Amazon AWS Certified Data Engineer — Associate DEA-C01 Exam Dumps and Practice Test Questions Set1 Q1-15
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Question 1:
You are designing a data pipeline on AWS to ingest large volumes of streaming IoT sensor data from multiple regions. You need a solution that can ingest, store, and process the data in real-time with minimal latency while ensuring durability and scalability. Which AWS service or combination of services is the best choice?
A) Amazon S3 + AWS Lambda
B) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics
C) Amazon RDS + AWS Glue
D) Amazon DynamoDB Streams + AWS Step Functions
Answer:
B) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics
Explanation:
In this scenario, the primary requirements are real-time ingestion, processing, minimal latency, durability, and scalability. Each option has different characteristics relevant to these requirements.
Option A, Amazon S3 + AWS Lambda, is often used for event-driven processing and batch workloads but is not ideal for high-throughput real-time streaming ingestion. S3 is an object storage system optimised for durability and scalability, but it works best in scenarios where files or batches of data are stored and then processed. While Lambda can respond to S3 events, the latency is not consistently real-time for high-volume IoT streams, and throughput can be limited by concurrent execution limits. Therefore, while this combination can work for moderate real-time use cases, it is less optimal for massive IoT data ingestion.
Option B, Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics, is specifically designed for real-time streaming data. Kinesis Data Streams provides a highly scalable service that can ingest millions of events per second from multiple sources, ensuring durability through replication across availability zones. Each data stream can be partitioned into shards, allowing fine-grained control over throughput and parallel processing. Kinesis Data Analytics can process this data in real-time using SQL queries or application code, providing immediate insights or transformations. This combination addresses all requirements: real-time ingestion, low-latency processing, high durability, and scalability, making it the best choice.
Option C, Amazon RDS + AWS Glue, is suited for structured relational data storage and ETL workflows. RDS is optimised for transactional workloads with strong consistency guarantees, while AWS Glue can extract, transform, and load data into various destinations. However, RDS is not designed for high-velocity streaming data ingestion. Large volumes of streaming IoT data would overwhelm RDS’s transactional model, leading to performance bottlenecks. AWS Glue is typically batch-oriented, and although it supports streaming ETL, it is not as low-latency or scalable as Kinesis for high-throughput streaming. Thus, this option is unsuitable for real-time IoT pipelines.
Option D, Amazon DynamoDB Streams + AWS Step Functions, can handle high-volume change data capture from DynamoDB tables and orchestrate workflows with Step Functions. This combination works well for event-driven applications and some near-real-time processing, but it assumes that the initial data is stored in DynamoDB. For IoT sensors generating millions of events per second, DynamoDB may not provide optimal ingestion without careful partition key design, and Step Functions introduces orchestration latency, making this option less ideal for low-latency streaming analytics.
Question 2:
Your company needs to move an on-premises data warehouse to AWS. The warehouse processes structured data from multiple sources and must provide near real-time query performance for analytics dashboards. Which AWS service is the most appropriate to replace the on-premises warehouse?
A) Amazon Redshift
B) Amazon Aurora
C) Amazon DynamoDB
D) Amazon EMR
Answer:
A) Amazon Redshift
Explanation:
The scenario specifies moving an on-premises data warehouse that processes structured data and requires near real-time query performance. Evaluating each option:
Option A, Amazon Redshift, is a fully managed, petabyte-scale data warehouse service optimised for analytic workloads. It supports SQL-based queries, columnar storage, data compression, and massively parallel processing (MPP) to deliver high-performance query execution on structured datasets. Redshift also integrates with AWS analytics tools, BI platforms, and real-time data ingestion services like Kinesis or S3 data lakes, making it highly suitable as a cloud replacement for an on-premises warehouse. Redshift offers features such as Redshift Spectrum, which allows querying data directly in S3, and concurrency scaling, which enables handling multiple queries efficiently. Its architecture is designed for structured and semi-structured data, making it the optimal choice for this scenario.
Option B, Amazon Aurora, is a relational database designed for transactional workloads with high availability and scalability. While Aurora is excellent for OLTP applications, it is not optimised for large-scale analytical queries over massive datasets. It lacks MPP architecture, and query performance for complex aggregations and analytics at scale will be inferior to Redshift. Aurora is more suited for transactional applications rather than replacing a full-featured on-premises data warehouse.
Option C, Amazon DynamoDB, is a NoSQL key-value and document database designed for ultra-low-latency transactional workloads at any scale. While DynamoDB offers fast read/write performance and can store large datasets, it does not support complex SQL-based analytical queries efficiently. Performing aggregation, joins, or analytics across multiple sources would require additional services or custom processing. Therefore, DynamoDB cannot serve as a full replacement for a traditional structured data warehouse with analytics dashboards.
Option D, Amazon EMR, is a managed Hadoop and Spark framework for large-scale data processing. EMR is excellent for big data processing, machine learning workflows, and unstructured or semi-structured data transformations. While it can execute SQL queries using Hive or Spark SQL, it is not designed primarily for low-latency, interactive analytics dashboards like a traditional data warehouse. EMR is more suited to batch or complex analytical pipelines rather than real-time dashboard workloads.
Considering the requirements of structured data processing, near real-time queries, and integration with BI tools, Amazon Redshift is the optimal choice. It provides performance, scalability, and analytics capabilities that closely mirror or exceed on-premises warehouse functionality, making it the best solution for this scenario.
Question 3:
You are designing a data lake solution to store and analyse petabytes of structured and unstructured data. The architecture must allow multiple AWS analytics services to query the data without moving it between services. Which combination of services best meets this requirement?
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:
The scenario specifies a data lake for both structured and unstructured data, with the ability for multiple analytics services to query the data directly. Each option offers different capabilities:
Option A, Amazon S3 + AWS Glue + Amazon Athena, provides a robust data lake architecture. S3 is a highly durable and scalable storage solution capable of holding petabytes of data. AWS Glue serves as the metadata catalogue and ETL service, enabling schema discovery, data transformation, and governance. Athena allows users to run SQL queries directly against data stored in S3 without moving it, supporting multiple data formats like Parquet, ORC, CSV, JSON, and Avro. This combination enables multiple analytics services, such as Redshift Spectrum, Athena, and EMR, to access the same data, avoiding duplication and movement. This approach is highly cost-effective and simplifies management while ensuring accessibility for analytics workloads.
Option B, Amazon RDS + Amazon Redshift, is not suitable for a large-scale data lake. RDS is designed for transactional workloads and structured data, while Redshift is a data warehouse for structured analytics. This combination does not provide a unified storage layer for unstructured data or allow querying by multiple analytics services without moving data.
Option C, Amazon DynamoDB + Amazon EMR, can store semi-structured data and perform batch or streaming analytics using EMR. However, DynamoDB is not optimised for unstructured data, and EMR requires data movement or processing to perform analytics. Multiple services querying the same dataset directly are not native to this combination, making it less suitable.
Option D, Amazon Kinesis Data Firehose + Amazon S3, can ingest streaming data into S3, forming a component of a data lake. However, Firehose is primarily for ingestion and not a full solution for data storage, cataloguing, or enabling multiple analytics services to query the same data directly. Without Glue and Athena, the querying capabilities are limited.
Therefore, Amazon S3 combined with AWS Glue and Amazon Athena provides the most complete, scalable, and versatile architecture for a multi-service-accessible data lake supporting both structured and unstructured data.
Question 4:
You need to build a cost-effective solution for processing daily batch data that arrives in Amazon S3 and generates reports within hours. The solution must scale automatically based on workload and support multiple programming frameworks. Which AWS service is most appropriate?
A) Amazon EMR
B) AWS Lambda
C) Amazon RDS
D) Amazon Redshift
Answer:
A) Amazon EMR
Explanation:
The key requirements are batch processing, scaling based on workload, support for multiple programming frameworks, and integration with S3. Each option offers different capabilities:
Option A, Amazon EMR, is a fully managed big data platform that supports frameworks like Hadoop, Spark, Presto, and Hive. EMR can read data directly from S3, process it efficiently, and scale dynamically based on cluster size or workload. EMR’s auto-scaling feature allows cost optimisation, adjusting resources during periods of low demand, and ensuring timely processing during peak workloads. This aligns directly with the requirements for daily batch processing, flexibility in programming frameworks, and cost-effectiveness.
Option B, AWS Lambda, is serverless and can process small to moderate workloads with event-driven triggers. While Lambda can be integrated with S3 for batch processing, large datasets or long-running jobs may exceed execution limits. Lambda is better suited for small-scale, real-time transformations rather than heavy batch analytics requiring multiple frameworks and extended processing.
Option C, Amazon RDS, is a relational database service for transactional workloads. It is not designed for large-scale batch processing of files in S3 or for supporting frameworks like Spark or Hadoop. Using RDS would require moving data and managing processing manually, making it inefficient and costly for this use case.
Option D, Amazon Redshift, is optimised for analytical queries and structured data. While Redshift can ingest batch data from S3 using COPY commands, it is not a general-purpose processing engine and does not natively support multiple programming frameworks. Redshift is more suited for structured analytics rather than batch processing pipelines.
Amazon EMR directly addresses all requirements: batch processing, scalability, S3 integration, multi-framework support, and cost-effectiveness through auto-scaling. Therefore, EMR is the correct choice.
Question 5:
Your analytics team wants to implement a solution to track user activity on a website in real-time, detect anomalies, and send alerts for unusual patterns. The solution must handle high volumes of streaming data and integrate with AWS analytics services for further insights. Which combination of services best fits this use case?
A) Amazon Kinesis Data Streams + Amazon CloudWatch + AWS Lambda
B) Amazon S3 + Amazon Athena
C) Amazon RDS + AWS Glue
D) Amazon DynamoDB + Amazon Redshift
Answer:
A) Amazon Kinesis Data Streams + Amazon CloudWatch + AWS Lambda
Explanation:
This scenario requires real-time tracking, anomaly detection, alerting, and integration with analytics services for high-volume streaming data. Each option provides different capabilities:
Option A, Amazon Kinesis Data Streams + Amazon CloudWatch + AWS Lambda, meets all requirements. Kinesis Data Streams can ingest high-velocity streaming data from website activity logs with low latency and high durability. Lambda can process each event in real-time, applying anomaly detection logic and triggering alerts. CloudWatch provides monitoring, alerting, and visualisation for detected anomalies. This architecture is scalable, serverless, and integrates seamlessly with other AWS analytics services, such as Kinesis Data Analytics or Redshift, for further insights.
Option B, Amazon S3 + Amazon Athena, is suitable for batch analytics of stored data. While Athena can query historical logs in S3 efficiently, it does not provide real-time processing or immediate anomaly detection. Alerts based on real-time patterns cannot be achieved with this combination.
Option C, Amazon RDS + AWS Glue, is suitable for structured data transformation and ETL pipelines. RDS handles relational transactional workloads, and Glue provides batch or streaming ETL. However, detecting real-time anomalies in high-volume streaming data would require moving data from RDS and Glue, introducing latency and inefficiency.
Option D, Amazon DynamoDB + Amazon Redshift, provides a mix of fast transactional storage and analytical querying. DynamoDB can store user events, and Redshift can analyse aggregated data. However, real-time anomaly detection and immediate alerts require additional services and orchestration, making this combination less suitable for immediate real-time processing.
Therefore, the combination of Kinesis Data Streams, Lambda, and CloudWatch provides an end-to-end solution for ingesting, processing, monitoring, and alerting on streaming user activity in real-time, making it the best choice.
Question 6:
You are designing a data pipeline to process clickstream data from an e-commerce website. The pipeline must handle bursts of data traffic, scale automatically, provide exactly-once processing, and allow downstream analytics applications to consume processed data in real-time. Which combination of AWS services is most suitable?
A) Amazon S3 + AWS Glue + Amazon Athena
B) Amazon Kinesis Data Streams + AWS Lambda + Amazon DynamoDB
C) Amazon SQS + Amazon RDS
D) Amazon Redshift + Amazon Kinesis Data Firehose
Answer:
B) Amazon Kinesis Data Streams + AWS Lambda + Amazon DynamoDB
Explanation:
Clickstream data processing requires handling high-velocity, bursty data while providing reliability, scalability, and low latency for downstream analytics. Each option has different strengths and limitations relevant to these requirements.
Option A, Amazon S3 + AWS Glue + Amazon Athena, is a common pattern for batch analytics and data lake queries. S3 provides durable and cost-effective storage for structured and unstructured data, Glue allows ETL transformation, and Athena provides SQL-based analytics directly on S3 objects. While this combination can process large volumes of data, it is primarily batch-oriented. It cannot provide real-time analytics or exactly-once processing guarantees without additional orchestration. For clickstream data that must be analysed as it arrives, this option is less suitable.
Option B, Amazon Kinesis Data Streams + AWS Lambda + Amazon DynamoDB, is optimised for real-time streaming data. Kinesis Data Streams provides sharded streams that can scale horizontally to handle bursts of traffic, ensuring durability and ordered delivery. Lambda can consume Kinesis records in near real-time, executing processing logic and writing results to DynamoDB or other downstream systems. DynamoDB ensures low-latency storage of processed results with high availability and strong consistency, supporting exactly-once processing patterns. This combination meets all requirements: automatic scaling, real-time processing, durability, low latency, and support for downstream analytics. It also integrates seamlessly with other AWS services for monitoring, alerting, or additional processing.
Option C, Amazon SQS + Amazon RDS, provides queuing and relational storage. SQS ensures decoupling and reliable delivery of messages, and RDS provides structured storage. However, SQS does not support real-time stream processing with exactly-once semantics out of the box, and RDS is optimised for transactional workloads rather than high-velocity analytics. Scaling RDS to handle bursts of streaming clickstream data is difficult and may result in performance bottlenecks. Consequently, this combination is better suited for asynchronous, moderate-volume processing, not real-time streaming analytics.
Option D, Amazon Redshift + Amazon Kinesis Data Firehose, is often used for near real-time analytics pipelines. Kinesis Firehose can ingest and batch streaming data into Redshift for analysis. While this option supports low-latency analytics for structured data, Firehose batches records before writing to Redshift, which can introduce latency and reduce granularity. Firehose also does not provide exactly-once semantics in the same manner as Kinesis Data Streams. For clickstream pipelines that require real-time, granular processing with exactly-once delivery, this solution is less optimal.
Therefore, the combination of Kinesis Data Streams, Lambda, and DynamoDB provides the most appropriate architecture for scalable, real-time clickstream analytics with exactly-once processing and low-latency access for downstream analytics.
Question 7:
You need to build a highly available, cost-efficient data storage solution for storing historical financial transaction records that will rarely be accessed but must be retained for regulatory compliance. Which AWS storage service provides the best solution?
A) Amazon S3 Standard
B) Amazon S3 Glacier Deep Archive
C) Amazon EBS
D) Amazon DynamoDB
Answer:
B) Amazon S3 Glacier Deep Archive
Explanation:
The scenario requires storing historical financial transaction records with infrequent access while ensuring durability and compliance. Each storage option has distinct characteristics:
Option A, Amazon S3 Standard, provides high durability, availability, and low-latency access. While S3 Standard is suitable for frequently accessed data and active workloads, it is more expensive for long-term retention of rarely accessed data. For records that need to be retained for compliance purposes but are rarely accessed, S3 Standard’s cost structure makes it suboptimal for archival use.
Option B, Amazon S3 Glacier Deep Archive, is specifically designed for long-term archival storage. It provides 99.999999999% (11 nines) durability across multiple Availability Zones, ensuring that critical financial records are protected against data loss. It is the most cost-effective option for data that is rarely accessed, with retrieval times ranging from hours to minutes, depending on retrieval options. Glacier Deep Archive supports regulatory compliance, retention policies, and lifecycle management, allowing seamless transition from other S3 storage classes if necessary. This makes it the optimal choice for historical financial data that must be preserved for extended periods but does not require frequent access.
Option C, Amazon EBS, is block-level storage attached to EC2 instances. It is optimised for low-latency workloads such as databases or applications requiring fast random access. EBS is not cost-efficient for long-term, infrequently accessed data because it requires ongoing allocation of provisioned storage and does not provide the durability guarantees of S3 Glacier. Additionally, EBS volumes are tied to a single availability zone, which may limit redundancy and durability for compliance purposes.
Option D, Amazon DynamoDB, is a NoSQL database designed for high-performance transactional workloads. While DynamoDB offers high availability and durability, it is not intended for archival storage of historical data. Storing rarely accessed financial records in DynamoDB would be cost-prohibitive and unnecessary, as the service is optimised for low-latency access rather than long-term retention at minimal cost.
Question 8:
A company wants to implement an automated pipeline to transform raw social media data into analytics-ready tables in near real-time. The data is unstructured and arrives continuously in large volumes. Which architecture best meets these requirements?
A) Amazon Kinesis Data Firehose + AWS Lambda + Amazon Redshift
B) Amazon S3 + AWS Glue ETL + Amazon Athena
C) Amazon RDS + AWS Glue
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon Kinesis Data Firehose + AWS Lambda + Amazon Redshift
Explanation:
The requirements specify an end-to-end ETL pipeline for high-volume, unstructured social media data. Each option provides different levels of suitability:
Option A, Amazon Kinesis Data Firehose + AWS Lambda + Amazon Redshift, supports continuous ingestion and transformation of streaming data. Kinesis Firehose can receive streaming data from multiple sources, buffer it, and invoke Lambda functions for lightweight transformations. Processed data is then loaded into Redshift for analytics and reporting. This architecture provides near real-time processing, scales automatically with traffic, and supports both structured and semi-structured data ingestion. Redshift’s columnar storage and MPP architecture make querying analytics-ready tables fast and efficient. This combination ensures low-latency processing, scalability, and analytics readiness, fully meeting the scenario’s requirements.
Option B, Amazon S3 + AWS Glue ETL + Amazon Athena, is ideal for batch-oriented data lakes. S3 provides durable storage, Glue can perform batch ETL, and Athena enables querying via SQL without moving data. While this solution is cost-effective for batch analytics, it does not meet the near-real-time processing requirement. The pipeline introduces latency as Glue jobs typically run on schedules rather than continuously streaming transformations.
Option C, Amazon RDS + AWS Glue, is more suitable for structured transactional datasets. RDS handles relational data with transactional guarantees, and Glue can perform ETL from RDS to other destinations. However, RDS cannot efficiently handle unstructured, high-velocity social media data. Real-time ingestion and processing are challenging due to RDS’s limitations in scaling for streaming workloads.
Option D, Amazon DynamoDB + Amazon EMR, supports scalable storage and big data processing. DynamoDB can handle high-velocity writes, and EMR can process data using Hadoop or Spark frameworks. However, EMR is primarily batch-oriented; near-real-time transformation is more complex to implement and maintain. This architecture is operationally heavier and less efficient than Kinesis Firehose + Lambda for continuous streaming transformations.
Thus, Kinesis Firehose combined with Lambda and Redshift is the optimal architecture for automatically transforming large, unstructured streaming datasets into analytics-ready tables in near real-time.
Question 9:
You are designing a data analytics solution to process millions of IoT device telemetry messages per day. The system must provide near real-time dashboards, detect anomalies, and allow batch analysis of historical data. Which AWS service combination is most appropriate?
A) Amazon Kinesis Data Streams + Amazon DynamoDB + Amazon QuickSight
B) Amazon S3 + AWS Glue + Amazon Athena + Amazon QuickSight
C) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + Amazon S3 + Amazon QuickSight
D) Amazon RDS + AWS Lambda + Amazon CloudWatch
Answer:
C) Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + Amazon S3 + Amazon QuickSight
Explanation:
The requirements include handling millions of telemetry messages, real-time dashboards, anomaly detection, and batch analysis. Evaluating each option:
Option A, Amazon Kinesis Data Streams + Amazon DynamoDB + Amazon QuickSight, supports real-time ingestion and storage. Kinesis streams can handle high-velocity data, and DynamoDB provides fast, low-latency storage. QuickSight can visualise the stored data. However, DynamoDB is less efficient for large-scale historical batch analysis. Processing anomalies across millions of events in DynamoDB alone requires additional complexity or custom processing.
Option B, Amazon S3 + AWS Glue + Amazon Athena + Amazon QuickSight, is ideal for batch analytics. S3 stores the raw data, Glue transforms it, Athena enables SQL queries, and QuickSight provides dashboards. While excellent for historical analysis, this option does not provide near-real-time streaming dashboards or low-latency anomaly detection.
Option C, Amazon Kinesis Data Streams + Amazon Kinesis Data Analytics + Amazon S3 + Amazon QuickSight, combines real-time and batch analytics. Kinesis Data Streams ingests streaming telemetry, Kinesis Data Analytics processes the streams for anomaly detection and real-time dashboards, S3 stores historical data for batch analytics, and QuickSight provides both real-time and historical visualisation. This architecture supports near real-time insights, anomaly detection, scalability, and historical batch processing, fulfilling all requirements efficiently.
Option D, Amazon RDS + AWS Lambda + Amazon CloudWatch, provides low-latency processing for transactional data, Lambda for event-driven computation, and CloudWatch for monitoring. However, RDS is not designed for high-volume IoT telemetry, and this architecture lacks a scalable streaming analytics layer and storage for large historical datasets.
Therefore, the combination of Kinesis Data Streams, Kinesis Data Analytics, S3, and QuickSight is the most suitable architecture for near real-time IoT telemetry processing with batch analytics and anomaly detection.
Question 10:
You need to design a cost-efficient data archival and analytics solution for sensor data that must be retained for regulatory compliance for seven years. The solution should allow occasional querying for analysis without restoring all archived data. Which combination of services is most appropriate?
A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena
Explanation:
The scenario requires long-term retention, cost-efficiency, and occasional query capability. Evaluating each option:
Option A, Amazon S3 Glacier Deep Archive + Amazon Athena, is ideal. Glacier Deep Archive provides extremely low-cost, highly durable storage suitable for seven-year retention requirements. With features like S3 Select and integration with Athena, analysts can query specific subsets of archived data without restoring the entire dataset. This reduces retrieval costs and allows analytics on demand. Glacier Deep Archive supports compliance requirements, lifecycle policies, and regulatory retention, ensuring the solution is both cost-efficient and functional for occasional analysis.
Option B, Amazon S3 Standard + AWS Lambda, provides low-latency storage with event-driven processing using Lambda. S3 Standard is expensive for long-term storage of rarely accessed data, and Lambda does not provide an efficient solution for querying large archival datasets. This option is not cost-efficient for seven-year retention.
Option C, Amazon RDS + Amazon Redshift, provides relational storage and data warehousing capabilities. While Redshift allows analytics, long-term archival of rarely accessed sensor data would be cost-prohibitive. RDS is not designed for large-scale archival storage, making this combination unsuitable.
Option D, Amazon DynamoDB + Amazon EMR, provides fast NoSQL storage and big data processing. While DynamoDB can handle high-velocity writes, it is expensive for long-term storage of rarely accessed data, and EMR is better suited for batch processing, not low-cost archival with occasional queries.
Thus, S3 Glacier Deep Archive combined with Athena allows cost-effective, regulatory-compliant long-term retention with the capability for occasional selective queries, making it the optimal solution.
Question 11:
Your organisation needs to build a real-time recommendation system for an e-commerce platform. The system must ingest user activity events at high velocity, process them for pattern detection, and update recommendation models continuously. Which combination of AWS services provides the best architecture for this solution?
A) Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker
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 SageMaker
Explanation:
Real-time recommendation systems require ingestion of high-volume events, near real-time processing, pattern detection, and continuous model updates. Option A, Amazon Kinesis Data Streams + AWS Lambda + Amazon SageMaker, provides an architecture explicitly designed for real-time streaming analytics. Kinesis Data Streams enables ingestion of millions of user activity events per second across multiple regions, supporting scalability, durability, and ordered delivery. AWS Lambda allows lightweight, serverless event processing in real-time, including feature extraction, anomaly detection, and preparation for machine learning inference. Amazon SageMaker can continuously train or update models using processed data and perform real-time inference to provide recommendations. This combination meets all key requirements: low-latency processing, scalability, integration with machine learning, and high durability, making it the optimal choice.
Option B, Amazon S3 + AWS Glue + Amazon Athena, is suitable for batch analytics and historical analysis. S3 provides durable object storage, Glue allows ETL transformation, and Athena supports SQL-based querying. While this combination is highly cost-effective for batch reporting and data lake analytics, it does not support real-time ingestion, continuous processing, or immediate model updates, which are critical for a recommendation engine.
Option C, Amazon RDS + Amazon Redshift + Amazon QuickSight, is ideal for structured analytics and reporting. RDS handles relational workloads, Redshift provides fast querying for analytical datasets, and QuickSight enables visualisation. However, RDS and Redshift are not optimised for high-velocity streaming event processing or real-time machine learning inference. They introduce latency, making them unsuitable for dynamic recommendation updates based on live user activity.
Option D, Amazon DynamoDB + Amazon EMR, can store high-velocity data and process it in batches. DynamoDB offers fast, scalable transactional storage, and EMR supports distributed processing with Hadoop or Spark. While EMR can perform large-scale analytics, it is primarily batch-oriented. Near real-time recommendation generation is challenging with this architecture, as streaming ingestion and model updates are less direct and operationally heavier than the Kinesis + Lambda + SageMaker solution.
Therefore, option A provides the best architecture for building a high-velocity, real-time recommendation system that can continuously update models and deliver timely insights.
Question 12:
You are tasked with designing a solution for long-term archival and analysis of large-scale genomic data. The data is rarely accessed but must be retained for regulatory compliance, and analysts occasionally need to perform queries on subsets of the data. Which AWS service combination is most appropriate?
A) Amazon S3 Glacier Deep Archive + Amazon Athena
B) Amazon S3 Standard + AWS Lambda
C) Amazon RDS + Amazon Redshift
D) Amazon DynamoDB + Amazon EMR
Answer:
A) Amazon S3 Glacier Deep Archive + Amazon Athena
Explanation:
Genomic data often involves extremely large datasets, regulatory retention requirements, and occasional selective querying. Option A, Amazon S3 Glacier Deep Archive + Amazon Athena, addresses these requirements effectively. Glacier Deep Archive provides extremely low-cost, long-term storage with 99.999999999% durability across multiple Availability Zones, ideal for regulatory compliance and long-term archival. Athena allows direct querying of data stored in S3 Glacier via features like S3 Select, which enables analysts to query specific subsets of data without restoring the entire dataset, reducing retrieval costs and operational overhead. This combination supports occasional analysis without compromising storage efficiency, fulfilling both cost and compliance goals.
Option B, Amazon S3 Standard + AWS Lambda, provides low-latency storage and event-driven processing. While S3 Standard is durable and highly available, it is cost-prohibitive for long-term retention of rarely accessed data. Lambda is useful for automation but does not support efficient querying of large archival datasets. This option is unsuitable for scenarios with extensive archival requirements and infrequent access.
Option C, Amazon RDS + Amazon Redshift, offers structured relational storage and analytical querying. While Redshift supports complex queries and RDS supports structured data storage, both are costly for long-term storage of rarely accessed data at the petabyte scale. Additionally, RDS is not optimised for archival compliance or regulatory retention, making this combination suboptimal.
Option D, Amazon DynamoDB + Amazon EMR, provides scalable storage and batch processing. DynamoDB handles high-velocity writes, and EMR can process large datasets in batches. However, this solution is operationally complex for archival storage and querying, and DynamoDB is cost-prohibitive for rarely accessed long-term data. Near-real-time or selective queries on archival data would require additional ETL or operational effort.
Therefore, S3 Glacier Deep Archive combined with Athena is the most cost-efficient, durable, and compliant solution for long-term storage and selective querying of genomic data.
Question 13:
A company collects telemetry data from thousands of industrial sensors. The system must support real-time anomaly detection, provide dashboards for operational monitoring, and store historical data for batch analytics. Which AWS architecture best meets 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 telemetry systems require both real-time and historical analysis, scalable ingestion, anomaly detection, and visualisation. Option A provides a comprehensive architecture: Kinesis Data Streams enables ingestion of high-velocity sensor data with durability and scalability, Kinesis Data Analytics processes the streams in real-time to detect anomalies, S3 provides low-cost storage for historical data, enabling batch analytics, and QuickSight visualizes both real-time and historical insights. This approach balances scalability, latency, and operational simplicity while providing actionable insights for both real-time monitoring and strategic analytics.
Option B, Amazon S3 + AWS Glue + Amazon Athena + QuickSight, supports batch analytics on historical data. S3 provides storage, Glue performs ETL, and Athena allows querying. QuickSight delivers visualisations. While suitable for historical reporting, this architecture cannot handle real-time anomaly detection or provide live dashboards for operational monitoring. The latency introduced by batch ETL workflows makes it unsuitable for immediate insights.
Option C, Amazon RDS + AWS Lambda + CloudWatch, enables structured data storage with event-driven processing and monitoring. RDS supports transactional workloads, Lambda allows event-based processing, and CloudWatch provides monitoring and alerts. While functional for small-scale telemetry or transactional data, RDS is not designed for high-velocity, high-volume streaming telemetry data. annear-real-timeme anomaly detection across millions of events would be difficult to scale.
Option D, Amazon DynamoDB + Amazon EMR + QuickSight, can store high-volume data and support batch processing. DynamoDB scales for transactional workloads, EMR can process large datasets using distributed frameworks, and QuickSight provides analytics. However, EMR is batch-oriented and introduces latency, making it less effective for real-time anomaly detection and live dashboards. Operational complexity and latency are higher than the Kinesis Data Streams + Kinesis Data Analytics solution.
Thus, option A offers the most comprehensive and efficient solution for handling high-velocity industrial telemetry, providing real-time anomaly detection, historical batch analytics, and visual dashboards.
Question 14:
You are implementing a cost-efficient data lake solution for storing and analysing IoT device logs. The system should allow multiple analytics and machine learning services to query data without duplication or movement. Which architecture best satisfies this requirement?
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 device logs of semi-structured or unstructured data. Option A, Amazon S3 + AWS Glue + Amazon Athena, is the standard architecture for a cost-efficient, multi-service-accessible data lake. S3 provides durable and scalable storage for petabytes of data. Glue serves as a centralised metadata catalogue and performs ETL transformations if needed. Athena allows multiple analytics or ML services to query the data directly using SQL without moving it between services, reducing cost and operational complexity. This architecture supports structured, semi-structured, and unstructured data, integrates seamlessly with other AWS analytics services, and provides a unified, cost-effective platform for querying and machine learning.
Option B, Amazon RDS + Amazon Redshift, provides relational and data warehousing capabilities. While Redshift allows complex queries and analytics, RDS is limited to structured datasets and both services are optimised for large-scale semi-structured IoT logs. Data movement is often required between services, increasing cost and operational effort.
Option C, Amazon DynamoDB + Amazon EMR, can store high-volume data and process it using distributed frameworks. However, this combination is more suited to batch analytics and large-scale processing, rather than a cost-efficient, central cataloguing lake for querying across multiple services. EMR processing introduces latency and operational overhead.
Option D, Amazon Kinesis Data Firehose + Amazon S3, handles streaming ingestion into S3. While Firehose is excellent for ingesting streaming logs, it does not provide metadata cataloging or query capabilities directly. Additional services like Glue and Athena are required to enable multi-service queries, making this option incomplete on its own.
Therefore, S3 + Glue + Athena offers a fully integrated, cost-effective data lake solution, supporting analytics and machine learning without duplication or data movement.
Question 15:
A company wants to migrate an on-premises transactional data warehouse to AWS. The warehouse must support structured analytics queries, near real-time reporting, and integration with BI tools. Which service is best suited for this migration?
A) Amazon Redshift
B) Amazon RDS
C) Amazon DynamoDB
D) Amazon S3 + AWS Glue
Answer:
A) Amazon Redshift
Explanation:
Migrating an on-premises data warehouse requires a service that supports structured analytics, near real-time reporting, and seamless integration with BI tools. Amazon Redshift is a fully managed, columnar, MPP (massively parallel processing) data warehouse that provides high-performance analytics for structured data at scale. Redshift supports near real-time query execution, allowing dashboards to refresh quickly, and integrates with BI tools like QuickSight, Tableau, and Power BI. Its architecture supports large volumes of historical and transactional data with features like Redshift Spectrum to query data directly in S3, concurrency scaling to handle multiple simultaneous queries, and automated backups and security controls for compliance.
Option B, Amazon RDS, is optimised for transactional workloads and relational data. While suitable for OLTP applications, it does not scale efficiently for large-scale analytics and lacks an MPP architecture for high-performance queries over large datasets. Near real-time reporting at scale is limited, and complex analytical queries can strain performance.Option B, Amazon RDS, while excellent for transactional workloads, lacks MPP capabilities and cannot handle analytics at scale efficiently.
Option C, Amazon DynamoDB, is optimised for key-value and document-based transactional workloads but cannot perform complex joins or aggregations needed for analytical queries.
Option D, S3 with AWS Glue, serves as a scalable data lake but often results in slower query performance compared to Redshift and is better suited for semi-structured or unstructured data.
Option C, Amazon DynamoDB, is a NoSQL database optimised for key-value and document workloads with low-latency transactional access. While excellent for high-volume application workloads, it is not suitable for structured analytics, complex joins, aggregations, or integration with BI tools for data warehouse use cases.
Option D, Amazon S3 + AWS Glue, provides a data lake and ETL capabilities. While cost-effective and scalable for large datasets, querying S3 directly (even with Athena) is typically slower than Redshift for interactive analytics and does not provide the same level of near-real-time reporting performance. Additionally, S3 + Glue is better suited for semi-structured or unstructured data rather than a full relational warehouse replacement.
Therefore, Amazon Redshift is the optimal solution for migrating an on-premises data warehouse to AWS while ensuring performance, analytics capabilities, and integration with BI tools.
When migrating an on-premises data warehouse to the cloud, the primary considerations are performance for analytical workloads, seamless integration with business intelligence tools, scalability for large volumes of data, and the ability to support near real-time reporting. Amazon Redshift is designed specifically for these requirements, making it the most suitable choice for this scenario.
Redshift is a fully managed, columnar, massively parallel processing (MPP) data warehouse. Its MPP architecture allows it to distribute data and query processing across multiple nodes, enabling high-speed query execution even on very large datasets. This architecture is particularly advantageous for organisations transitioning from on-premises warehouses, where large-scale analytics and complex queries were historically slow or resource-intensive. Redshift ensures that these queries execute efficiently without requiring extensive tuning or manual management of the underlying infrastructure.
Integration with BI tools is another critical factor for warehouse migration. Redshift provides native support for popular tools such as Amazon QuickSight, Tableau, Power BI, and Looker. This enables organisations to maintain their existing analytics workflows and dashboards with minimal changes while benefiting from cloud-scale performance. Additionally, Redshift supports Redshift Spectrum, which allows queries on structured data stored directly in Amazon S3, providing flexibility to combine historical data in S3 with current data in Redshift for comprehensive analytics.
Data security, automated backups, and compliance are crucial for enterprise workloads. Redshift offers built-in encryption, network isolation using VPC, automated snapshots, and audit logging, ensuring that migrated workloads adhere to security and regulatory requirements without adding operational complexity.
Overall, Amazon Redshift balances performance, scalability, analytics capability, and integration with BI tools, making it the optimal choice for organisations migrating from an on-premises data warehouse to AWS while maintaining near real-time reporting and high-performance analytics. Its architecture and features are specifically aligned with the requirements of enterprise-scale analytical workloads.