Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 6 Q76-90
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Question 76:
A global video streaming platform wants to implement a real-time recommendation engine that can handle millions of user interactions per second, provide dynamic personalised suggestions, and store historical data for trend analysis 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 continuous ingestion of large volumes of user interaction data, real-time processing, and personalised response generation. Option A is optimal. Kinesis Data Streams ingests millions of events per second with low latency, providing scalable and reliable data streaming. AWS Lambda processes these events in real-time, performing transformations, filtering, and enrichment to make the data ready for immediate use by machine learning models. Amazon SageMaker hosts and serves models that generate personalised recommendations dynamically, using both real-time inputs and historical behavioural data. Amazon S3 stores raw and processed datasets for long-term trend analysis and model retraining, ensuring continuous improvement of recommendation accuracy.
Option B, S3 + Glue + Redshift, is batch-oriented. While S3 can store large datasets and Glue can perform ETL transformations, these are scheduled tasks, which introduce delays. Redshift is suitable for analytics but not for real-time recommendations, meaning user interactions would be processed too slowly to provide immediate personalisation.
Option C, RDS + QuickSight, is limited by throughput and latency. RDS cannot handle millions of events per second efficiently, and QuickSight dashboards cannot provide real-time recommendation feedback. Scaling RDS for such high-velocity data introduces complexity and cost without providing the required real-time processing.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR processes data in batches, which introduces latency that is incompatible with real-time recommendation systems. Orchestrating dashboards or alerts adds operational complexity compared to the fully integrated Option A architecture.
Therefore, Option A provides a low-latency, scalable, and fully integrated solution for dynamic personalised recommendations, historical analysis, and model retraining.
Question 77:
A global logistics company wants to monitor its fleet in real-time using IoT devices. The solution must ingest telemetry continuously, detect anomalies such as vehicle malfunctions, generate immediate alerts, and maintain historical records 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 fleet monitoring requires high-velocity ingestion, continuous anomaly detection, immediate alerting, and durable storage. Option A satisfies all these requirements. Kinesis Data Streams ingests telemetry data from potentially thousands of vehicles, providing durability and scalability. Kinesis Data Analytics continuously processes incoming streams, enabling immediate detection of anomalies such as engine failures, unsafe driving patterns, or route deviations. AWS Lambda triggers operational alerts in real-time, notifying teams instantly or initiating automated workflows. Amazon S3 provides cost-efficient long-term storage for historical telemetry data, supporting trend analysis, reporting, and compliance audits.
Option B, S3 + Glue + Athena, introduces latency since Glue ETL jobs run on a schedule, making it unsuitable for real-time anomaly detection. Athena can query historical data but cannot react to live events immediately, reducing operational effectiveness.
Option C, RDS + QuickSight, is unsuitable due to throughput limitations. RDS cannot ingest high-frequency telemetry efficiently, and QuickSight dashboards cannot perform real-time monitoring. Scaling RDS to manage global fleet telemetry adds operational complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR’s batch processing introduces latency, preventing timely anomaly detection. Orchestrating alerts and dashboards requires additional operational overhead.
Thus, Option A provides a fully integrated, low-latency, scalable architecture for telemetry ingestion, anomaly detection, alerting, and historical storage.
Question 78:
A healthcare organisation needs a long-term storage solution for patient imaging data. The solution must provide high durability, cost efficiency, and occasional query capabilities for research or audit purposes 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 data requires long-term, highly durable storage, cost efficiency, and occasional query capability. Option A is optimal. Glacier Deep Archive offers extremely low-cost, durable storage with eleven nines of durability, ensuring patient imaging data is secure over long periods. Lifecycle policies can automatically transition data from more expensive S3 classes to Glacier Deep Archive, reducing storage costs. Athena allows selective querying of archived datasets without restoring the full dataset, enabling audits and research efficiently. This architecture minimises operational effort while maintaining compliance with healthcare regulations.
Option B, S3 Standard + Lambda, is cost-prohibitive for long-term archival and cannot efficiently query infrequently accessed datasets. Lambda cannot replace Athena’s query functionality.
Option C, RDS + Redshift, provides structured storage and analytics. RDS is expensive for archival storage, and Redshift requires active clusters to query archived data, increasing operational overhead.
Option D, DynamoDB + EMR, allows batch analytics but introduces latency and complexity. DynamoDB is costly for large, long-term datasets, and EMR does not provide immediate query capabilities on archival data.
Therefore, Option A offers a compliant, durable, cost-effective, and queryable archival solution for patient imaging data.
Question 79:
An online retailer wants to implement clickstream analytics capable of ingesting millions of user interactions per second, performing near real-time transformations, storing raw and processed data, and providing reporting and business intelligence dashboards. 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 ingestion of high-volume events, near real-time processing, storage, and reporting. Option A provides a fully integrated solution. Kinesis Data Firehose ingests millions of events per second, scaling automatically. AWS Lambda transforms data in real-time, performing enrichment, filtering, or aggregation. Amazon S3 stores raw and processed data for long-term analysis and audit purposes. Athena allows SQL-based queries directly on S3, supporting dashboards and business intelligence reporting without moving data.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs run on schedules, introducing delays in data transformation and analytics. Redshift is suited for structured data but cannot handle high-velocity streaming ingestion efficiently.
Option C, RDS + QuickSight, cannot handle millions of events per second. QuickSight does not provide real-time dashboards, limiting operational insights. Scaling RDS to manage such volumes is costly and complex.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency incompatible with near real-time processing, and orchestrating dashboards requires additional operational effort.
Therefore, Option A provides a scalable, low-latency solution for clickstream ingestion, transformation, storage, and analytics.
Clickstream analytics involves capturing, processing, and analysing user interaction data from websites, applications, or digital platforms. Each click, page view, or interaction generates events that must be collected at high velocity, processed in near real-time, and stored for both operational and analytical purposes. Businesses use clickstream data to understand user behaviour, optimise user experience, personalise content, and make data-driven decisions. To achieve this, the architecture must be capable of ingesting millions of events per second, processing them immediately for enrichment or filtering, storing both raw and transformed data securely, and supporting analytics without significant delays. Latency and scalability are critical because insights must be available almost immediately to adjust campaigns, detect anomalies, or feed personalisation engines.
Option A: Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
Option A offers a fully managed, low-latency architecture for clickstream analytics. Kinesis Data Firehose handles high-throughput data ingestion, automatically scaling to accommodate varying event volumes. It ensures that all user interactions are captured reliably and delivered to downstream services without manual scaling or complex management. AWS Lambda provides real-time transformation capabilities, allowing enrichment of incoming clickstream data, aggregation of key metrics, or filtering of irrelevant events. This immediate processing ensures that data is actionable as soon as it is ingested. Amazon S3 serves as durable storage for both raw and processed datasets, enabling long-term retention, auditing, and historical analysis. The combination of raw and transformed data allows analysts to perform trend analysis and retraining of predictive models while maintaining a single source of truth. Amazon Athena facilitates direct SQL-based queries on S3, allowing business intelligence teams to generate dashboards, perform ad hoc analysis, or create reports without moving large datasets to a separate data warehouse. This reduces operational overhead and provides near real-time visibility into user behaviour. Together, these services form a highly scalable, low-latency solution that meets the critical requirements of modern clickstream analytics pipelines.
Option B: Amazon S3 + AWS Glue + Amazon Redshift
Option B is primarily a batch-oriented architecture. While Amazon S3 provides storage for raw data and Redshift is suitable for structured analytics, the processing and transformation rely on AWS Glue ETL jobs. Glue jobs are scheduled and do not process data in real-time, which introduces delays. As a result, insights and dashboards based on this data are always delayed relative to the actual user interactions. While this option is strong for historical trend analysis and complex queries on structured datasets, it cannot provide the immediate visibility and operational responsiveness required for dynamic clickstream analytics.
Option C: Amazon RDS + Amazon QuickSight
Option C leverages relational storage and visualisation. Amazon RDS can store structured clickstream events, and QuickSight can generate dashboards. However, relational databases are not designed to handle millions of events per second efficiently. Scaling RDS to accommodate such high throughput introduces operational complexity and increased cost. QuickSight, while useful for visualisation, is not optimised for real-time analysis, limiting the ability to respond to emerging trends or user behaviour in near real-time.
Option D: Amazon DynamoDB + Amazon EMR
Option D combines scalable NoSQL storage with distributed batch processing. DynamoDB can store large volumes of clickstream events with low latency, but Amazon EMR is primarily designed for batch processing. EMR introduces processing latency incompatible with near-real-time analytics. Additionally, orchestrating dashboards or deriving operational insights requires additional integration and complexity, making this option less suitable for immediate decision-making based on live user interactions.
Question 80:
A financial services company requires a real-time fraud detection system that can ingest millions of transactions per second, detect anomalies instantly, trigger operational alerts, and store all transactions durably for auditing and compliance. Which AWS architecture is most suitable?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Explanation:
Real-time fraud detection requires scalable ingestion, immediate anomaly detection, alerting, and durable storage. Option A is optimal. Kinesis Data Streams ingests millions of transactions per second with low latency and durability. AWS Lambda processes transactions in real-time, applying anomaly detection models to flag suspicious activity. CloudWatch monitors operational metrics and triggers alerts for immediate response. S3 stores transactions durably, supporting auditing and compliance while enabling historical analysis and model retraining.
Option B, S3 + Glue + Athena, is batch-oriented and cannot detect fraud in real-time. Alerts would be delayed, reducing operational effectiveness.
Option C, RDS + Redshift, provides structured storage and analytics but cannot ingest high-frequency streaming data efficiently. Real-time detection is infeasible, and scaling RDS is operationally complex.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency incompatible with real-time detection, and alerts require additional orchestration.
Thus, Option A delivers a fully integrated, low-latency, scalable architecture for fraud detection, alerting, and compliance.
Question 81:
A global online gaming company wants to implement a real-time leaderboard system. The system must handle millions of player score updates per second, compute rankings dynamically, and store historical scores for player statistics and analytics. Which AWS architecture is most appropriate?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon DynamoDB + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon EMR + Amazon S3
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon DynamoDB + Amazon S3
Explanation:
Real-time leaderboard systems require high-velocity ingestion, immediate processing, dynamic ranking, and durable storage for historical analytics. Option A is the most suitable. Kinesis Data Streams ingests millions of score updates per second, scaling automatically during peak gaming activity. AWS Lambda processes incoming events in real-time, updating player scores and computing rankings. DynamoDB serves as a low-latency, scalable database capable of handling frequent reads and writes necessary for real-time leaderboard updates. S3 stores historical player scores for analytics, trend analysis, and audits.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs run on a schedule, which introduces latency incompatible with real-time leaderboards. Redshift is suitable for analytics, but cannot handle dynamic ranking in near real-time.
Option C, RDS + QuickSight, is unsuitable due to throughput limitations. RDS cannot manage millions of updates per second, and QuickSight dashboards are not real-time. Scaling RDS introduces operational complexity and cost.
Option D, EMR + S3, provides batch analytics but cannot support real-time processing needed for leaderboard updates. Batch processing introduces unacceptable delays for the user experience.
Therefore, Option A provides a fully integrated, low-latency, scalable solution for real-time leaderboard computation and historical analytics.
Question 82:
A healthcare provider wants to analyse streaming patient vital signs from IoT devices in real-time to detect abnormalities. The system must trigger alerts immediately, allow historical analysis, and comply with regulations for data retention. Which AWS architecture 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:
Monitoring patient vital signs in real-time requires high-throughput ingestion, immediate anomaly detection, alerting, and durable historical storage. Option A fulfils these requirements. Kinesis Data Streams ingests continuous vital signs data from IoT devices globally, ensuring durability and scalability. Kinesis Data Analytics performs continuous computations, identifying anomalies such as abnormal heart rates or oxygen levels instantly. AWS Lambda triggers operational alerts in real-time to notify medical staff or initiate automated workflows. S3 stores historical telemetry for audits, trend analysis, and compliance with regulatory standards.
Option B, S3 + Glue + Athena, is batch-oriented, making real-time anomaly detection and immediate alerts impossible. Athena allows query-based analysis of historical data but cannot operate on streaming inputs.
Option C, RDS + QuickSight, cannot handle the ingestion rate or provide real-time analytics. QuickSight dashboards are delayed and cannot generate instant alerts. Scaling RDS is operationally complex and costly.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency incompatible with real-time monitoring, and orchestrating alerts requires additional complexity.
Thus, Option A offers a scalable, low-latency solution for real-time patient monitoring, alerting, and historical analysis.
Question 83:
A financial organisation wants to store decades of transactional records securely and cost-effectively. They need to query historical data occasionally 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 high durability, cost efficiency, and selective query capability. Option A provides this. Glacier Deep Archive offers extremely low-cost, durable storage with eleven nines of durability, ideal for decades of transactional records. Lifecycle policies can automatically move older data from standard S3 to Glacier Deep Archive, optimising costs. Athena allows SQL-based queries on archived datasets without restoring the entire dataset, enabling regulatory audits to be conducted efficiently. This reduces operational overhead while meeting compliance standards.
Option B, S3 Standard + Lambda, is expensive for long-term archival and cannot efficiently query archived datasets. Lambda cannot provide Athena-like query functionality.
Option C, RDS + Redshift, supports structured storage and analytics but is costly for decades of archival data. Redshift requires active clusters for queries, increasing complexity and expense.
Option D, DynamoDB + EMR, allows batch analytics but introduces latency and complexity. DynamoDB is expensive for long-term archival, and EMR cannot perform ad-hoc queries efficiently on archival datasets.
Thus, Option A provides a compliant, durable, cost-effective, and queryable solution for long-term financial record storage.
Question 84:
An e-commerce company wants to perform real-time clickstream analytics to monitor millions of user interactions 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 of high-velocity data, near real-time processing, storage, and analytics. Option A provides a fully integrated solution. Kinesis Data Firehose ingests millions of events per second, automatically scaling with traffic. AWS Lambda performs real-time transformations, enriching and filtering events before storage. Amazon S3 holds both raw and processed datasets for long-term analysis, audits, and trend evaluation. Athena enables SQL-based queries directly on S3, supporting dashboards and reporting without moving data, reducing latency and operational complexity.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs run on schedules, introducing delays in data processing. Redshift supports structured analytics but cannot handle high-velocity streaming ingestion efficiently.
Option C, RDS + QuickSight, is unsuitable for real-time ingestion and analytics. RDS cannot process millions of events per second, and QuickSight dashboards are delayed.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency incompatible with near real-time analytics, and orchestrating dashboards adds operational overhead.
Therefore, Option A provides a low-latency, scalable, and integrated architecture for clickstream ingestion, transformation, storage, and reporting.
Question 85:
A global payment provider requires a real-time fraud detection system that can ingest millions of transactions per second, detect anomalies instantly, generate operational alerts, and store all transactions for auditing and regulatory 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, providing durability and auto-scaling. AWS Lambda processes transactions in real-time, applying fraud detection models to flag suspicious activity instantly. CloudWatch monitors system metrics and triggers alerts for immediate response. S3 stores all transactions durably, supporting auditing, compliance, and historical model retraining.
Option B, S3 + Glue + Athena, is batch-oriented and cannot provide real-time detection or alerts, reducing operational effectiveness.
Option C, RDS + Redshift, is limited in ingesting high-frequency streams and cannot support real-time fraud detection. Scaling RDS or Redshift for millions of transactions per second adds complexity and cost.
Option D, DynamoDB + EMR, offers scalable storage and batch processing, but EMR introduces latency incompatible with real-time detection. Alerts and dashboards require additional orchestration, adding complexity.
Thus, Option A delivers a fully integrated, low-latency, and scalable architecture for real-time fraud detection, alerting, and regulatory compliance.
Question 86:
A global online education platform wants to provide personalised course recommendations in real-time. The system must process millions of user interactions per second, update recommendations dynamically, and store historical user interactions for trend analysis 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 personalised recommendations require ingestion of high-volume interactions, dynamic processing, and historical data storage. Option A is optimal. Kinesis Data Streams handles millions of events per second, providing durability and scalability. AWS Lambda enables near real-time processing of events, performing transformations and enrichment to make data immediately usable by machine learning models. Amazon SageMaker hosts models that generate personalised recommendations dynamically based on real-time and historical user behaviour. Amazon S3 stores raw and processed interactions for trend analysis, audits, and model retraining.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue jobs run periodically, introducing latency that makes real-time recommendations impossible. Redshift supports analytics but cannot handle high-velocity streaming ingestion efficiently.
Option C, RDS + QuickSight, cannot process millions of events per second. QuickSight dashboards are not real-time, and scaling RDS adds complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency incompatible with real-time recommendations, and orchestrating dashboards requires additional operational effort.
Thus, Option A delivers a low-latency, scalable, and fully integrated architecture for personalised course recommendations and historical analysis.
Real-time personalised recommendation systems are a cornerstone for modern e-learning platforms, e-commerce websites, video streaming services, and digital content providers. The core objective of such systems is to dynamically predict and suggest content, courses, or products that align with each user’s unique behaviour, preferences, and historical interactions. To achieve this, the architecture must handle continuous ingestion of high-volume events, provide low-latency processing to generate actionable insights instantly, integrate machine learning models for personalisation, and maintain long-term storage for historical analysis and retraining. The system must be scalable, resilient, and capable of handling millions of concurrent users interacting with the platform simultaneously. Moreover, it should ensure operational simplicity, as complex architectures can introduce latency, errors, and maintenance overhead, which may undermine the user experience.
Option A: Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker + Amazon S3
Option A represents a complete and optimised architecture for real-time personalised recommendation systems. Amazon Kinesis Data Streams is a fully managed, scalable, and durable service designed to handle extremely high-throughput event ingestion. It can process millions of events per second, ensuring that no user interaction is lost and that the recommendation engine receives a continuous flow of real-time data. AWS Lambda provides serverless compute to process these events immediately. This processing includes transformations, enrichment, and preparation of data to be fed directly into machine learning models. Lambda’s serverless nature eliminates the need for pre-provisioned servers, supports auto-scaling, and enables immediate reaction to spikes in traffic, which is crucial for maintaining personalisation accuracy and speed. Amazon SageMaker serves as the platform for training, hosting, and deploying machine learning models. By integrating directly with real-time event streams, SageMaker can generate dynamic, contextually relevant recommendations as users interact with the platform, leveraging both current session data and historical behavioural patterns. Amazon S3 serves as a durable and scalable repository for storing raw and processed data. This ensures that historical interactions are available for auditing, trend analysis, and retraining of machine learning models to improve accuracy over time. Option A thus provides a cohesive ecosystem that balances low-latency real-time processing with long-term storage and analytical capabilities, fulfilling all requirements for a highly effective recommendation system.
Option B: Amazon S3 + AWS Glue + Amazon Redshift
Option B represents a batch-oriented architecture that excels at analysing large datasets after the fact but is unsuitable for real-time personalisation. Amazon S3 provides storage, AWS Glue orchestrates ETL workflows, and Amazon Redshift enables complex analytical queries. While this combination is powerful for reporting, historical trend analysis, and offline insights, it introduces significant latency. Glue ETL jobs run periodically, meaning that user interactions must accumulate over time before being processed. Redshift, although optimised for analytical queries on large datasets, cannot ingest or process real-time streaming data efficiently. Consequently, recommendations generated using this architecture will always be delayed and cannot reflect the user’s immediate behaviour or context. For a personalised recommendation system, where timely insights are crucial for engagement and retention, this option does not meet operational requirements.
Option C: Amazon RDS + Amazon QuickSight
Option C relies on traditional relational databases and visualisation tools. Amazon RDS provides structured storage for transactional or interaction data, while QuickSight enables the creation of dashboards and reports. While RDS can handle structured data and QuickSight can provide insights, this architecture is unsuitable for high-velocity, real-time personalisation. Relational databases are not optimised for ingesting millions of concurrent events per second, and scaling RDS to support such loads is complex and costly. QuickSight dashboards are designed for visualisation and analytics rather than real-time recommendation generation. This combination may suffice for periodic insights into user behaviour or historical trend analysis, but it cannot deliver the low-latency, immediate feedback required to provide personalised recommendations dynamically as users interact with the system.
Option D: Amazon DynamoDB + Amazon EMR
Option D provides a hybrid approach combining scalable NoSQL storage with distributed batch processing. DynamoDB is capable of handling high-throughput read and write operations and offers low-latency access to stored data. Amazon EMR enables distributed processing and analytics using frameworks like Apache Spark or Hadoop. While this combination works for analysing large datasets and producing offline recommendations, it introduces latency incompatible with real-time personalisation. EMR jobs are inherently batch-oriented and cannot process individual user interactions instantaneously. Moreover, orchestrating dynamic dashboards or feeding insights into a recommendation engine would require additional operational effort and integration complexity. This makes Option D less suitable for delivering immediate, individualised recommendations compared to an architecture specifically designed for real-time processing.
For a real-time personalised recommendation system, the architecture must simultaneously handle high-throughput data ingestion, instantaneous processing, machine learning-based personalisation, and durable storage for historical analysis. Option A fulfils all these requirements. Kinesis Data Streams ensures reliable ingestion of millions of user events per second, Lambda enables immediate processing and enrichment, SageMaker generates dynamic recommendations using both real-time and historical data, and S3 provides durable storage for long-term analytics, auditing, and model retraining. Options B, C, and D, while capable of batch processing, historical analytics, or scalable storage, cannot provide the immediacy and low-latency processing essential for dynamic personalisation. Implementing Option A ensures that recommendations are not only accurate but also delivered in real-time, significantly enhancing user engagement, learning outcomes, and overall platform satisfaction while maintaining operational simplicity and scalability.
Question 87:
A logistics company wants to implement a global fleet monitoring system using IoT sensors. The system 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 fleet monitoring requires high-throughput ingestion, continuous anomaly detection, alerting, and long-term storage. Option A satisfies these requirements. Kinesis Data Streams ingests telemetry data from global fleets, ensuring durability and scalability. Kinesis Data Analytics continuously analyses streams, identifying anomalies such as engine failures or unsafe driving behaviours. AWS Lambda triggers real-time alerts, notifying teams or initiating automated responses. Amazon S3 provides cost-efficient, durable storage for historical telemetry, supporting trend analysis, audits, and compliance reporting.
Option B, S3 + Glue + Athena, is batch-oriented. Scheduled ETL jobs introduce latency, preventing real-time detection and alerting. Athena allows querying historical data but cannot operate on streaming data.
Option C, RDS + QuickSight, is unsuitable due to throughput limitations. RDS cannot handle high-frequency telemetry efficiently, and QuickSight dashboards are not real-time. Scaling RDS introduces operational complexity.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency incompatible with real-time anomaly detection, and orchestrating alerts requires additional operational overhead.
Thus, Option A delivers a scalable, low-latency, fully integrated solution for global fleet monitoring, anomaly detection, alerting, and historical data storage.
Question 88:
A healthcare provider must store decades of patient imaging data securely and cost-effectively while allowing occasional queries for audits or research without restoring entire datasets. 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:
Healthcare imaging requires durable, low-cost, long-term storage with occasional query capability. Option A is optimal. Glacier Deep Archive provides extremely durable storage at minimal cost, suitable for decades-long retention. Lifecycle policies can automatically transition data from S3 Standard to Glacier Deep Archive, reducing costs. Athena enables SQL-based queries on archived datasets without restoring the full data, facilitating audits, research, and compliance. This approach minimises operational overhead while meeting strict regulatory requirements.
Option B, S3 Standard + Lambda, is cost-prohibitive for long-term storage. Lambda cannot provide query capabilities on archived datasets.
Option C, RDS + Redshift, offers structured storage and analytics but is expensive for long-term archival and requires active clusters to query data, increasing complexity and cost.
Option D, DynamoDB + EMR, provides batch analytics but introduces latency and complexity. DynamoDB is expensive for long-term storage, and EMR cannot perform ad-hoc queries efficiently on archived datasets.
Thus, Option A delivers a compliant, durable, cost-effective, and queryable solution for long-term storage of patient imaging data.
Question 89:
An e-commerce platform wants to analyse clickstream data from millions of users 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, near real-time transformation, storage, and reporting. Option A meets these requirements. Kinesis Data Firehose ingests high-velocity events and scales automatically. AWS Lambda performs real-time transformations such as filtering, enrichment, or aggregation. Amazon S3 stores raw and processed data cost-effectively for long-term analysis, auditing, and trend evaluation. Athena enables SQL-based queries directly on S3, supporting dashboards and reporting without moving data, reducing latency and operational complexity.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs run on a schedule, introducing latency. Redshift is suitable for analytics, but cannot handle high-velocity streaming ingestion efficiently.
Option C, RDS + QuickSight, cannot process millions of events per second. QuickSight dashboards are delayed and do not provide real-time insights. Scaling RDS adds operational complexity and cost.
Option D, DynamoDB + EMR, offers scalable storage and batch processing. EMR introduces latency incompatible with near-real-time analytics, and orchestrating dashboards requires additional operational effort.
Therefore, Option A provides a fully integrated, scalable, low-latency architecture for clickstream ingestion, transformation, storage, and business intelligence reporting.
Question 90:
A financial services company requires 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 provides this. 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 immediate response. 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 efficiently. Scaling RDS or Redshift for millions of transactions per second adds operational complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency incompatible with real-time fraud detection, and orchestrating alerts requires additional operational effort.
Thus, Option A delivers a fully integrated, low-latency, scalable architecture for real-time fraud detection, alerting, and regulatory compliance.
Real-time fraud detection systems are critical for organisations that process high volumes of transactions, such as financial institutions, e-commerce platforms, and payment gateways. The system must ingest transactional data continuously, identify suspicious patterns immediately, and trigger alerts for potential fraudulent activity. The architecture must support extremely low latency, meaning the detection and response happen within milliseconds to seconds. Additionally, the system must provide durable storage of all transactional data to satisfy auditing, compliance, and future model retraining requirements. Reliability, scalability, and operational simplicity are also essential because financial workloads can fluctuate significantly, and downtime or delayed detection can lead to financial loss and reputational damage.
Option A: Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Option A represents a fully integrated architecture designed for real-time data processing. Kinesis Data Streams is a managed service capable of ingesting millions of events per second, offering both durability and automatic scaling. It ensures that transactional data flows continuously into the system without loss, making it ideal for fraud detection, where every transaction must be evaluated. AWS Lambda provides serverless compute that processes each transaction in real-time, applying fraud detection algorithms or rules. This setup eliminates the need for pre-provisioned servers, reduces operational overhead, and allows immediate scaling as the transaction volume grows. Amazon CloudWatch monitors the entire pipeline, tracking metrics such as processing latency, error rates, and stream throughput. Alerts triggered through CloudWatch ensure that system operators or automated response mechanisms can act instantly when suspicious patterns emerge. Finally, Amazon S3 offers durable, cost-effective storage for all incoming transactions, ensuring historical data is preserved for auditing and retraining machine learning models. Together, these services form a cohesive, low-latency, highly scalable, and operationally simple architecture capable of meeting the rigorous requirements of real-time fraud detection.
Option B: Amazon S3 + AWS Glue + Amazon Athena
Option B focuses on a batch-oriented data architecture. Data is first stored in Amazon S3, then catalogued and transformed using AWS Glue, and finally queried via Amazon Athena. While this combination excels at handling large-scale analytics and reporting on historical data, it is fundamentally unsuitable for real-time fraud detection. The batch processing model introduces inherent latency because transactions are accumulated over a period before processing. As a result, any fraudulent activity would not be identified instantaneously, which defeats the purpose of a real-time monitoring system. Alerts cannot be triggered immediately, and the system cannot provide instantaneous responses to suspicious activity. Additionally, maintaining continuous ETL workflows with Glue and frequent Athena queries adds operational overhead and complexity. Although this option supports compliance and historical analysis effectively, it fails to deliver the core requirement of low-latency fraud detection.
Option C: Amazon RDS + Amazon Redshift
Option C relies on structured databases and analytical warehouses. Amazon RDS provides relational database storage, while Redshift offers analytical capabilities for running complex queries across large datasets. This architecture works well for reporting, historical analytics, and structured queries, but it is ill-suited for high-frequency transaction ingestion. Real-time fraud detection demands processing millions of events per second, and relational databases typically introduce constraints in terms of write throughput and scaling. While it is possible to scale RDS or Redshift, doing so for massive transactional volumes introduces operational complexity, increased cost, and potential performance bottlenecks. Furthermore, this architecture lacks built-in real-time alerting mechanisms, meaning that immediate identification of fraudulent activity requires additional orchestration and infrastructure. Consequently, although suitable for post-facto analysis or auditing, Option C cannot meet the operational speed and low-latency requirements of a live fraud detection system.
Option D: Amazon DynamoDB + Amazon EMR
Option D combines a scalable NoSQL storage solution with a distributed data processing framework. Amazon DynamoDB can handle high-frequency writes and provides low-latency access to data, making it capable of storing transactional information efficiently. Amazon EMR, however, is designed primarily for batch or large-scale distributed analytics using frameworks like Apache Spark or Hadoop. While EMR can analyse large datasets effectively, the processing introduces latency incompatible with real-time detection, and alerting is not native to the EMR framework. To implement immediate alerts, the architecture would require additional components and orchestration, increasing operational complexity. While this combination provides excellent storage and analytical capabilities for historical data, it does not fulfil the critical requirement of processing and acting on transactions in near real-time.
Real-time fraud detection demands an architecture that balances low latency, high throughput, durability, scalability, and operational simplicity. Option A, consisting of Kinesis Data Streams, AWS Lambda, CloudWatch, and S3, addresses all these requirements. Kinesis ensures uninterrupted ingestion of millions of transactions, Lambda applies detection logic instantly, CloudWatch provides monitoring and alerting, and S3 retains all transactional data for auditing and model retraining. Options B, C, and D each have strengths in batch processing, structured analytics, or large-scale storage, but none provide the fully integrated real-time capabilities essential for proactive fraud detection. Implementing Option A ensures the organisation can detect fraudulent activity as it occurs, respond immediately to prevent financial losses, maintain compliance, and continuously improve detection models based on historical data, all with minimal operational complexity.