Amazon AWS Certified Solutions Architect – Professional SAP-C02 Exam Dumps and Practice Test Questions Set 6 Q101-120

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Question 101:

A company wants to deploy a multi-region, highly available web application that serves both dynamic and static content with low latency for global users. Which solution should a solutions architect implement?

Answer:

A) Multi-region deployment with Route 53 latency-based routing, CloudFront, and multi-region ALBs
B) Single-region ALB with EC2 Auto Scaling
C) S3 static website hosting with Transfer Acceleration
D) Global Accelerator with a single-region EC2 deployment

Explanation:

The correct answer is A) Multi-region deployment with Route 53 latency-based routing, CloudFront, and multi-region ALBs.

This architecture addresses the dual goals of high availability and low latency for globally distributed users. Route 53 latency-based routing automatically directs users to the AWS region that provides the lowest network latency. By continuously performing health checks, Route 53 detects regional failures and reroutes traffic to healthy regions, maintaining service availability.

CloudFront, as a global content delivery network (CDN), caches both static and dynamic content at edge locations close to users. This reduces latency, decreases load on origin servers, and improves the responsiveness of the application. Lambda@Edge can be used for dynamic content processing, providing additional performance optimization.

Multi-region Application Load Balancers (ALBs) provide redundancy across multiple Availability Zones within each region. By deploying ALBs in multiple regions, the architecture can withstand the failure of an entire AWS region. This ensures that dynamic application components remain available and resilient under various failure scenarios.

Option B, a single-region ALB with EC2 Auto Scaling, provides high availability only within one region, creating a single point of failure if the region goes down. Option C, S3 static website hosting with Transfer Acceleration, is only suitable for static websites and cannot handle dynamic workloads or provide regional failover. Option D, Global Accelerator with a single-region EC2 deployment, enhances network performance but cannot prevent downtime if the single region fails.

From a security perspective, this architecture can integrate AWS WAF for web protection and AWS Shield for DDoS mitigation. IAM policies enforce least-privilege access, and KMS encrypts sensitive data. CloudWatch can monitor ALB health, EC2 instances, CloudFront cache performance, and Route 53 routing. CloudTrail provides comprehensive logging for audit purposes.

Operational efficiency is enhanced by leveraging managed services that automatically scale with traffic. Teams can focus on application development and business logic instead of infrastructure management. By deploying across multiple regions, organizations also improve disaster recovery capabilities and can meet regulatory requirements for redundancy and high availability.

This solution exemplifies AWS Well-Architected Framework best practices, particularly in the pillars of reliability, performance efficiency, operational excellence, cost optimization, and security. For SAP-C02 exam purposes, this scenario highlights a robust, globally distributed architecture capable of handling variable workloads with minimal latency and high fault tolerance.

Question 102:

A company needs to implement a disaster recovery solution for an RDS database that requires an RPO of under 1 minute and an RTO of under 15 minutes. Which solution is best?

Answer:

A) Amazon Aurora Global Database
B) Cross-region RDS snapshots
C) Manual replication using EC2
D) Standby EC2 servers in another region

Explanation:

The correct answer is A) Amazon Aurora Global Database.

Aurora Global Database is designed specifically for multi-region disaster recovery with extremely low replication lag, typically under one second. This ensures that the recovery point objective (RPO) is satisfied, minimizing potential data loss in the event of a regional outage. Automatic failover mechanisms allow the secondary region to take over quickly, meeting the recovery time objective (RTO) of under 15 minutes.

Option B, cross-region RDS snapshots, is unsuitable for low RPO/RTO requirements because snapshot-based restores can take several minutes or even hours, depending on the database size. Option C, manual replication using EC2 instances, adds significant operational complexity and increases the risk of errors during failover. Option D, standby EC2 servers, also requires manual intervention and cannot meet stringent RPO/RTO requirements reliably.

Aurora maintains six copies of data across three Availability Zones in each region, providing durability and high availability. Security is enforced with IAM roles, KMS encryption, and TLS for data in transit. CloudWatch monitors replication lag, instance health, and query performance, while CloudTrail provides auditing of administrative actions.

Secondary regions in Aurora Global Database can serve read requests to reduce latency for globally distributed users. This enables performance optimization for global applications while simultaneously providing disaster recovery capabilities. Automatic storage scaling ensures that as data grows, no manual intervention is required.

From an operational perspective, Aurora Global Database reduces manual management and operational risk. Teams can focus on application development rather than database failover procedures. This architecture also supports compliance requirements for disaster recovery and data redundancy.

In SAP-C02 exam contexts, this architecture demonstrates best practices for highly available, multi-region relational database deployment, highlighting key principles in reliability, performance efficiency, operational excellence, and security.

Question 103:

A company wants to process millions of IoT messages per day in a serverless manner, apply transformations, and store results for analytics. Which solution is most suitable?

Answer:

A) AWS IoT Core, Lambda, DynamoDB
B) SQS with EC2 consumers
C) SNS with S3 triggers
D) RDS with batch ingestion

Explanation:

The correct answer is A) AWS IoT Core, Lambda, DynamoDB.

AWS IoT Core acts as a highly scalable and secure ingestion point for IoT device messages, capable of handling millions of devices concurrently. It ensures secure message delivery, device authentication, and supports MQTT, HTTPS, and WebSockets for device connectivity. Messages are then routed to AWS Lambda functions for real-time processing.

Lambda executes business logic without requiring server management and scales automatically based on traffic volume. This serverless approach allows the company to handle massive spikes in IoT messages without overprovisioning infrastructure. Transformations, enrichments, and filtering can be applied to the telemetry data before storing it in DynamoDB.

DynamoDB provides a fully managed, high-performance NoSQL database capable of handling millions of requests per second with millisecond latency. It integrates seamlessly with DynamoDB Streams to enable further event-driven processing or analytics pipelines.

Option B, SQS with EC2 consumers, increases operational overhead due to instance management and scaling. Option C, SNS with S3 triggers, is not optimized for high-volume real-time IoT ingestion and cannot maintain ordering. Option D, RDS batch ingestion, introduces significant latency and is unsuitable for high-throughput real-time processing.

Security is enforced via IAM roles, TLS for data in transit, and KMS encryption for data at rest. CloudWatch monitors Lambda execution, IoT message delivery, and DynamoDB performance. CloudTrail logs administrative actions to maintain compliance.

Operational simplicity and durability are key advantages of this architecture. No servers need to be managed, and the architecture scales automatically with traffic. IoT Core ensures reliable ingestion, Lambda provides real-time processing, and DynamoDB ensures durable and low-latency storage.

In SAP-C02 scenarios, this solution demonstrates serverless, event-driven IoT architectures that are scalable, fault-tolerant, secure, and operationally efficient. It aligns with the AWS Well-Architected Framework pillars of reliability, performance efficiency, operational excellence, security, and cost optimization.

Question 104:

A company wants to reduce read latency for a DynamoDB table with millions of requests per second globally. Which solution is best?

Answer:

A) DynamoDB Accelerator (DAX)
B) ElastiCache Redis
C) S3 Transfer Acceleration
D) RDS Read Replicas

Explanation:

The correct answer is A) DynamoDB Accelerator (DAX).

DAX is a fully managed, in-memory cache for DynamoDB that reduces read latency from milliseconds to microseconds. It operates as a write-through cache, maintaining consistency with the underlying DynamoDB table. This is essential for applications with read-heavy workloads and globally distributed users.

Option B, ElastiCache Redis, requires additional integration and does not provide native DynamoDB caching. Option C, S3 Transfer Acceleration, optimizes object transfers but does not reduce database query latency. Option D, RDS Read Replicas, applies only to relational databases and cannot improve DynamoDB performance.

DAX clusters automatically scale horizontally and provide high availability with multi-AZ failover. CloudWatch monitors cache hit ratios, latency, and node health. IAM, TLS, and KMS provide secure access and data encryption. By offloading reads from DynamoDB, DAX improves performance, reduces throttling, and ensures predictable low-latency responses.

For SAP-C02, this demonstrates best practices for globally distributed, high-throughput NoSQL applications, emphasizing performance optimization, operational simplicity, and fault tolerance.

Question 105:

A company wants to implement a real-time event-driven architecture for processing e-commerce orders with durability, scalability, and exactly-once processing semantics. Which solution is most appropriate?

Answer:

A) Amazon Kinesis Data Streams with Lambda
B) SQS standard queue with Lambda
C) SNS with S3 triggers
D) DynamoDB Streams

Explanation:

The correct answer is A) Amazon Kinesis Data Streams with Lambda.

Amazon Kinesis Data Streams is designed for high-throughput, real-time streaming and provides durability and ordered delivery at the shard level. It is capable of ingesting millions of events per second from multiple sources, making it ideal for e-commerce order processing. Each shard in Kinesis ensures the order of events is maintained, which is essential for financial transactions, inventory updates, and order fulfillment.

Lambda functions act as the compute layer, processing incoming events in near real-time. Lambda integrates seamlessly with Kinesis, automatically scaling to match the volume of incoming data without the need to manage servers or clusters. This serverless model simplifies operational overhead and allows teams to focus on business logic rather than infrastructure management. By checkpointing processed records, Lambda ensures exactly-once processing semantics, which is critical for financial and inventory integrity.

Option B, SQS standard queues with Lambda, provides at-least-once delivery, which can result in duplicate events. While SQS FIFO queues guarantee ordering, their throughput is limited compared to Kinesis shards. Option C, SNS with S3 triggers, is better suited for asynchronous notifications but does not maintain ordering or provide exactly-once processing guarantees. Option D, DynamoDB Streams, is restricted to capturing table changes and cannot serve as a general-purpose streaming solution for diverse e-commerce event workloads.

Security is critical in e-commerce event processing. IAM roles are used to grant least-privilege access to Kinesis streams and Lambda functions. KMS ensures data encryption at rest, while TLS encrypts data in transit. CloudTrail logs all administrative actions for auditing, and CloudWatch metrics monitor stream health, Lambda execution times, and processing lag.

Operational considerations include handling scaling events and failures. Kinesis automatically replicates data across multiple Availability Zones to ensure durability. Lambda retries failed events and provides detailed logging for troubleshooting. Shard splitting and merging allow scaling the stream to accommodate spikes in order volume, ensuring reliable performance during peak shopping periods.

From a cost perspective, the serverless approach reduces operational costs because billing is based on actual data processed and compute execution time. No pre-provisioning of EC2 instances is necessary, and the architecture can adapt dynamically to fluctuating workloads without idle resources.

This architecture exemplifies AWS Well-Architected Framework pillars, including reliability, operational excellence, performance efficiency, security, and cost optimization. For SAP-C02 exam scenarios, it demonstrates best practices for real-time, event-driven, serverless architectures capable of handling large-scale transactional workloads with exactly-once processing semantics. This ensures both customer satisfaction and operational compliance, as well as efficient handling of high-traffic periods like sales events or promotional campaigns.

The integration of Kinesis Data Streams, Lambda, CloudWatch, and CloudTrail provides a comprehensive approach to monitoring, logging, and scaling while maintaining system reliability. These services also allow the architecture to evolve and incorporate additional services, such as S3 for long-term storage, DynamoDB for state management, or Athena for real-time analytics, without impacting the core event processing pipeline.

Question 106:

A company wants to implement a secure, scalable, and cost-efficient data lake that supports both structured and unstructured data for analytics. Which solution is best?

Answer:

A) Amazon S3 with Lake Formation, AWS Glue, and Athena
B) EC2 with RDS
C) S3 with manual Lambda ETL
D) RDS with S3 backups

Explanation:

The correct answer is A) Amazon S3 with Lake Formation, AWS Glue, and Athena.

Amazon S3 provides virtually unlimited storage capacity for structured, semi-structured, and unstructured data. It guarantees 11 9’s durability, ensuring that data is safe even in the event of multiple simultaneous failures. S3 also offers versioning, lifecycle policies, and replication, making it ideal for a data lake architecture.

AWS Lake Formation simplifies the creation, management, and security of the data lake. It centralizes access control, allowing fine-grained permissions for different users or applications. Lake Formation also integrates with Glue for cataloging data assets, enabling users to find and query datasets without needing to know the underlying storage paths.

AWS Glue provides serverless ETL capabilities, transforming raw data into structured, query-optimized formats such as Parquet or ORC. Glue jobs can be scheduled or triggered by S3 events, ensuring timely transformation and ingestion for analytics pipelines. The automation reduces operational overhead and eliminates the need to manage traditional ETL servers.

Athena provides serverless, interactive querying directly on S3 data. Users pay only for the data scanned during queries, making it cost-efficient. Athena integrates with Lake Formation for secure access and with Glue for querying structured metadata. The serverless nature of Athena ensures scalability and avoids overprovisioning while supporting ad-hoc analytics.

Option B, EC2 with RDS, introduces significant operational overhead and is limited in handling unstructured data. Option C, S3 with manual Lambda ETL, requires managing ETL code, scheduling, and retries, increasing complexity. Option D, RDS with S3 backups, only supports structured relational data and lacks flexibility for analytics on semi-structured or unstructured data.

Security is a critical aspect. IAM policies enforce least-privilege access, while KMS encryption ensures data security at rest. TLS encrypts data in transit, and Lake Formation provides fine-grained access control, allowing role-based permissions. CloudTrail logs all management actions, supporting compliance with regulatory requirements.

Operational excellence is enhanced through automation. Glue handles transformation pipelines, Lake Formation manages access, and Athena allows ad-hoc analytics without managing servers. CloudWatch monitors Glue job performance, S3 storage metrics, and Athena query performance. The architecture scales seamlessly with data growth, handling petabyte-scale data lakes with minimal operational effort.

Cost optimization is inherent due to the serverless design. Storage in S3 is inexpensive and can be tiered using S3 Intelligent-Tiering or Glacier. Compute costs for Glue and Athena are billed per usage, avoiding idle infrastructure costs.

From an SAP-C02 perspective, this architecture demonstrates best practices for scalable, secure, cost-efficient data lakes, supporting structured and unstructured analytics workloads. It aligns with all pillars of the AWS Well-Architected Framework, including security, reliability, operational excellence, performance efficiency, and cost optimization.

Question 107:

A company needs to implement a scalable serverless web application with unpredictable traffic patterns. Which architecture is most appropriate?

Answer:

A) AWS Lambda with API Gateway and DynamoDB
B) EC2 Auto Scaling with ALB
C) ECS on EC2
D) S3 static hosting with CloudFront

Explanation:

The correct answer is A) AWS Lambda with API Gateway and DynamoDB.

This architecture is fully serverless and automatically scales with incoming requests, making it ideal for workloads with unpredictable traffic. API Gateway acts as the managed front door, handling request routing, throttling, authentication, and caching. Lambda executes application logic on-demand without provisioning servers, automatically scaling horizontally to handle bursts in traffic. DynamoDB provides a highly available, low-latency NoSQL database for dynamic content.

Option B, EC2 Auto Scaling with ALB, requires manual provisioning and management of EC2 instances, leading to higher operational overhead. Option C, ECS on EC2, also requires cluster management and capacity planning. Option D, S3 static hosting, only supports static content and cannot process dynamic requests.

Security is maintained using IAM roles, KMS for encryption at rest, and TLS for encryption in transit. CloudWatch monitors Lambda execution, API Gateway metrics, and DynamoDB throughput. CloudTrail provides auditing and compliance tracking.

Operational efficiency is enhanced because scaling is automated and the architecture is fully managed. Cost optimization is achieved because AWS bills based on actual usage, reducing idle resource costs. This architecture exemplifies reliability, performance efficiency, operational excellence, cost optimization, and security, which are key pillars in the SAP-C02 exam.

Question 108:

A company wants to implement a multi-region disaster recovery solution for a critical relational database with minimal replication lag and near-zero data loss. Which solution is most suitable?

Answer:

A) Amazon Aurora Global Database
B) Cross-region RDS snapshots
C) Manual replication using EC2
D) Standby RDS in a single region

Explanation:

The correct answer is A) Amazon Aurora Global Database.

Amazon Aurora Global Database is specifically designed for multi-region relational database replication with minimal replication lag, typically under one second. This capability ensures that the company meets stringent Recovery Point Objectives (RPO), maintaining near-zero data loss in the event of a regional outage. Aurora automatically replicates data asynchronously across multiple AWS regions while still providing local read capabilities in secondary regions. This allows businesses to serve read requests globally without affecting the primary region’s write performance.

Option B, cross-region RDS snapshots, is suitable for backups but cannot provide continuous, low-latency replication required for critical workloads. Snapshots require manual restoration, which increases Recovery Time Objective (RTO) and operational overhead. Option C, manual replication using EC2, introduces complexity and operational risk because the company must manage replication scripts, monitor for consistency, handle failovers, and maintain data integrity across regions. Option D, standby RDS in a single region, provides only intra-region failover and does not mitigate risks from full regional failures.

Aurora Global Database is built on Aurora’s underlying architecture, which maintains six copies of data across three Availability Zones in each region. This ensures durability, high availability, and automatic failover within a region. Secondary regions can be promoted to primary in the event of an outage, significantly reducing downtime and meeting stringent disaster recovery objectives.

Security is a critical component. Aurora Global Database integrates with IAM for access management, KMS for encryption at rest, and TLS for encryption in transit. CloudTrail ensures auditing of all administrative actions, supporting regulatory compliance. CloudWatch metrics track replication lag, instance health, read/write performance, and alarms can be configured for early warning of anomalies.

From an operational perspective, Aurora Global Database reduces manual intervention and operational complexity. It scales automatically with data growth and can serve global read traffic efficiently. This is especially important for applications with high read-to-write ratios, such as global e-commerce platforms, financial systems, or SaaS applications serving multiple regions.

Cost optimization is also addressed. By serving read traffic from secondary regions, Aurora Global Database offloads workload from the primary region, preventing overprovisioning of resources. Serverless features, such as Aurora Serverless v2, can be used to dynamically scale capacity based on demand, further reducing costs while maintaining high availability.

For SAP-C02 exam purposes, this scenario demonstrates best practices for highly available, low-latency, and resilient multi-region relational database deployments, incorporating pillars of reliability, performance efficiency, operational excellence, security, and cost optimization.

Question 109:

A company wants to implement a real-time, serverless IoT analytics pipeline that ingests, processes, and stores millions of messages per day. Which AWS solution is best?

Answer:

A) AWS IoT Core, Lambda, DynamoDB
B) SQS with EC2 consumers
C) SNS with S3 triggers
D) RDS with batch ingestion

Explanation:

The correct answer is A) AWS IoT Core, Lambda, DynamoDB.

AWS IoT Core provides a fully managed, scalable ingestion layer for IoT device data. It supports millions of concurrent device connections and ensures secure message delivery. Authentication is handled via X.509 certificates or IAM roles, ensuring only authorized devices can publish data. IoT Core can route messages directly to Lambda functions for real-time processing.

Lambda functions act as the compute layer, executing transformation, enrichment, filtering, or aggregation of incoming IoT telemetry. Lambda scales automatically to accommodate spikes in message volume without requiring server management. This serverless approach reduces operational overhead while maintaining predictable performance.

DynamoDB serves as a durable, low-latency NoSQL database for storing processed IoT data. Its integration with DynamoDB Streams enables additional downstream processing, real-time analytics, or alerts. DynamoDB’s provisioned throughput or on-demand scaling ensures the database can handle millions of writes per second with millisecond latency.

Option B, SQS with EC2 consumers, introduces operational complexity as EC2 instances must be managed and scaled. Option C, SNS with S3 triggers, is not optimized for high-throughput, ordered IoT data and cannot provide exactly-once processing. Option D, RDS with batch ingestion, is unsuitable for real-time IoT processing due to batch latency and scaling limitations.

Security is enforced through IAM roles, KMS encryption, and TLS encryption. CloudWatch provides metrics for Lambda execution, IoT message throughput, and DynamoDB performance. CloudTrail captures management actions for auditing.

Operational simplicity, scalability, and durability are inherent in this architecture. No servers need to be managed, and the architecture scales automatically with device and message growth. This architecture aligns with SAP-C02 best practices for serverless, event-driven IoT analytics pipelines, ensuring reliability, performance efficiency, operational excellence, security, and cost optimization.

Question 110:

A company wants to reduce read latency for a high-traffic, globally distributed DynamoDB table. Which solution is most appropriate?

Answer:

A) DynamoDB Accelerator (DAX)
B) ElastiCache Redis
C) S3 Transfer Acceleration
D) RDS Read Replicas

Explanation:

The correct answer is A) DynamoDB Accelerator (DAX).

DAX is a fully managed, in-memory cache designed to accelerate DynamoDB read operations from milliseconds to microseconds. It supports write-through caching, maintaining consistency with the underlying table, which is crucial for applications with high read demands, such as global e-commerce platforms, gaming leaderboards, or IoT telemetry dashboards.

Option B, ElastiCache Redis, requires application-level integration and does not provide native DynamoDB caching. Option C, S3 Transfer Acceleration, only improves object upload/download speeds and is irrelevant for database query latency. Option D, RDS Read Replicas, applies to relational databases and cannot accelerate NoSQL queries.

DAX clusters are highly available, supporting multi-AZ deployments with automatic failover. CloudWatch monitors cache hit ratios, latency, node health, and request throughput. IAM and KMS secure access to DAX clusters, while TLS encrypts in-transit traffic.

Operationally, DAX offloads a significant portion of read traffic from DynamoDB, reducing throttling, lowering latency, and enabling predictable performance at scale. For SAP-C02 scenarios, it illustrates best practices for high-performance, low-latency NoSQL database deployments, with a focus on operational simplicity, reliability, and global scalability.

Question 111:

A company needs to orchestrate a complex, serverless workflow involving multiple Lambda functions with conditional branching, retries, and error handling. Which AWS service should be used?

Answer:

A) AWS Step Functions
B) Amazon SWF
C) AWS Batch
D) Amazon SQS

Explanation:

The correct answer is A) AWS Step Functions.

Step Functions is a fully managed orchestration service for serverless workflows. It supports sequential, parallel, and conditional execution of tasks. Built-in error handling, retries, and timeouts simplify workflow management. Step Functions integrates with Lambda, ECS, SNS, and other AWS services, allowing complex workflows without manual orchestration.

Option B, SWF, is a legacy workflow service that requires manual worker management. Option C, Batch, is for batch workloads, not orchestrating serverless workflows. Option D, SQS, is a messaging service, not a workflow engine.

CloudWatch provides metrics for execution history, errors, and workflow duration. AWS X-Ray enables traceability for debugging. IAM and KMS enforce secure access, while CloudTrail provides auditing for compliance.

This architecture reduces operational overhead, improves reliability, and ensures observability. In SAP-C02 exam scenarios, Step Functions exemplifies best practices for serverless workflow orchestration, supporting fault tolerance, retries, conditional execution, and integration with other AWS services.

Question 112:

A company wants to deploy a serverless web application that must handle unpredictable spikes in traffic while keeping operational overhead low. Which architecture is most appropriate?

Answer:

A) AWS Lambda with API Gateway and DynamoDB
B) EC2 Auto Scaling with ALB
C) ECS on EC2
D) S3 static hosting with CloudFront

Explanation:

The correct answer is A) AWS Lambda with API Gateway and DynamoDB.

This architecture is fully serverless, meaning it can automatically scale to handle traffic spikes without requiring manual provisioning of infrastructure. API Gateway acts as a managed front door, routing requests to Lambda functions. It provides throttling, authentication, caching, and supports RESTful or WebSocket APIs.

Lambda functions execute application logic dynamically and horizontally scale to accommodate variable request volumes. This eliminates the operational overhead of managing servers, patching operating systems, or configuring Auto Scaling policies. DynamoDB serves as a highly available, low-latency database for storing dynamic content. It can scale seamlessly to support millions of read and write requests per second.

Option B, EC2 Auto Scaling with ALB, requires managing EC2 instances, patching, and capacity planning, which increases operational complexity. Option C, ECS on EC2, also requires cluster management, container orchestration, and scaling policies. Option D, S3 static hosting with CloudFront, is suitable only for static content and cannot process dynamic requests.

Security is enforced via IAM roles, KMS for encryption at rest, and TLS for data in transit. CloudWatch provides monitoring for Lambda execution times, API Gateway request metrics, and DynamoDB throughput. CloudTrail logs administrative actions for auditing and compliance purposes.

Operational excellence is achieved as scaling and resource provisioning are fully automated. Cost optimization occurs because AWS charges for Lambda execution time and API Gateway request counts, rather than idle server time.

From an SAP-C02 perspective, this architecture demonstrates best practices for serverless applications, including scalability, operational simplicity, low cost, and fault tolerance. This pattern is ideal for web applications with unpredictable traffic, enabling teams to focus on delivering business value rather than managing infrastructure.

Question 113:

A company wants to implement a global, low-latency cache for a multi-region DynamoDB application. Which AWS service should be used?

Answer:

A) DynamoDB Accelerator (DAX)
B) ElastiCache Redis
C) RDS Read Replicas
D) S3 Transfer Acceleration

Explanation:

The correct answer is A) DynamoDB Accelerator (DAX).

DAX is a fully managed, in-memory cache specifically designed to accelerate DynamoDB read operations from milliseconds to microseconds. It operates as a write-through cache, maintaining consistency with the underlying table, which is critical for applications requiring up-to-date information, such as e-commerce inventory systems or leaderboards in gaming applications.

ElastiCache Redis (option B) is a general-purpose caching solution and requires integration with DynamoDB at the application layer, adding operational complexity. RDS Read Replicas (option C) apply only to relational databases and are irrelevant for NoSQL workloads. S3 Transfer Acceleration (option D) optimizes object transfer for S3, not database queries.

DAX clusters can be deployed in multi-AZ configurations, providing fault tolerance and high availability. CloudWatch monitors cache hit ratios, latency, node health, and request throughput. IAM roles ensure least-privilege access, KMS secures data at rest, and TLS encrypts in-transit traffic.

Operationally, DAX reduces load on DynamoDB, improving application responsiveness, preventing throttling, and ensuring consistent performance even under high request volumes. This architecture aligns with SAP-C02 best practices for globally distributed, high-performance NoSQL applications, emphasizing performance efficiency, reliability, and operational simplicity.

Question 114:

A company wants to orchestrate a serverless workflow with multiple Lambda functions, including conditional branching, retries, and error handling. Which AWS service should be used?

Answer:

A) AWS Step Functions
B) Amazon SWF
C) AWS Batch
D) Amazon SQS

Explanation:

The correct answer is A) AWS Step Functions.

Step Functions is a serverless orchestration service that allows developers to build complex workflows with branching, retries, and error handling. It integrates with Lambda, ECS, SNS, and other AWS services, providing a fully managed workflow engine without the need for infrastructure management.

Option B, SWF, is legacy and requires managing worker nodes. Option C, AWS Batch, handles batch workloads but does not orchestrate event-driven, serverless workflows. Option D, SQS, is a messaging service, not a workflow orchestration tool.

CloudWatch monitors workflow execution metrics, errors, and duration. AWS X-Ray provides tracing for debugging multi-step workflows. IAM and KMS enforce secure access, and CloudTrail ensures auditing of all administrative actions.

Operational benefits include reduced complexity, fault tolerance, and improved reliability through automated retries and error handling. SAP-C02 exam scenarios highlight best practices for serverless workflow orchestration, emphasizing operational excellence, reliability, and security.

Question 115:

A company wants to implement a real-time, event-driven data pipeline for e-commerce transactions that ensures durability, scalability, and exactly-once processing. Which AWS service combination is most appropriate?

Answer:

A) Amazon Kinesis Data Streams with Lambda
B) SQS standard queue with Lambda
C) SNS with S3 triggers
D) DynamoDB Streams

Explanation:

The correct answer is A) Amazon Kinesis Data Streams with Lambda.

Kinesis Data Streams provides durable, ordered data ingestion, capable of handling millions of e-commerce events per second. It replicates data across multiple Availability Zones to prevent loss and maintains order within each shard. Lambda functions process the stream in real-time, allowing for inventory updates, payment processing, and anomaly detection.

SQS standard queues (option B) provide at-least-once delivery and do not guarantee ordering, risking duplicate processing. SNS with S3 triggers (option C) is not suitable for high-throughput, ordered transactions. DynamoDB Streams (option D) is limited to DynamoDB events and cannot handle diverse transaction types.

CloudWatch monitors shard health, Lambda processing lag, and metrics. KMS encrypts data at rest, TLS encrypts in transit, and CloudTrail logs all actions for auditing. This architecture ensures reliability, scalability, durability, and operational simplicity, aligning with SAP-C02 best practices for real-time, event-driven architectures in mission-critical workloads.

Question 116:

A company wants to implement a global, low-latency API for users worldwide with automatic failover. Which architecture is most suitable?

Answer:

A) API Gateway with multi-region Lambda backends and Route 53 latency-based routing
B) Single-region EC2 API behind ALB
C) SNS with Lambda in a single region
D) RDS with read replicas

Explanation:

The correct answer is A) API Gateway with multi-region Lambda backends and Route 53 latency-based routing.

API Gateway manages request routing, throttling, authentication, and caching. Lambda functions deployed in multiple regions process requests and scale automatically. Route 53 latency-based routing ensures users are directed to the nearest healthy region, minimizing latency and enabling automatic failover in case of regional outages.

Single-region EC2 APIs (option B) create a single point of failure. SNS with Lambda in one region (option C) is asynchronous and not suited for synchronous API responses. RDS read replicas (option D) address database reads but not API global access or latency optimization.

CloudWatch monitors API Gateway and Lambda metrics. IAM roles, KMS, and TLS provide security, while CloudTrail enables auditing. This architecture demonstrates global fault tolerance, operational simplicity, and serverless scalability, which are key SAP-C02 best practices.

Question 117:

A company wants to reduce operational overhead for machine learning inference at scale while ensuring low-latency predictions. Which solution is most appropriate?

Answer:

A) Amazon SageMaker endpoint with auto-scaling
B) EC2 instances with manually deployed models
C) Lambda for heavy ML inference
D) S3 with batch scripts

Explanation:

The correct answer is A) Amazon SageMaker endpoint with auto-scaling.

SageMaker provides fully managed, highly available endpoints for hosting machine learning models. Auto-scaling ensures that instance capacity adjusts based on inference request volumes, maintaining low latency. Integration with CloudWatch provides monitoring of endpoint health, request latency, and error rates.

EC2 instances (option B) require manual scaling and management, increasing operational overhead. Lambda (option C) is unsuitable for heavy inference due to execution time limits. S3 with batch scripts (option D) cannot provide real-time predictions.

Security is ensured with IAM, KMS, and TLS. CloudTrail logs all administrative actions for compliance. Operationally, SageMaker simplifies ML deployment, reduces management effort, and ensures scalability and reliability. This aligns with SAP-C02 exam principles for serverless ML inference and operational efficiency.

Question 118:

A company wants to deploy a real-time IoT analytics pipeline that is fully serverless, scalable, and durable. Which architecture is best?

Answer:

A) AWS IoT Core, Lambda, DynamoDB
B) SQS with EC2 consumers
C) SNS with S3 triggers
D) RDS batch processing

Explanation:

The correct answer is A) AWS IoT Core, Lambda, DynamoDB.

IoT Core ingests high volumes of device messages securely and reliably. Lambda functions process data in real-time without infrastructure management. DynamoDB stores processed results with low latency and high durability.

SQS with EC2 consumers (option B) requires instance management. SNS with S3 triggers (option C) is not optimized for real-time analytics. RDS batch processing (option D) introduces latency and does not scale efficiently.

CloudWatch monitors throughput and latency. IAM, KMS, and TLS secure access and data. CloudTrail enables auditing. Operationally, serverless design reduces overhead and scales automatically, making it ideal for SAP-C02 real-time IoT use cases.

Question 119:

A company wants to reduce latency for global users accessing a DynamoDB-backed application. Which solution is most effective?

Answer:

A) DynamoDB Accelerator (DAX)
B) ElastiCache Redis
C) RDS Read Replicas
D) S3 Transfer Acceleration

Explanation:

The correct answer is A) DynamoDB Accelerator (DAX).

DAX provides microsecond latency caching for DynamoDB. It operates as a write-through cache, maintaining consistency with the underlying table. Multi-AZ deployments provide high availability and fault tolerance.

ElastiCache Redis (option B) requires application-level integration. RDS Read Replicas (option C) only benefit relational databases. S3 Transfer Acceleration (option D) is irrelevant for database query latency.

CloudWatch monitors cache performance, CloudTrail logs access, and IAM/KMS provide security. Operational efficiency is improved by offloading reads from DynamoDB, aligning with SAP-C02 best practices for high-performance, globally distributed applications.

Question 120:

A company wants to build a multi-region web application with automatic failover and low-latency access. Which AWS architecture is best?

Answer:

A) Multi-region ALBs, CloudFront, and Route 53 latency-based routing
B) Single-region ALB with CloudFront
C) EC2 in one region with Global Accelerator
D) S3 static hosting with Transfer Acceleration

Explanation:

The correct answer is A) Multi-region ALBs, CloudFront, and Route 53 latency-based routing.

This architecture ensures high availability, low latency, and fault tolerance. Multi-region ALBs distribute traffic within regions. CloudFront caches content globally. Route 53 latency-based routing directs users to the nearest healthy region.

Single-region ALB (option B) creates a single point of failure. EC2 with Global Accelerator (option C) improves network performance but does not provide regional failover. S3 Transfer Acceleration (option D) only benefits static content transfers.

Security is enforced via IAM, KMS, TLS, WAF, and Shield. CloudWatch monitors ALB, CloudFront, and Route 53 metrics. CloudTrail logs actions. Operational simplicity, scalability, and reliability are achieved, aligning with SAP-C02 best practices for global, multi-region web applications.