Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 3 Q31-45 - Certbolt

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Question 31:

A company wants to implement a real-time analytics pipeline for e-commerce transactions. The system must ingest high-volume transaction data, perform anomaly detection, and provide dashboards for business intelligence while storing historical data for regulatory compliance. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon QuickSight
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon Redshift + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon QuickSight

Explanation:

High-volume e-commerce transactions require real-time ingestion, immediate processing for anomaly detection, and integration with dashboards, while historical storage is needed for compliance. Option A, Kinesis Data Streams + Lambda + S3 + QuickSight, provides a complete solution. Kinesis handles streaming ingestion with automatic scaling for spikes. Lambda processes events in real-time, performing transformations or anomaly detection as transactions arrive. S3 stores historical data durably and cost-effectively for auditing or compliance purposes. QuickSight visualises real-time and historical data for business insights.

Option B, S3 + Glue + Athena, is suitable for batch analytics and historical analysis but cannot support real-time dashboards or immediate anomaly detection. Data must be transformed and queried in batches, introducing latency unsuitable for operational intelligence.

Option C, RDS + Redshift + QuickSight, supports structured storage and batch analytics. RDS handles transactional data but struggles with high-throughput streaming, and Redshift is designed for large-scale batch analytics. This combination cannot handle unpredictable spikes or near-real-time anomaly detection efficiently.

Option D, DynamoDB + EMR, provides scalable storage and distributed batch analytics. EMR is batch-oriented, introducing latency for real-time dashboards or anomaly detection. Operational complexity increases, and integration with real-time visualization is more difficult.

Thus, Kinesis + Lambda + S3 + QuickSight is the optimal architecture for near-real-time analytics, anomaly detection, historical storage, and BI dashboards.

Question 32:

A manufacturing company collects telemetry data from thousands of sensors. The system must ingest high-volume streaming data, perform near-real-time anomaly detection, allow historical analytics, and provide visualization dashboards. Which AWS services best meet these requirements?

A) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + Amazon S3 + Amazon QuickSight
B) Amazon S3 + AWS Glue + Amazon Athena + Amazon QuickSight
C) Amazon RDS + AWS Lambda + Amazon CloudWatch
D) Amazon DynamoDB + Amazon EMR + Amazon QuickSight

Answer:
A) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + Amazon S3 + Amazon QuickSight

Explanation:

Industrial sensor telemetry requires real-time processing, anomaly detection, historical storage, and visualization. Option A provides a full solution. Kinesis Data Streams ingests high-velocity sensor data and scales automatically. Kinesis Data Analytics performs streaming analytics for anomaly detection using SQL or real-time queries. S3 stores historical data for auditing, compliance, or ML model training. QuickSight provides visualization dashboards for both real-time and historical data.

Option B, S3 + Glue + Athena + QuickSight, is batch-oriented. S3 stores data, Glue performs ETL, and Athena enables SQL queries. QuickSight visualizes historical analytics. However, this architecture cannot perform near-real-time anomaly detection or dashboards because batch processing introduces latency.

Option C, RDS + Lambda + CloudWatch, supports structured storage, event-driven processing, and monitoring. While suitable for transactional workloads, RDS cannot handle high-velocity streaming telemetry efficiently. Lambda processing is limited by throughput, and CloudWatch does not support large-scale real-time anomaly detection.

Option D, DynamoDB + EMR + QuickSight, provides scalable storage and batch processing. EMR introduces latency incompatible with near-real-time anomaly detection. DynamoDB is fast for transactions but does not natively integrate with real-time analytics or visualization dashboards without additional orchestration.

Thus, option A offers the best architecture for streaming telemetry ingestion, real-time anomaly detection, historical storage, and dashboards.

Question 33:

A company needs a long-term archival solution for healthcare records that must remain cost-efficient, durable, and allow occasional auditing. Which AWS service combination is most suitable?

A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena

Explanation:

Healthcare records require extremely durable, cost-efficient storage with occasional query capabilities for auditing. Option A, Glacier Deep Archive + Athena, meets these requirements. Glacier Deep Archive provides 11 nines of durability at very low cost for rarely accessed data. Athena allows querying specific subsets of archived records using SQL without restoring the entire dataset, minimizing retrieval costs. This combination supports regulatory compliance, auditing, and occasional analytics with minimal operational overhead.

Option B, S3 Standard + Lambda, is unsuitable for archival because S3 Standard is expensive for long-term retention. Lambda cannot query large datasets for auditing purposes efficiently.

Option C, RDS + Redshift, supports structured storage and analytics but is cost-prohibitive for long-term archival. Redshift requires active cluster resources and data ingestion, increasing operational complexity.

Option D, DynamoDB + EMR, provides scalable storage and batch processing. DynamoDB is expensive for rarely accessed data, and EMR introduces latency, making occasional querying operationally complex.

Thus, Glacier Deep Archive + Athena offers the most cost-efficient, durable, and queryable archival solution for compliance.

Question 34:

You are designing a real-time clickstream analytics system for a global e-commerce platform. The system must handle high-velocity data ingestion, scale automatically, perform near-real-time transformations, and store historical data for analytics. Which architecture is most appropriate?

A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena

Explanation:

Clickstream analytics requires high-volume ingestion, real-time processing, and historical storage. Option A, Kinesis Data Firehose + S3 + Lambda + Athena, satisfies these requirements. Firehose ingests streaming clickstream data and scales automatically. Lambda performs near-real-time transformations or enrichment of the data. S3 stores historical data for long-term analytics, and Athena allows querying without moving data, enabling cost-efficient analytics for multiple services.

Option B, S3 + Glue + Redshift, is batch-oriented. Glue transforms data on a schedule, and Redshift queries it. This approach cannot support near-real-time dashboards or immediate transformation of streaming clickstream data.

Option C, RDS + QuickSight, is suitable for structured transactional data and visualization but cannot handle high-velocity streams or real-time transformations efficiently. Scaling RDS for unpredictable spikes is challenging.

Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR introduces latency for transformations and analytics, making it unsuitable for real-time processing. Multi-service access requires additional orchestration, increasing operational complexity.

Thus, option A provides scalable, near-real-time clickstream ingestion, transformation, and historical analytics support.

Question 35:

A company needs to implement a fraud detection system for online payments. The system must ingest transaction data at high volume, detect anomalies in near-real-time, trigger alerts, and store data durably. Which AWS services are most suitable?

A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch + Amazon S3

Explanation:

Fraud detection requires high-volume ingestion, real-time anomaly detection, alerting, and durable storage. Option A, Kinesis + Lambda + CloudWatch + S3, provides an integrated architecture. Kinesis Data Streams ingests millions of transactions per second with durability. Lambda processes transactions in real-time to detect anomalies or apply fraud detection logic. CloudWatch monitors metrics and triggers alerts for suspicious activity. S3 ensures durable, cost-efficient storage for auditing and compliance. This architecture scales automatically, reduces latency for operational decision-making, and provides comprehensive monitoring.

Option B, S3 + Glue + Athena, is batch-oriented. It is suitable for historical analysis but not real-time fraud detection, as Lambda and CloudWatch are required for near-real-time processing and alerting.

Option C, RDS + Redshift, supports structured storage and batch analytics but cannot process high-velocity streaming data in real-time. Scaling RDS for bursts is complex, and Redshift is better suited for batch queries rather than operational fraud detection.

Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency and is not optimised for near-real-time anomaly detection. Operational complexity increases when integrating with alerting and monitoring.

Thus, option A is the optimal solution for high-volume real-time fraud detection, immediate alerting, and durable storage.

Question 36:

A global e-commerce company wants to implement a real-time recommendation engine. The system must process high-velocity user clickstream data, scale automatically to handle traffic spikes, generate recommendations dynamically, and store historical data for model training and auditing. 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:

Recommendation engines for e-commerce platforms require processing of high-velocity clickstream data, dynamic inference of recommendations, scalability to handle variable user traffic, and historical storage for model retraining and auditing purposes. Option A, Kinesis Data Streams + Lambda + SageMaker + S3, is the most suitable architecture. Kinesis Data Streams provides durable, high-throughput ingestion of streaming clickstream data and automatically scales to handle unpredictable spikes in user activity, ensuring data is ingested in near-real time without loss. AWS Lambda allows event-driven, serverless processing of each streaming event, such as feature extraction or transformation, immediately as data arrives. Amazon SageMaker provides a fully managed machine learning environment for real-time inference; it can generate dynamic personalized recommendations for each user based on their clickstream activity. Amazon S3 stores historical clickstream and processed data, enabling model retraining, batch analytics, and compliance auditing while maintaining cost efficiency due to S3’s scalable storage.

Option B, S3 + Glue + Redshift, supports batch analytics workflows. S3 provides scalable storage, Glue can perform ETL transformations, and Redshift is designed for analytical queries on structured data. While suitable for historical analysis, this combination cannot handle real-time recommendation generation. The inherent latency in batch ETL and the non-streaming nature of Redshift prevents dynamic inference and immediate personalization. Thus, it is less suitable for near-real-time, high-velocity recommendation engines.

Option C, RDS + QuickSight, is optimized for structured relational data and visualization. RDS handles transactional storage, while QuickSight can visualize data for business intelligence. However, it cannot efficiently ingest high-velocity clickstream data or support dynamic model inference. Real-time recommendations are impractical due to the limited throughput of RDS and the batch-oriented nature of QuickSight analytics.

Option D, DynamoDB + EMR, provides scalable NoSQL storage and distributed batch processing. DynamoDB allows rapid transactional writes and low-latency reads, while EMR can perform batch transformations and analytics. However, EMR is batch-focused, introducing latency that prevents real-time recommendation generation. Integration with machine learning models for dynamic inference requires additional orchestration and increases operational complexity, making this architecture less ideal than Kinesis + Lambda + SageMaker + S3.

Therefore, Option A offers a fully integrated, scalable, and cost-efficient architecture for processing high-velocity clickstream data, generating real-time recommendations, and storing historical data for auditing and model retraining, making it the most suitable solution.

Question 37:

A company collects telemetry data from thousands of industrial sensors and requires near-real-time anomaly detection, historical data retention, and visualization dashboards. The system must be highly scalable, resilient, and allow multiple analytics and ML services to query data simultaneously. Which AWS architecture best meets these requirements?

Answer:
A) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + Amazon S3 + Amazon QuickSight

Explanation:

Industrial telemetry data demands continuous, high-throughput ingestion, near-real-time anomaly detection, durable storage, and visualization. Option A, Kinesis Data Streams + Kinesis Data Analytics + S3 + QuickSight, satisfies these requirements comprehensively. Kinesis Data Streams ingests high-velocity sensor data with automatic scaling, ensuring that spikes in telemetry traffic do not overwhelm the system. Kinesis Data Analytics performs streaming analytics, applying SQL or custom algorithms for anomaly detection in near-real timeS3 provides cost-efficient, durable storage for historical telemetry data, supporting auditing, regulatory compliance, and machine learning model training. QuickSight visualizes both real-time and historical data, giving operators actionable insights. The integration between Kinesis, S3, and QuickSight enables multi-service access without duplicating data.

Option B, S3 + Glue + Athena + QuickSight, is suitable for batch-oriented analytics. S3 stores telemetry data, Glue performs ETL, and Athena allows SQL queries. QuickSight can visualize historical data. However, batch processing introduces latency, making near-real-time anomaly detection impractical. The lack of streaming analytics prevents immediate operational responses to anomalies.

Option C, RDS + Lambda + CloudWatch, supports transactional storage, event-driven processing, and monitoring. RDS cannot handle large-scale streaming data efficiently, and Lambda’s processing throughput is limited compared to Kinesis Data Streams. CloudWatch can monitor metrics but does not provide near-real-time anomaly detection or complex streaming analytics at scale.

Option D, DynamoDB + EMR + QuickSight, offers scalable storage and distributed batch analytics. EMR is batch-oriented, which introduces latency incompatible with real-time anomaly detection. DynamoDB handles transactional writes efficiently but does not integrate natively with analytics dashboards or streaming anomaly detection without additional orchestration. Operational complexity and cost are higher than Option A.

Therefore, Option A provides a scalable, resilient, and fully integrated solution for streaming telemetry ingestion, near-real-time anomaly detection, historical data storage, and dashboard visualization, fulfilling the requirements comprehensively.

Question 38:

A financial institution must implement a long-term archival solution for regulatory compliance. The system must store structured transaction records cost-effectively, ensure high durability, and allow occasional querying for audits. Which AWS service combination is most suitable?

A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena

Explanation:

Regulatory-compliant financial archives require cost-efficient, highly durable storage with query capabilities for auditing. Option A, S3 Glacier Deep Archive + Athena, satisfies these requirements. Glacier Deep Archive provides extremely low-cost storage for rarely accessed data with eleven nines of durability, ensuring compliance with regulatory retention standards. Lifecycle policies can automatically transition records from other S3 storage classes to Glacier Deep Archive. Athena allows auditors to query specific subsets of archived data using SQL without restoring entire datasets, minimizing retrieval costs. This approach reduces operational complexity and ensures regulatory compliance while enabling occasional analytics.

Option B, S3 Standard + Lambda, provides low-latency storage and event-driven computation. However, S3 Standard is cost-prohibitive for long-term archival of rarely accessed records. Lambda cannot directly query large archival datasets for auditing purposes, making this combination unsuitable for compliance-focused archival storage.

Option C, RDS + Redshift, supports structured data storage and analytics. RDS can store transactional data, and Redshift allows analytical queries. However, RDS is expensive for long-term storage, and Redshift requires active clusters for querying, increasing operational complexity and costs. It is not optimized for rarely accessed archival data.

Option D, DynamoDB + EMR, offers scalable NoSQL storage and distributed batch analytics. DynamoDB is cost-prohibitive for long-term archival, and EMR introduces latency for querying. Operational complexity is higher, and occasional audits are less efficient compared to S3 Glacier Deep Archive + Athena.

Thus, Option A is the most cost-efficient, durable, and queryable solution for long-term regulatory-compliant financial data archival.

Question 39:

A company wants to build a near-real-time clickstream analytics system for a global e-commerce website. The system must ingest data at high velocity, scale automatically, perform near-real-time transformations, and store data for historical analysis. Which AWS architecture best meets these requirements?

A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena

Explanation:

Clickstream analytics requires high-volume ingestion, real-time processing, and historical data storage. Option A, Kinesis Data Firehose + S3 + Lambda + Athena, is the most suitable architecture. Firehose ingests clickstream events, automatically scaling to handle traffic spikes and providing durable streaming to S3. Lambda performs near-real-time transformations, enriching or aggregating the data as it arrives. S3 stores raw and transformed data for long-term analysis. Athena enables querying historical data directly on S3 without moving it, allowing cost-efficient analytics for multiple services or dashboards. This architecture supports both real-time and historical analytics efficiently.

Option B, S3 + Glue + Redshift, is batch-oriented. Glue performs scheduled ETL, and Redshift provides analytical querying. Real-time processing and transformation of streaming data are not supported, making this approach unsuitable for near-real-time analytics.

Option C, RDS + QuickSight, supports structured data and visualization but cannot ingest high-velocity streams or perform real-time transformations efficiently. Scaling for unpredictable spikes is difficult.

Option D, DynamoDB + EMR, provides scalable storage and distributed batch analytics. EMR introduces latency incompatible with near-real-time transformation. Integration with analytics dashboards is more complex and operationally intensive than Option A.

Thus, Option A provides the most complete and scalable architecture for near-real-time clickstream ingestion, transformation, and historical analytics.

Question 40:

A payment processing platform requires a fraud detection system that ingests transaction data at high volume, performs near-real-time anomaly detection, triggers alerts for suspicious activity, and stores data durably for auditing. Which AWS services best meet these requirements?

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 processing, high-volume ingestion, immediate alerting, and durable storage. Option A, Kinesis Data Streams + Lambda + CloudWatch + S3, provides a complete solution. Kinesis ingests millions of transactions per second with durability and automatic scaling. Lambda processes transactions in near-real time, applying anomaly detection logic or scoring models to identify fraudulent activity. CloudWatch monitors metrics and triggers alerts for suspicious events. S3 provides durable storage for auditing, compliance, and historical analysis. This architecture ensures scalability, operational monitoring, low latency, and minimal operational complexity.

Option B, S3 + Glue + Athena, is batch-oriented and suitable for historical analysis but cannot detect anomalies in near-real-time or trigger immediate alerts, making it unsuitable for operational fraud detection.

Option C, RDS + Redshift, is structured storage and batch analytics. RDS cannot handle high-velocity streaming transactions efficiently, and Redshift is batch-focused, preventing near-real-time detection and alerting. Scaling RDS for spikes is difficult.

Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency incompatible with near-real-time detection. Operational complexity increases for integrating alerting and monitoring.

Thus, Option A is the optimal architecture for high-volume, real-time fraud detection with immediate alerts and durable storage.

Fraud detection systems require an architecture capable of ingesting vast volumes of transactional data continuously, processing it in near-real time, identifying anomalies, and generating alerts while maintaining durable storage for auditing and compliance purposes. Industrial-scale e-commerce platforms, financial services, and digital payment systems demand such capabilities because fraudulent activities can occur at any moment, and even small delays in detection can result in significant financial losses. Amazon Kinesis Data Streams addresses the first critical requirement: low-latency, high-throughput ingestion. It is designed to handle millions of events per second, providing seamless scalability as transaction volumes fluctuate. Each data record is stored durably across multiple availability zones, ensuring no transaction is lost even during hardware failures or regional outages. This durability is critical in fraud detection, where missing a single transaction could prevent the detection of coordinated fraud patterns.

Once data is ingested, real-time processing is necessary to evaluate transactions instantly. AWS Lambda integrates tightly with Kinesis Data Streams, offering serverless event-driven processing. Lambda functions can execute fraud detection algorithms, score transactions against historical behaviour, or apply machine learning models in near-real time. The serverless nature of Lambda eliminates the need to manage compute infrastructure, automatically scaling in response to incoming transaction volume. This ensures consistent low-latency processing, even during peak traffic periods, without human intervention or complex provisioning. Real-time processing allows for immediate flagging of suspicious transactions, which is essential for operational fraud prevention and for initiating rapid response workflows such as alerting account holders or halting suspicious transactions before they are completed.

Monitoring and alerting form the third pillar of this architecture. Amazon CloudWatch enables continuous observability of metrics from Kinesis Data Streams and Lambda. Custom metrics, such as the frequency of anomalous transactions, transaction success rates, and processing latency, can be monitored in real-time. CloudWatch alarms can trigger automated notifications or invoke additional Lambda functions to handle incident response, ensuring that fraudulent events are not only detected but also escalated promptly. Operational monitoring through CloudWatch ensures the system remains reliable, performance thresholds are maintained, and anomalies in the processing pipeline itself are detected immediately, preventing blind spots in fraud detection coverage.

Durable storage for historical and regulatory purposes is provided by Amazon S3. Storing processed and raw transactional data in S3 ensures long-term availability, immutability, and cost-efficient storage for large datasets. Historical transaction data in S3 can be leveraged for retrospective analysis, model training, and trend evaluation. Organisations can use this data to refine fraud detection models, evaluate patterns of recurring fraudulent behaviour, and support regulatory reporting. S3’s scalability ensures that even with millions of daily transactions, storage limits are never a concern, and data access remains consistent for analytics and compliance purposes.

The combination of these four services creates a fully integrated fraud detection architecture. Kinesis Data Streams handles ingestion at scale and with durability, Lambda provides low-latency computation for anomaly detection, CloudWatch enables operational oversight and alerting, and S3 guarantees reliable long-term storage. This integration reduces operational complexity, as there is no need for external orchestration, custom queueing systems, or batch pipelines to process incoming transactions. Each service complements the others, creating a cohesive workflow from ingestion through processing to alerting and storage.

The architecture also inherently supports elasticity and high availability. Kinesis Data Streams allows dynamic scaling through shards, which ensures that sudden increases in transaction volume do not overwhelm the system. Lambda scales horizontally, processing multiple shards concurrently without manual intervention. CloudWatch continuously monitors both processing performance and transaction anomalies, maintaining operational visibility across dynamic workloads. S3 offers virtually unlimited storage capacity, accommodating continuous data growth. This elasticity ensures that the fraud detection system remains performant, responsive, and resilient, even under unpredictable workloads or traffic spikes.

Security is another critical component of this architecture. Data in Kinesis Data Streams, Lambda, and S3 can be encrypted in transit and at rest using AWS Key Management Service (KMS). Fine-grained access control through IAM allows organisations to restrict access to sensitive transaction data, ensuring that only authorised personnel or services can process, monitor, or retrieve data. CloudWatch monitoring can further enhance security by providing logs and alerts for unauthorised access attempts or unusual patterns in system usage. This ensures both operational security and compliance with financial regulations.

Compared to alternative approaches, this architecture clearly meets the stringent requirements of real-time fraud detection. Option B, S3, Glue, and Athena are batch-oriented and lack the ability to detect anomalies in real-time. Option C, RDS and Redshift, offers structured storage and analytics capabilities but cannot handle high-throughput streaming transactions or trigger immediate alerts. Option D, DynamoDB and EMR, provides scalable storage and batch processing but introduces latency due to EMR’s batch nature and adds complexity for integrating real-time alerting and monitoring.

By leveraging the combination of Kinesis Data Streams, Lambda, CloudWatch, and S3, organisations can implement a highly responsive, scalable, and secure fraud detection platform. This architecture ensures that high-volume transactions are ingested and processed with minimal latency, anomalous activity is detected immediately, alerts are generated proactively, and transaction records are securely archived for historical analysis and compliance. It enables continuous monitoring, operational oversight, and long-term analytical capabilities while reducing infrastructure management overhead.

Question 41:

A global streaming platform needs to process real-time user interaction events, detect unusual viewing patterns, and update personalised content recommendations dynamically. The system must also store historical interaction data for analytics and machine learning model training. Which AWS architecture best meets these requirements?

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 streaming platform must handle real-time ingestion of user interaction events at scale, process them with minimal latency, dynamically generate personalised recommendations, and maintain historical data for analytical and machine learning purposes. Option A, Kinesis Data Streams + Lambda + SageMaker + S3, provides a complete architecture to meet these requirements. Kinesis Data Streams allows ingestion of high-volume streaming events, ensuring scalability, durability, and low-latency processing. AWS Lambda processes each event in near-real time, applying feature extraction or transformations needed for recommendation models. Amazon SageMaker serves as the managed environment to run machine learning inference, producing real-time recommendations based on streaming input. Amazon S3 stores historical events, ensuring cost-efficient durability and availability for model retraining, auditing, and batch analytics.

Option B, S3 + Glue + Redshift, is designed primarily for batch processing and analytical workloads on structured data. While it is efficient for historical analysis, the batch-oriented nature introduces latency that is incompatible with real-time recommendations. Glue requires ETL jobs that are scheduled or triggered, and Redshift is best suited for large-scale queries rather than real-time streaming data, making it unsuitable for dynamic recommendation generation.

Option C, RDS + QuickSight, focuses on transactional storage and visualisation. RDS is ideal for structured relational data but is not optimised for high-volume, low-latency event ingestion. QuickSight enables dashboards but cannot process high-frequency real-time events or dynamically update recommendations. Scaling RDS to handle bursts of streaming interactions is complex and cost-intensive.

Option D, DynamoDB + EMR, provides scalable NoSQL storage and distributed batch analytics. DynamoDB handles fast writes but does not natively process streaming analytics. EMR processes batch data, introducing latency that prevents near-real-time recommendations. Orchestrating real-time inference on EMR is operationally complex and requires additional integration, making it less suitable than Option A.

Therefore, Option A delivers a fully integrated, scalable, low-latency solution capable of supporting real-time personalised recommendations while retaining historical data for analytics and machine learning.

Question 42:

An industrial IoT company collects sensor telemetry from thousands of devices every second. They need to detect anomalies in near-real time, store the data durably, and provide dashboards for operational monitoring. The system must also allow multiple analytics services to query the data simultaneously. Which AWS architecture should be used?

Answer:
A) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + Amazon S3 + Amazon QuickSight

Explanation:

Industrial IoT telemetry requires near-real-time ingestion, streaming analytics for anomaly detection, durable storage, and visualisation dashboards. Option A provides the most suitable architecture. Kinesis Data Streams ensures ingestion of high-velocity sensor data with scalability and durability. Kinesis Data Analytics allows processing of streaming events in near-real time, enabling immediate anomaly detection. Amazon S3 stores raw and processed telemetry data for historical analysis, ML model training, and auditing. QuickSight visualises both real-time and historical data, providing actionable insights to operations teams. The architecture supports multiple analytics and ML services querying data without duplication, ensuring efficient multi-service access.

Option B, S3 + Glue + Athena + QuickSight, is batch-oriented. Data is stored in S3, transformed with Glue, and queried using Athena. QuickSight visualises the batch results. While this is suitable for historical analysis, it introduces latency that prevents near-real-time anomaly detection. This architecture cannot meet the operational need for immediate insights or dashboards reflecting live sensor activity.

Option C, RDS + Lambda + CloudWatch, supports structured storage, event-driven processing, and monitoring. However, RDS cannot handle high-frequency streaming data efficiently, and Lambda’s throughput is limited. CloudWatch provides alerting and metric monitoring but does not perform complex streaming analytics, making it inadequate for real-time anomaly detection at scale.

Option D, DynamoDB + EMR + QuickSight, provides scalable storage and distributed batch processing. EMR is designed for batch operations and introduces latency that makes real-time detection unfeasible. DynamoDB handles transactional writes but does not integrate natively with streaming analytics or near-real-time dashboards. Operational complexity increases when integrating batch analytics with real-time visualisation.

Thus, Option A provides a fully scalable, real-time, and resilient solution that supports anomaly detection, historical storage, multi-service analytics, and visualisation dashboards.

Question 43:

A financial organisation needs to implement a long-term archival system for regulatory compliance. The system must store structured transaction data cost-efficiently, ensure high durability, and allow occasional queries for audits without significant operational overhead. Which AWS services are most suitable?

A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena

Explanation:

Financial regulatory compliance requires cost-effective, durable, long-term storage with the ability to query specific records for auditing. Option A, S3 Glacier Deep Archive + Athena, meets these requirements. Glacier Deep Archive provides extremely low-cost storage with eleven nines of durability, ideal for retaining rarely accessed transactional data for regulatory periods. Lifecycle policies can automate transitioning older data from other S3 storage classes to Glacier Deep Archive. Athena allows auditors to perform SQL-based queries on archived data subsets without restoring entire datasets, minimising retrieval time and costs. This combination reduces operational overhead while ensuring compliance and query accessibility.

Option B, S3 Standard + Lambda, offers low-latency storage and event-driven processing. However, S3 Standard is expensive for long-term archival of rarely accessed records. Lambda does not provide direct query capabilities for archived data, making it unsuitable for audit requirements.

Option C, RDS + Redshift, supports structured storage and analytics. RDS can store transactional records, and Redshift allows analytical queries. However, RDS is cost-prohibitive for long-term archival, and Redshift requires active clusters for queries, increasing operational complexity. This combination is not optimised for rarely accessed archival data.

Option D, DynamoDB + EMR, provides scalable NoSQL storage and batch processing. DynamoDB is expensive for long-term archival, and EMR’s batch processing introduces latency that is incompatible with audit query requirements. Operational complexity is higher compared to S3 Glacier Deep Archive + Athena.

Therefore, Option A is the most suitable solution for cost-efficient, durable, and queryable long-term archival of structured financial transaction data.

Question 44:

An e-commerce platform wants to implement a near-real-time clickstream analytics system. The system must ingest high-velocity user activity data, scale automatically, perform near-real-time transformations, and store the data for historical analysis and reporting. Which AWS architecture is most appropriate?

A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR

Answer:
A) Amazon Kinesis Data Firehose + Amazon S3 + AWS Lambda + Amazon Athena

Explanation:

Clickstream analytics requires ingestion of high-velocity data, near-real-time processing, and historical storage for analysis. Option A, Kinesis Data Firehose + S3 + Lambda + Athena, addresses all these needs. Firehose provides automatic scaling for high-volume clickstream ingestion and delivers data reliably to S3. Lambda performs near-real-time transformations, such as enrichment or aggregation, enabling immediate analytics. S3 stores both raw and transformed clickstream data cost-effectively, supporting historical analysis. Athena allows querying data /directly on S3 without moving it, enabling efficient reporting for business intelligence. This architecture supports both real-time and historical analytics efficiently, with minimal operational overhead.

Option B, S3 + Glue + Redshift, is batch-oriented. Glue performs scheduled ETL, and Redshift enables analytical queries. This approach cannot provide real-time transformation or immediate insight, introducing latency that limits operational responsiveness.

Option C, RDS + QuickSight, supports structured data storage and visualisation but cannot handle high-frequency streaming data or near-real-time transformations. Scaling RDS for variable traffic is complex, and QuickSight does not provide streaming analytics.

Option D, DynamoDB + EMR, provides scalable storage and batch analytics. EMR’s batch nature introduces latency, preventing real-time processing. Integrating EMR with dashboards increases operational complexity.

Thus, Option A provides the most complete and scalable solution for real-time clickstream ingestion, transformation, and historical analytics.

Question 45:

A payment processing platform needs a fraud detection system capable of ingesting high-volume transaction data, performing near-real-time anomaly detection, triggering alerts for suspicious activity, and storing data durably for auditing. Which AWS services best meet these requirements?

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, alerting, and durable storage. Option A, Kinesis Data Streams + Lambda + CloudWatch + S3, is ideal. Kinesis ingests millions of transactions per second with durability and automatic scaling. Lambda processes transactions in near-real time, applying fraud detection algorithms or scoring models. CloudWatch monitors metrics and triggers alerts when suspicious patterns are detected. S3 provides durable storage for auditing, compliance, and historical analysis. This architecture ensures scalability, operational monitoring, low latency, and cost efficiency.

Option B, S3 + Glue + Athena, is suitable for batch analytics but cannot support real-time detection or alerts, making it unsuitable for operational fraud prevention.

Option C, RDS + Redshift, supports structured storage and batch analytics. RDS cannot handle high-volume streaming, and Redshift’s batch-oriented nature prevents near-real-time detection and alerting. Scaling RDS for bursts is complex and costly.

Option D, DynamoDB + EMR, provides scalable storage and batch processing. EMR introduces latency that prevents near-real-time anomaly detection. Operational complexity increases when integrating alerting and monitoring, making it less suitable than Option A.

Therefore, Option A provides a fully integrated, scalable, and low-latency architecture for real-time fraud detection with immediate alerts and durable storage.

Fraud detection in financial services, e-commerce, or digital payment platforms demands an architecture capable of processing massive volumes of transactions in near-real time while identifying anomalies that indicate potential fraudulent activity. Low-latency ingestion, instantaneous evaluation, alerting, and durable record-keeping are essential components. Amazon Kinesis Data Streams serves as the backbone for ingesting transactional data at high velocity. This service is designed to handle millions of events per second while ensuring durability and fault tolerance by replicating data across multiple availability zones. It enables continuous collection of transactions without risk of data loss or throughput bottlenecks, which is critical in high-stakes fraud monitoring scenarios.

Once data is ingested, real-time processing and evaluation are crucial to prevent fraudulent transactions from completing. AWS Lambda integrates seamlessly with Kinesis Data Streams to provide event-driven processing. Each transaction can be processed immediately upon arrival, allowing Lambda functions to apply fraud detection algorithms, scoring models, or business rules in real time. This architecture supports dynamic evaluation of patterns, such as unusual spending behaviour, transaction velocity anomalies, or deviations from historical user behaviour. The serverless nature of Lambda automatically scales with incoming transaction volume, eliminating the need to provision and manage underlying infrastructure while maintaining sub-second processing latency.

Operational monitoring and alerting are vital to ensure that suspicious transactions trigger immediate attention. Amazon CloudWatch monitors metrics such as transaction throughput, error rates, and custom fraud-detection indicators from Lambda functions. It can be configured to trigger alarms when certain thresholds are crossed, such as a spike in declined transactions or abnormal transaction patterns. Alerts generated by CloudWatch can notify security teams, trigger automated responses, or invoke additional Lambda functions for further analysis. This continuous monitoring loop ensures that fraudulent activity is detected promptly, enabling immediate action to protect customers and organisational assets.

Durable storage of transactional data is necessary for auditing, compliance, historical analysis, and model retraining. Amazon S3 provides highly durable, cost-effective object storage that can retain raw transaction logs and processed fraud alerts over long periods. S3’s scalability ensures that even extremely large datasets from millions of daily transactions can be stored without limitations. Historical datasets stored in S3 also support retrospective analysis, trend identification, and machine learning model refinement. Combining S3 with Kinesis Data Streams and Lambda enables organisations to maintain a reliable, end-to-end workflow where data flows seamlessly from ingestion to real-time processing and long-term archival.

This architecture is fully managed and integrates naturally across AWS services. Kinesis Data Streams handles high-throughput ingestion with minimal operational overhead. Lambda provides a scalable, serverless compute layer that reacts to incoming data without requiring manual provisioning. CloudWatch ensures observability, alerting, and operational governance, while S3 guarantees durable, cost-efficient storage. The tight integration between these services reduces operational complexity, eliminates latency introduced by moving data between different systems, and supports compliance and security requirements by retaining immutable records of all transactions and detected anomalies.

The ability to scale automatically is another crucial factor. Kinesis Data Streams can increase the number of shards to match incoming transaction volume, while Lambda scales horizontally to process the stream in parallel. This ensures that the architecture can handle peak transaction loads, seasonal spikes, or sudden surges without performance degradation. CloudWatch provides continuous visibility into these metrics, enabling proactive management and tuning of the system to meet operational requirements. S3’s virtually unlimited capacity ensures that growth in transactional data does not require infrastructure reconfiguration or capacity planning.

Security is an inherent advantage of this architecture. Data in transit between Kinesis Data Streams, Lambda, and S3 can be encrypted using AWS Key Management Service (KMS), ensuring that sensitive transactional information remains protected. IAM policies provide granular access control, allowing only authorised users or services to interact with the fraud detection pipeline. CloudWatch monitoring can also track unauthorised attempts or unusual activity within the system, supporting regulatory and compliance standards.

Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 3 Q31-45

Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 3 Q31-45

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