Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 8 106-120
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Question 106:
A global online retail platform wants to implement a real-time dynamic pricing system. The system must ingest millions of user interactions and inventory events per second, adjust prices dynamically based on demand, and store historical pricing data for analytics and trend modelling. 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:
Dynamic pricing requires real-time ingestion, processing, and decision-making at a massive scale. Option A is optimal. Amazon Kinesis Data Streams can handle millions of events per second, providing a highly scalable ingestion layer for both user interactions and inventory changes. AWS Lambda processes the incoming streams instantly, applying business rules, aggregating data, and transforming it for model consumption. Amazon SageMaker hosts machine learning models that predict optimal pricing dynamically based on real-time demand, competitor pricing, historical sales patterns, and inventory levels. Amazon S3 provides durable storage for raw and processed data, allowing trend analysis, auditing, and retraining of models to continuously improve accuracy.
Option B, Amazon S3 + AWS Glue + Redshift, is batch-oriented. Glue jobs run on scheduled intervals, introducing latency that makes real-time dynamic pricing infeasible. Redshift provides structured analytics but cannot process real-time high-volume streaming data efficiently.
Option C, Amazon RDS + QuickSight, is unsuitable for real-time updates. RDS cannot scale to millions of events per second, and QuickSight dashboards are delayed, preventing timely price adjustments. Scaling RDS adds operational complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces processing latency incompatible with real-time pricing updates. Orchestrating dashboards or alerts requires additional operational overhead.
Option A provides a fully integrated, low-latency, scalable solution for real-time dynamic pricing, analytics, and historical trend modelling.
Question 107:
A healthcare organisation must implement a real-time patient telemetry monitoring system. The system must ingest IoT sensor data continuously, detect anomalies instantly, trigger alerts for medical staff, and store historical data for trend analysis, audits, and regulatory compliance. Which AWS service combination is best suited?
A) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + AWS Lambda + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + AWS Lambda + Amazon S3
Explanation:
Real-time patient telemetry monitoring demands continuous ingestion, immediate anomaly detection, real-time alerting, and durable long-term storage. Option A meets these requirements. Amazon Kinesis Data Streams ingests high-velocity telemetry data from patient IoT devices, ensuring scalable and fault-tolerant data capture. Kinesis Data Analytics performs continuous computations on streaming data to detect anomalies such as abnormal heart rate, blood pressure, or oxygen saturation. AWS Lambda triggers real-time alerts to medical personnel or automated workflows, enabling rapid intervention and improved patient safety. Amazon S3 stores historical telemetry data cost-effectively and durably, supporting trend analysis, research, audits, and regulatory compliance.
Option B, S3 + Glue + Athena, is batch-oriented. Glue ETL jobs run on a scheduled basis, introducing delays incompatible with real-time monitoring and alerting. Athena provides historical analysis but cannot operate on live streaming telemetry data.
Option C, RDS + QuickSight, cannot handle high-throughput telemetry ingestion. QuickSight dashboards are delayed, and scaling RDS globally is operationally complex and costly.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency, making real-time anomaly detection impossible. Orchestrating alerts adds operational complexity.
Option A delivers a scalable, low-latency, integrated architecture for real-time telemetry monitoring, anomaly detection, alerting, and historical analysis.
Question 108:
A financial institution must store decades of transactional data in a secure, durable, and cost-effective manner. Occasionally, auditors need to query the data without restoring entire datasets. Which AWS solution is best suited?
A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena
Explanation:
Long-term archival of transactional data requires durability, cost efficiency, and query capabilities. Option A is optimal. Glacier Deep Archive offers extremely low-cost storage for decades-long retention with eleven nines of durability. Lifecycle policies can automatically move data from S3 Standard to Glacier Deep Archive, reducing operational costs. Amazon Athena enables SQL queries directly on archived data without full restoration, allowing auditors to perform compliance checks efficiently and cost-effectively.
Option B, S3 Standard + Lambda, is expensive for decades-long storage. Lambda does not provide the query capabilities required for audits and compliance.
Option C, RDS + Redshift, supports structured storage and analytics but is costly for long-term retention. Redshift requires active clusters for queries, increasing operational overhead.
Option D, DynamoDB + EMR, provides batch analytics but introduces latency and operational complexity. DynamoDB is costly for long-term archival, and EMR cannot perform ad-hoc queries efficiently on archived datasets.
Option A delivers a compliant, durable, cost-effective, and queryable solution for decades of financial transaction data.
Question 109:
An e-commerce platform wants to perform clickstream analytics on millions of user interactions per second. The system must process data in near real-time, store both raw and processed datasets, and provide dashboards for business intelligence. Which AWS architecture is most suitable?
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
Explanation:
Clickstream analytics requires ingestion of high-frequency events, near real-time transformation, storage, and reporting. Option A is fully integrated and optimal. Kinesis Data Firehose ingests millions of events per second and scales automatically to accommodate spikes. AWS Lambda transforms and enriches the data in real-time, allowing it to be immediately usable for analytics. Amazon S3 stores raw and processed datasets cost-effectively, supporting long-term trend analysis and auditing. Amazon Athena enables SQL-based queries directly on S3, allowing business intelligence dashboards without moving data, minimising latency and operational complexity.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs introduce latency, making near-real-time insights impossible. Redshift supports structured analytics but cannot handle high-velocity streaming data efficiently.
Option C, RDS + QuickSight, is unsuitable for millions of events per second. QuickSight dashboards are delayed, preventing timely analysis. Scaling RDS increases complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency incompatible with near-real-time analytics. Orchestrating dashboards and alerts requires additional effort.
Option A provides a scalable, low-latency, fully integrated architecture for clickstream ingestion, transformation, storage, and business intelligence reporting.
Question 110:
A financial services company requires a real-time fraud detection system capable of ingesting millions of transactions per second, detecting anomalies instantly, triggering operational alerts, and storing all transactions for auditing and compliance. Which AWS architecture is most appropriate?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Explanation:
Real-time fraud detection requires scalable ingestion, immediate anomaly detection, alerting, and durable storage. Option A addresses all these requirements effectively. Kinesis Data Streams ingests millions of transactions per second, providing durability and auto-scaling. AWS Lambda processes each transaction in real-time, applying fraud detection logic to identify suspicious activity instantly. CloudWatch monitors system metrics and triggers alerts for operational teams or automated workflows. Amazon S3 stores all transactions durably, supporting auditing, regulatory compliance, and historical analysis for model retraining.
Option B, S3 + Glue + Athena, is batch-oriented and cannot provide real-time detection or alerts. Queries are delayed, reducing operational effectiveness.
Option C, RDS + Redshift, provides structured storage and analytics but cannot ingest high-frequency transactions efficiently. Scaling RDS or Redshift for millions of transactions per second increases complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency incompatible with real-time fraud detection, and orchestrating alerts adds operational overhead.
Option A delivers a fully integrated, low-latency, scalable architecture for real-time fraud detection, alerting, auditing, and regulatory compliance.
Scalability and High-Velocity Data Handling
In real-time fraud detection, the system must process extremely high volumes of transactions with minimal delay. Option A excels in this regard because Kinesis Data Streams can handle millions of events per second while maintaining durability and ordered processing. This ensures that no transaction is lost and that the sequence of events is preserved for accurate analysis. During peak periods, such as sales events or high-traffic financial operations, the auto-scaling capability of Kinesis allows the architecture to dynamically adjust, maintaining performance without manual intervention.
Real-Time Processing and Anomaly Detection
AWS Lambda enables near-instant processing of incoming transactions. By applying fraud detection logic to each transaction as it arrives, the system can immediately flag suspicious behaviour. This real-time evaluation is critical in preventing fraudulent transactions before they are completed, protecting both financial assets and customer trust. Lambda’s serverless nature also reduces operational complexity, as there is no need to provision or manage underlying servers, and the service scales automatically to meet demand.
Monitoring, Alerting, and Operational Readiness
Amazon CloudWatch provides continuous monitoring of the system’s health and performance. It can track metrics such as transaction volume, processing latency, and error rates. CloudWatch can also trigger automated alerts or notifications to operational teams when anomalies are detected, ensuring timely intervention. This proactive monitoring is essential for minimising the impact of potential fraud and maintaining operational resilience.
Durable Storage and Compliance
Amazon S3 offers durable, cost-effective storage for all transaction data. This allows organisations to maintain comprehensive audit trails and meet regulatory compliance requirements. Historical data stored in S3 can also be used for trend analysis and retraining fraud detection models, enabling continuous improvement of detection strategies.
Question 111:
A global e-commerce platform wants to implement a recommendation engine that can process millions of events per second, generate personalised recommendations in real-time, and store historical interaction data for analytics and model retraining. Which AWS architecture is best suited for this requirement?
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:
For a high-velocity recommendation engine, low-latency processing and scalable data ingestion are critical. Option A provides a fully integrated architecture. Kinesis Data Streams ingests millions of events per second, ensuring reliable, scalable, and durable data ingestion. AWS Lambda processes streaming data in real-time, performing transformations, aggregations, and forwarding enriched data to SageMaker models. Amazon SageMaker generates personalised recommendations instantly, leveraging both real-time and historical data. Amazon S3 stores raw and processed datasets, enabling historical analysis, auditing, and model retraining.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs execute periodically, introducing latency incompatible with real-time personalisation. Redshift supports analytics but cannot handle the scale and immediacy required for live recommendations.
Option C, RDS + QuickSight, is unsuitable for high-volume, real-time data. RDS cannot ingest millions of events per second, and QuickSight dashboards are delayed, preventing timely recommendations. Scaling RDS adds operational complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency incompatible with real-time recommendation generation. Operational overhead increases when orchestrating alerts or dashboards.
Option A ensures low-latency processing, high scalability, and seamless integration for real-time recommendations and historical analytics, making it the optimal choice.
Question 112:
A healthcare provider requires a system to monitor patient telemetry in real-time. The system must ingest IoT device data continuously, detect anomalies immediately, trigger alerts for medical staff, and store historical data for trend analysis, compliance, and auditing. Which AWS architecture best meets 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:
Real-time telemetry monitoring requires high-throughput data ingestion, immediate anomaly detection, alerting, and durable historical storage. Option A is ideal. Kinesis Data Streams ingests high-frequency IoT telemetry globally, providing scalability, durability, and fault tolerance. Kinesis Data Analytics continuously processes streaming data to detect anomalies, such as abnormal heart rate, oxygen saturation, or blood pressure readings. AWS Lambda triggers alerts instantly to medical staff or automated workflows, ensuring timely interventions. Amazon S3 provides cost-effective, durable storage for raw and processed telemetry, enabling trend analysis, audits, compliance, and research.
Option B, S3 + Glue + Athena, is batch-oriented. ETL jobs run periodically, introducing latency that prevents real-time monitoring and alerting. Athena allows historical queries but cannot operate on streaming telemetry for immediate anomaly detection.
Option C, RDS + QuickSight, cannot handle high-frequency data ingestion. QuickSight dashboards are delayed, and scaling RDS globally is costly and operationally complex.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency incompatible with real-time anomaly detection. Orchestrating alerts requires additional operational overhead.
Option A delivers a fully integrated, low-latency, and scalable architecture for real-time patient telemetry monitoring, anomaly detection, alerting, and historical data analysis.
Question 113:
A financial institution needs to securely store decades of transactional data. The system must allow occasional querying for audits and regulatory compliance 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:
Long-term archival of financial transactions requires durability, cost-effectiveness, and selective query capabilities. Option A is optimal. Glacier Deep Archive offers extremely low-cost storage with eleven nines of durability for decades-long retention. Data can be automatically transitioned from S3 Standard to Glacier Deep Archive using lifecycle policies, optimising storage cost. Athena allows direct querying of archived datasets without full restoration, enabling auditors to perform compliance checks efficiently.
Option B, S3 Standard + Lambda, is expensive for long-term storage and does not provide query capabilities suitable for audits.
Option C, RDS + Redshift, is not cost-effective for decades-long storage. Redshift requires active clusters for queries, increasing operational complexity and costs.
Option D, DynamoDB + EMR, introduces latency and operational complexity. DynamoDB is expensive for long-term archival, and EMR cannot perform ad-hoc queries efficiently on archived data.
Option A delivers a compliant, durable, and cost-effective solution with query capabilities for auditing and regulatory compliance.
Question 114:
An e-commerce company wants to perform clickstream analytics on millions of user interactions per second. The system must transform data near real-time, store both raw and processed datasets, and provide dashboards for business intelligence. Which AWS architecture is most suitable?
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
Explanation:
Clickstream analytics requires ingestion of high-frequency events, near real-time transformation, storage, and reporting. Option A provides a fully integrated solution. Kinesis Data Firehose ingests millions of events per second and scales automatically. AWS Lambda transforms and enriches data in near real-time, making it immediately usable for analytics. Amazon S3 stores raw and processed datasets cost-effectively, supporting trend analysis, auditing, and long-term storage. Athena enables SQL-based queries directly on S3, supporting dashboards and business intelligence reporting without moving data, minimising latency and operational complexity.
Option B, S3 + Glue + Redshift, is batch-oriented. ETL jobs introduce latency, preventing near-real-time analytics. Redshift supports structured analytics but cannot handle high-velocity streaming efficiently.
Option C, RDS + QuickSight, cannot handle millions of events per second. QuickSight dashboards are delayed, preventing timely insights. Scaling RDS adds operational complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency, making near-real-time analysis impossible. Additional orchestration is required for dashboards and alerts.
Option A provides a scalable, low-latency, and fully integrated architecture for clickstream ingestion, transformation, storage, and business intelligence.
Question 115:
A financial services company requires a real-time fraud detection system. The system must ingest millions of transactions per second, detect anomalies instantly, trigger operational alerts, and store all transactions for auditing and compliance. Which AWS architecture is most appropriate?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Explanation:
Real-time fraud detection requires scalable ingestion, immediate anomaly detection, alerting, and durable storage. Option A is optimal. Kinesis Data Streams ingests millions of transactions per second with durability and auto-scaling. AWS Lambda processes transactions in real-time, applying fraud detection logic instantly. CloudWatch monitors metrics and triggers alerts to operational teams or automated workflows. S3 provides durable storage for all transactions, supporting auditing, regulatory compliance, and historical analysis for model retraining.
Option B, S3 + Glue + Athena, is batch-oriented. Delayed queries prevent real-time anomaly detection and alerting, reducing operational effectiveness.
Option C, RDS + Redshift, supports structured analytics but cannot handle high-frequency transaction ingestion. Scaling clusters for millions of transactions per second adds complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency, making real-time fraud detection impossible. Additional orchestration is required for alerting.
Option A provides a fully integrated, low-latency, scalable architecture for real-time fraud detection, alerting, auditing, and compliance.
Fraud detection in financial transactions, e-commerce platforms, or online payment systems is a critical and time-sensitive challenge. Fraudsters exploit any delay in detection, so the system must operate in real-time to prevent financial losses and protect customer trust. Real-time fraud detection is fundamentally different from batch-oriented analytics because it requires immediate evaluation of each transaction, rapid identification of anomalies, and instant alerting or intervention. The system must also store transaction data durably for auditing, compliance, and model retraining. The challenge lies in handling extremely high volumes of transactional data with low latency while maintaining operational reliability.
Option A: Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Option A is specifically designed to meet the demands of real-time fraud detection. Amazon Kinesis Data Streams enables streaming ingestion of massive amounts of data with automatic scaling. It ensures that each transaction, whether a single payment or thousands of concurrent requests, is captured reliably without loss. The durability and ordering guarantees of Kinesis allow downstream components to process events accurately and consistently.
AWS Lambda provides real-time transaction processing. Lambda evaluates each incoming transaction against fraud detection rules, scoring systems, or machine learning models. Its serverless architecture ensures automatic scaling during spikes in traffic, which is crucial during high-transaction periods such as holiday sales or promotional campaigns. Lambda’s ability to process events in parallel ensures minimal latency, so potentially fraudulent transactions can be identified immediately and handled appropriately.
Amazon CloudWatch serves as the operational monitoring and alerting mechanism. It captures system metrics, transaction anomalies, processing latencies, and errors. Alerts can be configured to notify operational teams or trigger automated workflows, such as temporarily blocking suspicious accounts or flagging transactions for review. This ensures operational teams can intervene in real-time, reducing potential financial loss and reputational damage.
Amazon S3 provides durable, cost-effective storage for all transaction data. It ensures compliance with regulatory requirements, supports historical audit trails, and enables retraining of fraud detection models based on past incidents. S3’s scalability and reliability make it ideal for storing large volumes of transaction records over long periods. Historical analysis of this data allows organisations to refine fraud detection rules, improve machine learning models, and identify emerging fraud patterns, making the system progressively more effective over time.
Option B: Amazon S3 + AWS Glue + Amazon Athena
Option B is designed for batch-oriented analytics. While it is effective for historical analysis, reporting, and large-scale queries, it is not suitable for real-time fraud detection. Data ingested into S3 must be processed through AWS Glue ETL pipelines and queried via Athena before insights can be obtained. This introduces significant delays between transaction occurrence and anomaly detection, which is incompatible with real-time alerting. In fraud detection, even a few minutes of delay can result in substantial losses. Furthermore, operational alerting is not natively supported in this architecture, requiring additional tools or custom solutions to notify teams of suspicious activity.
Option C: Amazon RDS + Amazon Redshift
Option C combines a relational database for transactional storage (RDS) with a data warehouse (Redshift) for analytics. While suitable for structured analytics and historical reporting, this architecture is not optimised for high-throughput, low-latency transaction processing. RDS is effective for transactional consistency but requires significant scaling to handle millions of transactions per second. Redshift is a powerful analytical engine, but it operates primarily in batch mode. Queries against Redshift cannot deliver immediate results for real-time fraud detection, and scaling Redshift clusters for near-instant processing is expensive and operationally complex. Alerts and monitoring also require additional integration, adding to operational overhead.
Option D: Amazon DynamoDB + Amazon EMR
Option D leverages DynamoDB for scalable storage and EMR for large-scale analytics. DynamoDB can handle high-velocity writes, making it suitable for storing transactions. However, EMR is designed for batch processing and introduces processing delays, making real-time detection impossible. Additional orchestration is required to detect anomalies and trigger alerts, which increases system complexity and response times. While this architecture may be effective for historical trend analysis and batch-oriented fraud investigations, it cannot prevent or respond to fraudulent activity as it happens.
Comparative Assessment
When comparing these options, Option A is the only architecture that fully satisfies the essential requirements for real-time fraud detection: high-throughput ingestion, low-latency processing, real-time alerting, and durable storage for auditing and compliance. Options B, C, and D are either batch-oriented, introduce latency, or require significant operational overhead to achieve similar capabilities.
Option A’s fully integrated design ensures that every transaction is immediately processed, suspicious activity is flagged without delay, and operational teams are notified in real-time. Historical data is stored durably in S3 for compliance and model improvement, creating a feedback loop that strengthens fraud detection over time. The serverless and managed nature of Lambda, Kinesis, CloudWatch, and S3 reduces infrastructure management effort while allowing seamless scalability. Organisations adopting this architecture can focus on improving fraud detection strategies rather than managing complex operational pipelines.
This combination of low-latency, scalable, and fully managed services makes Option A the optimal choice for any environment where fraud prevention, operational efficiency, and compliance are critical.
Question 116:
A multinational e-commerce company wants to implement a real-time product recommendation system that can handle millions of user interactions per second. The system must dynamically generate personalised recommendations, store historical interactions for analytics, and allow retraining of machine learning models. Which AWS architecture is most appropriate?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker + Amazon S3
Explanation:
Implementing a real-time recommendation system requires a combination of high-throughput ingestion, low-latency processing, dynamic model inference, and durable storage for historical data. Option A provides a fully integrated architecture optimised for these requirements. Amazon Kinesis Data Streams is capable of ingesting millions of user events per second, providing scalability and fault tolerance. It ensures that the event stream is durable and available to multiple consumers for real-time processing. AWS Lambda processes the streaming data immediately, applying transformations, enrichment, and aggregations required for the recommendation engine. This serverless compute layer eliminates the need to manage infrastructure and allows the system to scale automatically with incoming events.
Amazon SageMaker hosts machine learning models that predict personalised recommendations in real-time. These models leverage both the real-time data from Kinesis and the historical data stored in S3 to optimise predictions. SageMaker endpoints provide low-latency inference, which is essential for delivering dynamic recommendations to users instantaneously. Amazon S3 serves as a highly durable, cost-effective storage layer for both raw and processed datasets. Historical interaction data in S3 can be used to retrain machine learning models periodically, ensuring continuous improvement in recommendation accuracy and relevance.
Option B, Amazon S3 + AWS Glue + Redshift, is batch-oriented. While this combination can process large amounts of data and support analytical queries, it cannot handle real-time ingestion or generate immediate recommendations. Glue ETL jobs run on scheduled intervals, introducing latency incompatible with dynamic personalisation, and Redshift, while suitable for structured analytics, cannot process millions of events per second in real-time.
Option C, Amazon RDS + QuickSight, is inadequate for this scenario. RDS cannot handle millions of inserts per second, and QuickSight dashboards are not real-time. While RDS supports structured storage, it would quickly become a bottleneck at this scale, requiring costly and complex scaling strategies. QuickSight is designed for visualisation and business intelligence reporting, not real-time inference, making it unsuitable for dynamic recommendations.
Option D, DynamoDB + EMR, provides scalable storage and batch processing but introduces latency. EMR is optimised for large-scale batch analytics, not real-time processing. Using this combination would require additional orchestration for processing streams and delivering recommendations, increasing operational complexity. DynamoDB provides fast read/write performance but does not natively support real-time model inference or analytics without supplementary services.
Option A ensures low-latency processing, high scalability, seamless integration for real-time recommendations, and historical analytics, making it the best choice for a global real-time recommendation system.
Question 117:
A healthcare provider needs a real-time patient monitoring solution. The system must continuously ingest telemetry from IoT devices, detect anomalies immediately, trigger alerts to medical staff, and store historical data for trend analysis, auditing, and regulatory compliance. Which AWS architecture is most suitable?
A) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + AWS Lambda + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + AWS Lambda + Amazon S3
Explanation:
Real-time patient monitoring demands high-frequency ingestion, immediate processing for anomaly detection, operational alerting, and long-term durable storage. Option A provides a comprehensive solution. Kinesis Data Streams allows continuous ingestion of telemetry data from IoT devices, supporting millions of events per second with high durability and availability. Kinesis Data Analytics performs real-time streaming computations to detect anomalies such as abnormal heart rates, oxygen saturation, or blood pressure readings. The processing latency is minimal, ensuring timely detection of critical conditions.
AWS Lambda triggers alerts in real-time to medical personnel or automated systems, allowing immediate intervention to improve patient safety. Lambda’s serverless nature ensures scalability without requiring manual infrastructure management. Amazon S3 provides cost-effective, durable storage for both raw and processed telemetry data. This historical data supports trend analysis, audits, regulatory compliance, and research studies, allowing healthcare providers to maintain a complete and secure record of patient data.
Option B, S3 + Glue + Athena, is batch-oriented. Glue ETL jobs run on a scheduled basis, introducing delays that prevent real-time anomaly detection and immediate alerts. Athena provides ad-hoc query capabilities but is unsuitable for real-time monitoring.
Option C, RDS + QuickSight, cannot ingest high-velocity telemetry data. RDS is constrained by throughput limitations, and QuickSight dashboards introduce delays, preventing timely anomaly detection and alerting. Scaling RDS globally increases cost and operational complexity.
Option D, DynamoDB + EMR, supports scalable storage and batch analytics, but EMR introduces latency incompatible with real-time anomaly detection. Orchestrating alerts and dashboards further increases operational complexity.
Option A delivers a low-latency, fully integrated, scalable architecture for real-time telemetry ingestion, anomaly detection, alerting, and historical analytics, ensuring patient safety and regulatory compliance.
Question 118:
A financial institution must securely store decades of transactional data. Occasionally, auditors must query the data without restoring entire datasets. Which AWS solution is optimal?
A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena
Explanation:
Long-term storage of financial transactions requires durability, cost efficiency, and selective querying for compliance. Option A provides the best solution. Glacier Deep Archive offers the lowest-cost storage with eleven nines of durability, suitable for decades-long retention. Data lifecycle policies can automatically move less-frequently accessed data from S3 Standard to Glacier Deep Archive, optimising cost management.
Athena allows querying archived datasets without restoring the entire dataset, enabling auditors to efficiently perform compliance checks and reporting. This eliminates delays associated with full data retrieval and reduces operational complexity.
Option B, S3 Standard + Lambda, is expensive for multi-decade storage. Lambda does not provide sufficient query capabilities for auditing purposes and does not address cost optimisation for long-term retention.
Option C, RDS + Redshift, is structured storage with analytical capabilities but is not cost-effective for long-term archival. Redshift requires active clusters for queries, significantly increasing costs and operational overhead.
Option D, DynamoDB + EMR, introduces latency and complexity. DynamoDB is expensive for decades-long storage, and EMR is suitable for batch analytics but cannot efficiently support ad-hoc queries on archived datasets.
Option A ensures secure, durable, cost-effective storage and query capabilities for auditing and regulatory compliance over decades, making it the ideal choice.
Question 119:
An e-commerce company wants to perform clickstream analytics on millions of interactions per second. The system must perform near real-time transformations, store raw and processed datasets, and provide dashboards for business intelligence. Which AWS architecture is most suitable?
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
Explanation:
Clickstream analytics involves the ingestion of high-frequency events, near real-time processing, storage, and reporting. Option A is fully integrated and optimised. Kinesis Data Firehose ingests millions of events per second and scales automatically with data volume spikes. AWS Lambda transforms and enriches data near real-time, making it immediately usable for analytics. Amazon S3 stores raw and processed datasets cost-effectively, supporting long-term analytics and auditing. Athena enables SQL-based queries on S3 directly, reducing latency and operational complexity for generating business intelligence dashboards.
Option B, S3 + Glue + Redshift, is batch-oriented. Glue ETL jobs run on a schedule, introducing latency incompatible with near-real-time analytics. Redshift supports structured analytics but cannot efficiently handle streaming data at high velocity.
Option C, RDS + QuickSight, cannot handle millions of events per second. QuickSight dashboards introduce delays, limiting actionable insights. Scaling RDS adds cost and complexity.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency, preventing near real-time analysis. Additional orchestration for dashboards and alerts increases operational effort.
Option A provides a scalable, low-latency, and fully integrated architecture for clickstream ingestion, transformation, storage, and reporting.
Question 120:
A financial services company requires a real-time fraud detection system capable of ingesting millions of transactions per second, detecting anomalies instantly, triggering operational alerts, and storing all transactions for auditing and compliance. Which AWS architecture is most appropriate?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Explanation:
Real-time fraud detection demands high-velocity data ingestion, immediate anomaly detection, alerting, and durable storage. Option A addresses all requirements efficiently. Kinesis Data Streams ingests millions of transactions per second with auto-scaling and durability. AWS Lambda processes each transaction in real-time, applying fraud detection logic to identify suspicious activities instantly. CloudWatch monitors system metrics and triggers alerts to operational teams or automated workflows, ensuring timely intervention. Amazon S3 stores all transactions durably, enabling auditing, regulatory compliance, and historical analysis for model retraining.
Option B, S3 + Glue + Athena, is batch-oriented and cannot support real-time anomaly detection or alerts. Query delays reduce operational effectiveness.
Option C, RDS + Redshift, provides structured analytics but cannot handle high-frequency transaction ingestion efficiently. Scaling clusters for millions of transactions per second increases cost and operational complexity.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency incompatible with real-time fraud detection. Additional orchestration is required for alerts, increasing operational effort.
Option A ensures low-latency, fully integrated, scalable real-time fraud detection with alerting, auditing, and compliance capabilities, making it the best solution.
Real-time fraud detection is one of the most critical applications in financial services, e-commerce, and online payment systems. The primary requirement is the ability to detect and respond to fraudulent transactions immediately as they occur. Fraudulent activities can range from unusual payment patterns, unauthorised account access, identity theft, or rapid multiple transactions from a single source. The system must handle high-velocity, high-volume data streams while maintaining low latency and ensuring operational reliability. Additionally, it must provide mechanisms for alerting, monitoring, auditing, and historical analysis to continuously improve fraud detection models. In this context, the architecture must balance performance, scalability, durability, and operational simplicity.
Option A: Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Option A addresses these requirements comprehensively. Amazon Kinesis Data Streams is designed for real-time streaming data, capable of ingesting millions of events per second. Its auto-scaling capability ensures that during peak transaction periods, the system can scale horizontally without manual intervention. This feature is particularly critical during events such as holiday sales, flash sales, or sudden spikes in transactions, where fraudulent activities often increase. Kinesis ensures that no data is lost even under high throughput, which is essential for both real-time detection and compliance purposes.
AWS Lambda complements Kinesis by providing serverless compute that processes each transaction as it arrives. This real-time processing enables the immediate application of fraud detection logic, such as anomaly detection, velocity checks, or rules-based evaluations. The serverless nature of Lambda eliminates the need to provision and manage servers, reducing operational overhead. Lambda’s ability to scale dynamically ensures that each incoming transaction is evaluated without delay, allowing the system to respond to fraud almost instantaneously.
Amazon CloudWatch provides the monitoring backbone for the system. It tracks key metrics, logs errors, and can trigger alerts to operational teams or automated workflows. For example, if the volume of suspicious transactions exceeds a predefined threshold, CloudWatch can send notifications to fraud investigation teams or initiate automated account restrictions. This ensures timely intervention, limiting potential financial losses and customer impact. CloudWatch’s metrics and dashboards also provide long-term visibility into system performance, helping teams optimise throughput, latency, and resource allocation.
Amazon S3 provides durable, scalable storage for all transaction data. This ensures compliance with regulatory requirements, enables historical auditing, and supports retraining of fraud detection models. By storing raw and processed transaction data, organisations can perform in-depth analysis to identify evolving fraud patterns, enhance machine learning models, and refine detection rules. S3’s durability and lifecycle policies ensure that data remains available for years, supporting both operational and compliance requirements.
Option B: Amazon S3 + AWS Glue + Amazon Athena
Option B represents a batch-oriented architecture. While S3 provides durable storage, AWS Glue enables ETL (extract, transform, load) processes, and Athena allows querying of stored data. This setup is ideal for historical analysis, reporting, and ad-hoc queries, but is unsuitable for real-time fraud detection. The batch processing model introduces latency, as data must first be ingested, transformed, and then queried before any actionable insights can be obtained. Fraudulent transactions could go undetected for hours or even days, exposing organisations to financial and reputational risk. Additionally, there is no mechanism for immediate alerts or operational intervention, which is essential in fraud scenarios where rapid response is critical.
Option C: Amazon RDS + Amazon Redshift
Option C offers structured relational and analytical storage. Amazon RDS provides transactional databases, and Redshift supports large-scale analytics. While this architecture can handle structured historical transaction data and perform detailed analytics, it is not optimised for real-time, high-frequency ingestion. Processing millions of transactions per second would require significant scaling of both RDS and Redshift clusters, resulting in high operational complexity and costs. Moreover, Redshift is primarily a data warehouse designed for batch queries rather than low-latency real-time processing. Alerts and monitoring would also require additional custom orchestration, further complicating the architecture and increasing response times.
Option D: Amazon DynamoDB + Amazon EMR
Option D combines a scalable NoSQL database with batch processing via EMR. DynamoDB provides fast key-value access and can handle large volumes of reads and writes, making it suitable for storing transaction data. However, Amazon EMR is designed for big data batch processing rather than real-time event-driven computation. Introducing EMR into the fraud detection pipeline adds processing latency, making it impossible to identify and respond to fraud as it occurs. Additional layers of orchestration and monitoring would be required to generate alerts, increasing system complexity and operational overhead. This architecture is better suited for historical analytics and batch-based fraud pattern discovery rather than immediate fraud prevention.