requirements. Kinesis Data Streams handles continuous ingestion of telemetry data from thousands of vehicles, maintaining durability and supporting auto-scaling for peak traffic periods. Kinesis Data Analytics allows continuous processing and anomaly detection, identifying unusual patterns such as traffic congestion or accidents in near-real time.AWS Lambda triggers operational alerts based on detected anomalies, notifying traffic management teams or initiating automated workflows. Amazon S3 stores historical telemetry data for long-term trend analysis, reporting, and regulatory compliance, ensuring cost-efficiency and durability.
Option B, S3 + Glue + Athena, is batch-oriented. S3 provides storage, Glue performs scheduled ETL, and Athena allows querying of historical data. This architecture does not support near-real-time anomaly detection or immediate alerting, making it unsuitable for operational traffic monitoring.
Option C, RDS + QuickSight, is optimised for structured data storage and visualisation. RDS cannot handle high-velocity streaming telemetry data efficiently, and QuickSight cannot provide near-real-time analytics or anomaly detection. Scaling RDS to manage bursts of telemetry events increases operational complexity.
Option D, DynamoDB + EMR, provides scalable NoSQL storage and batch processing. EMR processes data in batches, introducing latency incompatible with real-time detection and alerting. Integrating dashboards and notifications requires additional orchestration and increases complexity.
Thus, Option A delivers a fully integrated, scalable, real-time solution for IoT-based traffic monitoring, anomaly detection, alerting, and historical analytics.
Question 58:
A healthcare organisation must implement a long-term archival solution for patient imaging records. The system must provide highly durable, cost-efficient storage and allow occasional querying for audits or research without restoring entire datasets. Which AWS architecture 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:
Healthcare imaging records require highly durable, cost-efficient long-term archival storage with selective query capabilities for audits and research. Option A is the optimal architecture. Glacier Deep Archive offers extremely low-cost storage with eleven nines of durability, making it suitable for storing large volumes of imaging data over extended periods. Lifecycle policies enable automatic data transitions from higher-cost S3 classes to Glacier Deep Archive, optimising operational costs. Athena allows SQL-based queries on subsets of archived data without restoring entire datasets, facilitating audits and research with minimal operational effort.
Option B, S3 Standard + Lambda, is suitable for frequently accessed data. S3 Standard is cost-prohibitive for long-term archival, and Lambda cannot query archived datasets, making it unsuitable for regulatory or audit requirements.
Option C, RDS + Redshift, provides structured storage and analytics. RDS is costly for long-term storage, and Redshift requires active clusters for queries, increasing operational complexity. This approach is inefficient for rarely accessed archival data.
Option D, DynamoDB + EMR, offers scalable storage and batch analytics. EMR introduces latency, making occasional queries cumbersome, and DynamoDB is expensive for long-term archival storage. Operational overhead for querying archived datasets is high compared to Option A.
Therefore, Option A provides a compliant, cost-efficient, and highly durable solution for long-term archival of healthcare imaging records with query capabilities for audits and research.
Question 59:
A retail company wants to implement a clickstream analytics system to track millions of user interactions on its e-commerce website. The system must ingest high-velocity data, perform near-real-time transformations, store raw and processed data, and support reporting and business intelligence dashboards. Which AWS architecture is best suited for this scenario?
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 handling high-velocity user interactions, performing near-real-time processing, and storing data for historical analysis and reporting. Option A is the most suitable architecture. Kinesis Data Firehose ingests millions of clickstream events per second, automatically scaling to manage traffic surges. AWS Lambda enables near-real-time data transformations such as enrichment, aggregation, or filtering. Amazon S3 stores raw and processed clickstream data efficiently, supporting long-term historical analysis. Athena allows querying of S3 data directly, enabling business intelligence dashboards and analytics without moving data, thus minimising operational overhead.
Option B, S3 + Glue + Redshift, is batch-oriented. ETL jobs introduce latency, preventing near-real-time transformation of clickstream data. Redshift provides analytical querying but is not suitable for real-time ingestion and transformation, making it less suitable for operational dashboards.
Option C, RDS + QuickSight, supports structured data storage and dashboards. RDS cannot scale to handle millions of high-velocity events, and QuickSight cannot process live streams for immediate reporting. Operational complexity increases when scaling RDS for high-frequency data.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency, preventing real-time transformations and reporting. Integrating dashboards requires additional orchestration and operational overhead, making it less efficient than Option A.
Therefore, Option A offers a fully integrated, low-latency, and scalable solution for clickstream ingestion, transformation, storage, and business intelligence.
Question 60:
A financial services platform wants to implement a real-time fraud detection system that ingests millions of transactions per second, detects anomalies immediately, triggers alerts for suspicious activity, and stores all transactions durably 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:
Fraud detection at scale requires low-latency ingestion, immediate anomaly detection, operational alerting, and durable storage for auditing and compliance. Option A provides a complete solution. Kinesis Data Streams ingests millions of transactions per second, ensuring durability and automatic scaling to accommodate peaks. AWS Lambda processes transactions in real-time, applying anomaly detection models to flag suspicious activity immediately. Amazon CloudWatch monitors operational metrics, triggers alerts, and integrates with notifications for rapid response. Amazon S3 stores all transactions durably, ensuring auditability and regulatory compliance while supporting historical analysis and machine learning model retraining.
Option B, S3 + Glue + Athena, is batch-oriented. While suitable for historical analysis, it cannot provide real-time detection or alerting, making it unsuitable for operational fraud prevention.
Option C, RDS + Redshift, provides structured storage and analytical querying. RDS cannot handle high-velocity streaming data efficiently, and Redshift’s batch-oriented nature prevents real-time anomaly detection and alerting. Scaling RDS for millions of transactions per second is operationally complex.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR processes data in batches, introducing latency that is incompatible with near-real-time detection and alerting. Orchestrating alerts and dashboards increases operational complexity.
Therefore, Option A delivers a fully integrated, scalable, low-latency, and durable architecture for real-time fraud detection, alerting, and compliance.
Low-Latency Ingestion with Amazon Kinesis Data Streams
Amazon Kinesis Data Streams is designed to handle high-throughput, low-latency ingestion of streaming data, which is critical in fraud detection systems where decisions must be made in milliseconds. Millions of financial transactions, login attempts, or other sensitive events can flow into Kinesis simultaneously, without data loss. Its ability to scale automatically ensures that sudden spikes in activity, such as during Black Friday or high-volume trading periods, do not overwhelm the system. The distributed nature of Kinesis shards allows parallel processing, which reduces processing latency and improves throughput. This is particularly important because fraud patterns can emerge rapidly, and even a few seconds of delay in detection can lead to financial losses or compromised security.
Real-Time Processing with AWS Lambda
Once the data enters Kinesis, AWS Lambda provides a serverless compute layer that can trigger automatically on each new record or batch of records. Lambda’s event-driven architecture ensures that every transaction is analysed as it arrives. Machine learning models or rule-based algorithms can be applied immediately to detect anomalies, such as unusually large transactions, atypical IP addresses, or multiple rapid logins. Lambda’s automatic scaling allows it to handle bursts of events without requiring manual provisioning. The serverless nature of Lambda also reduces operational overhead, eliminating the need to manage servers or clusters for real-time processing. Importantly, Lambda can integrate with other AWS services like SNS or EventBridge to immediately trigger alerts when suspicious behaviour is detected.
Operational Monitoring and Alerting with Amazon CloudWatch
Amazon CloudWatch is integral for monitoring system health, tracking metrics, and generating operational alerts. In a fraud detection architecture, CloudWatch can continuously monitor Kinesis stream metrics, Lambda execution metrics, error rates, and throughput. Threshold-based alarms can be configured to notify security teams or trigger automated remediation workflows. For example, if Lambda processing latency exceeds a defined threshold or if anomaly detection identifies a pattern exceeding normal transaction volumes, CloudWatch alarms can initiate notifications to security operations teams. This near-real-time alerting capability ensures that potential fraud is addressed promptly, reducing exposure to losses and mitigating risk.
Durable Storage and Auditability with Amazon S3
All incoming transactions and detection results can be stored durably in Amazon S3. This ensures that no data is lost and provides a complete, immutable record for compliance, auditing, and regulatory reporting. S3’s virtually unlimited storage capacity allows long-term retention of historical data, which is crucial for forensic analysis, trend detection, and retraining machine learning models. Analysts can query historical data to identify emerging fraud patterns, refine detection algorithms, or investigate specific incidents. Additionally, S3 integrates seamlessly with other AWS analytics and machine learning services, allowing organisations to leverage historical data for predictive modelling and improving fraud detection accuracy over time.
Comparison with Option B: S3 + AWS Glue + Athena
Option B offers a batch-processing architecture. While S3, Glue, and Athena are excellent for historical data analysis and generating reports, they operate on scheduled or on-demand batch queries rather than real-time streams. This latency is incompatible with operational fraud detection, where immediate detection and response are required to prevent losses. Athena can query stored data efficiently, and Glue can transform and catalogue large datasets, but the architecture cannot detect anomalies at the moment they occur. Therefore, it is unsuitable for systems that require instant alerts and automated response mechanisms.
Comparison with Option C: Amazon RDS + Redshift
Option C focuses on structured storage and analytical querying. Amazon RDS is optimised for transactional workloads with structured relational data, but scaling it to ingest millions of transactions per second in real time is operationally complex and costly. Redshift excels at analytical queries on large datasets but operates in a batch-oriented manner, meaning it cannot process data as it arrives. Combining RDS and Redshift would allow analysis of historical trends, but would fail to detect or alert on anomalies immediately. This latency poses a risk in fraud scenarios where quick detection is essential to prevent financial losses or system abuse.
Comparison with Option D: DynamoDB + EMR
DynamoDB offers highly scalable NoSQL storage, and EMR provides distributed big data processing. However, EMR is primarily batch-oriented, introducing delays between data ingestion and anomaly detection. While DynamoDB supports fast read/write operations, it cannot process streams and trigger real-time detection on its own. Coordinating batch analytics with alerting workflows introduces additional operational complexity. Security teams would need to implement custom mechanisms to monitor, detect, and alert on anomalies, increasing the risk of delayed detection and response.
Integrated Architecture Advantages of Option A
Option A offers a fully integrated approach: Kinesis ensures ingestion and durability, Lambda provides real-time processing, CloudWatch handles monitoring and alerting, and S3 offers secure, durable storage. Each component complements the others, creating a seamless workflow where data flows from ingestion to detection to alerting to storage without latency bottlenecks. This architecture also allows flexibility in adding additional detection algorithms or integrating with other services, such as Amazon SageMaker for advanced machine learning-based anomaly detection, without disrupting existing workflows.
The combination also provides operational efficiency. Serverless Lambda eliminates the need for managing processing infrastructure. CloudWatch automates monitoring and alerting without manual intervention. S3 ensures data durability, compliance, and analytics readiness. The overall system is resilient, scalable, and able to adapt to varying transaction volumes, which is critical for industries such as finance, e-commerce, and online payments.
Scalability and Resilience Considerations
Kinesis’ ability to shard streams and Lambda’s automatic concurrency scaling allow this architecture to handle millions of events per second, maintaining low latency even under high traffic conditions. CloudWatch ensures operational resilience by providing automated alerts and metrics dashboards, enabling rapid identification and mitigation of failures. S3 ensures that even if downstream processes encounter issues, raw data remains intact, allowing reprocessing or forensic analysis without data loss. This combination of scalability, resilience, and automation is difficult to achieve with batch-oriented architectures like Glue + Athena, Redshift + RDS, or DynamoDB + EMR.