Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set 2 Q16-30
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Question 16:
A company collects sensor data from industrial machinery across multiple factories. The system must support high-throughput ingestion, near real-time anomaly detection, and long-term storage for compliance purposes. Which AWS service combination provides the most suitable architecture?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3
B) Amazon S3 + AWS Glue + Amazon Athena
C) Amazon RDS + AWS Lambda + Amazon CloudWatch
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3
Explanation:
Industrial sensor data requires high-throughput ingestion, real-time processing, and long-term retention. Option A, Amazon Kinesis Data Streams + AWS Lambda + Amazon S3, provides a complete architecture for these needs. Kinesis Data Streams handles massive volumes of events from multiple sources with durability and scalability, enabling ingestion across factories. AWS Lambda processes the events in real-time, executing anomaly detection logic as data arrives without the need for server provisioning. Amazon S3 provides durable, cost-effective storage for long-term retention, fulfilling regulatory requirements. This architecture ensures low-latency anomaly detection while maintaining historical records.
Option B, Amazon S3 + AWS Glue + Amazon Athena, is a common data lake approach. S3 provides durable storage, Glue allows batch ETL transformations, and Athena enables SQL queries directly on S3. While this architecture is cost-effective and scalable for analytics, it is primarily batch-oriented. Real-time anomaly detection is limited because Glue jobs run on schedules or triggers rather than continuously streaming data, making it less suitable for immediate alerts on industrial sensor data.
Option C, Amazon RDS + AWS Lambda + Amazon CloudWatch, provides transactional storage, event-driven computation, and monitoring. RDS is optimised for structured relational workloads but does not scale efficiently for high-throughput, high-velocity sensor data. While CloudWatch can alert based on processed data, real-time anomaly detection for millions of events would be difficult to implement and costly. This architecture cannot efficiently handle high-volume streaming telemetry.
Option D, Amazon DynamoDB + Amazon EMR, provides fast NoSQL storage and distributed batch processing. DynamoDB scales for transactional workloads, and EMR supports distributed analytics. However, EMR is batch-oriented and introduces latency, making it less suitable for real-time anomaly detection. Operational complexity is higher compared to the Kinesis + Lambda + S3 solution.
Thus, Kinesis Data Streams + Lambda + S3 provides the most suitable architecture for high-throughput ingestion, near real-time processing, and long-term storage for compliance.
Question 17:
A company wants to implement a scalable, cost-effective solution to process clickstream data for analytics dashboards. The system should support variable traffic, automatically scale, and allow integration with analytics and machine learning services. Which architecture is most appropriate?
A) Amazon Kinesis Data Firehose + Amazon S3 + 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 + Amazon Athena
Explanation:
Clickstream analytics require handling variable traffic, scaling automatically, and supporting integration with analytics and ML services. Option A, Amazon Kinesis Data Firehose + Amazon S3 + Amazon Athena, addresses these requirements efficiently. Kinesis Firehose ingests streaming data from user activity, automatically scales to accommodate bursts of traffic, and buffers it to S3 for durable storage. S3 provides scalable storage for both recent and historical clickstream events. Athena allows analytics directly on stored data using SQL without moving data, enabling integration with downstream analytics and machine learning services. This architecture supports near real-time dashboards by enabling incremental analytics queries and leveraging the serverless nature of Firehose and Athena to minimise operational overhead.
Option B, Amazon S3 + AWS Glue + Amazon Redshift, supports batch ETL pipelines. S3 stores data, Glue performs transformations, and Redshift enables analytics queries. While this architecture is robust for batch processing and large-scale analytics, it does not provide automatic scaling in response to real-time traffic spikes or immediate integration with machine learning pipelines. Real-time dashboards may experience latency due to batch processing cycles.
Option C, Amazon RDS + Amazon QuickSight, provides relational storage with visualisation. RDS supports structured data but is not designed for high-throughput streaming ingestion. Automatic scaling is limited, and RDS is not cost-effective for variable clickstream traffic or high-volume analytics. QuickSight can visualise data but relies on timely access to processed data, which may be delayed due to RDS limitations.
Option D, Amazon DynamoDB + Amazon EMR, combines scalable NoSQL storage and distributed analytics. DynamoDB is ideal for transactional workloads, and EMR processes batch data at scale. However, near real-time analytics is complex to implement, and EMR introduces latency for processing. Handling variable traffic efficiently and integrating seamlessly with ML services requires additional orchestration, making this architecture less optimal than Kinesis Firehose + S3 + Athena.
Thus, option A provides a scalable, cost-effective, and near real-time architecture for clickstream analytics dashboards that automatically adapts to traffic changes.
Question 18:
You need to build a solution to store and query semi-structured IoT device logs cost-effectively. The logs must be accessible by multiple analytics services without duplication or movement. Which architecture best meets these requirements?
A) Amazon S3 + AWS Glue + Amazon Athena
B) Amazon RDS + Amazon Redshift
C) Amazon DynamoDB + Amazon EMR
D) Amazon Kinesis Data Firehose + Amazon S3
Answer:
A) Amazon S3 + AWS Glue + Amazon Athena
Explanation:
Semi-structured IoT logs require scalable, cost-effective storage and analytics without duplication. Option A, Amazon S3 + AWS Glue + Amazon Athena, offers a fully integrated data lake solution. S3 stores petabytes of data durably and cost-effectively. Glue provides schema discovery, cataloguing, and ETL for semi-structured data. Athena enables direct SQL queries without moving data, allowing multiple analytics or ML services to access the same dataset. This architecture reduces operational complexity, minimises storage costs, and enables interoperability for diverse analytics and machine learning tools.
Option B, Amazon RDS + Amazon Redshift, provides relational storage and data warehousing capabilities. While Redshift supports structured analytics queries, RDS is limited to structured transactional data. Semi-structured IoT logs require transformation before ingestion, increasing operational overhead. Multiple analytics services cannot directly query the same dataset without duplication or ETL, reducing cost-effectiveness and simplicity.
Option C, Amazon DynamoDB + Amazon EMR, allows scalable storage and distributed processing. DynamoDB is optimal for key-value or document data, and EMR can process batch datasets. However, EMR is primarily batch-oriented, and near real-time access by multiple analytics services is operationally complex. Storage and query costs are higher than using S3 + Glue + Athena.
Option D, Amazon Kinesis Data Firehose + Amazon S3, supports ingestion and storage of streaming logs. While Firehose writes data to S3 automatically, it does not provide metadata cataloguing or query capabilities by itself. Multiple analytics services cannot efficiently query the same dataset without Glue or Athena, making this solution incomplete for full analytics interoperability.
Therefore, S3 + Glue + Athena provides a fully integrated, cost-effective, multi-service-accessible architecture for semi-structured IoT logs.
Question 19:
A company wants to implement a long-term archival solution for regulated healthcare records. The solution must be cost-efficient, durable, and allow occasional querying for audits or analytics. Which 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:
Regulated healthcare records require cost-efficient, durable storage with occasional querying. Option A, Amazon S3 Glacier Deep Archive + Amazon Athena, meets these requirements. Glacier Deep Archive provides 11 nines of durability, highly cost-effective long-term storage, and supports lifecycle policies for automated archival management. Athena allows analysts to query subsets of archived data without restoring the full dataset, reducing retrieval costs. This architecture supports compliance with retention requirements, auditing, and regulatory needs while enabling cost-efficient analytics.
Option B, Amazon S3 Standard + AWS Lambda, provides low-latency storage and event-driven processing. While suitable for active workloads, S3 Standard is cost-prohibitive for rarely accessed healthcare records. Lambda does not provide querying capabilities for long-term archival data, making this option less suitable.
Option C, Amazon RDS + Amazon Redshift, supports transactional and analytical workloads. RDS is costly for long-term archival, and Redshift, while capable of analytics, is optimised for structured data and not cost-efficient for rarely accessed archival data. Data movement may be required for queries, increasing complexity and cost.
Option D, Amazon DynamoDB + Amazon EMR, provides scalable storage and batch processing. DynamoDB is expensive for long-term storage of rarely accessed data, and EMR introduces operational complexity for occasional queries. This architecture is inefficient for regulatory compliance archival.
Thus, Glacier Deep Archive combined with Athena provides a cost-efficient, durable, and compliant solution with the capability for occasional analytics or auditing.
Question 20:
You are designing a near-real-time fraud detection system for an online payments platform. The system must ingest transaction events at high volume, detect anomalies immediately, and trigger alerts for suspicious activity. Which combination of AWS services is most suitable?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch
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
Explanation:
Fraud detection requires high-volume ingestion, low-latency anomaly detection, and immediate alerting. Option A, Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch, provides an optimal architecture. Kinesis Data Streams handles real-time ingestion of millions of transaction events with durability and scalability. AWS Lambda allows serverless processing of each event to detect anomalies based on defined rules or models. CloudWatch monitors the processed events and triggers alerts for suspicious activity in near real-time. This architecture ensures scalability, low-latency processing, and immediate visibility for operational teams.
Option B, Amazon S3 + AWS Glue + Amazon Athena, is batch-oriented. While suitable for large-scale analytics, it cannot process real-time transactions or trigger immediate alerts for fraud detection. The latency introduced by batch ETL makes it unsuitable for near-real-time operational monitoring.
Option C, Amazon RDS + Amazon Redshift, provides structured storage and analytics. RDS supports transactional data, and Redshift enables structured queries. However, high-volume streaming ingestion and real-time anomaly detection are challenging to implement. Latency for alerting would be too high for operational fraud detection needs.
Option D, Amazon DynamoDB + Amazon EMR, supports scalable storage and distributed processing. EMR is primarily batch-oriented, making real-time fraud detection and immediate alerting complex. DynamoDB can store events, but does not provide low-latency real-time analytics directly without additional services, resulting in higher operational complexity.
Thus, Kinesis Data Streams + Lambda + CloudWatch is the most suitable architecture for high-volume, near-real-time fraud detection and immediate alerting.
Question 21:
You are designing a near-real-time marketing analytics system that collects and analyses website clickstream data. The system must handle unpredictable traffic spikes, support real-time dashboards, and allow historical analytics on stored data. Which AWS architecture best meets these requirements?
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:
Marketing analytics systems require ingestion of high-volume clickstream data, near real-time processing for dashboards, and long-term historical storage for analytics. Option A, Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon QuickSight, is the most suitable solution. Kinesis Data Streams handles unpredictable traffic spikes efficiently by providing durable, scalable ingestion of streaming data across multiple shards. Lambda provides real-time event processing, enabling transformation, aggregation, or anomaly detection as the data flows. Amazon S3 serves as cost-effective, durable storage for historical clickstream data, ensuring long-term availability for batch analytics. QuickSight visualises both real-time and historical insights, supporting dashboards that update as data streams in.
Option B, Amazon S3 + AWS Glue + Amazon Athena, is suitable for batch-oriented analytics. S3 provides durable storage, Glue performs ETL jobs, and Athena enables SQL queries directly on stored data. While cost-effective for historical analysis, this solution cannot handle real-time dashboards efficiently due to the latency introduced by batch ETL jobs and scheduled Glue workflows.
Option C, Amazon RDS + Amazon Redshift + Amazon QuickSight, is appropriate for structured data analytics and BI reporting. RDS supports transactional workloads, and Redshift provides data warehousing for analytical queries. While QuickSight can generate dashboards, this architecture cannot efficiently process high-velocity streaming clickstream data. Scaling RDS for unpredictable spikes is challenging, and Redshift is primarily suited for batch analytics rather than continuous real-time processing.
Option D, Amazon DynamoDB + Amazon EMR, supports scalable transactional storage and distributed batch processing. DynamoDB provides low-latency reads and writes, and EMR allows processing of large-scale datasets with Spark or Hadoop. However, EMR is batch-oriented, adding latency to real-time dashboards. Handling near real-time analytics requires additional orchestration and infrastructure, making it more complex than the Kinesis + Lambda + S3 + QuickSight solution.
Thus, option A offers a complete solution for high-volume streaming ingestion, real-time dashboards, and cost-efficient historical analytics.
Question 22:
A company wants to store logs from thousands of IoT devices in a cost-effective, durable, and queryable way. The system should allow analytics and machine learning services to access the data without moving it. Which architecture best meets these requirements?
A) Amazon S3 + AWS Glue + Amazon Athena
B) Amazon RDS + Amazon Redshift
C) Amazon DynamoDB + Amazon EMR
D) Amazon Kinesis Data Firehose + Amazon S3
Answer:
A) Amazon S3 + AWS Glue + Amazon Athena
Explanation:
IoT devices generate semi-structured logs that need cost-effective, durable storage with query capabilities for analytics and ML services. Option A, Amazon S3 + AWS Glue + Amazon Athena, is the ideal architecture. S3 provides highly durable and scalable storage at minimal cost, suitable for petabytes of IoT data. AWS Glue discovers schema and creates a central data catalogue, enabling structured queries on semi-structured datasets. Athena allows multiple analytics and ML services to query the data directly using SQL without duplication or movement, reducing operational complexity and cost. This architecture provides flexibility, scalability, and interoperability while supporting analytics and machine learning on the same dataset.
Option B, Amazon RDS + Amazon Redshift, is optimised for structured transactional and analytical workloads. RDS supports relational storage, while Redshift enables high-performance analytics. However, RDS is not cost-effective for storing large-scale IoT logs, and Redshift requires data ingestion into its cluster for queries, which adds complexity and increases storage costs. Multi-service querying without duplication is limited, making this architecture less suitable for large-scale IoT data.
Option C, Amazon DynamoDB + Amazon EMR, supports high-volume transactional storage and batch processing. DynamoDB scales efficiently for key-value or document data, while EMR allows distributed processing using Spark or Hadoop. However, EMR is batch-oriented and introduces latency for analytics, making it less suitable for querying by multiple services in real time. Operational complexity is higher, and costs increase for rarely accessed historical logs.
Option D, Amazon Kinesis Data Firehose + Amazon S3, allows streaming ingestion of IoT logs into S3. While Firehose handles streaming efficiently, it does not provide a metadata catalogue or query capability directly. Analytics and ML services cannot access the data seamlessly without Glue and Athena. Therefore, this architecture alone is incomplete for full queryability and multi-service access.
Thus, S3 + Glue + Athena provides the most cost-efficient, durable, and queryable solution for IoT device logs while enabling analytics and machine learning integration.
Question 23:
You are designing a long-term archival solution for financial transaction records that must be retained for regulatory compliance. Occasionally, auditors need to query subsets of this data. Which AWS services best meet these requirements?
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 transaction records must be stored for long-term retention at minimal cost, with durability and occasional query capabilities. Option A, Amazon S3 Glacier Deep Archive + Amazon Athena, provides a cost-effective, compliant solution. Glacier Deep Archive offers extremely low-cost storage with 11 nines of durability, suitable for multi-year retention of financial records. Lifecycle policies can automatically transition data from other S3 classes to Glacier Deep Archive. Athena enables querying of archived subsets using S3 Select or standard SQL queries without restoring the entire dataset, reducing retrieval costs and operational overhead. This combination supports compliance audits, regulatory reporting, and occasional analytics while minimising storage expenses.
Option B, Amazon S3 Standard + AWS Lambda, provides low-latency storage and event-driven processing. S3 Standard is not cost-effective for rarely accessed long-term archival data. Lambda does not offer querying capabilities for historical data, making it unsuitable for compliance-driven, archival use cases.
Option C, Amazon RDS + Amazon Redshift, supports transactional and analytical workloads. RDS is cost-prohibitive for multi-year retention of rarely accessed data, and Redshift, although capable of querying, requires active cluster resources and data movement, increasing costs and complexity. This combination does not optimise for archival cost efficiency.
Option D, Amazon DynamoDB + Amazon EMR, supports high-volume storage and batch processing. DynamoDB is expensive for long-term archival of rarely accessed records, and EMR introduces operational complexity and latency. Querying subsets for occasional audits would require additional orchestration and processing, making this approach less optimal.
Therefore, S3 Glacier Deep Archive + Athena provides a highly durable, cost-effective, and compliant solution with the ability to query specific data subsets for auditing or analytics.
Question 24:
A company wants to process social media streaming data for sentiment analysis. The system must ingest data continuously, handle unpredictable spikes, perform near real-time transformations, and store processed data for historical analysis. Which AWS architecture is most suitable?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon Athena
B) Amazon S3 + AWS Glue + Amazon Redshift
C) Amazon RDS + Amazon QuickSight
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon Athena
Explanation:
Social media streaming data is high-volume, unstructured, and requires near real-time processing for sentiment analysis while retaining historical data for analytics. Option A, Amazon Kinesis Data Streams + AWS Lambda + Amazon S3 + Amazon Athena, satisfies these requirements. Kinesis Data Streams ingests streaming data continuously and scales automatically to handle spikes. Lambda performs transformations and preprocessing in near real time, enabling sentiment analysis or enrichment. Processed data is stored in S3 for durable long-term storage. Athena allows querying of historical processed data without moving it, supporting batch analytics or ML model training. This architecture balances real-time processing, scalability, and cost efficiency while ensuring durability and easy analytics access.
Option B, Amazon S3 + AWS Glue + Amazon Redshift, supports batch ETL pipelines. S3 stores raw data, Glue performs transformations, and Redshift allows analytics queries. While this architecture is suitable for large-scale batch analytics, it does not provide near-real-time transformations or support unpredictable spikes efficiently. This leads to latency in analysis and processing.
Option C, Amazon RDS + Amazon QuickSight, is suitable for structured data analytics and reporting. RDS supports transactional workloads, and QuickSight visualises queries. However, this architecture cannot scale efficiently for high-velocity streaming data, making near-real-time sentiment analysis challenging.
Option D, Amazon DynamoDB + Amazon EMR, supports high-volume storage and distributed batch processing. EMR is primarily batch-oriented, making near-real-time transformation difficult. DynamoDB provides fast transactional storage but lacks direct integration with analytics and ML services for streaming social media data. Operational complexity increases with this solution.
Therefore, Kinesis Data Streams + Lambda + S3 + Athena provides the most suitable architecture for continuous ingestion, near real-time transformation, and historical analysis of social media streaming data.
Question 25:
You are building a fraud detection system for an online payment platform. The system must ingest transaction data at high speed, detect anomalies in near real-time, and trigger alerts for suspicious activity. Which combination of AWS services is most appropriate?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch
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
Explanation:
Fraud detection requires near real-time processing, high-volume ingestion, and immediate alerting. Option A, Amazon Kinesis Data Streams + AWS Lambda + Amazon CloudWatch, provides a complete solution. Kinesis Data Streams enables ingestion of millions of transactions per second with durability and scalability. Lambda processes each event as it arrives, applying fraud detection logic, scoring, or model inference. CloudWatch monitors metrics and triggers alerts for suspicious activity. This architecture allows immediate action on fraudulent transactions, scales dynamically with traffic, and provides operational monitoring and alerting.
Option B, Amazon S3 + AWS Glue + Amazon Athena, is suitable for batch analytics on large datasets. While cost-effective for historical analysis, it cannot provide near real-time anomaly detection or immediate alerts, making it unsuitable for operational fraud detection.
Option C, Amazon RDS + Amazon Redshift, is optimised for transactional storage and analytical queries. High-volume streaming ingestion, near real-time detection, and alerting are difficult to implement with this architecture. Scaling RDS for bursts in transaction volume is challenging, and Redshift is better suited for batch analytics rather than continuous anomaly detection.
Option D, Amazon DynamoDB + Amazon EMR, provides scalable storage and batch processing. EMR is primarily batch-oriented, so near real-time detection and alerting are complex and introduce latency. DynamoDB supports fast transactional storage but lacks the real-time analytics capabilities required for immediate fraud detection without additional orchestration.
Thus, Kinesis Data Streams + Lambda + CloudWatch is the most suitable architecture for near-real-time fraud detection with high-volume ingestion and immediate alerts.
Question 26:
You need to build a scalable, cost-efficient data lake for storing machine-generated logs from multiple applications. The system must allow multiple analytics and machine learning services to query data without duplication or movement. Which AWS architecture best meets these requirements?
A) Amazon S3 + AWS Glue + Amazon Athena
B) Amazon RDS + Amazon Redshift
C) Amazon DynamoDB + Amazon EMR
D) Amazon Kinesis Data Firehose + Amazon S3
Answer:
A) Amazon S3 + AWS Glue + Amazon Athena
Explanation:
Machine-generated logs from multiple applications can be large, semi-structured, and require long-term retention. Option A, Amazon S3 + AWS Glue + Amazon Athena, provides a highly scalable, cost-efficient, and flexible solution. S3 offers virtually unlimited storage capacity and durability with low operational overhead. AWS Glue enables schema discovery and metadata cataloguing, allowing consistent data definitions across services. Athena allows direct SQL querying on S3 data without moving or duplicating it, making it possible for multiple analytics and machine learning services to access the same dataset. This architecture reduces storage and operational costs while providing interoperability and flexibility for analytics workloads.
Option B, Amazon RDS + Amazon Redshift, is suitable for structured, relational data with strong analytical needs. RDS handles transactional workloads, and Redshift provides high-performance analytics. However, for semi-structured logs, transforming and loading data into relational schemas is complex, costly, and introduces latency. This architecture requires duplication or ETL for other services to query, increasing operational effort and reducing cost efficiency.
Option C, Amazon DynamoDB + Amazon EMR, provides high-volume transactional storage and distributed batch processing. DynamoDB scales well for key-value or document data, and EMR supports large-scale batch analytics. However, EMR is batch-oriented, adding latency for querying and analytics, and multiple services cannot access the same dataset without additional orchestration. Operational complexity and costs are higher than with S3 + Glue + Athena.
Option D, Amazon Kinesis Data Firehose + Amazon S3, supports streaming ingestion of logs into S3. Firehose scales automatically for high-volume streaming, but it does not provide metadata cataloguing or query capabilities. Multiple analytics or machine learning services cannot query data efficiently without Glue and Athena, making this architecture incomplete for full analytics integration.
Thus, S3 + Glue + Athena is the most suitable architecture for a scalable, cost-efficient data lake accessible by multiple services without data duplication.
Question 27:
You are designing a system to process high-velocity financial transaction streams and detect anomalies in near real-time. The system must automatically scale, trigger alerts for suspicious activity, and maintain a durable log of transactions. Which AWS services should you use?
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:
Financial transactions require ingestion at high velocity, near real-time anomaly detection, alerting, and durable storage. Option A provides a complete architecture for these requirements. Kinesis Data Streams supports high-throughput streaming ingestion, maintaining ordered and durable event streams. AWS Lambda processes incoming transactions immediately, applying anomaly detection rules or models in near real-time. CloudWatch monitors processed events and triggers alerts for suspicious activity. S3 ensures durable long-term storage of all transaction data, fulfilling compliance and auditing requirements. The combination provides automated scaling, operational monitoring, and low-latency processing.
Option B, Amazon S3 + AWS Glue + Amazon Athena, is suitable for batch analytics and historical data analysis. While S3 stores the data and Glue allows ETL transformations, Athena is for query-based analysis rather than real-time detection. This architecture introduces latency and is unsuitable for real-time fraud or anomaly detection, as immediate alerts cannot be generated efficiently.
Option C, Amazon RDS + Amazon Redshift, supports structured data storage and analytics. RDS handles transactional workloads, while Redshift enables analytics. However, high-velocity streaming ingestion and near real-time anomaly detection are difficult to implement. Scaling RDS for sudden spikes is challenging, and Redshift is primarily batch-oriented, making it unsuitable for operational fraud detection.
Option D, Amazon DynamoDB + Amazon EMR, provides fast transactional storage and distributed batch analytics. While DynamoDB scales well, EMR is batch-focused, introducing latency that is incompatible with near-real-time anomaly detection. Multi-service integration for alerting is complex, making this combination less effective operationally than the Kinesis + Lambda + CloudWatch + S3 solution.
Thus, option A offers the optimal architecture for real-time anomaly detection, alerting, durable storage, and automatic scaling in financial transaction systems.
Question 28:
A company needs to build a near-real-time recommendation engine for an e-commerce platform. The system must process high-velocity user clickstream data, generate recommendations dynamically, and support historical analysis. Which 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 require high-velocity data processing, low-latency inference, and access to historical data. Option A, Kinesis Data Streams + Lambda + SageMaker + S3, meets all requirements. Kinesis Data Streams ingests user clickstream events in real-time, scaling automatically to handle traffic spikes. Lambda performs preprocessing or feature extraction on incoming events. SageMaker generates predictions dynamically using trained machine learning models, providing personalised recommendations with minimal latency. S3 stores historical clickstream and processed data for training models, auditing, or batch analytics. This architecture ensures seamless real-time inference combined with historical analysis, low operational overhead, and high scalability.
Option B, Amazon S3 + AWS Glue + Amazon Redshift, is suitable for batch analytics on historical datasets. S3 stores the data, Glue performs ETL, and Redshift enables structured queries. This architecture cannot provide real-time recommendation generation due to batch processing delays. Near real-time personalisation is not achievable with this combination.
Option C, Amazon RDS + Amazon QuickSight, provides transactional storage and reporting. RDS is not optimised for high-velocity streaming data, and QuickSight relies on structured queries. Real-time recommendations are challenging because latency is introduced by transactional processing and the lack of integrated ML inference.
Option D, Amazon DynamoDB + Amazon EMR, supports scalable storage and batch processing. DynamoDB handles fast writes, and EMR enables distributed processing. However, EMR is batch-oriented, preventing real-time recommendations. Operational complexity increases when integrating EMR with ML inference for dynamic user personalisation.
Thus, option A provides a fully integrated, scalable solution for real-time recommendation engines with historical data support and low-latency machine learning inference.
Question 29:
You are tasked with designing a long-term archival system for regulatory compliance that allows occasional querying for audits. The dataset consists of structured financial transactions. Which AWS services should you use?
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 compliance requires cost-effective, durable storage with occasional query capabilities. Option A, S3 Glacier Deep Archive + Athena, meets these needs. Glacier Deep Archive provides extremely low-cost, long-term storage with 11 nines of durability, ideal for multi-year retention. Lifecycle policies can automatically transition data from other S3 classes. Athena enables SQL queries on subsets of archived data using S3 Select, avoiding full dataset restores and reducing retrieval costs. Auditors can query historical records as needed while maintaining regulatory compliance and minimising storage expenses.
Option B, Amazon S3 Standard + AWS Lambda, provides low-latency storage and event-driven processing. S3 Standard is not cost-efficient for rarely accessed archival data, and Lambda does not support querying of large historical datasets. This option is unsuitable for compliance-driven archival storage.
Option C, Amazon RDS + Amazon Redshift, supports structured data storage and analytics. RDS is cost-prohibitive for long-term archival of rarely accessed datasets, and Redshift, although capable of analytics, requires cluster maintenance and data ingestion, increasing operational complexity and cost. It is not optimised for rarely accessed, long-term archival.
Option D, Amazon DynamoDB + Amazon EMR, allows high-volume transactional storage and batch analytics. DynamoDB is expensive for long-term archival, and EMR introduces latency for querying. The combination is operationally complex and less cost-efficient compared to Glacier Deep Archive + Athena.
Thus, option A provides a durable, cost-efficient, and queryable archival system for compliance and auditing purposes.
Question 30:
You need to design a telemetry data processing system for industrial sensors. The system must ingest high-volume streaming data, perform near real-time anomaly detection, store historical data for analytics, and integrate with visualisation dashboards. Which architecture is most appropriate?
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 telemetry data requires real-time processing, anomaly detection, historical storage, and visualisation. Option A, Kinesis Data Streams + Kinesis Data Analytics + S3 + QuickSight, provides a complete solution. Kinesis Data Streams ingests high-velocity sensor data with durability and scalability. Kinesis Data Analytics allows near real-time anomaly detection using SQL or streaming analytics applications. S3 stores historical telemetry for batch analytics, regulatory compliance, or model training. QuickSight visualises both real-time and historical data in dashboards, providing operational insights. This architecture minimises latency, scales automatically, and reduces operational overhead while meeting real-time and historical analytics requirements.
Option B, Amazon S3 + AWS Glue + Athena + QuickSight, is batch-oriented. S3 stores telemetry data, Glue performs ETL, and Athena enables SQL queries. While QuickSight can visualise historical analytics, near real-time anomaly detection, and dashboards are not supported, this introduces delays in operational monitoring.
Option C, Amazon RDS + AWS Lambda + CloudWatch, provides structured storage, event processing, and monitoring. RDS cannot efficiently handle high-volume streaming telemetry data, and Lambda processing is limited by throughput constraints. Real-time anomaly detection at scale would be challenging.
Option D, Amazon DynamoDB + Amazon EMR + QuickSight, provides scalable storage and batch analytics. EMR is batch-oriented, so near-real-time anomaly detection is not feasible. DynamoDB scales for transactional writes but does not provide direct real-time analytics. Integrating QuickSight requires additional orchestration, increasing complexity.
Thus, option A provides the most suitable architecture for high-volume streaming telemetry ingestion, near real-time anomaly detection, historical storage, and dashboard visualisation.
Industrial telemetry data represents continuous streams of information from sensors, machinery, or equipment in manufacturing, energy, or transportation environments. These data streams are high-volume, high-velocity, and often time-sensitive, requiring a platform capable of both real-time ingestion and processing to detect anomalies as they occur. Amazon Kinesis Data Streams serves as the foundation for this architecture, providing a fully managed, scalable solution for streaming data ingestion. Kinesis Data Streams can handle large-scale, continuous input from multiple sources simultaneously while guaranteeing durability and order of records. This ensures that every telemetry event is captured accurately and reliably, which is critical in industrial environments where missing a single event could compromise operational awareness or safety monitoring.
Once data is ingested, the ability to process and analyse it in near real-time is crucial. Amazon Kinesis Data Analytics provides a robust platform to perform continuous queries on streaming data using standard SQL or more advanced streaming analytics. This service enables organisations to detect patterns, identify anomalies, and trigger alerts as telemetry data is received. For example, sudden deviations in temperature, vibration, or pressure can be detected instantly, allowing operational teams to respond before issues escalate. Kinesis Data Analytics also supports windowed operations, aggregations, and complex event processing, which is particularly valuable when evaluating time-series sensor data or detecting correlated anomalies across multiple data streams.
For historical storage and batch analytics, Amazon S3 is an ideal component. As a highly durable and virtually unlimited object storage service, S3 allows telemetry data to be retained over long periods, supporting regulatory compliance, trend analysis, and machine learning model training. Historical data stored in S3 can be partitioned by time or device type, facilitating efficient query performance and enabling organisations to perform retrospective analyses, forecast future operational trends, and generate comprehensive reports. The decoupling of real-time ingestion from long-term storage ensures that high-throughput streaming does not interfere with storage performance or accessibility.
Visualisation and operational insight are essential for industrial telemetry monitoring. Amazon QuickSight provides interactive dashboards and visualisations for both real-time and historical data. QuickSight can consume data from Kinesis Data Analytics for live dashboards and from S3 for historical trend analysis. Users can create alerts, interactive charts, and drill-down reports, enabling operational teams to monitor equipment health, detect inefficiencies, and support decision-making. QuickSight also scales automatically to accommodate large numbers of users and data sources, making it suitable for enterprise deployments across multiple plants or facilities.
The combination of these services provides a unified solution that balances ingestion, real-time processing, historical storage, and visualisation. It allows telemetry data to be processed at the moment of arrival while retaining the ability to conduct in-depth analysis on accumulated historical data. Each component complements the others: Kinesis Data Streams ensures scalable, reliable ingestion; Kinesis Data Analytics enables immediate insights; S3 provides durable storage; and QuickSight delivers actionable visualisation.
This architecture also reduces operational overhead compared to managing separate ingestion pipelines, streaming frameworks, storage systems, and analytics platforms. Being fully managed, these services handle provisioning, scaling, fault tolerance, and maintenance automatically, which is critical in environments where operational continuity is paramount. Organisations can focus on analysing the data rather than maintaining infrastructure. Additionally, the integration between the services is seamless, reducing the complexity of building connectors or data transformation pipelines manually.
High availability and fault tolerance are inherent advantages of this architecture. Kinesis Data Streams replicates data across multiple availability zones, protecting against data loss. Kinesis Data Analytics ensures continuous query processing without the need for manual intervention, while S3 provides eleven 9s of durability for stored data. QuickSight’s serverless architecture ensures that dashboards remain responsive and accessible, even under variable load. Together, these services form a resilient, scalable, and efficient telemetry data processing ecosystem.
Industrial telemetry often involves multiple data sources with heterogeneous formats and varying frequencies of updates. Kinesis Data Streams accommodates this by supporting multiple shards per stream, enabling parallel processing and partitioning of data to match the velocity and volume of individual sources. Kinesis Data Analytics can normalise, filter, and aggregate these streams in-flight, providing a unified view for downstream storage and visualisation. This level of flexibility is crucial for complex industrial environments where sensors may produce irregular, bursty, or correlated events.
Furthermore, this architecture supports real-time decision-making and automation. Insights generated by Kinesis Data Analytics can be forwarded to operational systems, alerts, or machine learning endpoints for predictive maintenance, reducing downtime and operational costs. Historical analytics in S3 allows teams to refine anomaly detection models and optimise production efficiency over time. QuickSight dashboards serve both operational and strategic stakeholders, providing a holistic view of plant performance, safety indicators, and efficiency metrics.
Security and access control are critical in industrial telemetry scenarios. Kinesis Data Streams, Kinesis Data Analytics, and S3 integrate with AWS Identity and Access Management (IAM), allowing fine-grained permissions to control who can ingest, process, store, or visualise telemetry data. Data can also be encrypted at rest and in transit, meeting regulatory and corporate compliance requirements. QuickSight supports role-based access and secure sharing, ensuring that sensitive operational data is only visible to authorised personnel.
Scalability is another key advantage. Kinesis Data Streams automatically scales throughput by adding or removing shards, accommodating sudden spikes in telemetry volume. Kinesis Data Analytics can adjust processing capacity dynamically, and S3 can store virtually unlimited data without manual intervention. QuickSight can handle thousands of users simultaneously without performance degradation. This combination ensures that the telemetry system can grow with the organisation’s operational footprint without requiring costly infrastructure redesigns.