Amazon AWS Certified Solutions Architect – Professional SAP-C02 Exam Dumps and Practice Test Questions Set 1 Q1-20

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Question 1:

A company wants to migrate its on-premises database to AWS. The application requires a highly available, multi-AZ relational database with automated backups, point-in-time recovery, and the ability to scale read operations. Which AWS service should the solutions architect recommend?

Answer:

A) Amazon RDS for MySQL with Multi-AZ deployment
B) Amazon Aurora MySQL with Aurora Replicas
C) Amazon DynamoDB with global tables
D) Amazon Redshift with concurrency scaling

Explanation:

The correct answer is B) Amazon Aurora MySQL with Aurora Replicas.

Amazon Aurora is a fully managed relational database service compatible with MySQL and PostgreSQL. It offers multi-AZ replication, automated backups, and point-in-time recovery. Aurora Replicas allow read scaling without impacting the primary instance, providing low-latency read operations and high availability.

Option A (RDS MySQL Multi-AZ) provides failover and backups but read scaling requires separate read replicas, which are less performant than Aurora’s architecture.

Option C (DynamoDB global tables) is a NoSQL solution and not suitable for relational workloads with complex queries or transactions.

Option D (Redshift) is a data warehouse optimized for analytics, not transactional workloads, and does not meet the requirement for high-availability relational databases.

Aurora’s distributed architecture ensures durability by storing six copies across three AZs. It automatically scales storage and maintains backups. Aurora also integrates seamlessly with CloudWatch, CloudTrail, and IAM for monitoring, auditing, and security. Its fast failover capabilities and ability to promote read replicas to the primary database ensure minimal downtime. For enterprises migrating critical transactional workloads, Aurora offers superior performance, high availability, disaster recovery, and operational simplicity compared to traditional RDS or Redshift.

Question 2:

A solutions architect is designing a solution for a global e-commerce website hosted on AWS. The website experiences variable traffic patterns with sudden spikes. The architecture must ensure low latency and high availability. Which combination of services should the architect use?

Answer:

A) Amazon EC2 Auto Scaling, Elastic Load Balancing (ELB), Amazon CloudFront
B) Amazon EC2 with fixed instances, Application Load Balancer (ALB), Amazon S3
C) Amazon ECS with Fargate, AWS Global Accelerator, Amazon S3
D) Amazon RDS Multi-AZ, Amazon CloudFront, Amazon Route 53

Explanation:

The correct answer is A) Amazon EC2 Auto Scaling, ELB, Amazon CloudFront.

EC2 Auto Scaling automatically adjusts the number of instances based on traffic. ELB distributes traffic across multiple AZs, ensuring high availability. CloudFront caches content at edge locations to reduce latency for global users.

Option B lacks auto-scaling, which may cause performance issues during sudden spikes. Fixed instances could be underutilized during low traffic or overwhelmed during high traffic.

Option C combines serverless containers and Global Accelerator, but CloudFront is still needed to cache static content and reduce latency.

Option D includes RDS and CloudFront, which addresses database availability and content caching, but does not scale the web tier dynamically.

This solution ensures both application compute and content delivery scale automatically to handle unpredictable traffic, maintaining low latency and high availability. Using Auto Scaling with CloudFront caching improves cost efficiency and resilience, while ELB ensures failover between AZs.

Question 3:

A company runs a critical financial application on AWS. The application requires encryption of data at rest and in transit, fine-grained access control, and auditing of all user actions. Which combination of services and features should the solutions architect implement?

Answer:

A) AWS Key Management Service (KMS), Amazon S3 server-side encryption, AWS CloudTrail
B) AWS Secrets Manager, Amazon RDS with SSL, Amazon CloudWatch Logs
C) Amazon Macie, Amazon S3 bucket policies, AWS Config
D) AWS Certificate Manager (ACM), Amazon DynamoDB, AWS CloudTrail

Explanation:

The correct answer is A) AWS KMS, Amazon S3 server-side encryption, AWS CloudTrail.

KMS manages encryption keys for data protection. S3 server-side encryption ensures data at rest is encrypted. CloudTrail logs all API calls and user actions for auditing.

Option B provides secrets management and SSL, but CloudWatch Logs alone does not offer detailed auditing of all actions.

Option C focuses on data discovery and compliance, but does not encrypt data in transit.

Option D (ACM and DynamoDB) only secures network traffic and data storage but lacks automatic encryption with managed keys and audit logging.

Using KMS allows control over key rotation and access policies, ensuring only authorized users can decrypt data. CloudTrail integrates with CloudWatch and Athena to analyze logs for security monitoring. This architecture guarantees both data confidentiality and compliance with financial regulations. It also provides fine-grained IAM control and the ability to monitor suspicious activity in real-time.

Question 4:

A company runs a multi-tier web application on AWS. The front-end layer is deployed across multiple Availability Zones using EC2 instances behind an Application Load Balancer. The company wants to reduce latency for global users and improve fault tolerance. Which AWS service should the solutions architect recommend?

Answer:

A) Amazon CloudFront
B) Amazon Route 53 latency-based routing
C) AWS Global Accelerator
D) Amazon S3 Transfer Acceleration

Explanation:

The correct answer is B) Amazon Route 53 latency-based routing.

Route 53 directs users to the AWS region with the lowest latency. This ensures faster response times for global users while maintaining high availability.

Option A (CloudFront) caches static content but does not dynamically route users to the lowest latency region.

Option C (Global Accelerator) optimizes TCP/UDP traffic but is better suited for non-HTTP workloads and network-level optimization.

Option D (S3 Transfer Acceleration) only improves upload/download to S3, not general application performance.

Latency-based routing ensures traffic is sent to the healthiest endpoint closest to the user. Combined with multi-AZ EC2 deployments and ALB, this approach reduces latency, improves resilience, and provides fault tolerance without redesigning the application. It integrates seamlessly with health checks to reroute traffic in case of failures.

Question 5:

An application requires a relational database with high read throughput and minimal replication lag for multiple regions. Which AWS solution is the most appropriate?

Answer:

A) Amazon RDS MySQL with Read Replicas
B) Amazon Aurora Global Database
C) Amazon DynamoDB global tables
D) Amazon Redshift Spectrum

Explanation:

The correct answer is B) Amazon Aurora Global Database.

Aurora Global Database allows a primary region to replicate data with low latency to multiple secondary regions. It provides fast global reads and supports disaster recovery.

Option A requires managing read replicas manually across regions, which increases replication lag.

Option C is a NoSQL solution and may not meet relational database requirements.

Option D is for analytical queries and not suitable for transactional workloads.

Aurora Global Database uses physical replication to minimize lag, allowing applications in different regions to read data quickly without affecting the primary database. Failover to another region is possible within seconds, making it ideal for globally distributed, high-availability applications.

Question 6:

A solutions architect needs to design a secure, scalable file storage system for an enterprise application with frequent read/write access. Which service combination is most suitable?

Answer:

A) Amazon S3 with versioning and lifecycle policies
B) Amazon EFS with encryption at rest and in transit
C) Amazon FSx for Windows File Server with automated backups
D) Amazon Glacier with Vault Lock

Explanation:

The correct answer is B) Amazon EFS with encryption at rest and in transit.

EFS provides scalable file storage accessible across multiple EC2 instances simultaneously. It supports NFS protocol, automatically scales storage, and offers encryption both at rest and in transit.

Option A (S3) is object storage, not a shared file system suitable for frequent read/write by multiple servers simultaneously.

Option C (FSx Windows) is specific to Windows workloads, whereas EFS works across Linux and mixed environments.

Option D (Glacier) is for archival storage and not suitable for frequently accessed files.

EFS is ideal for applications requiring shared storage with high throughput and low latency. Features like lifecycle management reduce costs by moving infrequently accessed data to EFS Infrequent Access, while security is maintained using KMS and IAM policies.

When designing a secure and scalable file storage system for an enterprise application that requires frequent read and write access, it is essential to select a service that supports concurrent access, low-latency performance, and strong security controls. AWS provides several storage services, each optimized for different use cases, and choosing the right one depends on the workload requirements and access patterns.

Amazon Elastic File System (EFS) is a fully managed, elastic file storage service that is ideal for applications requiring shared access across multiple EC2 instances. EFS supports the Network File System (NFS) protocol, allowing Linux and Linux-based applications to mount the file system concurrently. It automatically scales storage capacity as files are added or removed, eliminating the need to provision or manage storage manually. Additionally, EFS provides encryption at rest and in transit, ensuring that data is protected both on disk and during network transfers. This makes it a highly secure solution for enterprise workloads. EFS also offers performance modes suitable for both general-purpose and high-throughput applications, enabling low-latency access to frequently read or written files. Features like lifecycle management allow automatically moving infrequently accessed files to EFS Infrequent Access, which helps optimize storage costs while maintaining accessibility.

Amazon S3 is an object storage service that provides durability and scalability. While S3 can manage versioning, lifecycle policies, and strong security through IAM and KMS, it is not a shared file system. Applications that require frequent read/write operations from multiple servers simultaneously cannot efficiently use S3 as a traditional file system due to its object-based architecture and eventual consistency model.

Amazon FSx for Windows File Server is designed for Windows-based workloads, offering native SMB support and automated backups. However, it is primarily suited for applications running on Windows servers, and may not integrate well with Linux or mixed environments, limiting its flexibility in heterogeneous enterprise setups.

Amazon Glacier with Vault Lock is intended for archival storage. It provides secure, durable, and cost-effective long-term storage but is not suitable for applications that require frequent or low-latency access to files. Retrieval from Glacier can take minutes to hours, which makes it impractical for real-time workloads.

Considering scalability, concurrent access, low latency, and strong security requirements, Amazon EFS with encryption at rest and in transit is the most suitable solution for an enterprise application requiring frequent file read and write operations.

The correct answer is B) Amazon EFS with encryption at rest and in transit.

Question 7:

A company wants to host a microservices application on AWS with minimal server management. Services should scale automatically based on demand. Which solution is most appropriate?

Answer:

A) Amazon EC2 Auto Scaling with ALB
B) AWS Lambda with API Gateway
C) Amazon ECS on EC2 instances
D) Amazon EMR with step scaling

Explanation:

The correct answer is B) AWS Lambda with API Gateway.

Lambda is serverless and automatically scales based on incoming requests. API Gateway exposes Lambda functions as REST endpoints.

Option A requires managing EC2 instances and scaling policies manually.

Option C involves container orchestration on EC2, requiring more operational overhead.

Option D (EMR) is for big data processing, not microservices.

Serverless architecture reduces operational complexity, automatically scales with workload, and integrates with monitoring, logging, and security features. It also reduces costs because you only pay for execution time rather than running instances continuously.

Question 8:

A company needs a highly available, durable storage system for logs, with infrequent retrieval. Cost optimization is a priority. Which AWS service should be used?

Answer:

A) Amazon S3 Standard
B) Amazon S3 Glacier Instant Retrieval
C) Amazon S3 Glacier Deep Archive
D) Amazon EBS Provisioned IOPS

Explanation:

The correct answer is C) Amazon S3 Glacier Deep Archive.

Glacier Deep Archive is the lowest-cost storage for long-term retention of infrequently accessed data. It provides durability of 99.999999999% and integrates with lifecycle policies.

Option A is more expensive for infrequent access.

Option B is for faster retrieval, not optimal for cost-sensitive long-term storage.

Option D is block storage for EC2 and not ideal for archival storage.

Using Glacier Deep Archive with automated lifecycle policies ensures compliance, durability, and minimal costs for log retention over years.

Question 9:

A solutions architect is designing a data analytics pipeline. Data is ingested continuously and requires near real-time analytics. Which AWS service combination is ideal?

Answer:

A) Amazon Kinesis Data Streams, AWS Lambda, Amazon Redshift
B) Amazon SQS, Amazon EMR, Amazon RDS
C) Amazon SNS, Amazon S3, Amazon Athena
D) Amazon Kinesis Data Firehose, Amazon S3, Amazon Athena

Explanation:

The correct answer is D) Amazon Kinesis Data Firehose, Amazon S3, Amazon Athena.

Firehose allows near real-time ingestion and delivery to S3. Athena enables serverless querying on stored data.

Option A involves Redshift, which is better for batch analytics and may introduce latency.

Option B (SQS and EMR) is suitable for batch, not real-time streaming.

Option C (SNS and S3) does not handle streaming analytics effectively.

This combination ensures low-latency ingestion, secure storage, and scalable querying without managing servers.

When designing a data analytics pipeline that requires continuous data ingestion and near real-time analytics, it is critical to choose services that can handle streaming data efficiently while enabling fast querying and minimal operational overhead. AWS provides several services suitable for these needs, but the combination of services must align with both low-latency ingestion and scalable analytics requirements.

Amazon Kinesis Data Firehose is a fully managed service for real-time streaming data delivery. It can continuously capture, transform, and load streaming data into destinations such as Amazon S3, Amazon Redshift, or Amazon Elasticsearch Service. In this scenario, Firehose is ideal because it allows data to be ingested near real-time and automatically delivered to S3 for storage. It also manages scaling, buffering, and retry logic, so developers do not need to build these capabilities themselves.

Amazon S3 serves as a durable and highly scalable storage layer for the ingested data. By storing streaming data in S3, the analytics pipeline gains a reliable, cost-effective repository for both raw and processed datasets. S3 also provides integration with various analytics services, making it an excellent choice for persistent storage in a streaming data pipeline.

Amazon Athena complements this combination by enabling serverless querying directly on data stored in S3 using standard SQL syntax. With Athena, users can run ad-hoc analytics or scheduled queries without provisioning or managing any infrastructure. This allows near real-time insights into the data without the latency or complexity associated with setting up and maintaining a traditional data warehouse.

Alternative options have limitations in this context. For example, Amazon Kinesis Data Streams combined with AWS Lambda and Redshift (option A) can support streaming ingestion, but Redshift is primarily optimized for batch analytics, and frequent streaming writes can introduce latency and increase management overhead. Amazon SQS, EMR, and RDS (option B) are better suited for batch processing pipelines rather than near real-time analytics. Finally, Amazon SNS, S3, and Athena (option C) do not efficiently handle continuous streaming data, as SNS is a messaging service rather than a streaming ingestion service.

Considering low-latency ingestion, reliable storage, and scalable querying, the combination of Amazon Kinesis Data Firehose, Amazon S3, and Amazon Athena is the optimal solution for a near real-time data analytics pipeline.

The correct answer is D) Amazon Kinesis Data Firehose, Amazon S3, Amazon Athena.

Question 10:

A company requires cross-account access to S3 buckets securely. Which solution is most appropriate?

Answer:

A) Bucket policies with cross-account IAM roles
B) Public S3 buckets with object ACLs
C) Copy data to another account manually
D) S3 replication without IAM permissions

Explanation:

The correct answer is A) Bucket policies with cross-account IAM roles.

Bucket policies define who can access resources. IAM roles allow temporary, secure cross-account access without sharing credentials.

Option B exposes data publicly, which is insecure.

Option C is operationally inefficient and error-prone.

Option D does not grant access unless permissions are configured.

Using IAM roles ensures least privilege access, auditability, and security compliance while enabling cross-account data sharing.

Question 11:

A web application must maintain session state for users across multiple EC2 instances. Which AWS solution is best?

Answer:

A) Amazon ElastiCache (Redis)
B) Local instance storage
C) Amazon S3 static files
D) Amazon RDS Multi-AZ

Explanation:

The correct answer is A) Amazon ElastiCache (Redis).

Redis provides a fast, in-memory data store for session data. It supports replication, high availability, and low-latency access.

Local instance storage is ephemeral and lost on instance termination.

S3 is object storage, not suitable for fast session reads/writes.

RDS is relational storage, which introduces latency for session management.

Redis ensures session consistency, high performance, and scalability for distributed applications.

When building a web application that runs on multiple EC2 instances, maintaining session state for users is crucial. Session state refers to information about a user’s interactions, such as login status, shopping cart contents, or user preferences. In a distributed environment where multiple EC2 instances handle requests, storing session data locally on each instance is not sufficient because requests from the same user might be routed to different instances. Therefore, a centralized, fast, and reliable solution for session storage is required.

Amazon ElastiCache with Redis is a fully managed, in-memory data store that is ideal for maintaining session state in such distributed applications. Redis provides extremely low-latency access to session data, which is critical for high-performance web applications. It supports data replication, persistence, and high availability, ensuring that session data remains consistent and durable even if an EC2 instance fails. Applications can store session identifiers and related data in Redis, allowing any EC2 instance to retrieve or update session state quickly, which ensures a seamless user experience across multiple requests.

Local instance storage on EC2 is not suitable for session management in distributed environments because the storage is ephemeral. When an instance is terminated or replaced during scaling operations, all session data stored locally is lost. This could result in users being unexpectedly logged out or losing their session information, which negatively impacts user experience.

Amazon S3 is an object storage service designed for storing large files, such as images, videos, and backups. While it is highly durable and scalable, it is not optimized for the low-latency, frequent read and write operations required for session management. Retrieving or updating session information in S3 would introduce significant latency, making it unsuitable for this use case.

Amazon RDS with Multi-AZ deployment provides a highly available relational database solution. While it can store session data, relational databases introduce additional latency compared to in-memory stores. Frequent read and write operations for session data could also increase database load and affect overall performance.

Considering performance, scalability, and reliability, Amazon ElastiCache with Redis is the best solution for maintaining session state across multiple EC2 instances. It provides fast, in-memory access, replication, high availability, and ensures session consistency for distributed web applications.

The correct answer is A) Amazon ElastiCache (Redis).

Question 12:

An enterprise wants to ensure disaster recovery for an application with RPO of 5 minutes and RTO of 15 minutes. Which AWS architecture is recommended?

Answer:

A) Multi-AZ deployment with synchronous replication
B) Backup and restore from S3 once daily
C) Cross-region replication with asynchronous Aurora Global Database
D) Standby EC2 instances in another AZ without replication

Explanation:

The correct answer is C) Cross-region replication with asynchronous Aurora Global Database.

Aurora Global Database replicates data to secondary regions with typical latency under a second, allowing near-zero data loss. Failover can be completed within minutes, satisfying RPO and RTO requirements.

Option A is within the same region and does not protect against region-level failures.

Option B cannot meet short RPO/RTO.

Option D is manual and error-prone, not suitable for business-critical applications.

Cross-region replication ensures resilience against region failures while providing fast recovery and minimizing data loss.

Question 13:

A company wants to decouple microservices and handle millions of messages per day. Which AWS service combination is suitable?

Answer:

A) Amazon SQS with Lambda consumers
B) Amazon SNS with S3
C) Amazon Kinesis Data Streams with EC2
D) Amazon RDS with SQS

Explanation:

The correct answer is A) Amazon SQS with Lambda consumers.

SQS provides a reliable, fully managed message queue. Lambda automatically scales to process messages, supporting high throughput without managing servers.

SNS with S3 is for notifications, not guaranteed queueing.

Kinesis streams are for event streaming rather than simple message decoupling.

RDS with SQS is inefficient for high-volume messaging.

SQS decouples components, ensures message durability, and Lambda ensures elasticity and automatic scaling.

Question 14:

A web application needs caching for frequently accessed database queries to improve performance. Which AWS solution is best?

Answer:

A) Amazon ElastiCache (Memcached)
B) Amazon RDS Read Replicas
C) Amazon DynamoDB Accelerator (DAX)
D) Amazon S3

Explanation:

The correct answer is C) Amazon DynamoDB Accelerator (DAX).

DAX is a fully managed, in-memory cache for DynamoDB, reducing read latency from milliseconds to microseconds.

Memcached is general-purpose but does not integrate directly with DynamoDB.

Read replicas reduce latency but still access the database.

S3 is object storage and not suitable for caching database queries.

DAX provides high throughput, low latency caching without application-level complexity.A web application that experiences frequent access to the same database queries can greatly benefit from caching to improve performance and reduce latency. Caching stores the results of database queries temporarily in memory, allowing subsequent requests to retrieve data quickly without hitting the underlying database each time. AWS provides multiple solutions for caching, and selecting the right one depends on the type of database and the application requirements.

Amazon ElastiCache (Memcached) is a fully managed, in-memory caching service that supports Memcached. It is designed to provide fast, temporary storage for frequently accessed data, which can significantly improve application response times. Memcached is general-purpose and works well for caching results from various databases, but it does not integrate directly with DynamoDB, meaning developers need to implement caching logic in the application layer, adding complexity.

 

Amazon RDS Read Replicas are primarily used to offload read-heavy workloads from the primary database. While they can reduce latency and improve scalability for relational databases, read replicas are not true caching solutions. They still involve querying the database, which means latency improvements are limited compared to in-memory caching. Read replicas are better suited for scaling read operations rather than reducing read latency to microseconds.

Amazon DynamoDB Accelerator (DAX) is a fully managed, in-memory caching service designed specifically for DynamoDB. DAX can reduce response times from milliseconds to microseconds by storing frequently accessed items in memory. Unlike general-purpose caching solutions, DAX integrates directly with DynamoDB, allowing applications to access cached data with minimal code changes. This seamless integration eliminates the need for developers to manage cache invalidation or implement complex caching logic, making it ideal for applications with high read demands.

Amazon S3 is an object storage service intended for storing files and large datasets. While it offers durability and scalability, it is not suitable for caching database queries. Retrieving objects from S3 is slower than in-memory caching and does not provide the low-latency access needed for high-performance database query caching.

Considering the above options, Amazon DynamoDB Accelerator (DAX) is the best solution for caching frequently accessed queries in a web application using DynamoDB. It provides high throughput, low latency, and seamless integration, significantly improving application performance without adding complexity to the code.

The final answer is C) Amazon DynamoDB Accelerator (DAX).

Question 15:

A solutions architect is designing a highly available architecture with EC2 instances across multiple Availability Zones. How can the architect efficiently distribute traffic and ensure fault tolerance for incoming requests?

Answer:

A) Elastic Load Balancer (ALB or NLB)
B) Amazon Route 53 only
C) EC2 instance DNS entries
D) Amazon CloudFront only

Explanation:

The correct answer is A) Elastic Load Balancer (ALB or NLB).

Elastic Load Balancing (ELB) is a fundamental component in designing highly available architectures on AWS. It automatically distributes incoming application traffic across multiple targets, such as Amazon EC2 instances, containers, and IP addresses, in one or more Availability Zones (AZs). By distributing traffic evenly, ELB prevents any single instance from being overwhelmed, thereby increasing fault tolerance and improving application responsiveness.

Option A: ALB (Application Load Balancer) operates at the HTTP/HTTPS layer (Layer 7), making it ideal for web applications. It provides advanced routing features, such as host-based and path-based routing, allowing requests to be sent to different backend services based on URL paths or host headers. NLB (Network Load Balancer), on the other hand, operates at Layer 4 (TCP/UDP) and can handle millions of requests per second with low latency, making it suitable for latency-sensitive applications or those requiring high throughput. Both ALB and NLB integrate seamlessly with Auto Scaling, allowing backend EC2 instances to scale in or out dynamically based on demand, ensuring that applications maintain performance during traffic spikes. Health checks configured within the ELB continuously monitor the status of registered instances, automatically removing unhealthy instances from the traffic distribution until they become healthy again. This contributes significantly to application availability and resiliency.

Option B: Amazon Route 53 is a highly available and scalable Domain Name System (DNS) web service. It provides routing policies, including latency-based, geolocation-based, and weighted routing. While Route 53 is excellent for directing traffic across regions or handling failover scenarios, it does not distribute traffic to multiple instances within a single region or AZ efficiently. Using Route 53 alone for load balancing would require additional logic and would lack the automatic health checks, SSL termination, and integration with Auto Scaling provided by ELB. Therefore, while Route 53 is essential for global routing and DNS-level failover, it is insufficient as a standalone solution for high-availability load distribution across multiple EC2 instances.

Option C: Directly using EC2 instance DNS entries is not a recommended practice for production workloads. While each EC2 instance has a public and private DNS name, routing traffic directly to individual instances introduces several limitations. There is no automatic health checking, load distribution, or scaling capability. In the event of instance failure, traffic cannot be automatically rerouted, which leads to downtime and degraded user experience. Additionally, manually managing DNS entries for multiple instances is operationally complex, especially as instances scale dynamically with Auto Scaling policies.

Option D: Amazon CloudFront is a content delivery network (CDN) that caches content at edge locations globally, reducing latency for end users. While CloudFront improves performance for static and dynamic content by serving cached content closer to users, it is not a replacement for a load balancer. CloudFront does not provide intelligent routing across EC2 instances, nor does it handle dynamic health checks or scaling for backend servers. It can complement ELB for global distribution of content and caching, but it cannot independently manage high availability or distribute traffic effectively among EC2 instances across multiple AZs.

Implementing ELB with Auto Scaling across multiple AZs ensures several critical advantages:

High Availability: Traffic is distributed evenly across instances in multiple AZs. If an instance fails or becomes unhealthy, the load balancer automatically reroutes traffic to healthy instances, minimizing downtime.

Fault Tolerance: ELB integrates with Auto Scaling and performs health checks on instances. Unhealthy instances are removed from rotation automatically, and new instances are launched as needed, preventing single points of failure.

Security Integration: ELB supports integration with AWS Certificate Manager (ACM) for SSL/TLS certificates, enabling encrypted traffic termination at the load balancer. It also integrates with AWS WAF (Web Application Firewall) for protection against common web exploits.

Scalability: ELB can handle sudden traffic spikes by distributing incoming requests across a dynamic number of backend instances. Combined with Auto Scaling, applications can scale seamlessly to handle variable workloads without manual intervention.

Advanced Routing: ALB supports host-based and path-based routing, enabling microservices architectures where requests can be routed to specific services based on request content. NLB supports static IPs and preserves the client’s IP address for applications that require it.

Monitoring and Logging: ELB integrates with Amazon CloudWatch for monitoring metrics like request count, latency, and instance health. Access logs provide detailed request-level information, which is crucial for troubleshooting and performance optimization.

In conclusion, Elastic Load Balancers (ALB/NLB) provide the most comprehensive solution for distributing traffic across multiple EC2 instances in multiple AZs. They ensure fault tolerance, high availability, and performance optimization while integrating seamlessly with other AWS services for security, monitoring, and scaling. Using Route 53, CloudFront, or manual DNS entries alone cannot achieve the same level of resilience and automation required for enterprise-grade web applications. ELB is, therefore, the recommended choice for designing highly available and fault-tolerant architectures.

Question 16:

A company wants a serverless workflow to orchestrate multiple AWS Lambda functions with conditional branching, error handling, and retries. 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.

AWS Step Functions is a serverless orchestration service that allows developers to design workflows for microservices, Lambda functions, and other AWS resources. Step Functions provides state machines that define the sequence of steps, branching logic, parallel execution, error handling, and retry policies. It abstracts the complexity of managing task dependencies, failure handling, and sequencing, which is critical for serverless architectures where each function may perform distinct operations.

Option A: Step Functions supports multiple workflow patterns, including sequential, parallel, and conditional branching. Each state in the workflow can execute a Lambda function or call another AWS service using service integrations. It also provides robust error handling capabilities, including catch blocks, retry policies, and timeout management. Step Functions automatically manages the orchestration, ensuring that failures are retried according to defined policies or alternative steps are executed for error scenarios. It integrates with CloudWatch for monitoring workflow executions, making it easy to track performance, success rates, and error trends. This serverless orchestration eliminates the need to write custom code for managing task sequencing, retries, and error handling, significantly simplifying development and operational overhead.

Option B: Amazon Simple Workflow Service (SWF) is a fully managed service for building distributed applications with coordinated tasks. While SWF provides workflow orchestration and task coordination, it requires more operational management, including worker implementation, heartbeat monitoring, and manual scaling. Step Functions, on the other hand, abstracts these complexities, provides a visual workflow designer, and integrates natively with modern serverless architectures like Lambda, making it a better fit for orchestrating serverless functions.

Option C: AWS Batch is designed for batch processing jobs, particularly compute-intensive workloads. While it automates scheduling and scaling of batch jobs, it does not provide advanced orchestration for serverless Lambda functions or conditional branching with retry policies. Batch jobs are more suitable for high-performance computing workloads and not for event-driven or real-time orchestration of microservices.

Option D: Amazon SQS is a fully managed message queue service used to decouple application components. While SQS supports asynchronous messaging and ensures message durability, it does not orchestrate workflows, handle sequential or conditional execution, or provide error-handling capabilities. SQS can be used as part of a Step Functions workflow but cannot replace the orchestration and state management provided by Step Functions.

Step Functions supports three primary workflow types: Standard Workflows for long-running, durable executions, Express Workflows for high-throughput, short-duration tasks, and service integrations for directly invoking AWS services without Lambda. It provides audit trails for compliance and detailed metrics to optimize performance. Using Step Functions ensures serverless orchestration is reliable, scalable, and maintainable, with minimal operational overhead.

Question 17:

A company wants to store sensitive customer documents in AWS and control access based on user identity while enforcing encryption at rest and in transit. Which architecture meets these requirements?

Answer:

A) Amazon S3 with bucket policies, KMS-managed keys, and IAM roles
B) Amazon EFS with NFS access and local encryption
C) Amazon S3 public bucket with HTTPS access
D) Amazon DynamoDB with client-side encryption

Explanation:

The correct answer is A) Amazon S3 with bucket policies, KMS-managed keys, and IAM roles.

Amazon S3 provides durable, highly available object storage. Bucket policies allow fine-grained access control, KMS-managed keys enable encryption at rest, and HTTPS ensures encryption in transit. IAM roles provide secure temporary credentials for access.

Option B (EFS) provides shared file storage with encryption, but access control is more limited, and NFS is less suitable for document distribution to multiple external users.

Option C exposes sensitive data publicly, violating security best practices.

Option D (DynamoDB) is optimized for key-value storage rather than document management.

S3 with IAM, bucket policies, and KMS enables least-privilege access, encryption, audit logging through CloudTrail, and lifecycle policies to manage retention, making it ideal for sensitive customer document storage.

Question 18:

A company wants a multi-region, active-active architecture for a web application, minimizing latency for global users. Which AWS service combination is recommended?

Answer:

A) Amazon Route 53 latency-based routing, CloudFront, multi-region ELB
B) Amazon CloudFront only
C) Single-region ELB with Route 53 failover
D) Amazon S3 with Transfer Acceleration

Explanation:

The correct answer is A) Amazon Route 53 latency-based routing, CloudFront, multi-region ELB.

Route 53 latency-based routing ensures users are directed to the closest region. CloudFront caches static content at edge locations. Multi-region ELB distributes requests to healthy instances in multiple AZs per region.

Option B (CloudFront only) improves performance but does not handle dynamic application traffic across regions.

Option C (single-region ELB) cannot support global low-latency access.

Option D (S3 Transfer Acceleration) is only for accelerating S3 uploads/downloads.

This architecture provides high availability, fault tolerance, low latency, and disaster recovery. Combining Route 53, CloudFront, and multi-region ELB ensures minimal user-perceived latency and robust resilience.

Question 19:

A solutions architect needs to implement a database migration with minimal downtime from on-premises MySQL to AWS. Which service is most appropriate?

Answer:

A) AWS Database Migration Service (DMS) with continuous replication
B) Manual export/import using mysqldump
C) AWS Snowball
D) Amazon S3 batch upload

Explanation:

The correct answer is A) AWS Database Migration Service (DMS) with continuous replication.

DMS enables near-zero downtime migrations. It supports continuous replication, keeping the source and target databases in sync until cutover.

Option B requires downtime for export/import.

Option C (Snowball) is for large offline data transfer, not near real-time migration.

Option D (S3 batch) is unsuitable for relational databases.

DMS also supports schema conversion and integrates with CloudWatch for monitoring replication performance. It ensures a smooth transition to AWS while minimizing operational disruption.

Question 20:

A company runs an event-driven architecture and wants to ensure exactly-once processing of events with durability and scalability. Which AWS service combination is ideal?

Answer:

A) Amazon Kinesis Data Streams with Lambda consumers
B) Amazon SQS standard queues with Lambda
C) Amazon SNS topics with S3 triggers
D) Amazon DynamoDB Streams only

Explanation:

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

Kinesis allows ordered, durable event streaming with shard-level sequencing. Lambda can consume records and handle retries with exactly-once semantics.

Option B (SQS standard queues) provides at-least-once delivery, which may cause duplicates. FIFO queues can be used, but throughput is lower.

Option C (SNS with S3 triggers) is event-driven but lacks ordering guarantees and exactly-once processing.

Option D (DynamoDB Streams) only captures changes to DynamoDB tables and cannot handle arbitrary application events.

Using Kinesis with Lambda ensures high throughput, durability, and precise control over event ordering and processing, making it suitable for financial systems, analytics pipelines, and event-driven microservices requiring exactly-once semantics.