Amazon AWS Certified Developer – Associate DVA-C02 Exam Dumps and Practice Test Questions Set 2 Q21-40

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Question 21 

Which AWS service allows you to store messages temporarily between distributed microservices?

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

Answer:  A) Amazon SQS

Explanation:

Amazon SQS, or Simple Queue Service, is a fully managed message queuing service designed to decouple and coordinate microservices, distributed systems, and serverless applications. It allows messages to be temporarily stored in queues until they are processed by the consuming service. By introducing a buffer between producers and consumers, SQS ensures that messages are not lost even if a service is temporarily unavailable or overwhelmed. This makes it ideal for asynchronous communication where reliability and message persistence are critical. Developers can choose between standard queues, which offer high throughput and at-least-once delivery, or FIFO queues, which guarantee order and exact-once processing, depending on the requirements of their application.

Amazon SNS, or Simple Notification Service, is a messaging service that follows the publish/subscribe model. It enables applications to send messages to multiple subscribers simultaneously, which could be other applications, mobile devices, or email endpoints. SNS is designed for real-time message delivery rather than temporary storage. Unlike SQS, it does not retain messages for later processing; if a subscriber is unavailable, the message may be lost unless integrated with a durable service like SQS. While SNS is excellent for broadcasting notifications or triggering multiple endpoints instantly, it does not provide the message persistence and decoupling features required for reliable microservice communication.

AWS Lambda is a serverless computer service that allows developers to run code in response to events without provisioning or managing servers. Lambda can be triggered by a wide range of AWS services, including SQS, SNS, and DynamoDB streams. While it can process messages and respond to events, it does not serve as a message storage mechanism. Messages cannot be queued or persisted directly in Lambda; they must be delivered to Lambda by other services. Lambda’s strength lies in executing short-lived tasks in response to triggers, which complements a message queue but cannot replace one for decoupled communication between services.

Amazon Kinesis is a service for real-time data streaming and analytics, primarily focused on collecting, processing, and analyzing large streams of data such as logs, telemetry, or clickstreams. Kinesis is designed for high-throughput and continuous ingestion of streaming data rather than temporary message storage between microservices. While Kinesis can buffer data for short periods, it is intended for analytics pipelines rather than reliable message queuing. Therefore, for the purpose of storing messages temporarily and ensuring asynchronous, decoupled processing between microservices, Amazon SQS is the correct choice. It combines reliability, persistence, and flexible delivery guarantees that align precisely with the requirements of distributed systems.

Question 22 

Which AWS service allows for continuous integration and continuous delivery (CI/CD) of application code?

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

Answer:  A) AWS CodePipeline

Explanation:

AWS CodePipeline is a fully managed continuous integration and continuous delivery service that helps automate the building, testing, and deployment of application code. It orchestrates the entire CI/CD workflow, allowing developers to define stages, actions, and approvals for code changes. By integrating with services such as CodeBuild, CodeDeploy, and third-party tools, CodePipeline enables the rapid and reliable release of software updates while minimizing manual intervention. Pipelines can be configured to trigger automatically on code commits, ensuring that the latest code passes through tests and deployment stages seamlessly.

AWS CloudFormation is an infrastructure-as-code service that allows developers to define and provision AWS resources using templates. While CloudFormation is excellent for deploying and managing infrastructure consistently, it does not handle the automation of building, testing, or deploying application code itself. CloudFormation templates can be used within a CI/CD workflow, but on its own, it does not serve as a pipeline for continuous integration or delivery, making it insufficient for the complete CI/CD lifecycle.

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces artifacts ready for deployment. While CodeBuild handles the build and test stages efficiently, it does not manage the orchestration of multiple stages, approvals, or deployment steps. To achieve full CI/CD automation, CodeBuild needs to be integrated into a pipeline service like CodePipeline. Its focus is on transforming source code into deployable artifacts, but it cannot coordinate the end-to-end process required for continuous delivery without a pipeline.

AWS CodeDeploy is a deployment automation service that handles the delivery of applications to EC2 instances, Lambda functions, or on-premises servers. CodeDeploy focuses on the deployment stage of CI/CD and supports strategies like blue/green and rolling deployments. However, it does not manage the build or testing stages and requires integration with pipeline services for full automation. CodePipeline is the correct choice because it orchestrates the complete CI/CD workflow, connecting CodeBuild, CodeDeploy, and other actions into a seamless automated release process, enabling faster and more reliable software delivery.

Question 23 

Which AWS service is designed for high-performance, managed relational databases compatible with MySQL and PostgreSQL?

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

Answer: B) Amazon Aurora

Explanation:

Amazon Aurora is a fully managed relational database service designed for high performance, scalability, and availability while maintaining compatibility with MySQL and PostgreSQL. Aurora uses a distributed, fault-tolerant, and self-healing storage system that automatically scales up to handle growing workloads. It provides advanced features like read replicas, multi-AZ replication, automated backups, and high throughput for transactional workloads. Aurora is specifically optimized to deliver superior performance compared to standard RDS databases, often achieving up to five times the throughput of MySQL and twice that of PostgreSQL.

Amazon RDS is a managed relational database service that supports multiple engines, including MySQL, PostgreSQL, Oracle, SQL Server, and MariaDB. While RDS simplifies database provisioning, patching, backup, and recovery, standard RDS instances do not offer the same high performance and automatic scaling features as Aurora. RDS is suitable for general-purpose relational workloads, but Aurora is specifically engineered to meet the demands of high-traffic applications requiring low latency, high throughput, and fault-tolerant storage.

Amazon DynamoDB is a fully managed NoSQL database that provides single-digit millisecond latency at scale. It is optimized for key-value and document-based data structures rather than relational data. DynamoDB does not support SQL-based queries in the same way as relational databases, and it cannot offer features like joins, foreign keys, or complex transactions typical in MySQL or PostgreSQL workloads. While DynamoDB excels in scalability and performance for non-relational workloads, it is not designed for applications that require relational database functionality.

Amazon Redshift is a managed data warehouse service designed for analytical queries on large datasets. Redshift uses columnar storage and massively parallel processing to optimize read-heavy workloads, aggregations, and complex queries for analytics. It is not intended for transactional workloads typical of relational databases, and it does not provide MySQL or PostgreSQL compatibility for everyday application use. Aurora is correct because it combines relational database features, MySQL/PostgreSQL compatibility, high availability, and performance optimization, making it the ideal choice for high-performance transactional applications.

Question 24 

Which AWS service allows you to encrypt data at rest and manage encryption keys centrally?

A) AWS KMS
B) AWS Secrets Manager
C) Amazon S3
D) Amazon RDS

Answer:  A) AWS KMS

Explanation:

AWS Key Management Service (KMS) provides centralized control for creating, managing, and auditing cryptographic keys across AWS services and applications. KMS allows developers to encrypt and decrypt data using symmetric or asymmetric keys, apply granular access controls, and track usage through detailed logging in AWS CloudTrail. It serves as the backbone for encryption in AWS, ensuring that sensitive data remains protected while enabling secure key lifecycle management. KMS integrates seamlessly with other AWS services, allowing data to be encrypted without developers needing to implement custom encryption solutions.

AWS Secrets Manager is designed to store, manage, and rotate sensitive information such as database credentials, API keys, and passwords. While it enhances security by centralizing secrets management and providing automated rotation, Secrets Manager does not provide general-purpose encryption key management. It focuses on access and secret rotation rather than encryption at rest across multiple services, making it complementary to KMS rather than a replacement.

Amazon S3 allows users to store objects with encryption at rest using server-side or client-side encryption. However, the encryption keys used for S3 objects can be managed via KMS, or customers can supply their own keys. S3 alone does not provide centralized key management for all AWS resources; it relies on integration with KMS to enable full key control and auditing. S3 is primarily a storage service, not a key management system.

Amazon RDS supports encryption at rest for database instances using AWS-managed keys or customer-managed keys stored in KMS. While RDS can encrypt stored data, it does not provide centralized key management for other AWS services independently. KMS is the correct choice because it provides a dedicated, centralized, and fully managed solution for encryption key creation, management, policy enforcement, and auditing, ensuring that data across services can be securely encrypted and controlled from one place.

Question 25 

Which AWS service enables developers to run containers without provisioning servers or managing clusters?

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

Answer:  A) Amazon ECS with Fargate

Explanation:

Amazon ECS with Fargate is a serverless container execution platform that allows developers to deploy and run containers without managing the underlying infrastructure. Fargate automatically provisions the necessary compute resources, handles scaling, and ensures isolation between containers. ECS manages orchestration, scheduling, and service discovery while Fargate eliminates the need to provision EC2 instances manually. This combination enables developers to focus entirely on building containerized applications rather than on operational overhead, reducing complexity and operational risk.

Amazon EKS is a managed Kubernetes service that provides the flexibility and features of Kubernetes while reducing cluster management overhead. However, users still need to manage worker nodes or configure Fargate profiles to achieve serverless container execution. EKS is suitable for teams familiar with Kubernetes and requiring advanced orchestration features but does not fully abstract away cluster management unless combined with Fargate. Therefore, it requires more operational knowledge compared to ECS with Fargate.

Amazon EC2 provides virtual servers in the cloud with complete control over operating systems, networking, and compute resources. While EC2 can run container workloads, developers are responsible for provisioning instances, installing container runtimes, managing scaling, and handling updates. This requires significant infrastructure management and does not provide the serverless experience that Fargate offers. EC2 is better suited for applications requiring full control over the server environment rather than abstracted container execution.

AWS Lambda can run container images up to certain size and runtime limits, enabling some containerized workloads to run serverlessly. However, Lambda is optimized for event-driven execution with short-lived tasks rather than long-running containerized applications or complex service architectures. ECS with Fargate is the correct choice because it allows fully containerized applications to run serverlessly, providing orchestration, scaling, isolation, and infrastructure management automatically, making it ideal for developers seeking a completely serverless container deployment platform.

Question 26

Which service allows storing frequently accessed data in-memory to reduce database load?

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

Answer:  A) Amazon ElastiCache

Explanation:

Amazon ElastiCache is a fully managed, in-memory caching service designed to improve application performance by storing frequently accessed data in memory. It supports both Redis and Memcached, two widely used caching engines, allowing developers to reduce database load and achieve low-latency access for high-throughput workloads. By keeping hot data in memory, applications can avoid repeatedly querying backend databases, which improves response times and overall scalability. ElastiCache is particularly useful for scenarios such as caching session data, leaderboards, or frequently queried database results.

Amazon RDS is a fully managed relational database service that provides features such as automated backups, replication, and high availability. While RDS delivers strong performance for structured data, it is not an in-memory cache. Requests to RDS typically involve disk I/O and network latency, which makes it less suited for scenarios where microsecond-level response times are needed. Developers can integrate RDS with ElastiCache to offload frequent reads, but RDS alone does not provide the caching capabilities required for high-speed data access.

Amazon S3 is an object storage service optimized for durability and availability. While it is excellent for storing large volumes of unstructured data like images, videos, and backups, S3 is not designed for low-latency in-memory operations. Accessing data in S3 involves network calls and object retrieval processes that are significantly slower compared to in-memory caching. S3 excels at persistent storage but does not reduce database load in real-time access scenarios, which is essential for high-performance applications.

Amazon DynamoDB is a fully managed NoSQL database offering single-digit millisecond latency. Although it provides very fast access to structured data, DynamoDB functions as a database, not a caching layer. Developers sometimes pair DynamoDB with DynamoDB Accelerator (DAX) to achieve caching-like performance, but out-of-the-box, DynamoDB does not store transient data in memory like ElastiCache. The primary purpose of DynamoDB is persistent, low-latency storage rather than short-term, high-speed caching. ElastiCache is the correct choice because it is specifically built to store hot data in memory and reduce load on databases while improving application performance.

Question 27 

Which AWS service allows you to trace requests and diagnose performance issues in microservices?

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

Answer: B) AWS X-Ray

Explanation:

AWS X-Ray enables developers to trace requests across distributed applications and microservices, providing end-to-end visibility into performance and errors. It allows you to identify latency issues, exceptions, and bottlenecks in complex workflows. X-Ray generates service maps, showing how requests propagate through services, and provides detailed insights into the response times of individual components, which is crucial for diagnosing performance issues in event-driven or microservices architectures.

AWS CloudTrail records all API calls made in an AWS account for auditing and compliance purposes. While it tracks the “who, what, and when” of service activity, CloudTrail does not provide application-level tracing or detailed insights into request flows. It is more suited for security and operational audits than for analyzing performance bottlenecks in microservices.

AWS CloudWatch collects metrics, logs, and events from AWS resources and applications. It can alert on threshold violations and visualize trends over time. However, CloudWatch alone does not trace individual requests through multiple services or provide the granular insights into service dependencies that X-Ray does. CloudWatch helps with monitoring, but not distributed request tracing.

Amazon CloudFront is a content delivery network that caches and serves content from edge locations to improve latency for end users. It is not designed for application-level tracing and does not provide detailed insight into how requests move through backend services. X-Ray is the correct solution because it focuses specifically on end-to-end request tracing, helping developers diagnose performance issues and optimize distributed applications effectively.

Question 28 

Which AWS service allows developers to decouple microservices with publish-subscribe messaging?

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

Answer:  A) Amazon SNS

Explanation:

Amazon SNS is a fully managed pub/sub messaging service that enables asynchronous communication between decoupled microservices. Publishers send messages to SNS topics, and multiple subscribers, such as Lambda functions, HTTP endpoints, or email addresses, can receive them simultaneously. This model allows immediate notification delivery and ensures loosely coupled, event-driven architectures, making it ideal for broadcast-style messaging.

Amazon SQS is a message queue service designed for decoupling microservices and processing messages asynchronously. While SQS ensures reliable delivery and allows consumers to process messages at their own pace, it does not natively support pub/sub. Each message is delivered to a single consumer, making it better suited for task queues rather than broadcast notifications.

AWS Lambda is a serverless compute service that executes code in response to events. Although it integrates with messaging services like SNS and SQS, Lambda itself is not a messaging platform. It can act as a subscriber in a pub/sub model but does not manage message distribution between producers and multiple consumers.

Amazon MQ is a managed message broker supporting standard protocols such as AMQP and MQTT. It allows message-based communication for existing applications requiring compatibility with traditional messaging standards. However, Amazon MQ does not provide the same seamless integration with AWS-native serverless and event-driven workflows as SNS. SNS is correct because it directly enables pub/sub communication with immediate delivery to multiple subscribers for building scalable, loosely coupled systems.

Question 29 

Which AWS service enables serverless file processing triggered by object uploads?

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

Answer:  A) Amazon S3 with Lambda

Explanation:

Amazon S3 is a highly durable and scalable object storage service that allows developers to store virtually unlimited amounts of data, ranging from documents and images to videos and backups. One of its powerful features is the ability to generate events when objects are created, modified, or deleted. These events can be configured to trigger AWS Lambda functions, enabling developers to build fully serverless workflows. This integration allows automated file processing tasks to occur immediately after an object is uploaded or changed, without the need to provision, manage, or scale any servers. Common use cases include automatic image resizing, format conversion, metadata extraction, virus scanning, or triggering notifications whenever new files are uploaded. By leveraging S3 events and Lambda, applications can implement event-driven architectures that respond instantly to data changes, improving efficiency and reducing manual intervention.

Amazon EC2 provides virtual servers in the cloud, giving developers full control over operating systems and software. While EC2 can run scripts or applications to process files, it does not inherently respond to S3 events. Developers would need to implement additional mechanisms, such as polling S3 buckets at regular intervals or using custom scripts to detect file changes. This approach introduces complexity, potential delays, and higher operational overhead compared to using a serverless event-driven workflow. EC2 requires manual scaling and management of the compute infrastructure, which adds both cost and administrative effort when compared to the automated, on-demand nature of Lambda functions triggered by S3 events.

Amazon RDS is a fully managed relational database service, and Amazon DynamoDB is a managed NoSQL database service. Both services are optimized for structured and semi-structured data storage and retrieval. However, neither RDS nor DynamoDB is designed to store files directly or provide event-based triggers for file processing. While they excel at managing transactional or queryable data, they cannot automatically initiate workflows in response to object uploads, making them unsuitable for serverless file processing scenarios.

Using S3 in combination with Lambda provides a true serverless, event-driven solution for file processing. When a file is uploaded to S3, a Lambda function can be triggered immediately, executing the necessary processing logic without requiring any manual intervention or server management. This approach is highly scalable, as Lambda automatically handles concurrent executions, and cost-efficient, as developers only pay for the compute time consumed by the function. The combination of S3 and Lambda ensures that file processing workflows are automated, responsive, and reliable, making it the ideal solution for event-driven, serverless file handling in the cloud.

Question 30 

Which AWS service allows developers to automate application deployments to EC2 instances?

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

Answer:  A) AWS CodeDeploy

Explanation:

AWS CodeDeploy is a fully managed deployment service designed to automate the process of deploying applications to EC2 instances, AWS Lambda functions, and even on-premises servers. Its primary goal is to ensure that application releases are consistent, repeatable, and reliable across different environments. By automating the deployment process, CodeDeploy reduces the risk of human error, minimizes downtime during updates, and allows teams to roll back changes quickly if a deployment fails. It supports multiple deployment strategies, including rolling updates, blue/green deployments, and canary releases. These strategies provide flexibility in how new versions of applications are introduced, allowing teams to test updates on a subset of instances before a full-scale rollout, which helps maintain system stability and reliability.

AWS CodePipeline, on the other hand, is a continuous integration and continuous delivery (CI/CD) orchestration service. It manages the end-to-end workflow of software delivery, including building, testing, and deploying applications. While CodePipeline automates the flow of changes through different stages, it does not directly perform deployments to compute resources. Instead, it relies on services like CodeDeploy to handle the actual deployment step. CodePipeline is essential for automating release workflows and coordinating multiple tools in a CI/CD process, but without CodeDeploy, it cannot push code changes to running servers or manage deployment strategies.

AWS CloudFormation is an infrastructure-as-code service that allows developers to define and provision AWS resources using templates. CloudFormation is excellent for automating the creation and configuration of infrastructure such as EC2 instances, networking components, and databases. However, it is focused on resource provisioning and management rather than deploying application code to those resources. While CloudFormation can provision the infrastructure needed to host applications, the actual deployment of new application versions must be handled by a deployment service like CodeDeploy.

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces deployable artifacts. Its main function is the build and test stages of a CI/CD pipeline. CodeBuild does not handle deploying these artifacts to EC2 instances or Lambda functions. While it integrates with other services like CodePipeline and CodeDeploy, its purpose is limited to creating and validating the application packages rather than delivering them to production environments.

CodeDeploy is the correct service for this scenario because it directly automates the deployment of applications to compute resources. By managing deployment strategies, rollbacks, and instance targeting, CodeDeploy ensures that releases are reliable, consistent, and scalable. It allows developers to focus on building and testing applications while providing a robust framework for safe, automated deployment.

Question 31 

Which service allows managing infrastructure using JSON or YAML templates?

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

Answer:  A) AWS CloudFormation

Explanation:

AWS CloudFormation is a service that allows developers and DevOps engineers to define and provision AWS infrastructure using declarative templates written in JSON or YAML. These templates describe the resources required, their configurations, and dependencies, allowing consistent, repeatable, and version-controlled infrastructure deployments. CloudFormation handles the creation, update, and deletion of resources automatically, reducing manual intervention and the risk of misconfigurations.

AWS CodePipeline is a continuous integration and continuous delivery (CI/CD) service that automates the build, test, and deployment of applications. While it orchestrates workflows across different AWS services and third-party tools, it does not define the underlying infrastructure in a declarative template. Its primary purpose is to automate code deployment, not infrastructure provisioning.

AWS OpsWorks is a configuration management service that uses Chef and Puppet to automate server configuration, deployment, and management. It is procedural rather than declarative, requiring users to specify actions in recipes or manifests rather than defining the infrastructure state in a template. While it can manage servers, it is not primarily designed for full infrastructure lifecycle management like CloudFormation.

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces deployable artifacts. It is an integral part of the CI/CD pipeline but does not provision or manage infrastructure resources. It focuses solely on code compilation and testing rather than infrastructure orchestration.

CloudFormation is the correct answer because it uniquely provides infrastructure as code, allowing developers to define AWS resources declaratively and manage them automatically. It integrates version control, supports parameterization, and ensures consistent environment replication, which distinguishes it from CodePipeline, OpsWorks, and CodeBuild.

Question 32 

Which AWS service is designed for long-term archival of data with infrequent access?

A) Amazon Glacier
B) Amazon S3 Standard
C) Amazon RDS
D) Amazon DynamoDB

Answer:  A) Amazon Glacier

Explanation:

Amazon Glacier is a low-cost storage service designed for long-term data archiving and backup. It provides secure, durable storage for data that is accessed infrequently, with retrieval times that range from minutes to hours depending on the retrieval option chosen. Glacier is ideal for compliance-driven storage, historical data archiving, or scenarios where large amounts of data need to be stored for extended periods without regular access.

Amazon S3 Standard is optimized for frequently accessed data. It provides immediate retrieval with high availability and low latency but comes at a higher cost compared to Glacier. S3 Standard is suitable for active data that is regularly used or updated, rather than long-term archival.

Amazon RDS is a managed relational database service that provides scalable database solutions. While it offers high availability and automated backups, it is designed for operational transactional workloads rather than cost-efficient long-term storage of rarely accessed data.

Amazon DynamoDB is a NoSQL database designed for high-performance, low-latency applications. It is not intended for archival storage, as it focuses on real-time access to structured data. Using DynamoDB for infrequently accessed archival data would be both inefficient and costly.

Glacier is correct because it balances extremely low cost with high durability, making it the preferred solution for storing large amounts of data over long periods with infrequent retrieval. Its integration with S3 lifecycle policies allows seamless migration from S3 Standard to Glacier for archival needs.

Question 33 

Which AWS service allows developers to provision serverless relational databases with MySQL compatibility?

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

Answer:  A) Amazon Aurora Serverless

Explanation:

Amazon Aurora Serverless is an on-demand, auto-scaling relational database service compatible with MySQL and PostgreSQL. It automatically adjusts capacity based on application traffic, eliminating the need for manual instance provisioning or scaling. Aurora Serverless provides high availability, fault tolerance, and automated backups, while reducing operational overhead and cost for variable workloads.

Amazon RDS is a fully managed relational database service that requires users to provision and manage database instances. While it offers automation features such as backups and minor version upgrades, it does not automatically scale capacity in response to workload fluctuations like Aurora Serverless.

Amazon DynamoDB is a NoSQL database that provides fast, predictable performance and scalability. It is non-relational and does not support SQL-based queries or MySQL compatibility, making it unsuitable for applications requiring relational databases.

Amazon Redshift is a data warehouse service designed for large-scale analytical workloads. While powerful for analytics, it is not designed for transactional workloads or relational database operations with serverless scaling.

Aurora Serverless is correct because it combines the advantages of a relational database with the flexibility of serverless computing, allowing developers to run MySQL-compatible databases without managing instances, making it ideal for variable workloads and cost optimization.

Question 34 

Which AWS service is best for storing and managing user identities for applications?

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

Answer:  A) AWS Cognito

Explanation: 

AWS Cognito provides authentication, authorization, and user management for web and mobile applications. It allows developers to manage user sign-up, sign-in, and access control securely, integrating with social identity providers and enterprise identity systems. Cognito also supports token-based authentication, user pools, and identity federation for flexible application security.

AWS IAM manages permissions and access for AWS resources at the account level. It is designed for controlling AWS service actions and not for managing application-level user identities. Using IAM for application users would be complex and insecure.

AWS Secrets Manager stores sensitive information such as database credentials, API keys, and tokens. It focuses on secure storage and automatic rotation of secrets rather than authentication or user identity management.

AWS KMS provides key management and encryption services to secure data at rest or in transit. It does not handle user identity or authentication for applications and is unrelated to user access control.

Cognito is correct because it provides application-level identity management, secure authentication, and authorization capabilities, making it the most appropriate choice for managing users, unlike IAM, Secrets Manager, or KMS, which serve different security functions.

Question 35 

Which AWS service helps analyze logs and metrics from distributed applications for troubleshooting?

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

Answer:  A) AWS CloudWatch

Explanation:

AWS CloudWatch is a monitoring and observability service that collects metrics, logs, and events from AWS resources and applications. It enables real-time monitoring, automated alerts, dashboards, and insights to troubleshoot performance issues across distributed systems. CloudWatch allows users to set alarms, visualize trends, and respond proactively to operational problems.

AWS CloudTrail records API calls and actions within AWS accounts, providing auditing, compliance, and governance capabilities. While valuable for security analysis, CloudTrail is not designed for real-time performance monitoring or application troubleshooting.

AWS X-Ray helps developers analyze and debug distributed applications by tracing requests as they travel through services. It provides latency and dependency insights but does not aggregate general logs or performance metrics for all resources like CloudWatch.

AWS Config tracks configuration changes and assesses compliance against rules. It provides visibility into resource configurations but is not focused on performance monitoring or troubleshooting application metrics.

CloudWatch is correct because it consolidates logs, metrics, and events from multiple services, enabling developers to monitor applications, detect anomalies, and troubleshoot issues efficiently. Its comprehensive observability features make it the primary service for application monitoring over CloudTrail, X-Ray, or Config.

Question 36 

Which service is ideal for creating data streams to ingest and process real-time data?

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

Answer:  A) Amazon Kinesis

Explanation:

Amazon Kinesis is a fully managed service designed specifically for ingesting, processing, and analyzing large volumes of streaming data in real time. It enables applications to continuously collect data from multiple sources, such as IoT devices, logs, social media feeds, and clickstreams, and process that data immediately. Kinesis provides capabilities like Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics, allowing developers to build applications that react to incoming data without delay, which is essential for real-time analytics, monitoring, and alerting.

Amazon SQS, on the other hand, is a message queuing service. It allows decoupled microservices or distributed systems to communicate by sending messages asynchronously. While it guarantees reliable delivery and scales easily, it is not designed for continuous data streaming. Messages in SQS are processed individually, and the service does not support real-time analytics or continuous ingestion of high-velocity data streams in the same way that Kinesis does.

Amazon SNS provides a pub/sub messaging model where publishers send messages to topics, and multiple subscribers receive them. SNS is excellent for broadcasting notifications, alerts, or updates to multiple endpoints simultaneously. However, SNS does not offer the real-time streaming and analytics capabilities that Kinesis does. It is more suited for event notifications rather than high-throughput data processing pipelines.

AWS Lambda is a serverless compute service that executes code in response to events. While Lambda can be triggered by Kinesis streams to process data as it arrives, Lambda itself does not provide a native data ingestion or streaming platform. Kinesis is the correct choice because it is built to handle real-time streaming data at scale, providing both ingestion and processing pipelines without requiring extensive infrastructure management.

Question 37 

Which AWS service allows you to create and manage serverless APIs?

A) Amazon API Gateway
B) AWS Lambda
C) Amazon EC2
D) AWS App Mesh

Answer:  A) Amazon API Gateway

Explanation:

Amazon API Gateway is a fully managed service that enables developers to create, deploy, and manage APIs at any scale. It supports both RESTful and WebSocket APIs, providing features such as request throttling, authentication, caching, monitoring, and traffic management. API Gateway allows developers to focus on building API endpoints without worrying about server management or scaling, making it ideal for serverless and microservices architectures.

AWS Lambda is a serverless compute platform that runs code in response to events. While Lambda is often used in combination with API Gateway to execute backend logic for APIs, Lambda itself does not provide the API management layer. Without API Gateway, exposing Lambda functions directly to clients would require additional configuration and infrastructure management.

Amazon EC2 provides virtual servers in the cloud and gives complete control over compute resources. Although it is possible to host APIs on EC2 instances, it requires manual management of servers, scaling, and networking. EC2 does not offer built-in API lifecycle management, so it is less suitable for purely serverless API deployment.

AWS App Mesh is a service mesh that manages communication between microservices. It focuses on service-to-service networking, routing, and observability rather than API exposure to external clients. It cannot create or manage APIs directly. API Gateway is the correct choice because it provides a fully managed platform for creating, securing, deploying, and monitoring APIs without managing servers.

Question 38

Which AWS service automatically scales Lambda function invocations based on incoming traffic?

A) AWS Lambda
B) Amazon EC2 Auto Scaling
C) AWS Step Functions
D) Amazon ECS

Answer:  A) AWS Lambda

Explanation:

AWS Lambda is designed to automatically scale based on the number of incoming events. Each invocation of a Lambda function occurs in a separate execution environment, which allows the service to handle multiple requests concurrently without manual provisioning. This scaling happens instantly and transparently, ensuring that serverless applications can accommodate sudden spikes in demand without any administrative overhead.

Amazon EC2 Auto Scaling is used to scale virtual server instances based on demand. It monitors metrics such as CPU utilization or network traffic and adjusts the number of EC2 instances accordingly. While EC2 Auto Scaling automates scaling for servers, it does not directly apply to serverless functions, which are managed differently.

AWS Step Functions orchestrates workflows across multiple AWS services. It defines the execution order of tasks, handles retries, and manages state transitions, but it does not automatically scale compute functions. Lambda functions within a Step Functions workflow may scale individually, but the orchestration itself is not responsible for scaling.

Amazon ECS manages containerized applications and can scale tasks based on metrics. However, it requires configuring cluster capacity and scaling policies. Unlike Lambda, ECS is not inherently serverless, and developers must manage the underlying infrastructure. Lambda is correct because it automatically scales in response to demand without any infrastructure management, making it ideal for event-driven and serverless workloads.

Question 39 

Which AWS service allows for visual workflow orchestration of serverless applications?

A) AWS Step Functions
B) AWS Lambda
C) Amazon EC2
D) Amazon API Gateway

Answer:  A) AWS Step Functions

Explanation:

AWS Step Functions is a serverless orchestration service that lets developers build complex workflows by connecting multiple AWS services. It provides a visual interface to design workflows using state machines, supporting sequential execution, parallel processing, branching logic, and error handling. Step Functions simplifies application logic, reduces operational complexity, and improves maintainability for serverless applications.

AWS Lambda executes individual functions in response to events. While Lambda can perform computation or processing, it does not provide a visual orchestration tool. Developers would need additional logic to coordinate multiple Lambda functions manually.

Amazon EC2 offers virtual servers and compute resources. It does not provide orchestration capabilities or a way to visualize the flow of an application. EC2 instances could host orchestration software, but this would require significant manual setup.

Amazon API Gateway exposes APIs to clients and manages request handling. It does not manage workflows or orchestrate multiple services internally. Step Functions is the correct answer because it allows developers to design, visualize, and manage workflows across multiple services, integrating seamlessly with Lambda and other AWS services.

Question 40 

Which AWS service is ideal for deploying machine learning models as APIs?

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

Answer:  A) Amazon SageMaker

Explanation:

Amazon SageMaker is a fully managed machine learning service that enables developers and data scientists to build, train, and deploy machine learning models at scale. It provides managed endpoints that can serve models as APIs, allowing applications to perform real-time inference without managing the underlying infrastructure. SageMaker also offers features for automated model tuning, monitoring, and scaling, which reduces operational overhead.

AWS Lambda can be used to invoke machine learning models for inference, but it is not a dedicated ML deployment platform. While Lambda supports serverless execution, developers would need to integrate it with a separate model hosting solution and handle scaling, versioning, and monitoring manually.

Amazon EC2 provides virtual machines where models can be hosted. While EC2 allows full control over deployment, it requires managing servers, scaling instances, and maintaining runtime environments, increasing operational complexity. It is less convenient for ML model deployment compared to managed services like SageMaker.

Amazon RDS is a relational database service and is not designed to host machine learning models. It stores and retrieves structured data but provides no facilities for model training, inference, or serving. SageMaker is the correct choice because it offers a fully managed, scalable, and integrated platform for deploying models as APIs, handling all aspects of inference and operational management.