Amazon AWS Certified Solutions Architect – Associate SAA-C03 Exam Dumps and Practice Test Questions Set 2 Q21-40

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Question 21:

A company wants to host a multi-tier web application in AWS. The application consists of a web layer, an application layer, and a database layer. The company requires high availability, fault tolerance, and automatic failover for the database. Which architecture should a Solutions Architect recommend?

Answer:

A) Single EC2 instance for all layers with EBS storage
B) Web and application layers across multiple AZs with RDS Multi-AZ for the database
C) Single EC2 instance for web and application layers, RDS Read Replica
D) AWS Lambda for all layers

Explanation:

Option B is correct. Deploying web and application layers across multiple Availability Zones (AZs) ensures high availability and fault tolerance. The use of an Application Load Balancer distributes traffic across healthy instances, supporting dynamic scaling and resilience to AZ failures. For the database layer, Amazon RDS Multi-AZ provides synchronous replication to a standby instance in a different AZ, allowing automatic failover if the primary database fails. This ensures minimal downtime and continuous availability. Option A creates a single point of failure and does not meet high availability requirements. Option C uses a read replica, which is primarily for read scaling and cannot provide automatic failover for writes. Option D with Lambda could handle stateless workloads but is unsuitable for complex multi-tier applications requiring persistent database connections and full transactional support. The recommended architecture aligns with AWS best practices for multi-tier applications: distributing compute resources across AZs, employing managed services for high availability, and ensuring automatic failover mechanisms to reduce operational complexity and meet Service Level Agreements (SLAs). Additionally, deploying across multiple AZs improves fault tolerance, ensuring that the application can withstand AZ-specific outages. Combining Auto Scaling with Multi-AZ RDS also reduces manual operational efforts and provides a cost-effective, reliable solution for enterprise applications, meeting both performance and compliance requirements.

Question 22:

A company wants to serve dynamic content globally with low latency. The content changes frequently, and caching is desired to reduce load on the origin servers. Which solution is most appropriate?

Answer:

A) Amazon CloudFront with origin pull from S3 or EC2
B) Amazon S3 with versioning enabled
C) Amazon RDS Multi-AZ
D) AWS Direct Connect

Explanation:

Option A is correct. CloudFront caches dynamic and static content at edge locations worldwide, reducing latency for end users. With origin pull from S3 or EC2, CloudFront fetches the latest content when the cache expires or is invalidated, ensuring frequently updated content is served efficiently. S3 with versioning alone does not provide edge caching or reduced latency for global users. RDS Multi-AZ ensures high availability but is a database solution, not a content distribution network. Direct Connect is for private network connectivity and does not serve content globally. CloudFront also supports cache invalidation, TTL configuration, and Lambda@Edge for executing code closer to users, enabling low-latency content customization and real-time content personalization. Integrating CloudFront reduces load on origin servers, saves operational costs, and provides DDoS protection through AWS Shield. Using CloudFront for dynamic content requires careful cache control strategies, such as setting appropriate Cache-Control headers and using query string caching, to ensure content is up to date without sacrificing performance. By using CloudFront globally, organizations achieve improved performance, lower latency, enhanced security, and operational efficiency for applications serving dynamic content worldwide.

A company that wants to serve dynamic content globally with low latency while reducing load on origin servers should consider Amazon CloudFront with origin pull from S3 or EC2. CloudFront is a content delivery network (CDN) that caches both static and dynamic content at edge locations around the world. By storing frequently accessed content closer to end users, CloudFront significantly reduces latency and improves the user experience. For dynamic content that changes frequently, CloudFront can fetch the latest version from the origin server, whether it is an Amazon S3 bucket or an EC2 instance, whenever cached content expires or is invalidated. This ensures that users receive up-to-date content while still benefiting from caching mechanisms that reduce the origin load.

Amazon S3 with versioning enabled provides storage and the ability to track changes to objects, but it does not offer edge caching or global content distribution, which limits performance for users located far from the region where the S3 bucket resides. Amazon RDS Multi-AZ provides high availability for relational databases but is not designed to distribute content or reduce latency for web requests. AWS Direct Connect establishes private network connections between on-premises infrastructure and AWS, but it does not provide content distribution or caching capabilities for global users.

CloudFront offers additional features such as cache invalidation, customizable time-to-live (TTL) settings, and Lambda@Edge for executing code at edge locations, enabling dynamic content personalization and low-latency updates. Proper cache control strategies, such as using Cache-Control headers and query string caching, allow organizations to balance freshness and performance effectively. CloudFront also integrates with AWS Shield and WAF for enhanced security, providing protection against DDoS attacks while optimizing delivery performance. By using CloudFront with origin pull from S3 or EC2, organizations can achieve fast, reliable, and secure delivery of frequently updated content worldwide, reduce operational costs, and improve scalability without managing additional infrastructure.

Question 23:

A company requires a solution to automate backups, patching, and scaling for its relational database. The database must support high availability and multi-region read scaling. Which AWS service is most suitable?

Answer:

A) Amazon RDS Multi-AZ with Read Replicas
B) Amazon EC2 with MySQL installed
C) Amazon DynamoDB Global Tables
D) Amazon Aurora Serverless

Explanation:

Option A is correct. RDS Multi-AZ deployments provide synchronous replication to a standby instance in a different AZ, ensuring automatic failover in case of failure. Read replicas allow read scaling across multiple regions, improving performance for globally distributed users. EC2 with MySQL requires manual backup, patching, and failover configuration, which increases operational overhead. DynamoDB is NoSQL and does not support relational queries. Aurora Serverless supports scaling and some multi-AZ capabilities but may not fully support specific read-replica configurations across regions. RDS Multi-AZ combined with Read Replicas ensures high availability, operational simplicity, and global read scalability. Additionally, automated snapshots, integration with CloudWatch for monitoring, and seamless patch management reduce operational complexity. This architecture aligns with AWS best practices for building fault-tolerant, highly available, and scalable relational databases. Organizations benefit from reduced risk of downtime, improved performance for read-heavy workloads, and a fully managed environment that minimizes operational effort while maintaining compliance and durability for critical data. Multi-region read replicas also allow applications to offload read-heavy traffic from the primary database, improving responsiveness for users distributed across different geographic regions.

Question 24:

A company wants to securely transfer large amounts of data to AWS while minimizing internet dependency and ensuring consistent bandwidth. Which service should be used?

Answer:

A) AWS Direct Connect
B) Amazon S3 Transfer Acceleration
C) AWS Snowball
D) Amazon CloudFront

Explanation:

Option A is correct. AWS Direct Connect establishes a dedicated network connection between an on-premises environment and AWS, providing consistent bandwidth, lower latency, and enhanced security compared to transferring data over the public internet. S3 Transfer Acceleration uses CloudFront edge locations for faster uploads but still relies on the public internet. AWS Snowball is suitable for offline bulk data transfer but does not provide continuous connectivity. CloudFront is a content delivery network, not a dedicated transfer solution. Direct Connect supports hybrid architectures by providing private connectivity to services such as VPC, S3, and EC2. It is especially beneficial for enterprises with large-scale, high-volume workloads that require predictable network performance. By avoiding the public internet, Direct Connect reduces the risk of interruptions and improves data transfer reliability for mission-critical applications. Additionally, Direct Connect integrates with AWS VPNs for redundancy, providing failover options to maintain availability in case of a primary connection failure. For ongoing transfers or hybrid cloud applications, Direct Connect ensures secure, high-performance, and low-latency connectivity, aligning with best practices for enterprise-grade AWS networking.

A company that needs to securely transfer large volumes of data to AWS while minimizing dependence on the public internet and ensuring consistent network performance should consider AWS Direct Connect. Direct Connect provides a dedicated, private network connection between an on-premises environment and AWS, bypassing the public internet. This dedicated connectivity offers predictable bandwidth, lower latency, and enhanced security, which are critical for enterprises that manage large-scale or mission-critical workloads. By providing a private connection to services such as Amazon VPC, S3, and EC2, Direct Connect supports hybrid cloud architectures and ensures reliable access to AWS resources for both ongoing operations and large data transfers.

Amazon S3 Transfer Acceleration improves upload speed to S3 by leveraging CloudFront edge locations to route traffic, but it still relies on the public internet. While it can reduce latency for geographically distant users, it does not provide the same level of bandwidth consistency or security as a dedicated Direct Connect connection. AWS Snowball is a physical device used for offline bulk data transfer. It is highly effective for initial migration of petabyte-scale datasets or archival data but does not offer continuous connectivity for real-time operations. Amazon CloudFront is a content delivery network that optimizes content delivery to end users, not a solution for secure, high-volume data transfer to AWS.

Direct Connect also integrates with AWS VPN for redundancy, enabling failover in case of connection issues and maintaining high availability. Organizations benefit from reduced data transfer costs compared to using the public internet for large workloads. Direct Connect ensures reliable, high-performance, and secure connectivity, which is especially important for enterprises requiring low-latency, predictable network behavior for mission-critical applications. For ongoing large-scale data transfers or hybrid cloud scenarios, Direct Connect aligns with best practices for enterprise networking on AWS, offering a scalable and resilient solution for secure data movement without reliance on internet-based transfers.

Question 25:

A company wants to implement a serverless application architecture that automatically scales with incoming requests and only charges for usage. Which services should be used together?

Answer:

A) AWS Lambda with Amazon API Gateway
B) Amazon EC2 with Auto Scaling
C) AWS Elastic Beanstalk with EC2
D) Amazon ECS with Fargate

Explanation:

Option A is correct. AWS Lambda executes code in response to events without provisioning servers, automatically scaling based on incoming requests. Amazon API Gateway exposes REST or HTTP endpoints that trigger Lambda functions, providing secure and scalable API access. EC2 with Auto Scaling provides automatic scaling but requires server management. Elastic Beanstalk automates deployment but provisions EC2 instances. ECS with Fargate abstracts container management but is not fully event-driven. Using Lambda and API Gateway together allows pay-per-use pricing, eliminating idle compute costs, and supporting microservices and event-driven architectures. Lambda integrates with S3, DynamoDB Streams, Kinesis, and CloudWatch, enabling end-to-end serverless solutions. API Gateway provides throttling, caching, and authorization features, enhancing performance, security, and scalability. This combination supports modern application design by allowing developers to focus on code and business logic while AWS handles infrastructure scaling and operational management. Additionally, using Lambda in conjunction with other AWS services like Step Functions enables orchestration of complex workflows without managing servers, making the solution highly resilient, cost-effective, and maintainable for enterprise applications.

A company looking to implement a fully serverless application architecture that automatically scales with incoming requests and charges only for actual usage should use AWS Lambda together with Amazon API Gateway. AWS Lambda is a compute service that allows developers to run code in response to events without provisioning or managing servers. It automatically scales based on the volume of incoming requests, ensuring that applications can handle sudden spikes in traffic without manual intervention. Lambda supports multiple programming languages and integrates seamlessly with a variety of AWS services, including S3, DynamoDB Streams, Kinesis, and CloudWatch, enabling developers to build complex, event-driven architectures without worrying about infrastructure management.

Amazon API Gateway complements Lambda by providing a fully managed service to create, deploy, and secure RESTful or HTTP APIs. API Gateway exposes endpoints that trigger Lambda functions in response to client requests, allowing developers to build scalable APIs without managing servers or load balancers. It also provides features such as request throttling, caching, and authorization through AWS IAM, Cognito, or Lambda authorizers, ensuring secure and efficient API access. By combining API Gateway with Lambda, organizations benefit from a true pay-per-use pricing model, paying only for the compute time consumed by functions and the number of API calls made, eliminating costs associated with idle infrastructure.

Alternative options like EC2 with Auto Scaling provide automatic scaling but require server management, patching, and monitoring. Elastic Beanstalk simplifies deployment but still provisions EC2 instances and manages servers, which increases operational overhead. Amazon ECS with Fargate abstracts container management but is not inherently event-driven and may involve idle resource costs. By using Lambda with API Gateway, organizations can focus entirely on business logic and application development while AWS handles all infrastructure scaling, fault tolerance, and availability. Additionally, integrating Lambda with Step Functions enables orchestration of complex workflows, creating a fully serverless, resilient, and maintainable architecture suitable for modern enterprise applications that demand efficiency, scalability, and cost-effectiveness.

Question 26:

A company wants to store sensitive files in S3 that can only be accessed by authorized users and are encrypted at rest. Which configuration is recommended?

Answer:

A) Enable SSE-KMS encryption with IAM policies restricting access
B) Store files in S3 with default encryption and allow public access
C) Encrypt files client-side without access control
D) Use S3 Standard storage with versioning

Explanation:

Option A is correct. SSE-KMS encrypts data at rest with keys managed in AWS KMS and allows fine-grained access control via IAM policies. This ensures that only authorized users or roles can access the files. Option B is insecure if public access is enabled. Option C lacks centralized key management and auditing. Option D preserves versions but does not encrypt files or enforce access restrictions. SSE-KMS also provides auditability through CloudTrail, showing who accessed the keys and when. Organizations can create separate KMS keys for different applications or departments, providing isolation and compliance. Access control can be further restricted using bucket policies and condition keys to enforce security best practices. Combining SSE-KMS with IAM ensures encryption, access control, compliance, and operational efficiency. This setup aligns with AWS Well-Architected Framework recommendations for securing data at rest and managing access at scale.

A company that needs to securely store sensitive files in Amazon S3 while ensuring that only authorized users can access them and that data is encrypted at rest should enable server-side encryption with AWS Key Management Service (SSE-KMS) and enforce access control through IAM policies. SSE-KMS provides strong encryption using keys managed in AWS KMS, offering both data protection and centralized key management. This approach allows organizations to define who can use specific keys to encrypt and decrypt data, supporting fine-grained access control and compliance requirements. With IAM policies in place, only authorized users, groups, or roles can access or manage the encrypted objects, minimizing the risk of unauthorized data exposure.

Simply storing files in S3 with default encryption and allowing public access is insecure, as public access bypasses access controls and could expose sensitive data to anyone on the internet. Encrypting files client-side without centralized key management provides encryption but lacks auditability, key rotation, and fine-grained access controls, making it difficult to meet regulatory or organizational compliance requirements. Using S3 Standard storage with versioning preserves previous object versions and protects against accidental deletions or overwrites, but it does not automatically encrypt data at rest or restrict access, leaving sensitive information vulnerable.

SSE-KMS integrates with AWS CloudTrail, enabling audit logging of key usage, showing which users accessed or attempted to access encryption keys and when. Organizations can create separate KMS keys for different departments or applications, isolating data and controlling access at a granular level. Bucket policies and condition keys can further restrict access based on attributes such as IP address, encryption status, or request context. By combining SSE-KMS encryption with IAM policies and strict bucket configurations, companies ensure that sensitive data is encrypted, securely accessed, auditable, and compliant with security best practices. This approach aligns with the AWS Well-Architected Framework, providing robust protection, operational efficiency, and centralized management of both data and encryption keys at scale.

Question 27:

A company wants to implement a cost-optimized storage solution for infrequently accessed but occasionally needed files. Which S3 storage class should be used?

Answer:

A) S3 Standard-IA
B) S3 Standard
C) S3 Glacier Deep Archive
D) Amazon EBS

Explanation:

Option A is correct. S3 Standard-Infrequent Access (Standard-IA) is designed for infrequently accessed data while providing low latency and high throughput when needed. Standard storage is more expensive for infrequent data. Glacier Deep Archive is cheaper but has longer retrieval times, making it less suitable for occasional access. EBS is block storage for EC2 and not optimized for cost-effective object storage. Standard-IA integrates seamlessly with lifecycle policies, enabling automatic tiering of objects based on access patterns, which reduces cost without sacrificing performance for occasional retrieval. Organizations can also combine S3 Intelligent-Tiering with Standard-IA for dynamic cost optimization. This approach supports compliance, performance, and operational simplicity, ensuring that storage costs are minimized while maintaining data availability when required.

Question 28:

A company wants to centralize auditing of all API calls across AWS accounts. Which service provides this capability?

Answer:

A) AWS CloudTrail
B) AWS Config
C) AWS Trusted Advisor
D) Amazon CloudWatch

Explanation:

Option A is correct. CloudTrail logs all API calls made across AWS accounts, including identity, time, source IP, and parameters. Config monitors configuration changes but does not capture API usage. Trusted Advisor provides best practice recommendations. CloudWatch monitors metrics and events, not auditing. CloudTrail enables centralized auditing, governance, and compliance tracking. Organizations can aggregate logs from multiple accounts into an S3 bucket and analyze them using Athena or CloudWatch Logs. Integration with AWS Security Hub allows for automated detection of suspicious activity. CloudTrail supports encryption, multi-region logging, and log file integrity validation, ensuring audit logs cannot be tampered with. Centralized API auditing helps organizations meet regulatory requirements, detect security incidents, and maintain operational transparency.

A company that aims to centralize auditing of all API calls across multiple AWS accounts should use AWS CloudTrail. CloudTrail is a fully managed service that records all API activity within an AWS environment, including actions taken through the AWS Management Console, SDKs, command-line tools, and other AWS services. Each log entry captures critical information, such as the identity of the caller, the time of the request, the source IP address, the specific action performed, and any request parameters. This detailed logging enables organizations to maintain full visibility into user and service activity, supporting governance, security monitoring, and compliance requirements.

While AWS Config tracks configuration changes to resources and helps monitor compliance against defined policies, it does not provide comprehensive auditing of API calls or user activity. AWS Trusted Advisor offers best practice recommendations for security, cost optimization, fault tolerance, and performance but does not capture real-time activity logs. Amazon CloudWatch is used for monitoring operational metrics, setting alarms, and observing events, but it does not provide auditing of API calls or user actions across accounts. CloudTrail fills this gap by offering a centralized, consistent logging mechanism for all API activity.

CloudTrail can aggregate logs from multiple AWS accounts into a single Amazon S3 bucket, making it easier to manage and analyze activity across an organization. Logs can be queried using Amazon Athena, allowing for ad hoc analysis of specific events or patterns, or forwarded to CloudWatch Logs for real-time monitoring and alerting. Integration with AWS Security Hub enables automated detection and response to suspicious activity, enhancing security operations. CloudTrail also supports encryption with AWS Key Management Service, multi-region logging to capture global activity, and log file integrity validation to ensure logs cannot be tampered with. By centralizing API auditing with CloudTrail, organizations gain improved transparency, operational oversight, regulatory compliance, and enhanced security visibility across all AWS accounts. This makes CloudTrail an essential component of a secure and well-governed AWS environment.

Question 29:

A company wants a fully managed NoSQL database that provides global replication and low-latency access for users worldwide. Which service should be used?

Answer:

A) Amazon DynamoDB Global Tables
B) Amazon RDS Multi-AZ
C) Amazon Redshift
D) Amazon Aurora

Explanation:

Option A is correct. DynamoDB Global Tables provide fully managed multi-region, multi-master replication, enabling low-latency read/write operations for users distributed worldwide. RDS Multi-AZ is relational and does not support active-active global writes. Redshift is analytical, and Aurora is relational. Global Tables automatically replicate data across regions and integrate with Lambda for event-driven applications. This design improves fault tolerance, provides high availability, and reduces latency for geographically dispersed users. Using Global Tables also eliminates the need to manage cross-region replication manually, simplifying operational management and ensuring consistency with eventual consistency or strong consistency options depending on application requirements. Organizations can leverage this service for highly available, globally distributed applications that demand low-latency data access, including gaming, social media, and IoT applications.

A company that requires a fully managed NoSQL database capable of providing low-latency access for users distributed globally should use Amazon DynamoDB Global Tables. Global Tables extend DynamoDB by enabling multi-region, multi-master replication, allowing applications to perform read and write operations in multiple AWS regions simultaneously. This ensures that users experience minimal latency when accessing data from any geographic location, which is critical for applications such as gaming platforms, social media services, e-commerce systems, and IoT solutions where real-time responsiveness is essential. By providing automatic replication across regions, Global Tables remove the complexity of manually managing cross-region replication, reducing operational overhead and the risk of replication errors.

Unlike Amazon RDS Multi-AZ deployments, which provide high availability within a single region but do not support active-active global writes, DynamoDB Global Tables enable active replication in multiple regions, allowing concurrent updates worldwide. Amazon Redshift, while excellent for analytical workloads and large-scale data warehousing, is not designed for low-latency transactional operations or real-time global access. Amazon Aurora is a relational database that provides high performance and read scalability within a region but does not natively support multi-region, multi-master writes for globally distributed applications.

DynamoDB Global Tables also integrate seamlessly with AWS Lambda, enabling event-driven architectures where data changes can trigger serverless workflows in real time. This integration allows organizations to build responsive, scalable, and highly available systems without managing infrastructure. Global Tables provide configurable consistency models, offering eventual consistency for maximum performance or strong consistency where applications require strict accuracy. This ensures that globally distributed applications maintain data integrity while optimizing performance.

By leveraging DynamoDB Global Tables, organizations benefit from fully managed replication, automatic conflict resolution, simplified operational management, and the ability to serve users worldwide with minimal latency. The service also supports fine-grained access control, encryption at rest, and auditing through CloudTrail, aligning with best practices for security and compliance. Overall, Global Tables are ideal for applications that demand high availability, global distribution, low-latency access, and operational simplicity.

Question 30:

A company wants to implement a high-performance caching solution to reduce latency for frequently accessed database queries. Which service should be used?

Answer:

A) Amazon ElastiCache
B) Amazon RDS
C) Amazon DynamoDB
D) Amazon S3

Explanation:

Option A is correct. ElastiCache provides fully managed, in-memory caching using Redis or Memcached, improving latency and throughput for frequently accessed data. RDS is relational storage and cannot serve as an in-memory cache. DynamoDB provides NoSQL storage with low latency but is not a cache. S3 is object storage. ElastiCache reduces load on databases by serving cached query results, supporting sub-millisecond latency for high-performance applications. Organizations can scale clusters to accommodate increased traffic, replicate nodes for high availability, and configure automatic failover to improve reliability. Combining ElastiCache with RDS or DynamoDB optimizes read-heavy workloads, reduces database costs, and enhances user experience. Best practices include cache invalidation strategies, TTL configuration, and monitoring performance using CloudWatch to ensure efficient caching and resource utilization.

A company that requires a high-performance caching solution to reduce latency for frequently accessed database queries should use Amazon ElastiCache. ElastiCache is a fully managed, in-memory caching service that supports Redis and Memcached engines. By storing frequently accessed data in memory, ElastiCache enables sub-millisecond response times, significantly improving application performance and reducing the load on underlying databases. This is particularly beneficial for read-heavy workloads, session management, leaderboards, real-time analytics, and caching results from relational or NoSQL databases.

Amazon RDS is a managed relational database service, but it is designed for persistent storage and cannot provide the ultra-low latency offered by in-memory caching. Amazon DynamoDB is a fully managed NoSQL database that provides low-latency access to data, but it is not designed as a cache and accessing frequently changing data repeatedly from DynamoDB can incur higher costs and latency compared to a dedicated caching layer. Amazon S3 is an object storage service optimized for durability and scalability rather than real-time access and cannot provide the performance characteristics required for caching.

ElastiCache clusters can be scaled horizontally to handle increasing traffic and support high availability with replication and automatic failover. Redis provides advanced data structures, persistence options, and pub/sub capabilities, making it suitable for more complex caching scenarios, while Memcached is ideal for simple key-value caching with minimal overhead. By placing ElastiCache in front of RDS or DynamoDB, organizations can reduce the number of direct database queries, lower operational costs, and improve user experience by delivering faster responses.

Best practices for ElastiCache include setting appropriate time-to-live (TTL) values, implementing cache invalidation strategies to prevent stale data, monitoring cache performance with Amazon CloudWatch, and configuring replication and backup for fault tolerance. Using ElastiCache as a caching layer not only enhances application performance but also ensures scalability, reliability, and efficient resource utilization, making it an essential component for high-performance applications that require low-latency access to frequently used data.

Question 31:

A company wants to implement an event-driven architecture to process S3 object uploads asynchronously. Which AWS service combination is recommended?

Answer:

A) Amazon S3 with Lambda triggers
B) Amazon EC2 with manual polling
C) AWS Elastic Beanstalk
D) Amazon CloudFront

Explanation:

Option A is correct. S3 can trigger Lambda functions on object creation events, enabling serverless, event-driven processing without managing servers. EC2 requires manual polling scripts, increasing operational overhead. Elastic Beanstalk manages applications but is not event-driven. CloudFront is a CDN. Using S3 with Lambda ensures automatic scaling, cost efficiency (pay-per-use), and real-time processing. This architecture supports a wide range of use cases, including image or video processing, ETL workflows, and automated notifications. Lambda’s integration with other services like SNS, SQS, and DynamoDB enables building fully serverless, decoupled applications. It also provides monitoring via CloudWatch, supporting observability and operational insights. Event-driven patterns improve responsiveness, reduce latency, and allow applications to react to changes in data immediately while minimizing operational complexity.

Question 32:

A company wants to encrypt data in transit for a web application running on EC2 instances. Which approach is recommended?

Answer:

A) Enable HTTPS using TLS/SSL certificates with Amazon Certificate Manager
B) Use unencrypted HTTP
C) Use S3 server-side encryption
D) Use CloudFront caching only

Explanation:

Option A is correct. HTTPS using TLS/SSL certificates encrypts data in transit between clients and EC2 instances, ensuring confidentiality and integrity. Certificates can be managed automatically via AWS Certificate Manager (ACM). HTTP transmits data in plaintext and is insecure. S3 server-side encryption protects data at rest, not in transit. CloudFront caching does not provide encryption on its own but can be configured with HTTPS. Using HTTPS with ACM reduces operational overhead, ensures automated certificate renewal, and aligns with security best practices such as PCI DSS compliance. It also provides end-to-end encryption when combined with other services, such as API Gateway, ELB, or CloudFront, securing sensitive user data during transmission. Encrypting data in transit prevents eavesdropping, man-in-the-middle attacks, and helps organizations maintain compliance with industry regulations while improving customer trust.

Question 33:

A company wants to analyze application logs stored in S3 using SQL queries without provisioning any infrastructure. Which AWS service is appropriate?

Answer:

A) Amazon Athena
B) Amazon Redshift
C) Amazon RDS
D) Amazon DynamoDB

Explanation:

Option A is correct. Athena allows querying data directly in S3 using standard SQL without provisioning servers. Redshift is a managed data warehouse that requires cluster management. RDS is relational storage for structured workloads, and DynamoDB is NoSQL. Athena supports multiple formats including CSV, JSON, Parquet, and ORC. It also integrates with Glue Data Catalog for schema management. Organizations can run ad-hoc queries, generate reports, and analyze logs cost-effectively. Pricing is based on the amount of data scanned, encouraging efficient queries. Athena also allows integration with visualization tools like QuickSight for dashboards. It is ideal for log analysis, auditing, and business intelligence tasks without managing infrastructure. Queries are executed serverlessly, scaling automatically, and providing near real-time insights into application behavior, system performance, and operational metrics.

Question 34:

A company wants to enforce least-privilege access to AWS resources for developers across multiple accounts. Which service combination is best?

Answer:

A) AWS IAM with AWS Organizations and Service Control Policies
B) AWS CloudTrail only
C) Amazon S3 bucket policies only
D) AWS Config only

Explanation:

Option A is correct. IAM manages users, roles, and permissions within accounts. Organizations allows centralized policy enforcement, and Service Control Policies (SCPs) define maximum allowed permissions across accounts. CloudTrail logs activity but does not enforce policies. S3 bucket policies control access to S3 only. Config monitors compliance but does not actively restrict actions. Combining IAM, Organizations, and SCPs ensures developers have only the required permissions, minimizes risk of privilege escalation, and enforces governance across accounts. This approach reduces operational errors, supports auditability, and aligns with security best practices. It allows scalable, centralized management of access controls while maintaining flexibility for development teams to perform their tasks without over-provisioning permissions. Organizations can define baseline security controls, restrict resource usage, and maintain compliance with internal policies and regulatory requirements.

Question 35:

A company wants to provide low-latency access to database queries for a read-heavy application. Which solution is most appropriate?

Answer:

A) Amazon ElastiCache
B) Amazon RDS
C) Amazon DynamoDB
D) Amazon S3

Explanation:

Option A is correct. ElastiCache provides an in-memory cache using Redis or Memcached, reducing latency for frequently accessed data and offloading read traffic from the primary database. RDS provides relational storage but cannot achieve sub-millisecond latency for high-volume reads. DynamoDB is NoSQL, suitable for low-latency access but not caching relational data. S3 is object storage and not designed for query caching. Using ElastiCache improves performance, reduces database load, and supports scaling read-heavy workloads. TTL and eviction policies ensure memory is efficiently used, and replication provides high availability. Combining ElastiCache with a relational database reduces cost and improves responsiveness, which is essential for applications such as gaming, e-commerce, or financial services that require high-speed query processing. Monitoring with CloudWatch ensures cache performance is optimized and helps identify bottlenecks before they impact user experience.

Question 36:

A company wants to ensure compliance with regulatory requirements for storing sensitive data in S3. Which combination of features should be used?

Answer:

A) SSE-KMS encryption, bucket policies, and CloudTrail logging
B) S3 Standard storage only
C) Public access enabled with versioning
D) CloudFront caching

Explanation:

Option A is correct. SSE-KMS provides encryption at rest, bucket policies enforce access controls, and CloudTrail tracks API activity for auditing. S3 Standard alone does not encrypt or log access. Public access with versioning exposes sensitive data. CloudFront is a CDN, not a compliance tool. Using SSE-KMS ensures encryption keys are managed securely, with access restricted through IAM policies. Bucket policies enforce the principle of least privilege. CloudTrail provides audit trails of who accessed or modified objects, supporting regulatory compliance. Together, these features ensure sensitive data is protected, auditable, and accessible only by authorized personnel. Additional best practices include enabling MFA delete, versioning, and lifecycle management to ensure data integrity, retention, and recoverability. This setup reduces operational risks, aligns with compliance frameworks like HIPAA and PCI DSS, and ensures sensitive information is securely managed throughout its lifecycle.

Question 37:

A company wants to decouple application components and buffer messages for asynchronous processing. Which service should be used?

Answer:

A) Amazon SQS
B) Amazon RDS
C) Amazon DynamoDB
D) Amazon CloudFront

Explanation:

Option A is correct. SQS provides a fully managed message queue that decouples components and ensures reliable delivery, allowing consumers to process messages at their own pace. RDS is a relational database and not a message queue. DynamoDB is a NoSQL database. CloudFront is a CDN. Using SQS enables fault-tolerant architectures, smooth handling of variable workloads, and asynchronous processing without requiring manual queue management. It integrates with Lambda, EC2, and other services, enabling event-driven architectures. Features like dead-letter queues and message visibility timeouts ensure reliable delivery and error handling. SQS supports high throughput and scales automatically, allowing developers to focus on business logic while AWS manages the underlying infrastructure. This design improves reliability, reduces coupling between services, and allows scaling independent components based on demand.

Question 38:

A company wants to build a highly available and scalable web application. Which combination of AWS services should be recommended?

Answer:

A) EC2 instances across multiple AZs with an Application Load Balancer and Auto Scaling
B) Single EC2 instance with EBS
C) Lambda only
D) S3 only

Explanation:

Option A is correct. Deploying EC2 instances across multiple AZs ensures high availability. An Application Load Balancer distributes incoming traffic and monitors instance health. Auto Scaling adjusts the number of instances based on demand. A single EC2 instance is a single point of failure. Lambda alone may not support stateful, complex multi-tier applications. S3 alone cannot host dynamic web applications. This architecture follows AWS best practices, providing fault tolerance, scalability, and resilience. Auto Scaling reduces operational overhead and optimizes costs while maintaining performance. Integration with CloudWatch enables monitoring and automated responses to load changes. By distributing instances across AZs, applications can survive failures without impacting user experience, supporting business continuity and SLAs.

A company that wants to build a highly available and scalable web application should deploy EC2 instances across multiple Availability Zones (AZs) behind an Application Load Balancer (ALB) with Auto Scaling. Distributing EC2 instances across multiple AZs ensures that the application can remain operational even if one AZ experiences an outage, providing high availability and fault tolerance. The ALB automatically distributes incoming traffic across healthy instances and performs health checks to ensure that requests are routed only to functioning resources. This prevents downtime caused by failed instances and optimizes user experience by maintaining consistent application performance.

Auto Scaling dynamically adjusts the number of EC2 instances based on traffic patterns or performance metrics such as CPU utilization or request count. During traffic spikes, additional instances are launched to maintain application responsiveness, and during low traffic periods, instances are terminated to optimize costs. This automated scaling reduces operational overhead while maintaining performance and reliability. CloudWatch integration allows monitoring of instance health, application metrics, and Auto Scaling events, enabling administrators to respond quickly to any performance or operational issues.

Alternative approaches like deploying a single EC2 instance with an EBS volume create a single point of failure and do not provide high availability or resilience. Using Lambda alone is suitable for serverless or event-driven applications, but it may not support complex, multi-tier, or stateful web applications. Storing content in S3 alone is suitable for static websites, but dynamic web applications require compute resources for processing requests, which S3 cannot provide.

The combination of EC2 across multiple AZs, an Application Load Balancer, and Auto Scaling aligns with AWS best practices for designing resilient, scalable, and highly available architectures. This setup ensures that the application can handle unpredictable workloads, survive component failures, optimize costs, and meet business continuity requirements. By implementing this architecture, organizations achieve a robust foundation for modern web applications, supporting service-level agreements, user satisfaction, and operational efficiency across geographically distributed users.

Question 39:

A company wants to analyze streaming data in real-time and trigger alerts when specific thresholds are exceeded. Which service combination is recommended?

Answer:

A) Amazon Kinesis Data Streams with Lambda and CloudWatch
B) Amazon S3 with Athena
C) Amazon RDS with Redshift
D) AWS CloudTrail only

Explanation:

Option A is correct. Kinesis Data Streams captures real-time streaming data. Lambda can process incoming records in real-time, enabling alerting or transformations. CloudWatch can monitor metrics and trigger alarms based on thresholds. S3 with Athena supports batch analytics, not real-time. RDS with Redshift is for relational storage and data warehousing, unsuitable for streaming. CloudTrail logs API calls but does not provide real-time processing. This architecture allows organizations to ingest, analyze, and respond to events in near real-time. It supports use cases like fraud detection, IoT telemetry processing, and operational monitoring. Lambda automatically scales with stream throughput, providing a serverless, low-maintenance solution for processing high-volume streaming data. Using CloudWatch alarms ensures timely notifications and automated responses, enhancing operational efficiency, performance monitoring, and business decision-making.

Question 40:

A company wants to provide secure, low-latency global access to static web assets stored in S3. Which solution is optimal?

Answer:

A) Amazon CloudFront with S3 origin and HTTPS
B) S3 Standard alone
C) EC2 instances in one region
D) AWS Direct Connect

Explanation:

Option A is correct. CloudFront caches content at edge locations globally, reducing latency. Using HTTPS ensures data security during transit. S3 serves as the origin for storing objects. S3 Standard alone does not provide caching or global edge access. EC2 instances in one region introduce latency for distant users. Direct Connect provides private connectivity but is not a CDN. CloudFront supports cache invalidation, geolocation routing, and integration with WAF for security. This setup ensures highly available, secure, and performant content delivery for users worldwide. By caching content at edge locations, CloudFront reduces load on the S3 origin, optimizes cost, and improves user experience. Using HTTPS ensures compliance with security best practices, protecting sensitive data during transmission and enhancing trust for end users. This architecture is ideal for hosting static websites, media files, and application assets that require global reach and low latency.