Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 5 Q61-75
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Question 61:
A global media company wants to build a near-real-time content recommendation engine for its streaming platform. The system must ingest millions of user interactions per second, analyse behavioural patterns, generate personalised recommendations dynamically, and store historical events for model retraining and analytics. Which AWS architecture is best suited?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker + Amazon S3
Explanation:
A near real-time content recommendation engine requires processing high-velocity streaming data, dynamically generating personalised recommendations, and maintaining historical records for retraining and analytics. Option A fulfils these requirements. Kinesis Data Streams handles the ingestion of millions of user interaction events, offering scalability and durability even during peak usage periods. AWS Lambda provides serverless event processing, enabling real-time transformations, filtering, and enrichment of data streams, which prepares them for input to the recommendation engine. Amazon SageMaker hosts machine learning models that generate personalised recommendations based on real-time behaviour and historical data patterns, allowing the platform to respond immediately to changing user preferences. Amazon S3 stores both raw and processed events cost-effectively, supporting long-term analytics, auditing, and retraining of machine learning models, ensuring continuous improvement of recommendation accuracy.
Option B, S3 + Glue + Redshift, is batch-oriented. While S3 stores raw data, Glue performs scheduled ETL transformations, and Redshift provides analytics for structured data. This approach introduces latency that is unsuitable for real-time recommendation generation, limiting the ability to provide dynamic personalisation to users.
Option C, RDS + QuickSight, provides relational storage and dashboards. RDS is not designed to ingest millions of events per second, and QuickSight cannot process streaming data in real-time. Scaling RDS to meet the demands of high-frequency streaming data would be operationally complex and costly.
Option D, DynamoDB + EMR, offers scalable NoSQL storage and batch analytics. EMR processes data in batch mode, which introduces latency incompatible with real-time recommendations. Orchestrating dashboards and real-time analytics with EMR adds significant complexity compared to the integrated architecture in Option A.
Thus, Option A delivers a fully integrated, scalable, and low-latency solution for real-time personalised recommendations, operational analytics, and historical data storage for model retraining and reporting.
Question 62:
A logistics company wants to monitor its fleet of trucks in near real-time. The system must ingest telemetry data continuously, detect anomalies such as engine failures or unsafe driving, trigger alerts, and store historical data for trend analysis and compliance reporting. Which AWS service combination is best suited?
A) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + AWS Lambda + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + AWS Lambda + Amazon S3
Explanation:
Monitoring a fleet of trucks in near real-time requires scalable data ingestion, immediate anomaly detection, alerting mechanisms, and durable historical storage. Option A fulfils these requirements comprehensively. Kinesis Data Streams ingests high-frequency telemetry data from thousands of trucks, maintaining durability and handling sudden spikes efficiently. Kinesis Data Analytics provides continuous processing and anomaly detection, enabling the identification of unusual patterns such as mechanical failures, excessive speed, or unsafe driving in near real-time. AWS Lambda triggers operational alerts based on anomaly detection, notifying maintenance teams or triggering automated workflows. Amazon S3 provides cost-efficient, highly durable storage for historical telemetry data, supporting trend analysis, operational reporting, and regulatory compliance.
Option B, S3 + Glue + Athena, is batch-oriented. While S3 stores telemetry data, Glue performs scheduled ETL, and Athena allows queries, this architecture cannot provide near-real-time anomaly detection or immediate alerting. Delayed detection reduces operational effectiveness for fleet monitoring.
Option C, RDS + QuickSight, is suitable for structured data storage and visualisation, but cannot ingest high-frequency streaming data efficiently. QuickSight dashboards cannot provide real-time operational insights, and scaling RDS for continuous telemetry ingestion is complex and costly.
Option D, DynamoDB + EMR, provides scalable NoSQL storage and batch analytics. EMR processes data in batches, introducing latency incompatible with real-time detection. Integrating alerts and dashboards requires additional orchestration, increasing operational overhead.
Thus, Option A provides a fully integrated, scalable, low-latency architecture for telemetry ingestion, anomaly detection, alerting, and historical analytics.
Question 63:
A healthcare organisation needs a long-term archival solution for patient imaging records that is highly durable, cost-efficient, and allows selective querying for audits or research without restoring entire datasets. Which AWS architecture is most appropriate?
A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena
Explanation:
Healthcare imaging records require highly durable, cost-efficient long-term storage with selective query capabilities. Option A is optimal. Glacier Deep Archive offers extremely low-cost storage with eleven nines of durability, ensuring long-term protection of large imaging datasets. Lifecycle policies automate the movement of older data from higher-cost S3 classes to Glacier Deep Archive, optimising operational costs. Athena enables SQL-based queries on subsets of archived data without restoring entire datasets, allowing audits and research with minimal operational effort. This approach simplifies compliance management and reduces cost while providing selective access.
Option B, S3 Standard + Lambda, is suitable for frequently accessed data but is cost-prohibitive for long-term archival of rarely accessed patient imaging data. Lambda cannot query archived datasets, limiting its usefulness for regulatory audits.
Option C, RDS + Redshift, provides structured storage and analytical capabilities. RDS is expensive for long-term archival, and Redshift requires active clusters for querying, increasing operational overhead. It is not optimised for rarely accessed archival data.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency for queries, and DynamoDB is expensive for long-term archival, making this combination less suitable.
Therefore, Option A delivers a compliant, durable, cost-efficient, and queryable archival solution for patient imaging records.
Question 64:
An e-commerce company wants to implement a clickstream analytics system to monitor millions of user interactions per second. The system must ingest high-velocity data, perform near real-time transformations, store raw and processed data, and support reporting and business intelligence dashboards. Which AWS architecture is best suited?
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 user interactions, near real-time transformation, and storage for historical analysis. Option A is the most suitable. Kinesis Data Firehose ingests millions of clickstream events per second, automatically scaling to handle traffic spikes. AWS Lambda performs near real-time transformations such as aggregation, filtering, and enrichment. Amazon S3 stores raw and processed data cost-effectively for long-term analytics. Athena allows SQL-based querying directly on S3, enabling reporting and business intelligence dashboards without moving data, reducing operational overhead.
Option B, S3 + Glue + Redshift, is batch-oriented. ETL jobs introduce latency, preventing near real-time transformation. Redshift is suitable for structured analytics, but cannot handle streaming ingestion efficiently.
Option C, RDS + QuickSight, is suitable for structured data storage and dashboards, but cannot handle millions of events per second. QuickSight cannot process real-time streaming data, limiting operational insights.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency, preventing real-time transformation and reporting. Integrating dashboards requires additional orchestration and complexity.
Thus, Option A offers a fully integrated, scalable, and low-latency solution for clickstream analytics, real-time transformation, storage, and reporting.
Question 65:
A financial services platform needs a real-time fraud detection system that can ingest millions of transactions per second, detect anomalies immediately, trigger operational alerts, and store all transactions durably for auditing and compliance. Which AWS architecture is most appropriate?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Explanation:
Fraud detection requires low-latency ingestion, real-time anomaly detection, operational alerting, and durable storage for auditing and compliance. Option A is the best solution. Kinesis Data Streams ingests millions of transactions per second, providing durability and automatic scaling for traffic spikes. AWS Lambda processes each transaction in real-time, applying anomaly detection models to flag suspicious activity. CloudWatch monitors metrics, triggers alerts, and integrates with notification systems for immediate operational response. S3 stores all transactions durably, ensuring compliance, auditability, and historical analysis for future fraud model improvements.
Option B, S3 + Glue + Athena, supports historical analysis but is batch-oriented, introducing delays that prevent real-time fraud detection and alerting.
Option C, RDS + Redshift, provides structured storage and analytics but cannot handle high-velocity streaming data or real-time anomaly detection efficiently. Scaling RDS for millions of transactions per second is operationally complex.
Option D, DynamoDB + EMR, offers scalable storage and batch processing. EMR introduces latency incompatible with near-real-time detection. Integrating alerts and dashboards increases complexity.
Thus, Option A delivers a fully integrated, scalable, low-latency architecture for real-time fraud detection, alerting, and compliance.
Question 66:
A global e-commerce company wants to implement a real-time recommendation system for its website. The system must ingest millions of user interactions per second, process behavioural patterns to generate personalised recommendations dynamically, and store historical interactions for model retraining and analytics. Which AWS architecture is best suited?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker + Amazon S3
Explanation:
A near real-time recommendation system for a global e-commerce platform requires continuous ingestion of high-volume user interactions, real-time data processing, personalised recommendation generation, and durable historical storage for model retraining and analytics. Option A is the most suitable architecture. Kinesis Data Streams handles high-velocity ingestion, ensuring low-latency data flow and automatic scaling during peak traffic. AWS Lambda processes events in near real-time, enriching and transforming user interactions so that they are immediately usable for recommendation algorithms. Amazon SageMaker hosts and serves machine learning models that dynamically generate personalised recommendations based on incoming user behaviour and historical patterns. Amazon S3 stores both raw and processed data cost-effectively, enabling historical analysis, audits, and model retraining to enhance recommendation accuracy over time.
Option B, S3 + Glue + Redshift, is primarily batch-oriented. While S3 can store user interactions, Glue runs scheduled ETL jobs, and Redshift provides analytics for structured data. This architecture introduces latency, which prevents real-time recommendation generation, reducing the system’s responsiveness to user behaviour.
Option C, RDS + QuickSight, provides relational storage and dashboards. RDS cannot efficiently ingest millions of events per second, and QuickSight cannot process streaming data in real-time. Scaling RDS to handle high-frequency streaming data is operationally complex and cost-prohibitive.
Option D, DynamoDB + EMR, provides scalable NoSQL storage and batch processing. EMR processes data in batches, introducing latency incompatible with real-time recommendations. Orchestrating analytics and dashboards adds operational complexity and reduces system efficiency compared to Option A.
Therefore, Option A offers an integrated, low-latency, and scalable solution for real-time personalised recommendations, operational analytics, and historical data storage for continuous model retraining.
Question 67:
A logistics company wants to monitor its global fleet of vehicles using IoT sensors. The system must ingest telemetry data continuously, detect anomalies such as engine failures or unsafe driving, trigger operational alerts, and store historical telemetry for trend analysis and compliance reporting. Which AWS service combination is best suited?
A) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + AWS Lambda + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + AWS Lambda + Amazon S3
Explanation:
Near real-time fleet monitoring requires scalable ingestion of high-frequency IoT telemetry data, continuous anomaly detection, alerting, and durable historical storage. Option A meets these requirements comprehensively. Kinesis Data Streams ingests telemetry from thousands of vehicles simultaneously, providing durability, automatic scaling, and low-latency processing. Kinesis Data Analytics processes the data continuously, detecting anomalies such as engine malfunctions, speeding, or unsafe driving behaviours in near real-time. AWS Lambda triggers operational alerts, notifying maintenance or operational teams immediately or initiating automated workflows for preventive measures. Amazon S3 stores historical telemetry data cost-effectively, supporting trend analysis, operational reporting, and compliance requirements across different regions.
Option B, S3 + Glue + Athena, is batch-oriented. S3 stores the data, Glue performs scheduled ETL, and Athena provides query-based analysis. This introduces delays, preventing real-time anomaly detection and immediate operational response, which are critical for fleet monitoring.
Option C, RDS + QuickSight, is designed for structured storage and dashboarding. RDS cannot handle high-frequency streaming telemetry data efficiently, and QuickSight cannot process real-time streaming data. Scaling RDS to manage telemetry from a global fleet introduces operational complexity and high costs.
Option D, DynamoDB + EMR, provides scalable NoSQL storage and batch analytics. EMR processes data in batches, which introduces latency incompatible with real-time detection and alerting. Integrating alerts and dashboards requires additional orchestration, increasing operational complexity.
Thus, Option A offers a fully integrated, scalable, low-latency architecture for telemetry ingestion, anomaly detection, alerting, and historical analysis.
Question 68:
A healthcare organisation needs to archive patient imaging records for long-term retention. The solution must provide extremely durable, cost-efficient storage while allowing occasional querying for audits or research without restoring entire datasets. Which AWS architecture is most appropriate?
A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena
Explanation:
Healthcare imaging records require highly durable and cost-efficient long-term archival with selective query capabilities. Option A is optimal. Glacier Deep Archive provides extremely low-cost storage with eleven nines of durability, ensuring protection of large datasets over decades. Lifecycle policies automate movement from higher-cost S3 storage classes to Glacier Deep Archive, optimising costs. Athena enables SQL-based queries on archived data without restoring entire datasets, facilitating audits and research with minimal operational effort. This architecture simplifies compliance management and reduces operational overhead while providing selective access for regulatory or research purposes.
Option B, S3 Standard + Lambda, is suitable for frequently accessed data but is cost-prohibitive for long-term archival. Lambda cannot query archived datasets, limiting its utility for audits or research.
Option C, RDS + Redshift, provides structured storage and analytics but is costly and operationally complex for long-term archival of rarely accessed datasets. Active Redshift clusters are required for queries, increasing cost and complexity.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR’s batch nature introduces latency for queries, and DynamoDB is expensive for long-term archival storage, making this architecture suboptimal for the scenario.
Thus, Option A delivers a compliant, durable, cost-efficient, and queryable solution for healthcare imaging records archival.
Question 69:
An online retail platform wants to implement a clickstream analytics system that can handle millions of user interactions per second. The system must ingest high-velocity data, perform near real-time transformations, store raw and processed data, and provide reporting and business intelligence capabilities. 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 real-time ingestion, transformation, storage, and reporting. Option A meets these requirements effectively. Kinesis Data Firehose ingests millions of clickstream events per second, automatically scaling for traffic spikes. AWS Lambda performs near real-time transformations such as filtering, enrichment, and aggregation, preparing data for analysis. Amazon S3 stores raw and processed data cost-efficiently for historical analysis, supporting audits and business intelligence. Athena allows SQL-based queries directly on S3, enabling dashboards and reporting without moving large datasets, reducing operational complexity.
Option B, S3 + Glue + Redshift, is batch-oriented. Scheduled ETL jobs introduce latency, preventing near real-time transformation and analysis. Redshift supports structured analytics but is not optimised for high-velocity streaming ingestion.
Option C, RDS + QuickSight, provides structured storage and dashboards. RDS cannot handle millions of high-frequency events, and QuickSight cannot process real-time streaming data, limiting operational insights.
Option D, DynamoDB + EMR, offers scalable storage and batch processing. EMR introduces latency incompatible with real-time transformations and reporting, while orchestrating dashboards adds complexity.
Thus, Option A provides an integrated, low-latency, scalable solution for clickstream ingestion, transformation, storage, and reporting.
Understanding Clickstream Analytics Requirements
Clickstream analytics involves the collection, processing, and analysis of user interactions with web applications, mobile apps, or digital platforms. Organisations use clickstream data to understand user behaviour, optimise user experience, personalise content, detect anomalies, and inform business decisions. The nature of clickstream data is high-velocity and high-volume, with potentially millions of events generated per second. Effective analytics requires real-time ingestion, transformation, and immediate availability for querying or reporting. Additionally, historical storage is necessary for trend analysis, auditing, and machine learning purposes. A robust architecture must therefore accommodate both streaming and batch requirements while minimising operational complexity.
Option A: Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
Option A represents a fully integrated solution for real-time clickstream analytics. Amazon Kinesis Data Firehose is a fully managed streaming service designed to reliably capture, transform, and load streaming data into destinations such as Amazon S3, Redshift, or Elasticsearch. Kinesis Data Firehose automatically scales to accommodate fluctuations in event rates, which is crucial for handling traffic spikes, seasonal surges, or viral campaigns that can produce sudden bursts of clickstream data.
AWS Lambda complements Firehose by enabling serverless, near-real-time data transformations. Lambda functions can filter irrelevant events, enrich raw data with additional metadata, or aggregate events into meaningful summaries suitable for analysis. These transformations occur immediately as the data flows through the pipeline, ensuring that the information is actionable within seconds of capture. This is essential for operational analytics, real-time personalisation, or monitoring user interactions for anomalies or engagement patterns.
Amazon S3 provides a highly durable, cost-efficient storage layer for both raw and transformed data. Storing data in S3 ensures that all historical clickstream events are preserved for auditing, compliance, or machine learning purposes. S3’s integration with Athena allows analysts and business users to execute SQL queries directly on stored data without the need for data movement, reducing operational overhead and improving efficiency. Athena supports ad hoc querying and can power dashboards for near real-time insights, bridging the gap between raw event collection and actionable intelligence.
The combination of Firehose, Lambda, S3, and Athena ensures end-to-end data flow from ingestion to storage to querying, all with minimal latency. This architecture also supports fault tolerance and automatic scaling, which are critical for maintaining performance under variable workloads. By using serverless components, operational management is simplified, avoiding the need for provisioning and maintaining dedicated infrastructure for ingestion or processing pipelines.
Option B: Amazon S3 + AWS Glue + Amazon Redshift
Option B relies on batch-oriented processing. AWS Glue is designed for extract, transform, and load (ETL) operations on scheduled intervals. While it can efficiently process large datasets, the batch nature of Glue introduces latency, making it unsuitable for real-time clickstream analysis. By the time Glue completes a scheduled job, the data may already be outdated for operational dashboards or personalisation features.
Redshift serves as a powerful analytical database optimised for structured, relational queries. However, it is primarily designed for batch ingestion rather than continuous high-velocity streaming events. Scaling Redshift to handle millions of events per second would require complex orchestration and costly resources, and even then, near real-time query capabilities would be limited. While this combination excels for historical reporting or business intelligence, it fails to meet the immediacy requirements of real-time analytics.
Furthermore, orchestrating data pipelines with Glue and Redshift introduces additional operational complexity. ETL scripts must be maintained, scheduling must be coordinated, and resource allocation optimised to handle varying workloads. For organisations aiming to respond to user behaviour in seconds rather than hours, Option B falls short of expectations.
Option C: Amazon RDS + Amazon QuickSight
Option C uses a relational database combined with a visualisation tool. Amazon RDS offers structured, managed database storage for transactions and records, while QuickSight provides dashboards and reporting capabilities. This setup is well-suited for traditional analytics on relational datasets where data volume is moderate, and updates occur periodically.
However, RDS is not optimised for ingesting millions of clickstream events per second. High-frequency events can overwhelm database connections, resulting in degraded performance or failed inserts. QuickSight is designed for visualisation and business intelligence, but does not provide native support for streaming analytics. Consequently, real-time insights and operational dashboards cannot be generated effectively using this combination. Data is only as current as the refresh interval, which is unsuitable for applications requiring immediate visibility into user interactions.
Moreover, managing schema changes, indexing, and optimising queries under high-throughput workloads increases operational overhead. While suitable for historical reporting or limited-scale analytics, RDS and QuickSight cannot deliver the low-latency, high-throughput pipeline required for robust clickstream analytics.
Option D: Amazon DynamoDB + Amazon EMR
Option D combines a NoSQL database with a big data processing platform. DynamoDB provides scalable, low-latency storage for key-value or document-based datasets. EMR allows distributed processing using frameworks like Spark or Hadoop, suitable for large-scale data transformations and analytics.
Although DynamoDB can handle high write throughput, EMR is inherently batch-oriented. Analysing clickstream events via EMR requires loading data from DynamoDB or S3, performing distributed computations, and then writing results back to a data store. This workflow introduces latency that prevents real-time visibility into user behaviour. Additionally, orchestrating alerts, dashboards, or aggregations with EMR requires complex pipelines and custom scheduling, increasing operational complexity.
While this architecture supports scalable storage and analytics, it is not optimised for real-time transformation, aggregation, or reporting. It is better suited for historical data processing, large-scale machine learning model training, or batch analytics rather than immediate, operational clickstream insights.
Question 70:
A financial services company requires a real-time fraud detection system capable of ingesting millions of transactions per second, detecting anomalies immediately, triggering operational alerts, and storing all transactions for auditing and compliance purposes. 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:
A real-time fraud detection system requires scalable ingestion, immediate anomaly detection, alerting, and durable storage for auditing and compliance. Option A provides a comprehensive solution. Kinesis Data Streams ingests millions of transactions per second, ensuring durability and automatic scaling for peak workloads. AWS Lambda processes transactions in real-time, applying anomaly detection models to flag suspicious activity immediately. Amazon CloudWatch monitors operational metrics, triggers alerts, and integrates with notification systems for prompt response. Amazon S3 stores all transactions durably and cost-effectively, supporting auditability and historical analysis for model retraining and compliance reporting.
Option B, S3 + Glue + Athena, is batch-oriented, suitable for historical analysis but incapable of providing real-time fraud detection or immediate alerts.
Option C, RDS + Redshift, supports structured storage and analytics but cannot handle high-velocity streaming transactions efficiently. Real-time anomaly detection is not feasible, and scaling RDS for millions of transactions per second is operationally challenging.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency incompatible with real-time detection, and orchestrating alerts adds complexity.
Therefore, Option A delivers a fully integrated, low-latency, scalable architecture for real-time fraud detection, alerting, and compliance.
Question 71:
A streaming music service wants to implement a personalised recommendation engine. The system must ingest millions of song play events per second, analyse listening behaviour in near real-time, provide dynamic recommendations, and store historical play data for model retraining and trend analysis. Which AWS architecture is best suited?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker + Amazon S3
Explanation:
A personalised recommendation engine for a streaming music service requires ingesting high-velocity user events, performing near real-time behavioural analysis, dynamically generating recommendations, and storing historical data for model retraining. Option A provides a fully integrated solution. Kinesis Data Streams captures millions of song play events per second, ensuring scalability and durability even during peak periods. AWS Lambda enables serverless processing of streaming data in real-time, performing filtering, aggregation, and enrichment necessary for recommendation algorithms. Amazon SageMaker hosts and serves machine learning models that generate personalised recommendations based on live user behaviour and historical data patterns. Amazon S3 stores raw and processed events cost-effectively, providing a historical record for auditing, trend analysis, and model retraining to continuously improve recommendation accuracy.
Option B, S3 + Glue + Redshift, is batch-oriented. While S3 stores raw data, Glue runs scheduled ETL jobs, and Redshift provides analytics, this architecture introduces latency that prevents real-time recommendation generation. Recommendations would be delayed, reducing personalisation effectiveness.
Option C, RDS + QuickSight, supports relational storage and dashboards. RDS is not designed for high-velocity streaming ingestion, and QuickSight cannot perform near real-time analytics. Scaling RDS to handle millions of events per second is operationally complex and expensive.
Option D, DynamoDB + EMR, provides scalable NoSQL storage and batch processing. EMR’s batch nature introduces latency, preventing real-time recommendations. Orchestrating dashboards or analytics adds operational complexity, making it less suitable than Option A.
Therefore, Option A delivers a low-latency, scalable, and fully integrated architecture for real-time personalised recommendations, operational analytics, and historical data retention for model retraining.
Question 72:
A global logistics company wants to implement a real-time fleet monitoring solution using IoT sensors. The system must ingest continuous telemetry data, detect anomalies such as mechanical failures, trigger alerts instantly, and maintain historical data for trend analysis and regulatory compliance. Which AWS service combination 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 fleet monitoring with IoT sensors requires high-velocity data ingestion, continuous anomaly detection, immediate alerting, and durable storage. Option A is the best solution. Kinesis Data Streams ingests telemetry data from thousands of vehicles in real-time, providing scalability and durability. Kinesis Data Analytics continuously processes incoming streams, detecting anomalies such as engine malfunctions, unsafe driving behaviours, or deviations from expected patterns. AWS Lambda triggers operational alerts immediately when anomalies are detected, enabling preventive measures or dispatching maintenance teams. Amazon S3 stores historical telemetry data for trend analysis, operational reporting, and regulatory compliance across multiple regions.
Option B, S3 + Glue + Athena, is batch-oriented. Data is stored in S3, Glue runs scheduled ETL, and Athena queries datasets, but this introduces latency, preventing real-time anomaly detection and alerting. Delayed detection reduces the operational effectiveness of fleet monitoring.
Option C, RDS + QuickSight, supports structured storage and dashboards but cannot ingest high-frequency streaming data efficiently. QuickSight cannot perform real-time analytics, and scaling RDS to handle global fleet telemetry introduces operational complexity and costs.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR processes data in batches, introducing latency incompatible with real-time monitoring. Alerts and dashboards require additional orchestration, increasing operational overhead.
Thus, Option A provides a fully integrated, low-latency, scalable solution for telemetry ingestion, anomaly detection, alerting, and historical analysis.
Question 73:
A healthcare organisation must archive patient imaging records for long-term retention. The solution must provide extremely durable, cost-efficient storage while allowing occasional querying for research or audits without restoring entire datasets. Which AWS architecture is most appropriate?
A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena
Explanation:
Healthcare imaging records require highly durable, cost-efficient long-term archival with selective query capabilities. Option A is optimal. Glacier Deep Archive offers extremely low-cost storage with eleven nines of durability, ideal for storing large imaging datasets for decades. Lifecycle policies automate moving older data from higher-cost S3 classes to Glacier Deep Archive, optimising costs. Athena enables SQL-based queries on archived datasets without restoring entire files, facilitating audits and research with minimal operational effort. This architecture ensures compliance with regulations while minimising operational overhead and cost.
Option B, S3 Standard + Lambda, is suitable for frequently accessed data but is cost-prohibitive for long-term archival. Lambda cannot query archived datasets, making it unsuitable for audits or research.
Option C, RDS + Redshift, provides structured storage and analytics. However, RDS is expensive for long-term archival, and Redshift requires active clusters to query data, increasing operational complexity and cost.
Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR processes data in batches, introducing latency, and DynamoDB is costly for long-term storage. This combination is suboptimal for archival use cases requiring infrequent queries.
Thus, Option A delivers a compliant, durable, cost-efficient, and queryable solution for long-term archival of patient imaging records.
Question 74:
An online retailer wants to implement clickstream analytics to monitor millions of user interactions per second. The system must ingest high-velocity data, perform near real-time transformations, store raw and processed data, and provide reporting and business intelligence dashboards. 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 high-velocity ingestion, near real-time data transformation, storage, and reporting. Option A satisfies these requirements. Kinesis Data Firehose ingests millions of clickstream events per second, automatically scaling to accommodate traffic fluctuations. AWS Lambda performs near-real-time data transformations such as filtering, enrichment, and aggregation. Amazon S3 stores both raw and processed clickstream data cost-effectively, supporting historical analysis, audits, and business intelligence. Athena allows SQL-based queries directly on S3, enabling dashboards and reporting without moving data, reducing operational complexity and latency.
Option B, S3 + Glue + Redshift, is batch-oriented. ETL jobs introduce delays, preventing near-real-time transformations. Redshift supports analytics but is not optimised for real-time streaming ingestion.
Option C, RDS + QuickSight, is suitable for structured storage and dashboards. RDS cannot handle millions of high-velocity events, and QuickSight cannot provide real-time analytics.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency incompatible with real-time transformations and reporting. Orchestrating dashboards requires additional complexity.
Thus, Option A provides an integrated, low-latency, scalable solution for clickstream ingestion, transformation, storage, and reporting.
Question 75:
A financial services company needs a real-time fraud detection system that can ingest millions of transactions per second, detect anomalies immediately, trigger operational alerts, and store all transactions durably for auditing and compliance. Which AWS architecture is most appropriate?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Explanation:
A real-time fraud detection system requires scalable ingestion, low-latency anomaly detection, alerting, and durable storage for auditing and compliance. Option A is the optimal solution. Kinesis Data Streams ingests millions of transactions per second, providing durability and automatic scaling for high-volume workloads. AWS Lambda processes transactions in real-time, applying anomaly detection models to flag suspicious activity immediately. Amazon CloudWatch monitors operational metrics, triggers alerts, and integrates with notification systems for prompt response. Amazon S3 stores all transactions durably and cost-effectively, ensuring auditability and compliance while supporting historical analysis and retraining of fraud detection models.
Option B, S3 + Glue + Athena, is batch-oriented. While suitable for historical analysis, it cannot provide real-time detection or immediate alerts, which are critical for operational fraud prevention.
Option C, RDS + Redshift, provides structured storage and analytics but cannot handle high-velocity streaming data. Real-time detection is not feasible, and scaling RDS for millions of transactions per second is operationally complex and expensive.
Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency incompatible with near real-time detection, and orchestrating alerts adds operational complexity.
Therefore, Option A delivers a fully integrated, low-latency, scalable architecture for real-time fraud detection, alerting, and compliance.
Understanding the Requirements for Real-Time Fraud Detection
A real-time fraud detection system is designed to identify anomalous behaviour or suspicious transactions as they occur. Such a system must satisfy several key requirements: high-velocity ingestion, immediate processing, real-time alerting, scalability, durability, and auditability. Financial institutions, e-commerce platforms, and payment processors rely heavily on these capabilities to prevent financial loss, reputational damage, and regulatory penalties. In this context, the system must handle millions of transactions per second, detect patterns indicative of fraud using machine learning or rule-based models, trigger alerts instantaneously, and store transaction history for compliance and retraining purposes.
Option A: Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
Option A is architected to address each of these requirements directly. Amazon Kinesis Data Streams provides a fully managed, high-throughput, low-latency streaming service capable of ingesting millions of events per second. Kinesis ensures durability by replicating data across multiple availability zones and supports auto-scaling to handle bursts of traffic. This is critical in financial services, where sudden spikes in transaction volume can occur due to seasonal events, promotions, or fraudulent attacks.
AWS Lambda complements Kinesis by enabling serverless, real-time processing of incoming transactions. Lambda functions can execute custom fraud detection logic, apply anomaly detection models, and make instant decisions about whether a transaction is suspicious. The event-driven nature of Lambda ensures near-zero delay between ingestion and detection, which is essential for operational fraud prevention.
Amazon CloudWatch integrates seamlessly to provide monitoring and alerting. It collects metrics from Kinesis, Lambda, and other components, triggering alarms when anomalies are detected or when system thresholds are breached. CloudWatch can automatically notify relevant teams or initiate workflows for further investigation, ensuring that fraud incidents are addressed promptly.
Finally, Amazon S3 serves as the durable, cost-effective storage layer for all transactions. It ensures compliance with audit regulations by maintaining historical transaction data and enables model retraining by providing a reliable dataset for machine learning pipelines. S3’s integration with services such as Athena and Redshift Spectrum allows for scalable historical analysis without disrupting real-time operations. Overall, Option A delivers an end-to-end architecture that satisfies the stringent requirements of a real-time fraud detection system, combining high ingestion rates, low latency processing, immediate alerting, and reliable long-term storage.
Option B: Amazon S3 + AWS Glue + Amazon Athena
Option B represents a batch-oriented approach rather than a streaming solution. Amazon S3 provides durable storage, AWS Glue facilitates ETL (extract, transform, load) operations, and Athena allows for serverless querying of historical data. While this architecture is excellent for post-facto analysis and generating reports or trends, it cannot meet the immediate detection and alerting requirements of real-time fraud prevention.
Batch processing introduces inherent latency, as data must first be stored, processed, and then analysed. In a scenario where financial transactions are being conducted in real time, delays of even a few minutes can result in significant financial loss. Additionally, triggering alerts based on batch analysis is reactive rather than proactive, which undermines the primary purpose of operational fraud detection. Option B is more suitable for periodic auditing, compliance reporting, or model retraining, but cannot replace a live, real-time detection system.
Option C: Amazon RDS + Amazon Redshift
Option C focuses on structured storage and analytics. Amazon RDS provides managed relational databases suitable for transactional workloads, while Redshift is optimised for large-scale analytical queries. While both are robust and reliable, they are not designed for high-velocity, low-latency stream processing.
Scaling RDS to handle millions of transactions per second is operationally challenging, requiring complex sharding, replication, and tuning. Redshift, although powerful for data warehousing and analytics, operates on batch-oriented queries and cannot provide instantaneous feedback for fraud detection. Implementing real-time alerts would require additional components and complex orchestration, increasing operational overhead and latency. While RDS and Redshift are valuable for reporting, analytics, and storing historical transaction data, they are insufficient for real-time operational fraud prevention.
Option D: Amazon DynamoDB + Amazon EMR
Option D provides a combination of scalable storage with batch processing. DynamoDB is a fully managed NoSQL database capable of handling high throughput and low latency for key-value or document-based storage. Amazon EMR is a managed big data platform for running distributed processing frameworks such as Spark or Hadoop.
While DynamoDB supports high-speed reads and writes, EMR introduces batch-oriented processing, which is not suited for instantaneous fraud detection. Detecting anomalies would require extracting data from DynamoDB, processing it in EMR, and then generating alerts—this workflow introduces latency that undermines real-time operational requirements. Additionally, integrating alerting mechanisms with EMR requires custom orchestration, which increases system complexity and operational cost. DynamoDB + EMR is more suitable for analytics on historical data or large-scale offline processing rather than real-time fraud prevention.
Considering the specific requirements of a real-time fraud detection system—scalable ingestion, low-latency processing, immediate alerting, durability, and auditability—Option A (Kinesis Data Streams + Lambda + CloudWatch + S3) is the most appropriate. It delivers an integrated solution where each component complements the others: Kinesis handles high-volume streaming, Lambda executes real-time detection, CloudWatch monitors and triggers alerts, and S3 provides durable storage for compliance and historical analysis. Options B, C, and D are valuable in analytics, batch processing, or historical data management but cannot satisfy the operational immediacy and scalability needed for real-time fraud detection. By leveraging serverless, event-driven architecture, Option A ensures robust, scalable, and compliant fraud prevention capable of responding instantly to suspicious activity, making it the clear optimal choice.