Amazon AWS Certified Developer – Associate DVA-C02 Exam Dumps and Practice Test Questions Set 7 Q121-140

Visit here for our full Amazon AWS Certified Developer – Associate DVA-C02 exam dumps and practice test questions.

Question 121 

Which AWS service allows developers to securely encrypt and decrypt application data?

A) AWS KMS
B) AWS Secrets Manager
C) AWS Systems Manager Parameter Store
D) Amazon S3

Answer:  A) AWS KMS

Explanation:

When developers need to secure data within their applications, whether stored, transmitted, or processed, they require a service that not only handles encryption but also manages the entire lifecycle of the keys used for this purpose. AWS offers a range of services that touch on security in different ways, but they do not all provide the same capabilities when it comes to encryption functions. AWS Key Management Service, commonly referred to as AWS KMS, is central to encryption across AWS because it provides the infrastructure needed for securely creating, storing, rotating, and auditing cryptographic keys.

AWS Secrets Manager is another security-related service, but its purpose differs substantially from AWS KMS. While Secrets Manager securely stores sensitive information such as database credentials, API keys, tokens, or other application secrets, it does not directly handle the encryption and decryption of arbitrary application data. Secrets Manager does use AWS KMS behind the scenes to encrypt the secrets it stores, but the service itself does not act as an encryption engine.

AWS Systems Manager Parameter Store also provides secure storage functionality, but similar to Secrets Manager, it is not primarily intended for encryption operations. The Parameter Store service can store both plaintext and encrypted configuration parameters. When encrypted parameters are used, AWS KMS again is responsible for the underlying key management and cryptographic operations. Parameter Store does not create or rotate encryption keys, nor does it provide the cryptographic APIs necessary for encrypting and decrypting arbitrary data within applications.

Amazon S3 is frequently associated with encryption because it supports multiple encryption modes, including server-side encryption with Amazon S3-managed keys, customer-provided keys, or AWS KMS-managed keys. However, this encryption is limited to object-level protection for stored data within S3 buckets. The service does not offer cryptographic operations to developers, nor does it provide a standalone key management solution. Instead, S3 relies on other encryption foundations, most often AWS KMS, to support secure storage.

AWS KMS stands out as the correct option because it is the only service designed explicitly for comprehensive and centralized cryptographic key management. It provides APIs that allow developers to perform encryption and decryption operations programmatically, enabling them to integrate secure data handling into applications without managing their own cryptographic infrastructure. KMS also supports key rotation, access control policies, audit logging through AWS CloudTrail, and integration with nearly all other AWS services. These features ensure consistent, enterprise-grade security while minimizing operational overhead. Considering all four options, AWS KMS is the only service capable of meeting the full requirements of secure application data encryption and decryption, which is why it is the correct answer.

Question 122 

Which AWS service allows developers to execute code without provisioning servers?

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

Answer:  A) AWS Lambda

Explanation:

The concept of executing code without provisioning or managing servers falls under the serverless computing paradigm. AWS Lambda is a core service in this space, allowing developers to focus entirely on writing application logic while AWS manages the underlying compute infrastructure. Lambda functions are triggered by events from more than 200 AWS sources, including S3 object changes, DynamoDB stream updates, API Gateway requests, and EventBridge rules. When a Lambda function runs, AWS automatically allocates CPU, memory, and runtime resources, scales capacity based on demand, and bills the user only for execution time consumed. There is no need to configure servers, manage capacity, patch systems, or scale infrastructure manually.

Amazon EC2 is a flexible computer service that provides virtual servers in the cloud. While EC2 offers high control and customization, it requires developers to choose instance sizes, configure networking, manage OS patches, handle scaling, and provision or deprovision instances manually or through automation frameworks. EC2 does not qualify as serverless because it involves continuous infrastructure management, even when automated through Auto Scaling groups or launch templates. Developers must still monitor instance performance, secure the environment, and maintain long-lived server infrastructure. Therefore, EC2 does not match the requirement of executing code without provisioning servers.

AWS Fargate provides a serverless compute engine for running containerized workloads without managing EC2 servers. While Fargate eliminates the need to provision or maintain EC2 instances, it still requires developers to build, package, and deploy containers. Fargate is suitable for container-based workloads where developers need full control over runtime environments, dependencies, and container orchestration. However, Fargate is not designed for running lightweight functions or event-driven code, nor does it abstract away execution details to the extent Lambda does. 

Amazon ECS is a container orchestration service that manages deployment and operation of containerized applications. ECS can run workloads on EC2 or Fargate. When ECS is used with EC2 launch type, it requires provisioning and managing the underlying infrastructure, including virtual machines, cluster scaling, and patching. Even when paired with Fargate, which removes infrastructure management, ECS still requires developers to focus on container orchestration, image management, and service definitions. 

Among all the available choices, AWS Lambda is the only service that enables developers to execute code directly, without building or managing servers or containers. It uniquely supports automatic scaling, event-driven triggers, per-invocation billing, dependency packaging through layers, and native integration with numerous AWS services. Lambda abstracts away virtually all infrastructure concerns, allowing developers to run code in response to events with minimal operational overhead. While EC2, Fargate, and ECS serve compute needs for traditional or containerized applications, they do not provide the serverless execution model that Lambda offers. Therefore, AWS Lambda is the correct answer for a service that allows developers to execute code without provisioning or managing servers.

Question 123 

Which AWS service allows developers to manage user authentication for web and mobile applications?

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

Answer:  A) AWS Cognito

Explanation:

When managing authentication for web and mobile applications, developers often need a service that handles user sign-up, sign-in, multi-factor authentication, user directories, token generation, and secure session management. AWS Cognito is specifically designed to handle these requirements through its User Pools and Identity Pools. User Pools give developers the ability to manage user registration, authentication, account recovery, password policies, and multi-factor authentication. Cognito integrates seamlessly with OAuth 2.0, OpenID Connect, and SAML-based identity providers, allowing developers to use social logins such as Google, Facebook, and Apple. It also integrates with API Gateway and Application Load Balancer to provide secure access to application endpoints. By offering features like hosted UI, user attribute verification, and token issuance, Cognito serves as a fully managed identity and access management solution for application users. These capabilities make AWS Cognito the ideal service for authentication and user management in web and mobile applications.

AWS Identity and Access Management (IAM) provides powerful controls over permissions and access to AWS resources, but it is not intended for managing the identities of end users of an application. IAM manages access for administrators, developers, service roles, and machine identities interacting with AWS services. It does not provide user sign-up pages, password recovery, MFA flows, or token-based authentication for application users. IAM is optimized for resource-level permissions and policies, not application-level authentication or user management.

AWS KMS is a cryptographic key management service designed for securing and encrypting data. Its role lies in generating and managing encryption keys, performing cryptographic operations, and supporting encryption across AWS workloads. It does not handle user session management, user identity verification, login flows, or OAuth tokens. While Cognito may use AWS KMS underneath to secure sensitive data, KMS itself is not an identity management system.

AWS Secrets Manager is a service used to store sensitive values such as passwords, API keys, database credentials, and third-party service tokens. While it ensures that secrets remain encrypted and supports automatic rotation for certain types of secrets, it does not manage user identities or authentication flows. Developers cannot use Secrets Manager to authenticate users or issue identity tokens. Like Parameter Store, its purpose is securely storing and retrieving secrets, not providing an authentication mechanism.

Among all the services listed, only AWS Cognito offers the full suite of features required to manage the authentication process for application users. It provides sign-in and sign-up functionality, handles account verification, integrates with user directories, supports multi-factor authentication, and issues secure JWT tokens. It reduces the operational burden of building authentication systems manually and aligns with modern application security standards. By supporting a variety of identity providers and enabling seamless integration with APIs and application backends, Cognito is purpose-built for managing authentication in web and mobile applications. This makes it the correct and most suitable answer to the question.

Question 124 

Which AWS service allows developers to build CI/CD pipelines for automated application deployment?

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

Answer:  A) AWS CodePipeline

Explanation:

A continuous integration and continuous delivery (CI/CD) pipeline automates the build, test, and deployment stages of application delivery. AWS CodePipeline is designed specifically to orchestrate these stages by connecting multiple AWS and third-party tools into a cohesive automation workflow. Developers can configure CodePipeline to trigger on code commits, run build processes using CodeBuild or Jenkins, perform testing, and deploy applications using CodeDeploy, CloudFormation, ECS, or Lambda. Each stage of the pipeline is fully automated, and changes flow from one stage to the next based on defined rules and conditions. CodePipeline also integrates with approval workflows, enabling teams to enforce manual checkpoints in regulated environments.

AWS CodeBuild is a service focused on compiling source code, running tests, and producing artifacts. While CodeBuild is a critical component of many pipelines, it does not orchestrate the full CI/CD workflow. Instead, CodeBuild serves as a build engine within a larger pipeline, often triggered by CodePipeline. Although developers can manually script custom pipelines using CodeBuild and CloudWatch Events, such approaches lack the flexibility, reliability, and ease of use that CodePipeline provides.

AWS CodeDeploy automates the deployment of application artifacts to a variety of compute platforms, including EC2 instances, on-premises servers, Lambda functions, and ECS services. It ensures consistent deployments and supports rollback, blue/green deployments, and traffic shifting. However, CodeDeploy does not manage the build or test stages of the development lifecycle. 

AWS CloudFormation enables infrastructure as code by allowing developers to define AWS resources through declarative templates. While CloudFormation can be used within CI/CD pipelines to automate infrastructure provisioning or updates, it is not a CI/CD orchestration tool by itself. CloudFormation manages infrastructure, not software delivery workflows. It does not support pipeline sequencing, test execution, build orchestration, or deployment approvals.

CodePipeline is the correct answer because it provides automation across every stage of the CI/CD lifecycle, from code commit to build, testing, approval, and deployment. It integrates with other AWS developer tools and external services, enabling continuous and repeatable application delivery. Unlike CodeBuild, it orchestrates the entire pipeline. Unlike CodeDeploy, it performs more than deployment. And unlike CloudFormation, it manages application workflows rather than infrastructure definitions. Therefore, CodePipeline is the only service listed that fully meets the requirement of building CI/CD pipelines.

Question 125 

Which AWS service provides a managed relational database for serverless applications?

A) Amazon Aurora Serverless
B) Amazon DynamoDB
C) Amazon RDS for MySQL
D) Amazon ElastiCache

Answer:  A) Amazon Aurora Serverless

Explanation:

Serverless relational databases require automatic scaling, on-demand capacity adjustments, and minimal operational overhead. Amazon Aurora Serverless satisfies these requirements by providing a fully managed, auto-scaling relational database compatible with MySQL and PostgreSQL. Unlike traditional relational databases, Aurora Serverless automatically adjusts the number of Aurora Capacity Units (ACUs) based on workload demand. If no connections or queries occur, Aurora Serverless can scale down to zero, reducing cost. It also resumes instantly when new activity begins.

Amazon DynamoDB is a fully managed NoSQL database that delivers single-digit millisecond performance at any scale. Although DynamoDB offers on-demand capacity scaling and serverless behavior, it is not a relational database. DynamoDB uses key-value and document data models, making it unsuitable when applications require joins, relational integrity, SQL queries, or complex transactional workflows typical of relational databases. 

Amazon RDS for MySQL is a managed relational database service, but it is not serverless. RDS instances require developers to provision specific instance sizes, storage capacity, networking configurations, and scaling strategies. Scaling is manual or scheduled, meaning the database cannot autonomously scale in response to workload spikes. RDS also does not scale down automatically during periods of inactivity. 

Amazon ElastiCache is an in-memory caching service that supports Redis and Memcached. It is not a relational database and cannot serve as a primary data store for relational queries. Developers typically use ElastiCache to accelerate application performance by caching frequently accessed data, reducing latency for database-heavy applications. 

Among the options, Amazon Aurora Serverless uniquely combines relational database functionality with serverless scalability. It provides the SQL compatibility, ACID transactions, high availability, durability, and managed operational features expected from relational systems, while also embracing auto-scaling and pay-per-use models characteristic of serverless designs. DynamoDB is serverless but not relational. RDS is relational but not serverless. ElastiCache is neither relational nor intended as a primary data store. Therefore, Aurora Serverless is the only service that satisfies all the conditions mentioned in the question.

Question 126 

Which AWS service allows developers to trigger Lambda functions in response to object changes?

A) Amazon S3
B) Amazon EC2
C) AWS Step Functions
D) Amazon RDS

Answer:  A) Amazon S3

Explanation:

Amazon S3 integrates natively with AWS Lambda by allowing event notifications to invoke Lambda functions when certain object-level operations occur. These operations include events such as object creation, deletion, restoration, and replication. Developers can configure S3 event triggers to launch Lambda automatically when new files are uploaded—for instance, to process images, generate thumbnails, extract metadata, run data transformations, or perform security scanning. S3’s event-driven integration with Lambda is direct, requiring no intermediary services or complex orchestration. 

Amazon EC2 is a computer service that provides virtual machines but does not generate event notifications tied to object-level operations like S3. EC2 instances do not act as event sources for Lambda, nor do changes within EC2 trigger serverless functions. While CloudWatch Events or CloudWatch Logs can sometimes be used to monitor EC2 state changes, this has nothing to do with object updates or storage events. EC2’s primary role is computer provisioning, not event triggering.

AWS Step Functions is a state machine orchestration service that coordinates microservices and serverless workflows. Although Step Functions can call Lambda and integrate with AWS services, it does not generate events based on object changes in S3. Instead, Step Functions react only when invoked directly through another service, an API call, or an event routed through EventBridge. It does not monitor S3 buckets or detect object operations. 

Amazon RDS is a relational database service and does not integrate with Lambda for object change events. While RDS can publish events to Amazon EventBridge for certain database-level activities such as instance failovers or maintenance events, these are not related to object storage operations. RDS does not manage or detect file uploads, deletions, or storage-level changes, and therefore cannot trigger Lambda in response to object-level modifications.

Among all the services listed, Amazon S3 is the only one that provides native event notifications capable of invoking AWS Lambda in response to object-level operations. This capability allows developers to build powerful and scalable event-driven applications that automate data processing workflows. EC2 lacks event triggers for storage operations, Step Functions is an orchestration tool rather than an event source, and RDS emits database events but not object storage events. For these reasons, S3 is the correct answer when developers need a service that triggers Lambda based on object changes.

Question 127 

Which AWS service allows developers to create a scalable, managed queue for message buffering?

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

Answer:  A) Amazon SQS

Explanation:

Amazon Simple Queue Service (SQS) is AWS’s fully managed message queuing service designed to help developers build scalable, decoupled, and fault-tolerant distributed applications. SQS enables asynchronous communication between microservices by temporarily storing messages in a queue until they are processed. It supports both standard queues with nearly unlimited throughput and FIFO queues that guarantee message ordering and exactly-once processing. SQS automatically handles load, scales seamlessly, and ensures reliable message delivery by maintaining multiple redundant copies of messages. Developers can use SQS to prevent message loss, offload processing workloads, smooth traffic spikes, and prevent components from becoming overloaded. 

Amazon Simple Notification Service (SNS) is a publish/subscribe messaging service designed for broadcasting messages to multiple subscribers. It is not a queueing service and does not store messages for later consumption. Instead, SNS immediately pushes messages to subscribers such as email endpoints, Lambda functions, HTTP/S endpoints, or SQS queues. SNS does not provide message persistence or buffering capabilities, making it unsuitable for scenarios where messages must be stored until explicitly processed by consumers. 

AWS Lambda is a serverless computer service designed to run code in response to events. Although Lambda can process messages from SQS, DynamoDB Streams, or EventBridge, it does not store or buffer messages on its own. Lambda functions respond to events but do not ensure message durability or queue management. Without an external queueing service such as SQS, messages could be lost or fail to trigger reliably.

Amazon Kinesis is a data streaming service optimized for real-time analytics, event ingestion, and large-scale data flows. It does not act as a traditional message queue but instead handles streaming records that require real-time processing. Kinesis streams must be consumed quickly, and unprocessed data expires after a retention period. Unlike SQS, Kinesis delivers ordered, time-series records for scenarios such as log ingestion, clickstream processing, and telemetry data analysis. 

Among the available choices, Amazon SQS is the only service designed specifically for message buffering and reliable asynchronous communication. SNS supports notifications, Lambda provides compute, and Kinesis specializes in streaming, but SQS alone offers persistent, scalable, and managed message queues. It ensures messages are stored until processed, supports distributed architectures, and enables high-throughput workloads without risk of message loss. Therefore, SQS is the correct and appropriate answer to this question.

Question 128 

Which AWS service allows developers to visualize request flows and detect performance bottlenecks?

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

Answer:  A) AWS X-Ray

Explanation:

AWS X-Ray is a distributed tracing service that gives developers detailed insights into how requests flow through an application. It provides visualizations of end-to-end request paths, latency breakdowns, service maps, and detailed traces of individual operations. X-Ray helps developers identify bottlenecks, detect errors, troubleshoot microservices, and optimize serverless or containerized architectures. By instrumenting applications running on AWS Lambda, Amazon ECS, Amazon EKS, EC2, or on-premises environments, X-Ray collects trace data that highlights dependencies, timing, and error propagation..

AWS CloudWatch is a monitoring and observability service that collects logs, metrics, alarms, and events from AWS resources and applications. While CloudWatch offers dashboards, metrics, log analytics, alerts, and application insights, it does not provide end-to-end distributed tracing. CloudWatch focuses on resource-level visibility such as CPU utilization, memory usage, network I/O, and application logs. Although CloudWatch ServiceLens can integrate with X-Ray to show traces, CloudWatch itself does not generate or visualize the detailed request flows required to identify per-request bottlenecks. 

AWS CloudTrail is an auditing service that records API calls made within an AWS account. It captures who performed an action, when the action was taken, and what AWS service was involved. CloudTrail is essential for governance, compliance, security auditing, and troubleshooting unauthorized access. 

AWS Config is a configuration monitoring and compliance service. It tracks changes to AWS resource configurations, evaluates them against compliance rules, and provides historical configuration timelines. Although Config is extremely helpful for maintaining governance and identifying misconfigurations, it has no capability to trace application requests, visualize flows, or analyze performance bottlenecks. 

Among all the services listed, AWS X-Ray is the only one specifically designed for tracing requests through distributed applications, visualizing execution flows, diagnosing latency issues, and identifying performance hotspots. CloudWatch monitors resources, CloudTrail audits API calls, and Config tracks configuration changes, but none of these services provide the depth of request-level visibility required to troubleshoot complex microservice architectures. Therefore, AWS X-Ray is the correct answer.

Question 129 

Which AWS service allows developers to deliver APIs at scale with security and throttling features?

A) Amazon API Gateway
B) AWS Lambda
C) Amazon EC2
D) Amazon CloudFront

Answer:  A) Amazon API Gateway

Explanation:

Amazon API Gateway is a fully managed service designed to create, publish, maintain, secure, monitor, and scale APIs. It supports RESTful APIs, WebSocket APIs, and HTTP APIs, allowing developers to expose application logic running on AWS Lambda, EC2, containerized services, or on-premises systems. API Gateway includes built-in security features such as rate limiting, throttling, request validation, WAF integration, IAM authorization, Cognito-based authentication, and API key usage controls. It also supports caching, versioning, and custom domain names. Developers can deploy APIs across multiple stages, monitor usage through CloudWatch metrics, and apply fine-grained access policies.

AWS Lambda executes serverless functions but does not provide API management capabilities such as throttling, access control, endpoint routing, or request validation. While Lambda can process requests behind an API Gateway endpoint, Lambda alone cannot expose public or private APIs without partnering services. Lambda is a compute engine, not an API delivery platform.

Amazon EC2 provides virtual servers but leaves API development, security, scaling, throttling, and routing entirely to application developers. Running an API on EC2 requires managing web servers, load balancers, TLS certificates, scaling groups, failover strategies, and operational maintenance. EC2 does not automatically secure or throttle API traffic and does not offer integrated monitoring or authorization. 

Amazon CloudFront is a content delivery network (CDN) that accelerates the delivery of static and dynamic content. While CloudFront can sit in front of API Gateway or application endpoints, it is not designed to manage APIs by itself. CloudFront provides caching, DDoS protection via AWS Shield, and global edge distribution, but it does not offer API-specific features such as request validation, throttling tiers, API keys, or usage plans. It enhances performance but does not deliver core API functionality.

Among all listed services, Amazon API Gateway is the only one that provides comprehensive API delivery features, including security controls, throttling, authorization, monitoring, and scaling. Lambda executes code, EC2 hosts applications, and CloudFront accelerates content, but only API Gateway offers end-to-end management for APIs. Therefore, API Gateway is the correct answer.

Question 130 

Which AWS service allows developers to implement event-driven architecture by routing events between services?

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

Answer:  A) Amazon EventBridge

Explanation:

Amazon EventBridge is a serverless event bus service designed to connect event producers with event consumers in complex, scalable, event-driven architectures. EventBridge collects events from AWS services, custom applications, and more than 100 SaaS integrations, and then routes them to targets such as Lambda, Step Functions, SQS, API Gateway, ECS, or Kinesis. It supports advanced features such as event filtering, pattern matching, schema discovery, and event transformation. These capabilities allow developers to build loosely coupled applications where components communicate via events instead of direct service calls. EventBridge automates routing logic based on rules and event patterns, enabling developers to create highly reactive architectures that scale dynamically.

Amazon SNS is a pub/sub messaging service that broadcasts messages to multiple subscribers. While SNS can deliver notifications and trigger Lambda functions, it does not offer the sophisticated event routing, filtering, or integration capabilities found in EventBridge. SNS operates on a push model where a message is published and immediately delivered to subscribers. It is not designed for multi-service event orchestration or rule-based routing. SNS lacks features such as event pattern matching, SaaS integration, or event buses. 

Amazon SQS is a managed queueing service used for message buffering and decoupling microservices. While SQS provides durable message storage and asynchronous processing, it is not an event routing service. SQS delivers messages to consumers based on polling rather than event-driven push mechanisms, and it cannot route events to multiple targets or apply filtering logic. It lacks the higher-level event patterns and orchestration capabilities needed to build event-driven systems involving numerous services. 

AWS Lambda is a compute service that executes code in response to events. While Lambda can act as a consumer of events from EventBridge, SNS, SQS, DynamoDB Streams, and other event sources, it does not route events between services. Lambda cannot transform, filter, or forward events on its own unless developers manually build such logic within the function code. Lambda executes code but does not determine how events move across an architecture.

EventBridge is the correct answer because it is the only service designed specifically for routing events between services using patterns, rules, and event buses. SNS publishes messages, SQS stores them, and Lambda executes code, but none of them provide the event-driven integration capabilities that EventBridge offers. Therefore, Amazon EventBridge is the correct choice for implementing event-driven architecture.

Question 131 

Which AWS service provides centralized log storage and analysis for troubleshooting applications?

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

Answer:  A) AWS CloudWatch Logs

Explanation:

AWS CloudWatch Logs is a fully managed logging service that allows developers to collect, store, analyze, and monitor logs from applications, AWS services, and on-premises systems. One of its main strengths is providing a centralized platform where multiple log sources converge, making analysis and troubleshooting significantly easier. CloudWatch Logs supports log filtering, metric generation, log retention policies, and integration with CloudWatch Alarms, allowing teams to trigger notifications when specific log patterns occur. Developers can search logs in real time, identify error trends, and correlate log sequences with system performance. 

AWS CloudTrail is another logging-related service, but its purpose differs significantly. CloudTrail focuses exclusively on auditing API activity across the AWS account. It records who made what request, from where, and when the request occurred. Because CloudTrail captures control-plane actions, it helps teams track changes and identify unauthorized or unexpected operations. However, CloudTrail does not store application-level logs, performance logs, or business logic logs. It cannot search or analyze logs created by an application itself. It is limited to auditing events for governance, compliance, and security investigations. 

AWS X-Ray is designed for distributed tracing and understanding application request flows across microservices. Instead of storing raw logs, X-Ray collects traces, segments, and subsegments that show where latency occurs within a distributed system. This makes X-Ray ideal for identifying bottlenecks, unusual delays, or anomalies in request paths. It is extremely useful for visualizing service-to-service communication, identifying slow dependencies, and pinpointing performance hotspots.

AWS Config focuses on tracking configuration changes in AWS resources and evaluating compliance against predefined rules. It provides historical visibility into resource states and helps detect drift or misconfiguration. Config is widely used in governance-oriented environments where monitoring resource configurations is essential.

When comparing all four services, the correct answer becomes clear. CloudWatch Logs is the only service that provides centralized log storage, supports diverse log sources, enables powerful querying, and allows real-time troubleshooting. CloudTrail focuses on API auditing, which is useful for security but unrelated to application debugging. X-Ray analyzes distributed applications at the tracing level but does not store raw logs. This centralized capability allows teams to speed up troubleshooting, correlate performance issues with application events, and maintain operational visibility across their workloads. Because it fulfills the requirements of log ingestion, retention, indexing, filtering, and real-time monitoring, CloudWatch Logs is the correct choice for a service providing centralized log storage and analysis for troubleshooting applications.

Question 132 

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

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

Answer:  A) Amazon ECS with Fargate

Explanation:

Amazon ECS with Fargate is a serverless compute engine for containers that allows developers to run containerized applications without provisioning, managing, or scaling underlying virtual machines. With Fargate, teams focus solely on defining container specifications, CPU, memory, and networking requirements, while AWS automatically launches and manages the infrastructure. This eliminates the need to handle EC2 instances, patch operating systems, manage clusters, or size servers for varying workloads. The service also provides task-level isolation and automatically scales to match the application’s resource needs. 

Amazon EKS is AWS’s managed Kubernetes service and provides orchestration for complex container environments using Kubernetes. While EKS simplifies Kubernetes management, it still requires administrators to manage worker nodes unless using Fargate as the execution layer. EKS clusters require decisions about node groups, scaling mechanisms, version upgrades, and operational monitoring. Because Kubernetes introduces additional complexity, EKS is more suited for organizations that already rely on Kubernetes or need its advanced scheduling and extensibility features. 

AWS Lambda supports running packaged container images, but it is fundamentally a serverless function execution platform rather than a general container orchestration system. Lambda’s execution environment is constrained by runtime limits, memory caps, and the event-driven invocation model. While lightweight containers can be deployed to Lambda, it is designed for short-lived, stateless tasks rather than running full microservices or long-running workloads. Lambda also cannot manage container clusters, networking rules, or service discovery in the way that ECS or EKS can. 

Amazon EC2 provides raw virtual machine instances that can run containers through ECS, EKS, or manually installed container engines like Docker. Although EC2 offers flexibility and control, it requires developers to manage server provisioning, scaling policies, operating system updates, and cluster lifecycle operations. This contradicts the requirement for a service that runs containers without server management. 

Given these distinctions, Amazon ECS with Fargate is the correct answer because it provides a fully serverless environment for container execution without the need to manage EC2 instances or infrastructure. Fargate handles scaling, isolation, provisioning, and task scheduling automatically. EKS still requires cluster and node management unless paired with Fargate, which introduces unnecessary complexity for users who simply want a managed container runtime. EC2 requires full infrastructure management, which goes against the requirements of the question. Therefore, ECS with Fargate is the ideal solution for running containerized applications without server management, providing a streamlined operational experience and allowing developers to focus on building applications instead of handling underlying compute resources.

Question 133 

Which AWS service provides managed document storage compatible with MongoDB APIs?

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

Answer:  A) Amazon DocumentDB

Explanation:

Amazon DocumentDB is a fully managed document database service built to support JSON-based workloads and provide compatibility with MongoDB APIs. It enables developers to run applications that depend on MongoDB drivers and tools with minimal or no code changes. DocumentDB separates compute from storage, providing high scalability, fast performance, and robust durability. The service automatically handles backups, encryption, patching, replication, and failover, reducing operational overhead significantly. Because it is optimized for document-oriented data models, DocumentDB efficiently stores and retrieves hierarchical JSON documents and supports indexing, querying, and aggregation features typically required by document-based applications.

Amazon DynamoDB is another fully managed NoSQL database service, but it is key-value and document-oriented rather than being MongoDB compatible. DynamoDB delivers single-digit millisecond latency, global replication, automatic scaling, and serverless performance characteristics, making it ideal for high-traffic workloads. However, it does not use the MongoDB query language or driver ecosystem, and its data modeling principles differ significantly. 

Amazon RDS is AWS’s managed relational database platform supporting engines such as MySQL, PostgreSQL, MariaDB, and Oracle. RDS is optimized for structured, tabular data and enforces relational schema rules. It supports SQL queries, transactions, and referential integrity but is not designed for flexible JSON document storage as a primary model. While some engines in RDS provide JSON column support, this feature is secondary and not comparable to a dedicated document store. 

Amazon Aurora is a high-performance relational database engine compatible with MySQL and PostgreSQL. Aurora provides improved availability, replication, and performance compared to traditional RDS engines. It is ideal for transactional and analytical workloads that benefit from relational modeling, strong ACID properties, and SQL functionality. Like RDS, Aurora may store JSON data inside specific column types, but it is fundamentally relational and lacks full document database and MongoDB API support. It cannot act as a drop-in replacement for MongoDB workloads.

After evaluating all four options, Amazon DocumentDB is the correct answer because it is purpose-built to offer MongoDB compatibility, flexible document modeling, and fully managed operations. DynamoDB, while excellent for NoSQL workloads, does not support MongoDB drivers or data modeling constructs. RDS and Aurora focus on relational databases and cannot store or query document data in the same manner as MongoDB. DocumentDB stands out by offering compatibility, scalability, automated maintenance, and performance optimizations specifically tailored for JSON document workloads. Applications relying on MongoDB APIs can migrate with minimal code changes, making DocumentDB the ideal managed service for MongoDB-compatible document storage on AWS.

Question 134 

Which AWS service allows developers to automatically rotate database credentials and API keys?

A) AWS Secrets Manager
B) AWS KMS
C) AWS Systems Manager Parameter Store
D) Amazon RDS

Answer:  A) AWS Secrets Manager

Explanation:

AWS Secrets Manager is a fully managed service that securely stores sensitive information such as database credentials, API keys, OAuth tokens, and other confidential configuration values. One of its greatest advantages is that it supports automatic rotation of secrets using built-in integration with services such as Amazon RDS, Amazon Redshift, Amazon DocumentDB, and custom rotation logic through Lambda functions. Secrets Manager encrypts secrets at rest, enforces fine-grained access control using IAM, and stores detailed audit logs of secret retrieval through CloudTrail. It also offers versioning for secrets, allowing applications to refer to the latest version or specific iterations of stored values. Automatic rotation helps organizations comply with security best practices and reduces the risk of long-lived credentials being compromised. 

AWS KMS (Key Management Service) focuses specifically on creating, managing, and controlling encryption keys. While KMS encrypts and decrypts sensitive data and helps developers secure secrets, it does not manage secret rotation for database credentials or API tokens. KMS primarily handles cryptographic operations such as generating encrypted envelopes, signing data, verifying integrity, and enabling encryption for S3, EBS, Lambda, and other AWS resources. KMS keys themselves can have automatic rotation for key material, but this rotation does not apply to credentials stored by the user. As such, KMS is complementary to Secrets Manager but cannot replace its credential rotation capabilities.

AWS Systems Manager Parameter Store provides a secure and scalable storage system for configuration data, parameters, and secrets. It supports both plaintext and encrypted values, with encryption performed using AWS KMS. Parameter Store is widely used because it integrates with EC2, ECS, Lambda, and other AWS services. However, Parameter Store does not support automatic rotation of secrets by default. Developers must implement custom logic through external workflows if they want secrets to rotate automatically. 

Amazon RDS manages relational databases such as MySQL, PostgreSQL, MariaDB, and SQL Server. RDS provides automated backups, patching, snapshots, and failover, simplifying database maintenance. However, it does not manage credentials on behalf of the user beyond initial creation. RDS relies on external services such as Secrets Manager to store and rotate credentials. 

Based on this comparison, AWS Secrets Manager is the correct answer because it is the only service that provides automated rotation of secrets, simplifies integration with AWS databases, and supports custom rotation workflows. KMS manages encryption keys, Parameter Store stores secrets without built-in rotation, and RDS does not handle credential management or rotation. Secrets Manager offers a dedicated, secure, and automated approach to secret lifecycle management, making it the ideal solution for rotating database credentials and API keys.

Question 135 

Which AWS service allows developers to orchestrate workflows using multiple AWS services?

A) AWS Step Functions
B) AWS Lambda
C) Amazon EC2
D) AWS CodePipeline

Answer:  A) AWS Step Functions

Explanation:

AWS Step Functions is a fully managed orchestration service that allows developers to combine multiple AWS services into coordinated workflows. It uses state machines and visual diagrams to represent sequential, parallel, branching, and error-handling logic. Step Functions integrates seamlessly with services like Lambda, ECS, Glue, SageMaker, DynamoDB, SNS, SQS, and more, allowing developers to build complex distributed applications. It manages retries, ensures fault-tolerance, tracks workflow progress, and provides detailed execution logs. 

AWS Lambda is a serverless computer service that runs code in response to events. While Lambda can be a component of a workflow, it does not orchestrate other services on its own. Lambda functions execute individual tasks, but they lack built-in mechanisms for sequencing operations, coordinating multiple services, or handling workflow state across steps. Developers would need to implement custom logic inside Lambda functions to manage orchestration, which increases code complexity and reduces observability. 

Amazon EC2 provides scalable virtual machine instances used to run applications, servers, and custom orchestration tools. However, EC2 does not include workflow orchestration capabilities natively. Teams would have to install and manage their own workflow engines such as Apache Airflow or custom scripts. 

AWS CodePipeline automates CI/CD workflows for application releases. It orchestrates stages such as source retrieval, build, test, and deployment, and integrates with CodeBuild, CodeDeploy, and third-party tools. However, CodePipeline is specifically designed for software delivery automation, not general-purpose application orchestration or coordination of AWS service tasks.

Considering these differences, AWS Step Functions is the correct answer because it provides serverless orchestration, built-in state management, visualization tools, error recovery, and integrations with a wide range of AWS services. Lambda runs individual tasks, EC2 offers infrastructure without orchestration, and CodePipeline focuses on CI/CD rather than application workflow execution. Step Functions is the dedicated AWS service for orchestrating multi-step, multi-service workflows in a scalable and reliable manner.

Question 136 

Which AWS service provides a managed key-value and document database for low-latency applications?

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

Answer:  A) Amazon DynamoDB

Explanation:

Amazon DynamoDB is a fully managed NoSQL database service designed to deliver consistent single-digit millisecond latency at any scale. It supports both key-value and document data models, making it suitable for applications requiring flexible schema design. DynamoDB automatically handles scaling, replication, encryption, backups, and partition management. It provides features such as on-demand capacity mode, TTL expiration, Streams for change tracking, and DAX for in-memory acceleration. Because DynamoDB is serverless, developers do not provision or manage servers, and the service dynamically adjusts capacity based on workload demands. This makes it ideal for gaming, mobile apps, IoT platforms, and real-time analytics workloads requiring fast and predictable performance.

Amazon RDS is a relational database service supporting engines like MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server. RDS is optimized for structured, relational workloads requiring SQL queries, ACID transactions, and rigid schema design. Although RDS provides high performance and automation for relational databases, it is not designed as a NoSQL key-value or document store. Applications requiring sub-millisecond latency or schema flexibility typically rely on NoSQL solutions instead of relational engines.

Amazon ElastiCache provides in-memory caching using Redis or Memcached. It offers microsecond-level latency and is ideal for accelerating read-heavy or compute-intensive applications. However, ElastiCache is not a persistent database and is not intended for durable key-value or document storage. Caches complement databases like DynamoDB or RDS but cannot replace them for long-term storage. 

Amazon S3 is an object storage service used to store files, media, backups, logs, and unstructured data at scale. It provides high durability and cost-efficient storage but is not a database and does not offer key-value or document querying capabilities. 

Given these differences, Amazon DynamoDB is the correct answer because it offers fully managed, low-latency, scalable storage supporting both key-value and document models. RDS is relational, ElastiCache is in-memory caching rather than durable storage, and S3 is object storage. DynamoDB’s serverless architecture, automatic scaling, consistent performance, and flexible data structures make it ideal for low-latency applications requiring a managed key-value and document database.

Question 137 

Which AWS service allows developers to automatically scale EC2 instances based on demand?

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

Answer:  A) Amazon EC2 Auto Scaling

Explanation:

Amazon EC2 Auto Scaling ensures that applications running on EC2 maintain sufficient capacity to handle changing demand by automatically increasing or decreasing the number of EC2 instances. Developers can configure scaling policies based on metrics such as CPU utilization, network traffic, or custom CloudWatch metrics. Auto Scaling also supports predictive scaling and scheduled scaling to optimize performance and cost. It continuously monitors instance health and replaces unhealthy instances automatically. 

AWS Lambda is a serverless compute service that automatically scales based on the number of incoming requests. While Lambda offers scaling, it applies only to serverless function execution rather than EC2 instances. Lambda cannot scale EC2 infrastructure or modify instance fleets. It is fundamentally different from EC2 Auto Scaling because it scales individual functions rather than virtual machines.

AWS Step Functions is an orchestration service that coordinates tasks and AWS services using state machines. It does not manage scaling of compute resources such as EC2 instances. While Step Functions can trigger Lambda functions or initiate ECS tasks, it does not automatically adjust infrastructure capacity. Its purpose is workflow coordination, not resource scaling.

Amazon S3 provides durable object storage and supports virtually unlimited scalability for storing files, logs, media, and other unstructured data. Although S3 scales storage capacity automatically, it does not handle compute scaling or manage EC2 instances. S3 operates entirely differently from compute services and cannot modify resources like EC2.

Given these comparisons, Amazon EC2 Auto Scaling is the correct answer because it is specifically designed to scale EC2 instances automatically based on demand, health checks, and defined policies. Lambda scales function executions, Step Functions orchestrates workflows, and S3 stores objects. Only EC2 Auto Scaling provides dynamic, automated control over EC2 capacity, making it essential for maintaining application performance while optimizing cost.

Question 138 

Which AWS service provides serverless in-memory caching for improving application performance?

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

Answer:  A) Amazon ElastiCache

Explanation:

Amazon ElastiCache is a fully managed in-memory caching service supporting Redis and Memcached. It improves application performance by storing frequently accessed data in memory, reducing the need to query databases repeatedly. ElastiCache offers extremely low latency, high throughput, automatic failover, backup support, and cluster scaling. Because it is managed by AWS, developers do not need to patch, monitor, or manually scale caching servers. It is widely used for accelerating read-heavy workloads, reducing database load, storing session data, caching API responses, and supporting real-time leaderboards. The serverless nature of ElastiCache abstracts cluster management and allows applications to benefit from fast, in-memory performance without operational overhead.

Amazon DynamoDB, while capable of delivering low-latency performance, is a durable NoSQL database rather than a caching service. DynamoDB stores data on disk with SSD-backed persistence. Although DynamoDB Accelerator (DAX) provides in-memory caching for DynamoDB workloads, it is a separate feature and not the same as ElastiCache. DynamoDB’s primary goal is to serve as a scalable database, not a dedicated in-memory cache.

Amazon RDS provides relational database engines such as MySQL and PostgreSQL and is designed for structured storage rather than in-memory caching. RDS offers caching at the database level through engine-specific mechanisms, but it is not a serverless caching system. It does not deliver the microsecond-level latency associated with in-memory caches.

Amazon S3 is an object storage service optimized for durability and large data objects. It provides cost-efficient, highly durable storage but is not suitable for in-memory caching or rapid, repeated data retrieval. S3 access patterns involve network calls and are not designed for caching application responses.

Given these distinctions, Amazon ElastiCache is the correct answer because it is the dedicated AWS service for serverless, in-memory caching that significantly improves application performance. DynamoDB, RDS, and S3 serve other storage purposes but do not replace a managed caching system.

Question 139 

Which AWS service allows developers to analyze streaming data in real-time using SQL queries?

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

Answer:  A) Amazon Kinesis Data Analytics

Explanation:

Amazon Kinesis Data Analytics is a fully managed service that allows developers to process streaming data in real time using SQL queries or Apache Flink. It integrates directly with Kinesis Data Streams and Kinesis Data Firehose, enabling teams to run continuous queries that filter, aggregate, and transform streaming data as it arrives. With its SQL-based interface, developers familiar with relational querying can build streaming applications without learning specialized stream-processing frameworks. Kinesis Data Analytics provides fault tolerance, automatic scaling, durable checkpoints, and built-in monitoring through CloudWatch. It is ideal for log processing, real-time analytics dashboards, anomaly detection, and event-driven pipelines requiring immediate insights.

Amazon SQS is a fully managed message queuing service designed for decoupling microservices, buffering requests, and ensuring reliable message delivery. SQS does not perform data analytics and cannot execute SQL queries. Its purpose is transport rather than transformation or analysis. While SQS plays a crucial role in distributed application architectures, it is not a stream processing or analytics tool.

Amazon SNS supports pub/sub messaging and broadcasting notifications to subscribers. SNS is used to send messages to applications, email, SMS, Lambda, or SQS, but it does not offer data transformation, querying, or real-time analytics. It focuses on message distribution rather than data processing.

AWS Lambda executes code in response to events, including streaming events from Kinesis. Although Lambda can process streaming data, it does not provide built-in analytics features or SQL querying capabilities. Developers would need to write custom code to handle analytics logic, which is more complex than using Kinesis Data Analytics’ declarative SQL interface.

Given these differences, Amazon Kinesis Data Analytics is the correct answer because it specifically provides real-time SQL-based analysis of streaming data. SQS and SNS handle messaging, and Lambda executes code but lacks built-in SQL analytics capabilities. Kinesis Data Analytics is the only service designed for real-time querying and transformation of streaming datasets.

Question 140 

Which AWS service allows developers to implement API throttling, authorization, and request validation?

A) Amazon API Gateway
B) AWS Lambda
C) Amazon EC2
D) Amazon CloudFront

Answer:  A) Amazon API Gateway

Explanation:

Amazon API Gateway is a fully managed service used to create, deploy, protect, and monitor APIs at scale. It provides features such as throttling to control the rate of incoming requests, authorization using IAM, Cognito, or custom authorizers, and request validation to ensure payloads meet required schemas. API Gateway supports REST APIs, WebSocket APIs, and HTTP APIs, enabling developers to expose backend services while maintaining security and reliability. It also integrates with Lambda, ECS, EC2, and other AWS services to route incoming traffic. Additional features include authentication, caching, usage plans, access logging, and detailed monitoring through CloudWatch. These capabilities collectively make API Gateway the central tool for managing API access and traffic control.

AWS Lambda is often paired with API Gateway as a backend compute service, but it does not provide API management capabilities by itself. Lambda executes code in response to invocations and supports scaling, environment variables, and role-based access control. 

Amazon EC2 provides raw virtual servers that can host applications and custom API management tools, but it does not include built-in API throttling or request validation features. Developers would need to manually implement or install third-party solutions, which increases operational overhead and complexity.

Amazon CloudFront is a global content delivery network designed to accelerate static and dynamic content delivery. While CloudFront can enforce some security measures and integrate with AWS WAF for request filtering, it does not replace API Gateway’s API management functionality. It cannot perform request validation, API throttling, or authentication at the application API level.

When comparing all four options, Amazon API Gateway is clearly the correct service because it is specifically designed for API traffic management, including throttling, authorization, and validation. Lambda runs backend code, EC2 provides compute infrastructure, and CloudFront optimizes content delivery. Only API Gateway provides the full suite of features required to secure and control API interactions.