Amazon AWS Certified Developer – Associate DVA-C02 Exam Dumps and Practice Test Questions Set 1 Q1-20

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Question 1 

Which AWS service allows you to run code without provisioning or managing servers?

A) Amazon EC2
B) AWS Lambda
C) Amazon RDS
D) Amazon S3

Answer: B) AWS Lambda

Explanation:

Amazon EC2 provides virtual servers in the cloud, giving users full control over operating systems, network configurations, and installed applications. While EC2 offers flexibility for running various workloads, it requires managing scaling, patching, monitoring, and infrastructure availability. Users are responsible for provisioning instances and ensuring fault tolerance, which introduces operational overhead. EC2 is highly suitable for applications that need a consistent runtime environment or legacy software that cannot be easily containerized or broken into small, event-driven components. However, it does not remove the need for server management, which is a key requirement in this question.

Amazon RDS is a managed relational database service. It simplifies database administration by handling patching, backups, and scaling of database instances, and it supports engines such as MySQL, PostgreSQL, Oracle, SQL Server, and MariaDB. Although RDS reduces operational complexity for databases, it is not a compute service designed for executing arbitrary code. Developers cannot run custom application logic directly on RDS; it is specifically for structured data storage and retrieval.

Amazon S3 is an object storage service designed to store and retrieve large volumes of unstructured data. While S3 offers durability, scalability, and lifecycle management for files, it does not provide any execution environment for code. S3 is optimized for storing data like images, backups, and logs, but it cannot run server-side logic or handle compute workloads.

AWS Lambda, in contrast, is a fully serverless compute service. It allows developers to execute code in response to events such as HTTP requests, file uploads to S3, or database changes. Lambda automatically provisions, scales, and maintains the infrastructure required to run the code, freeing developers from server management tasks. This serverless model makes it ideal for microservices, real-time data processing, and event-driven architectures. Lambda’s ability to handle scaling dynamically and its integration with multiple AWS services makes it the correct choice for running code without provisioning or managing servers.

Question 2 

Which AWS service is most suitable for storing large amounts of structured relational data?

A) Amazon S3
B) Amazon DynamoDB
C) Amazon RDS
D) AWS Lambda

Answer: C) Amazon RDS

Explanation:

Amazon S3 is primarily an object storage solution. It is optimized for storing unstructured data such as files, media, backups, or logs and does not provide relational capabilities such as SQL queries, joins, or transaction management. S3 offers high durability and availability but lacks native support for relational database features that structured applications require.

Amazon DynamoDB is a fully managed NoSQL database optimized for key-value and document data structures. It offers low-latency performance and seamless scalability, making it ideal for high-traffic applications, caching layers, or session storage. However, DynamoDB does not implement traditional relational concepts like foreign keys, joins, or SQL-based querying, making it unsuitable for applications that require a relational schema and ACID transactions.

AWS Lambda, as a serverless compute service, is designed for running code rather than storing structured data. While Lambda can interact with databases, it cannot serve as a storage solution for relational data. It complements database services by providing computation and business logic but is not a data storage platform.

Amazon RDS is a managed relational database service, offering engines such as MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server. It automates administrative tasks such as backups, patch management, scaling, and high availability. Applications can store structured relational data and perform SQL queries efficiently without worrying about the underlying infrastructure. RDS supports ACID transactions, complex queries, and referential integrity, making it the correct choice for large amounts of structured relational data.

Question 3 

Which AWS service is designed for real-time messaging between distributed applications?

A) Amazon SQS
B) Amazon SNS
C) Amazon MQ
D) AWS CloudTrail

Answer: B) Amazon SNS

Explanation:

Amazon SQS is a fully managed message queuing service that decouples application components. It supports asynchronous processing, where messages are stored until consumers retrieve and process them. While SQS is highly reliable for queuing workflows, it is not designed for pushing messages in real time to multiple subscribers simultaneously.

Amazon MQ is a managed message broker compatible with Apache ActiveMQ and RabbitMQ. It allows organizations to migrate legacy messaging systems to AWS with minimal changes. MQ supports traditional messaging protocols and patterns but is not optimized for large-scale, real-time notifications or serverless integration scenarios.

AWS CloudTrail is a logging service that records API activity for auditing, compliance, and security monitoring. While it is essential for tracking changes in your AWS environment, it does not facilitate messaging or event delivery between applications.

Amazon SNS is a publish-subscribe service designed to push messages instantly to multiple subscribers. It supports integration with Lambda, HTTP endpoints, email, and SMS, enabling real-time notifications and event-driven architectures. SNS ensures that messages are delivered immediately to all subscribers, making it the correct service for real-time messaging between distributed applications.

Question 4 

Which AWS service enables automatic scaling of compute resources based on demand?

A) Amazon EC2 Auto Scaling
B) Amazon S3
C) Amazon RDS
D) AWS Lambda

Answer:  A) Amazon EC2 Auto Scaling

Explanation:

Amazon S3 is a highly durable and scalable object storage service designed to store virtually unlimited amounts of data, including files, images, backups, and logs. While it can automatically handle increasing storage requirements without user intervention, S3 does not provide any compute resources. This means it cannot execute application logic or scale computing power based on workload demands. Its scalability is limited to storage capacity and data throughput rather than processing capabilities. S3 is ideal for storing and retrieving large amounts of data, but it does not contribute to adjusting application performance in response to traffic spikes, which is the core requirement for automatic compute scaling.

Amazon RDS is a managed relational database service that simplifies database administration tasks such as backups, patching, and replication. RDS supports horizontal scaling for read-heavy workloads through read replicas and vertical scaling for storage and instance size. However, RDS does not automatically scale the compute resources of EC2 instances hosting application servers. It is focused on database performance and availability rather than dynamically adjusting general-purpose compute resources for applications. While RDS helps maintain database responsiveness under variable loads, it does not address scaling the broader application infrastructure automatically.

AWS Lambda is a serverless compute service that automatically handles the provisioning and scaling of execution environments in response to incoming events. Lambda can efficiently scale up and down depending on the number of invocations, making it suitable for event-driven architectures or workloads with unpredictable demand. However, Lambda operates independently of EC2 instances and traditional virtual machines. It abstracts the infrastructure entirely, which means it cannot be used to scale EC2-based applications or manage virtual servers. Lambda’s automatic scaling is limited to its own execution environments rather than the broader compute resources in an AWS account.

Amazon EC2 Auto Scaling is specifically designed to automatically adjust the number of EC2 instances in response to changing application demands. It monitors metrics such as CPU utilization, network traffic, or custom application-level parameters and dynamically increases or decreases the instance count to maintain performance and optimize costs. During periods of high demand, Auto Scaling ensures sufficient compute resources are available to handle the load, and during low-demand periods, it scales in to reduce expenses. By directly managing the number of EC2 instances and their distribution across availability zones, EC2 Auto Scaling provides a reliable and flexible solution for maintaining application performance and resilience. This makes it the correct service for automatically scaling compute resources based on workload demand.

Question 5 

Which service allows you to store secrets such as database credentials securely in AWS?

A) AWS Secrets Manager
B) AWS Key Management Service
C) Amazon Cognito
D) Amazon CloudWatch

Answer:  A) AWS Secrets Manager

Explanation:

AWS Key Management Service (KMS) is a fully managed service designed to create, manage, and control cryptographic keys used for data encryption and decryption. KMS allows applications and services to encrypt sensitive data, ensuring confidentiality and regulatory compliance. While KMS provides robust key management capabilities, it is not intended to store secrets such as database credentials or API keys directly. Its primary function is cryptographic operations rather than secret lifecycle management. For example, you can use KMS to encrypt a password before storing it elsewhere, but KMS alone does not handle secret rotation or retrieval for applications.

Amazon Cognito is a user identity and access management service that handles user authentication, authorization, and user pools. It allows developers to manage sign-ups, sign-ins, and federated identities, providing secure access to applications. While Cognito stores user credentials for authentication purposes, it is not designed for general-purpose secret storage. Application secrets such as database passwords, API keys, or tokens are not part of its functionality. Cognito is primarily aimed at securing end-user identities and managing access, not securely storing sensitive application configuration data or rotating secrets.

Amazon CloudWatch is AWS’s monitoring and observability service, used for collecting logs, metrics, and events from applications and infrastructure. CloudWatch provides insights into system performance, helps detect anomalies, and supports automated responses via alarms. However, it is not a service designed to manage or store sensitive secrets. While operational logs may contain some sensitive information, CloudWatch does not provide the encryption, access control, or automated rotation features required for secure secret management. Its focus is on observability rather than confidential information storage.

AWS Secrets Manager is specifically designed to securely store, manage, and rotate secrets such as database credentials, API keys, and other sensitive information. It provides fine-grained access control through IAM policies, ensuring that only authorized entities can retrieve secrets. Secrets Manager also supports automatic rotation of secrets with minimal disruption to applications, which reduces the risk of credential exposure. Additionally, it integrates seamlessly with other AWS services like RDS, Lambda, and EC2, making it easier to securely manage secrets across applications and workloads. Its purpose-built features for storing, retrieving, and rotating sensitive information make AWS Secrets Manager the correct choice for secure secret management, providing both operational simplicity and enhanced security.

Question 6 

Which AWS database service is fully managed, NoSQL, and provides single-digit millisecond latency?

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

Answer: B) Amazon DynamoDB

Explanation:

Amazon RDS is a managed relational database service that supports engines such as MySQL, PostgreSQL, Oracle, and SQL Server. It provides automated backups, patching, and replication, making it easier to manage relational databases in the cloud. However, it is not designed as a NoSQL service, and it does not guarantee single-digit millisecond latency under high scale, which is often required for applications needing real-time responses.

Amazon DynamoDB, on the other hand, is a fully managed NoSQL database service that delivers consistent, single-digit millisecond latency at any scale. It automatically scales throughput capacity to accommodate workloads, supports key-value and document data structures, and integrates seamlessly with other AWS services. Its design makes it ideal for applications like gaming, IoT, mobile apps, and real-time analytics, where low latency and high availability are critical.

Amazon Redshift is AWS’s fully managed data warehouse service intended for large-scale analytical workloads. It is optimized for complex queries across petabytes of structured data and is primarily used for reporting and business intelligence rather than transactional or real-time NoSQL workloads. Redshift is not designed to provide millisecond-level response times for individual reads and writes, so it does not meet the criteria in the question.

Amazon Aurora is a fully managed relational database that is compatible with MySQL and PostgreSQL. Aurora provides high performance, fault tolerance, and availability, often exceeding standard RDS performance for relational workloads. While it is a powerful relational database with some NoSQL-like features (like Aurora Serverless), it is still a relational system and does not qualify as a pure NoSQL database with single-digit millisecond latency.

The correct choice is Amazon DynamoDB because it is specifically built for fully managed NoSQL operations with extremely low latency and automatic scaling, meeting all the requirements stated in the question.

Question 7 

Which service is used for monitoring AWS resources and applications in real time?

A) AWS CloudTrail
B) AWS CloudWatch
C) AWS Config
D) AWS X-Ray

Answer: B) AWS CloudWatch

Explanation:

AWS CloudTrail records API calls made within your AWS account and captures detailed event logs for auditing, compliance, and security analysis. While it is an essential service for tracking user and service activity, it is not primarily designed for real-time monitoring or metrics collection. CloudTrail focuses on recording actions rather than evaluating operational health or performance.

AWS CloudWatch is a monitoring and observability service that provides real-time visibility into AWS resources and applications. It collects and tracks metrics, logs, and events, enabling developers and operators to set alarms, visualize performance dashboards, and automate responses to operational changes. CloudWatch is ideal for proactive monitoring of system performance, availability, and resource utilization.

AWS Config tracks configuration changes of AWS resources and evaluates compliance with predefined rules. While it provides historical context and configuration snapshots for auditing and governance, it is not designed for real-time monitoring or alerting of operational metrics. Config helps organizations ensure their resources remain in a compliant state, but it does not provide the same real-time insight as CloudWatch.

AWS X-Ray allows developers to trace requests through distributed applications, helping diagnose performance bottlenecks and latency issues. While it provides detailed insights into application-level performance, it is focused on tracing and debugging rather than general real-time monitoring of metrics across all resources.

The correct service is AWS CloudWatch because it is designed to monitor applications and resources in real time, offering metrics, logs, alarms, and dashboards that enable immediate operational insights and automated responses.

Question 8 

Which AWS service allows developers to deploy containerized applications without managing servers?

A) Amazon EC2
B) Amazon ECS with Fargate
C) Amazon EKS
D) AWS Lambda

Answer: B) Amazon ECS with Fargate

Explanation:

Amazon EC2 provides virtual servers in the cloud where developers can run containers. However, EC2 requires manual server management, including scaling, patching, and infrastructure maintenance. Developers must handle container orchestration themselves if they rely solely on EC2, which increases operational complexity.

Amazon ECS with Fargate is a serverless container service that allows developers to run containers without managing the underlying EC2 instances. It handles scaling, patching, and infrastructure management automatically while providing isolation and security for each task. This makes it ideal for running containerized applications with minimal operational overhead.

Amazon EKS is a managed Kubernetes service that orchestrates containerized workloads. While it reduces some complexity compared to self-managed Kubernetes, developers still need to manage cluster nodes, scaling policies, and certain operational tasks. It is not fully serverless in the same way Fargate is.

AWS Lambda allows running code in response to events without provisioning servers, including support for container images. However, Lambda is generally used for event-driven, short-duration workloads and is not designed to run full containerized applications continuously.

The correct choice is Amazon ECS with Fargate because it provides fully managed, serverless container deployment, removing the need to manage EC2 instances while supporting full-scale container applications.

Question 9 

Which AWS service provides automatic backup and snapshot management for relational databases?

A) Amazon RDS
B) Amazon DynamoDB
C) AWS Lambda
D) Amazon S3

Answer:  A) Amazon RDS

Explanation:

Amazon RDS is a fully managed relational database service that simplifies database administration tasks such as provisioning, patching, monitoring, and scaling. One of its key features is automatic backup and snapshot management. RDS automatically performs daily backups of the database and transaction logs, allowing point-in-time recovery. It also provides the ability to create manual snapshots at any time, giving administrators flexibility in backup management and disaster recovery strategies.

Amazon DynamoDB is a managed NoSQL database that also supports backup and restore functionality. However, its backup system is designed for NoSQL key-value or document stores, not relational databases. While DynamoDB can create on-demand backups and continuous backups using point-in-time recovery, it does not manage relational data structures, SQL queries, or relational transaction integrity, which are essential for many enterprise applications requiring RDS.

AWS Lambda is a serverless compute service designed to execute code in response to events. Lambda does not provide any built-in database management or backup functionality. While Lambda functions could theoretically trigger backups for databases, the service itself does not perform automated backups or snapshots and cannot replace a fully managed database service for backup purposes.

Amazon S3 is an object storage service that can store files, backups, and snapshots. While you could store database backups in S3, S3 does not provide automated snapshot management, point-in-time recovery, or integration with relational database operations. It is a storage solution, not a managed relational database service.

The correct answer is Amazon RDS because it is specifically designed to manage relational databases with built-in features for automatic backups, snapshot creation, and point-in-time recovery. These capabilities reduce operational complexity for database administrators and ensure reliable disaster recovery without manual intervention.

Question 10 

Which AWS service allows applications to securely call APIs on behalf of a user without sharing credentials?

A) AWS IAM Roles
B) AWS Cognito
C) AWS KMS
D) AWS Secrets Manager

Answer: B) AWS Cognito

Explanation:

AWS IAM Roles allow assigning permissions to users or services to access AWS resources. Roles can grant temporary credentials, but they do not directly handle user authentication for applications. IAM roles are primarily designed for granting permissions to AWS services or federated users, not for enabling an application to call APIs on behalf of a user without exposing credentials.

AWS Cognito is an identity management service that enables applications to authenticate users and obtain temporary, limited-privilege credentials to access AWS resources securely. Cognito integrates with social identity providers and SAML-based enterprise identity systems and generates tokens that allow applications to call APIs without exposing the user’s long-term credentials. It handles authentication, authorization, and secure token management, which makes it ideal for scenarios where applications need to act on behalf of users securely.

AWS KMS (Key Management Service) is a service for creating and managing encryption keys. KMS allows developers to encrypt and decrypt data securely but does not handle user authentication or the delegation of API access. It is essential for securing sensitive data but does not provide a mechanism for applications to obtain temporary credentials for API calls on behalf of a user.

AWS Secrets Manager is a service for securely storing and retrieving secrets, such as database credentials or API keys. While it helps avoid hardcoding credentials in applications, it does not provide user authentication or generate temporary credentials for API calls. Applications would still need another mechanism, such as Cognito, to securely call APIs without exposing credentials.

The correct answer is AWS Cognito because it provides user authentication, token issuance, and temporary credential management, enabling applications to securely access AWS resources or APIs on behalf of authenticated users without exposing permanent credentials, meeting the requirements of the question.

Question 11 

Which service is best suited for building event-driven architectures in AWS?

A) Amazon S3
B) Amazon EventBridge
C) Amazon RDS
D) Amazon EC2

Answer: B) Amazon EventBridge

Explanation:

Amazon S3 is primarily an object storage service designed to store and retrieve large amounts of data. While S3 can trigger events such as object creation or deletion, its main role is not to serve as an event routing platform. It does have some event notification capabilities that can integrate with Lambda or SNS, but S3 alone cannot orchestrate complex event-driven workflows or route events between multiple AWS services and external applications.

Amazon EventBridge, on the other hand, is explicitly designed for event-driven architectures. It acts as a serverless event bus that enables applications to respond to events from AWS services, SaaS platforms, or custom applications. EventBridge supports filtering, transformation, and routing of events, which allows developers to create loosely coupled, reactive architectures. It simplifies the building of applications that need to respond to state changes or external triggers without requiring manual polling or tight integrations.

Amazon RDS is a managed relational database service. While it is excellent for storing structured data and providing automated backups and replication, it does not inherently support event-driven patterns or asynchronous messaging between services. Similarly, Amazon EC2 provides virtual servers in the cloud for running applications but lacks built-in capabilities to orchestrate events or trigger actions across multiple services automatically.

Therefore, EventBridge is the most suitable service for event-driven architectures because it enables scalable, loosely coupled event routing between systems. Its ability to integrate natively with other AWS services and SaaS applications, along with advanced filtering and transformation features, makes it the correct choice for creating event-driven workflows in AWS.

Question 12 

Which AWS service allows you to manage infrastructure as code using declarative templates?

A) AWS CloudFormation
B) AWS CodeDeploy
C) AWS Lambda
D) AWS OpsWorks

Answer:  A) AWS CloudFormation

Explanation:

AWS CloudFormation enables users to define and provision infrastructure using declarative templates written in JSON or YAML. These templates describe the desired state of AWS resources, including dependencies and configurations. CloudFormation then automatically creates, updates, and deletes resources to match the template, providing a repeatable and version-controlled approach to infrastructure management.

AWS CodeDeploy focuses on automating application deployments to EC2 instances, Lambda functions, or on-premises servers. It is primarily a deployment tool rather than a platform for managing infrastructure resources. Similarly, AWS Lambda is a serverless compute service designed to run code in response to triggers and is not intended for defining or managing infrastructure. AWS OpsWorks provides configuration management using Chef or Puppet and follows a procedural model, which differs from the declarative approach used by CloudFormation.

CloudFormation is particularly advantageous because it abstracts the underlying API calls and handles dependencies automatically, allowing teams to manage complex environments without manual intervention. By using templates, it becomes easier to reproduce environments, apply consistent configurations, and integrate with CI/CD pipelines. This declarative model, combined with automation and integration, makes CloudFormation the ideal service for infrastructure-as-code management.

Question 13 

Which service is ideal for storing session state in a distributed web application?

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

Answer: B) Amazon ElastiCache

Explanation:

Amazon S3 is an object storage service, which provides high durability but is not optimized for low-latency access required by session state in real-time web applications. Using S3 for session storage would result in slower performance compared to in-memory stores.

Amazon ElastiCache provides fully managed in-memory caching with Redis or Memcached. It is designed for low-latency, high-throughput access, making it ideal for storing session data for distributed web applications. Its in-memory architecture allows web applications to quickly retrieve and update session state across multiple instances without performance degradation.

Amazon DynamoDB is a NoSQL database that is suitable for persistent data storage and can handle large-scale workloads. However, while fast, it is not as low-latency as an in-memory caching solution for frequent session access. Amazon RDS provides managed relational databases, which are durable and transactional but also slower than in-memory solutions, making them less suitable for session state storage.

ElastiCache is the correct choice because it delivers fast, ephemeral storage for session data that allows distributed applications to maintain consistency and responsiveness. Its ability to handle high request rates and provide replication options ensures that session state remains reliable and accessible.

Question 14 

Which AWS service is designed to trace requests in microservices-based applications?

A) AWS CloudTrail
B) AWS X-Ray
C) AWS CloudWatch
D) Amazon CloudFront

Answer: B) AWS X-Ray

Explanation:

AWS CloudTrail records API calls made in your AWS account and tracks user activity for auditing and compliance purposes. While useful for security and governance, CloudTrail does not trace the path of requests through microservices or provide insights into service performance.

AWS X-Ray is a distributed tracing service that helps developers analyze and debug applications built on microservices. It captures end-to-end request paths, identifies bottlenecks, and provides visualization of service interactions, helping teams troubleshoot latency and errors effectively.

AWS CloudWatch collects metrics, logs, and events, and can trigger alarms. Although it provides monitoring and alerting, it does not provide detailed tracing or the ability to see how a request propagates through multiple services. Amazon CloudFront is a content delivery network used to cache and deliver content globally and does not trace internal application requests.

X-Ray is the correct choice because it enables end-to-end tracing of requests in complex microservices architectures. By offering detailed insight into performance and request flow, it helps developers identify issues quickly and optimize application behavior.

Question 15 

Which AWS service provides a managed queue for decoupling microservices?

A) Amazon SQS
B) Amazon SNS
C) Amazon MQ
D) AWS Lambda

Answer:  A) Amazon SQS

Explanation:

Amazon SQS (Simple Queue Service) is a fully managed message queuing service designed to decouple components of distributed systems and microservices. It enables asynchronous communication, allowing one component of an application to send messages that are stored in a queue until they are retrieved and processed by another component. This buffering ensures that services do not need to operate at the same speed or be available at the same time, which improves system resilience and fault tolerance. SQS automatically handles message storage, delivery retries, and scaling to accommodate high volumes of messages, making it highly reliable for workloads with varying traffic patterns.

Amazon SNS (Simple Notification Service) is a pub/sub messaging service that broadcasts messages to multiple subscribers simultaneously. It is ideal for sending notifications, alerts, or updates to endpoints such as email, HTTP/S, SMS, or Lambda functions. While SNS excels at real-time message delivery to many recipients, it does not provide persistent storage for messages or ensure that individual subscribers process each message exactly once. As such, SNS is more suitable for broadcasting information rather than decoupling services that require reliable point-to-point communication with guaranteed processing.

Amazon MQ is a managed message broker service compatible with industry-standard protocols like AMQP, MQTT, and JMS. It provides a familiar environment for organizations migrating from on-premises messaging systems. While it supports traditional broker-based messaging patterns, Amazon MQ is not as tightly integrated with AWS-native serverless services and does not scale automatically as easily as SQS. It is better suited for applications that require standard broker protocols or legacy system compatibility rather than modern event-driven architectures.

AWS Lambda is a serverless compute service that executes code in response to triggers such as API calls, S3 events, or DynamoDB streams. While Lambda can process messages from SQS or SNS, it is not a queuing mechanism itself. Lambda handles computation rather than message storage or delivery, so it cannot serve as a direct replacement for a message queue.

SQS is the correct choice because it allows components of distributed applications to communicate asynchronously, reducing tight coupling and dependencies between services. Its ability to reliably store messages until they are successfully processed, combined with automatic scaling and integration with other AWS services, makes it ideal for building resilient, scalable, and decoupled architectures. By ensuring that microservices can operate independently without waiting on each other, SQS enhances overall system reliability and flexibility.

Question 16

Which AWS service enables developers to store and retrieve unlimited amounts of objects with high durability?

A) Amazon S3
B) Amazon EBS
C) Amazon EFS
D) Amazon Glacier

Answer:  A) Amazon S3

Explanation:

Amazon S3, or Simple Storage Service, is a scalable object storage service designed for storing and retrieving any amount of data from anywhere on the web. Its key strength lies in durability and scalability. S3 guarantees 99.999999999% durability by automatically replicating objects across multiple Availability Zones within a region. This makes it highly suitable for applications that require storing vast amounts of unstructured data, such as media files, backups, or logs, without worrying about capacity constraints.

Amazon EBS, or Elastic Block Store, is a block-level storage service for EC2 instances. While it provides persistent storage that survives instance termination and allows for low-latency access, it is limited to the attached EC2 instance and is not designed for massive object storage. It is ideal for workloads like databases and operating systems, where block storage is necessary, but it cannot scale to the virtually unlimited object storage that S3 provides.

Amazon EFS, or Elastic File System, provides shared file storage for multiple EC2 instances and is a fully managed NFS file system. While it offers scalability and concurrent access, it is primarily intended for workloads that require a traditional file system interface. EFS throughput and storage costs differ from S3, and it is not ideal for storing an unlimited number of objects with high durability.

Amazon Glacier is an archival storage service designed for long-term storage and infrequent access. It is cost-effective for backups or archives that are rarely accessed, but retrieval times are slower compared to S3. Glacier is optimized for cold storage scenarios rather than active, frequently accessed object storage. Therefore, Amazon S3 is the correct choice because it combines unlimited scalability, extremely high durability, and instant access, making it the ideal service for developers who need reliable object storage for diverse applications.

Question 17 

Which AWS service allows you to automate code deployment to EC2 instances or Lambda functions?

A) AWS CodeDeploy
B) AWS CodePipeline
C) AWS CloudFormation
D) AWS OpsWorks

Answer:  A) AWS CodeDeploy

Explanation:

AWS CodeDeploy is a fully managed deployment service that automates the release of application code to EC2 instances, on-premises servers, or Lambda functions. It ensures that code updates are applied consistently and reliably across environments, reducing the risk of deployment errors. CodeDeploy supports rolling updates, blue/green deployments, and automatic rollback in case of failures, making it highly valuable for continuous delivery workflows.

AWS CodePipeline is a CI/CD orchestration service that automates the steps needed to build, test, and deploy applications. While it defines the workflow, it relies on CodeDeploy (or other deployment mechanisms) to execute the actual deployment step. CodePipeline handles pipeline orchestration but does not deploy code directly without integration.

AWS CloudFormation automates infrastructure provisioning by defining resources as code in templates. It allows the creation, updating, and deletion of AWS resources consistently but does not manage application deployment. CloudFormation ensures reproducible infrastructure but is not designed for the direct automation of application code releases.

AWS OpsWorks is a configuration management service that uses Chef or Puppet to manage servers and application stacks. While it can assist with deployment tasks, it is primarily focused on configuration automation rather than orchestrating application deployment to EC2 or Lambda. Therefore, CodeDeploy is the correct service because it directly automates and manages the deployment of code, ensuring consistent and reliable rollouts to compute resources.

Question 18 

Which AWS service allows for event-driven compute triggered by file uploads to a storage bucket?

A) Amazon S3 with AWS Lambda
B) Amazon EC2
C) Amazon RDS
D) Amazon DynamoDB

Answer:  A) Amazon S3 with AWS Lambda

Explanation:

Amazon S3 provides object storage and can generate event notifications whenever objects are created, modified, or deleted. By integrating S3 with AWS Lambda, developers can create serverless applications that automatically respond to events, such as image processing, data transformation, or triggering workflows upon file uploads. This enables real-time, event-driven compute without requiring servers to be constantly running.

Amazon EC2 offers virtual server instances but does not natively react to file uploads. EC2 would require custom scripts, cron jobs, or polling mechanisms to detect changes in S3, making it less efficient for event-driven processing.

Amazon RDS is a managed relational database service. It does not integrate directly with S3 events to trigger compute operations. Its primary focus is structured data management and transactional workloads rather than reacting to object storage events.

Amazon DynamoDB is a NoSQL database service that supports triggers via DynamoDB Streams. However, these triggers respond to database changes, not S3 storage events. Using S3 with Lambda is the correct combination because it allows fully automated, serverless event-driven workflows directly tied to storage operations, enabling efficient and scalable processing.

Question 19 

Which service allows developers to deploy containerized applications using Kubernetes?

A) Amazon ECS
B) Amazon EKS
C) AWS Lambda
D) Amazon Fargate

Answer: B) Amazon EKS

Explanation:

Amazon ECS, or Elastic Container Service, is AWS’s native container orchestration service that enables users to run and manage Docker containers at scale. ECS is highly integrated with AWS services such as IAM, CloudWatch, and ELB, making it convenient for teams building containerized applications entirely within the AWS ecosystem. It allows users to define tasks, services, and clusters for container deployments. However, ECS does not provide native Kubernetes APIs, which limits portability and the ability to leverage the vast Kubernetes ecosystem. Teams that require Kubernetes-specific tools, manifests, or integrations cannot rely solely on ECS, making it less suitable for Kubernetes-centric workloads.

Amazon EKS, or Elastic Kubernetes Service, is a fully managed Kubernetes service that allows developers to deploy, manage, and scale containerized applications using Kubernetes APIs and tools. EKS abstracts the management of the Kubernetes control plane, including patching, upgrades, and high availability, so teams can focus on application deployment rather than infrastructure maintenance. With EKS, developers benefit from the standard Kubernetes ecosystem, including Helm charts, CRDs, and community tools, providing flexibility and portability for applications. It supports integration with AWS networking, IAM, and monitoring services while maintaining Kubernetes-native functionality.

AWS Lambda is a serverless compute service designed to run short-lived functions in response to events such as API requests, file uploads, or database changes. While Lambda excels at executing event-driven workloads without managing servers, it is not designed to run long-lived containerized applications or manage orchestration frameworks like Kubernetes. Lambda’s execution model is based on stateless functions and ephemeral compute environments, making it unsuitable for applications that require full container lifecycle management or cluster orchestration.

Amazon Fargate is a serverless compute engine for containers that allows users to run containers without provisioning or managing servers. Fargate integrates with both ECS and EKS, removing the need to manage EC2 instances for container execution. While it abstracts the underlying infrastructure and provides scalability, Fargate itself is not a Kubernetes orchestrator. Running Kubernetes workloads specifically requires a managed Kubernetes control plane, which EKS provides. Fargate simply handles container execution within that framework.

Amazon EKS is the correct choice because it provides native Kubernetes support, including APIs, tools, and cluster management capabilities. It allows developers to deploy, scale, and manage containerized applications efficiently while integrating seamlessly with AWS services. EKS is ideal for teams that require Kubernetes compatibility, portability, and access to the Kubernetes ecosystem, enabling robust, production-grade container orchestration without the operational burden of managing the control plane.

Question 20 

Which AWS service provides fully managed in-memory caching for applications?

A) Amazon ElastiCache
B) Amazon DynamoDB
C) Amazon RDS
D) AWS Lambda

Answer:  A) Amazon ElastiCache

Explanation:

Amazon ElastiCache is a fully managed, in-memory caching service provided by AWS that supports Redis and Memcached. It significantly improves application performance by storing frequently accessed data in memory, allowing applications to retrieve data faster than querying a database repeatedly. ElastiCache reduces latency and helps applications scale by offloading read operations from backend databases. It also provides features such as clustering, replication, and automatic failover, ensuring high availability and reliability for critical workloads. With its seamless integration into AWS services, ElastiCache enables developers to implement caching layers that optimize performance and responsiveness across a variety of applications, from web applications to real-time analytics.

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance at scale. It is optimized for key-value and document data models and can handle large volumes of structured and semi-structured data with low latency. While DynamoDB is not a caching solution by itself, it can be paired with DynamoDB Accelerator (DAX), which is an in-memory caching layer that improves read performance. However, DAX is tightly coupled to DynamoDB, meaning it cannot be used as a general-purpose caching layer for other databases or workloads. DynamoDB focuses primarily on data persistence rather than caching transient, frequently accessed data in memory.

Amazon RDS is a managed relational database service that supports multiple database engines such as MySQL, PostgreSQL, Oracle, and SQL Server. It simplifies administrative tasks such as backups, patching, and scaling of relational databases. RDS can improve performance through read replicas or database-level caching strategies, but it is not inherently an in-memory cache. While RDS is excellent for structured, relational data storage, it does not provide the low-latency memory access that dedicated caching services like ElastiCache offer.

AWS Lambda is a serverless compute service designed to run event-driven functions without managing infrastructure. Lambda executes code in response to triggers and automatically scales based on demand. However, it does not provide persistent storage or caching across invocations. Each function execution is stateless, meaning that it cannot retain frequently accessed data in memory between invocations without external services such as ElastiCache or DynamoDB.

ElastiCache is the correct choice because it is purpose-built to provide a fast, fully managed in-memory caching layer that reduces database load and accelerates application responsiveness. Unlike DynamoDB, RDS, or Lambda, ElastiCache specifically addresses the need for low-latency, high-throughput data access, making it ideal for performance-critical applications that require rapid data retrieval and minimal query latency. Its robust features, including clustering, replication, and failover, further enhance reliability and scalability, solidifying its role as the go-to caching solution in AWS.