Amazon AWS Certified Developer – Associate DVA-C02 Exam Dumps and Practice Test Questions Set 6 Q101-120

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Question 101 

Which AWS service allows developers to decouple microservices and send messages asynchronously between them?

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

Answer:  A) Amazon SQS

Explanation: 

Amazon SQS is a fully managed message queuing service designed to help developers decouple microservices and enable asynchronous communication. The service works by allowing producers to send messages to a queue where they are stored until consumers retrieve and process them. This capability ensures that even if the receiving component is temporarily unavailable or experiencing high load, messages are not lost and processing can resume seamlessly once the consumer becomes available again. SQS supports high scalability, fault tolerance, and reliability, making it a fundamental building block for distributed application architectures. Its ability to handle large volumes of messages with minimal operational overhead makes it suitable for event-driven, microservice-based, and serverless architectures.

Amazon SNS, on the other hand, is a publish/subscribe messaging service that focuses on delivering messages to multiple subscribers simultaneously. SNS works by allowing publishers to send messages to a topic, from which the service immediately pushes those messages to all subscribed endpoints such as email, SMS, HTTP endpoints, or AWS Lambda functions. While SNS excels at fan-out scenarios and broadcasting notifications, it does not provide message retention or queueing for delayed processing. Once the message is delivered or attempted, it is not stored for future retrieval. This makes SNS less suitable for applications that need reliable, asynchronous task execution or systems where the processing component may be temporarily unavailable.

AWS Lambda is a serverless compute service that executes code in response to events and automatically manages the underlying compute resources. While Lambda can be triggered by various sources, including SQS queues, SNS topics, and API Gateway requests, it does not function as a message queue itself. Lambda processes events and returns results but does not store, manage, or buffer messages independently. It complements event-driven architectures by providing compute execution but cannot replace a queuing mechanism. Lambda is a consumer of messages rather than a tool for decoupling microservices through message storage or buffering.

Amazon Kinesis is a managed service designed specifically for real-time ingestion and processing of streaming data. Kinesis Data Streams enables ingestion of large amounts of continuously generated data such as logs, IoT device telemetry, or application events. Although Kinesis retains data for a configurable period and allows multiple consumers, it is optimized for high-throughput, low-latency streaming analytics rather than discrete asynchronous messages between microservices. It is ideal for scenarios involving continuous data flows but not suitable as a traditional message queue for discrete tasks or process handoffs.

The correct answer is Amazon SQS because it uniquely provides the queueing capabilities required for decoupling application components and enabling asynchronous message-based communication. SQS ensures message durability, reliable delivery, buffering during traffic spikes, and the ability for consumer services to process messages at their own pace. Unlike SNS, which broadcasts messages, SQS is designed for reliable handoff between one component and another. Lambda cannot store messages, and Kinesis is optimized for streaming rather than message-based workloads. SQS aligns perfectly with asynchronous microservice architectures, ensuring scalability, resilience, and operational simplicity.

Question 102 

Which AWS service allows developers to distribute content globally with low latency?

A) Amazon CloudFront
B) AWS Lambda
C) Amazon S3
D) Amazon API Gateway

Answer:  A) Amazon CloudFront

Explanation:

Amazon CloudFront is a global content delivery network designed to accelerate the delivery of static and dynamic content by caching it at AWS edge locations distributed around the world. CloudFront reduces latency by serving content from the nearest geographic location to the user rather than requiring every request to travel back to the origin server. It supports integration with Amazon S3, custom origins, elastic load balancers, and API Gateway. CloudFront also offers additional capabilities such as SSL/TLS support, DDoS mitigation through AWS Shield, and advanced caching controls. These features make it ideal for improving website performance, video streaming, API responsiveness, and overall user experience.

AWS Lambda is a serverless compute service that runs code in response to events and automatically handles scaling, provisioning, and management of compute. While Lambda can process data, transform requests, and integrate with services like CloudFront through Lambda@Edge, the core purpose of Lambda is computation, not content delivery. It does not replicate content to global locations or cache it for low-latency access. Lambda enhances edge functionality when paired with CloudFront, but on its own, it is not a content distribution service.

Amazon S3 is an object storage service that provides secure, durable, and scalable storage for files, documents, backups, static website hosting, and application assets. While S3 can serve content publicly and is commonly used as the origin for CloudFront, it does not provide global caching or performance optimization on its own. All requests to S3 must travel to the AWS region where the bucket resides, which can introduce latency for users in distant regions. Although reliable and highly available, S3 is not designed to accelerate global content delivery alone.

Amazon API Gateway is used to create, publish, maintain, and secure APIs. It enables developers to expose backend services, integrate with Lambda, perform request transformations, and manage API traffic. While API Gateway can work with CloudFront and supports edge-optimized configurations, its primary purpose is API management, not global content caching. It does not independently reduce latency by caching content at edge locations or distributing data globally.

The correct answer is Amazon CloudFront because it is the only service among the options specifically designed to deliver content globally with low latency. CloudFront caches data at AWS edge locations worldwide, reducing the load on origin servers and improving performance for users regardless of location. Lambda handles compute, S3 handles storage, and API Gateway handles API traffic, but none of these services provide global content acceleration capabilities. CloudFront uniquely fulfills the role of a content delivery network, offering optimized routing, caching, and global distribution—making it the correct choice for delivering low-latency content to end users.

Question 103 

Which AWS service enables real-time ingestion and processing of streaming data?

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

Answer:  A) Amazon Kinesis Data Streams

Explanation:

Amazon Kinesis Data Streams is a fully managed service designed specifically for ingesting and processing real-time streaming data. It enables developers to collect high-throughput, continuously generated information such as clickstreams, metrics, IoT telemetry, gaming data, and application logs. Kinesis Data Streams allows multiple consumers to process the same stream in parallel, offers configurable data retention periods, and provides sub-second latency. These capabilities make it ideal for applications requiring real-time analytics, anomaly detection, monitoring, and continuous data processing. Kinesis is built to handle massive volumes of constantly flowing data with high durability and low latency.

Amazon SQS is designed for asynchronous messaging between distributed application components, allowing developers to decouple microservices and enable reliable message queuing. Unlike Kinesis, SQS processes individual messages rather than continuous data streams. It stores messages until consumers retrieve them, but it is not optimized for real-time analytics or high-throughput streaming scenarios. SQS is suitable for tasks, workflows, and background processing, not for ingesting large volumes of continuously generated data requiring immediate or near-real-time processing.

Amazon SNS is a publish/subscribe messaging service used to broadcast notifications to multiple subscribers. While SNS can deliver messages quickly to many endpoints, it is not designed for continuous ingestion or real-time processing of high-volume data streams. SNS is best suited for alerting systems, event notifications, or fan-out messaging scenarios. It does not offer the ability to replay data, maintain ordered streams, or process multiple shards of data like Kinesis does. Therefore, SNS cannot replace a real-time streaming service.

AWS Lambda is a serverless compute service that executes code in response to events. While Lambda can be triggered by streaming data — including Kinesis Data Streams — Lambda itself is not a streaming ingestion or processing service. Instead, it consumes events generated by other services. Lambda functions are stateless and run for short durations, whereas real-time streaming workloads may require long-lived processing, partitioning logic, data retention, and reprocessing capabilities. Lambda complements Kinesis but does not serve the same function.

The correct answer is Amazon Kinesis Data Streams because it is purpose-built to handle real-time data ingestion and analysis at scale. The service’s ability to retain data for configurable periods, support multiple consumers, maintain ordering within shards, and handle extremely high throughput makes it uniquely suited for streaming workloads. SQS and SNS focus on messaging rather than streaming, and Lambda provides compute rather than streaming ingestion. Kinesis Data Streams remains the most appropriate choice for any scenario requiring low-latency, real-time processing of continuously flowing data.

Question 104 

Which AWS service provides a fully managed key-value and document database?

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

Answer:  A) Amazon DynamoDB

Explanation:

Amazon DynamoDB is a fully managed NoSQL database service that supports both key-value and document data models. It offers single-digit millisecond latency, high availability, and automatic scaling. With features like on-demand capacity mode, DynamoDB Streams, global tables, and built-in security, it enables developers to build applications that require consistent performance at scale. DynamoDB is ideal for serverless applications, gaming systems, IoT platforms, e-commerce workloads, and any scenario requiring flexible schema and low-latency data access. Its NoSQL architecture allows developers to store unstructured or semi-structured data with ease, and because it is fully managed, there is no need to handle patching, provisioning, or cluster management.

Amazon RDS is a managed relational database service that supports SQL databases such as MySQL, PostgreSQL, Oracle, MariaDB, and SQL Server. RDS is optimized for workloads requiring structured schema, relational integrity, and complex querying with SQL. While RDS is powerful for transactional applications, analytical queries, and relational data modeling, it is not designed to store document-based or flexible-schema data. It requires predefined tables and columns, making it less adaptable for workloads that benefit from a NoSQL approach. RDS focuses on relational capabilities rather than key-value or document storage.

Amazon S3 is an object storage service used for storing large volumes of unstructured data such as files, media assets, backups, and logs. While S3 is extremely durable and scalable, it is not a database and does not support key-value or document querying in the way DynamoDB does. S3 does not provide features like low-latency item-level access, conditional updates, or query operations that database workloads require. Although S3 can store JSON documents or objects, it cannot replace a performant NoSQL database for application-level data access.

Amazon ElastiCache is an in-memory caching service that supports Redis and Memcached. It is designed for low-latency, high-speed data retrieval and is often used to cache frequently accessed data from databases such as DynamoDB or RDS. While extremely fast, ElastiCache does not provide durable storage and is not suitable as a standalone database. It cannot store data persistently or serve as a key-value/document database with long-term durability. Its function complements databases rather than replacing them.

The correct answer is Amazon DynamoDB because it is the only option that provides a fully managed NoSQL database supporting both key-value and document data models. RDS supports only relational data, S3 stores objects rather than structured records, and ElastiCache provides temporary in-memory caching. DynamoDB’s flexibility, performance, scalability, and full management capabilities make it the correct service for applications requiring a key-value or document database. Its strong integration with AWS services and support for serverless architectures further reinforce why it is the most appropriate choice for this use case.

Question 105 

Which AWS service allows developers to trigger workflows composed of 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 service that orchestrates multiple AWS services into coordinated workflows. It enables developers to create state machines that define the flow of tasks, including sequential execution, branching logic, parallel operations, retries, error handling, and timeouts. With a visual workflow interface, Step Functions makes it easier to design complex processes and ensure that each step transitions smoothly to the next. It integrates with AWS Lambda, Amazon ECS, AWS Batch, DynamoDB, and many other services. This makes it ideal for microservice orchestration, data processing pipelines, automation, and long-running workflows.

AWS Lambda is a serverless compute service that executes individual functions in response to events. While Lambda can run tasks that are part of a workflow, it cannot orchestrate multiple tasks on its own. Lambda functions are stateless and are designed for short-lived execution, making them suitable for individual operations but not for managing multi-step processes. Although Step Functions can invoke Lambda, the reverse is not true; Lambda cannot visually or programmatically manage a sequence of actions across multiple AWS services. Lambda handles compute, but not workflow orchestration.

Amazon EC2 is a virtual server provisioning service that provides resizable compute capacity in the cloud. It allows developers to run applications, host services, and manage servers directly. However, EC2 does not provide workflow automation or orchestration capabilities. Developers must manually build their own orchestration logic at the application level, which increases complexity and operational overhead. EC2 excels at hosting workloads but does not coordinate multi-step workflows between services.

AWS CodePipeline is a fully managed continuous integration and continuous delivery service designed for automating software release pipelines. While CodePipeline orchestrates build, test, and deployment stages for application releases, it is not intended for orchestrating service-level workflows or integrating business logic across AWS services. CodePipeline focuses solely on DevOps workflows rather than general-purpose service orchestration. It cannot replace Step Functions in managing microservices, event-driven architectures, or stateful workflow logic.

The correct answer is AWS Step Functions because it is the only service among the options designed to orchestrate multiple AWS services into end-to-end workflows with visual modeling, state tracking, automated retries, and error handling. Lambda executes individual functions, EC2 provides compute resources, and CodePipeline automates software releases. None of them offer the ability to manage complex, multi-step workflows involving diverse AWS services. Step Functions simplifies building distributed applications by centralizing workflow logic, improving maintainability, and reducing complexity, making it the best choice for orchestrating multi-service workflows.

Question 106 

Which AWS service allows developers to securely store API keys, passwords, and credentials with automatic rotation?

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 designed specifically for secure, centralized storage of sensitive information such as database passwords, API keys, OAuth tokens, and application credentials. One of its main capabilities is automatic rotation, which allows developers to maintain strong security without manually updating secrets. Secrets Manager integrates with services such as Amazon RDS, Redshift, and DocumentDB to rotate credentials automatically, ensuring that applications always retrieve the latest version without downtime. It also supports fine-grained access control through IAM policies and provides detailed audit logging through CloudTrail. This combination of secure storage, encryption, auditing, rotation, and programmatic retrieval is what makes Secrets Manager highly effective for managing secrets at scale across applications and distributed systems.

AWS Key Management Service, or KMS, focuses primarily on the creation, storage, and management of cryptographic keys. While it provides envelope encryption and integrates with many AWS services to protect data at rest, KMS is not designed to store application secrets like passwords or API keys as a primary function. You can use KMS to encrypt data that you might store elsewhere, but it does not provide automatic rotation for secrets nor a native secret retrieval API. It only helps with key rotation, not with rotating credentials or dynamically updating application secrets in a managed way.

AWS Systems Manager Parameter Store is capable of storing configuration parameters and secure strings, including encrypted values that use KMS. It is a useful alternative for storing some types of sensitive data, but it does not provide out-of-the-box automatic rotation for secrets. Developers can build custom scripts or Lambda functions to simulate rotation, but these workflows require additional maintenance and lack the seamless rotation offered by Secrets Manager. Parameter Store is excellent for configuration management, environment variables, and versioning but not for automated credential lifecycle management.

Amazon RDS is a relational database service and does not function as a secrets storage tool. While RDS uses credentials, it is not responsible for storing or rotating application secrets. Instead, it integrates with Secrets Manager for credential rotation or requires developers to manage passwords manually. RDS focuses on database operations such as backups, patching, replication, and failover rather than secure application secret handling.

The correct answer is AWS Secrets Manager because it is the only option specifically designed for secure and centralized secrets storage, retrieval, encryption, auditing, and automated rotation without additional tooling. Secrets Manager reduces operational overhead and minimizes the security risks associated with hard-coded credentials, manual rotation, or configuration files stored in repositories. It integrates smoothly with AWS services, supports scalable application architectures, and maintains strict access controls for sensitive data. While KMS helps with key encryption, Parameter Store offers secure parameter storage, and RDS handles relational databases, none of these services provide the complete end-to-end secret management lifecycle that Secrets Manager delivers. Secrets Manager’s ability to automate credential rotation and maintain the integrity and confidentiality of sensitive data makes it the most suitable choice for developers who require a secure, managed solution for storing and rotating application secrets across microservices, serverless applications, and enterprise systems.

Question 107 

Which AWS service provides distributed tracing to monitor request performance in microservices?

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

Answer:  A) AWS X-Ray

Explanation:

AWS X-Ray is designed specifically for distributed tracing across microservices, serverless applications, and container-based architectures. When an application is composed of many small services communicating through APIs or asynchronous events, understanding how a request flows through the system becomes a challenge. X-Ray helps capture end-to-end latency data, visualize service maps, identify slowdowns, and track errors. It provides trace segments and subsegments that reveal how each component behaves during a request. X-Ray integrates with services like Lambda, ECS, EKS, API Gateway, and Elastic Beanstalk, enabling deep performance visibility. Its ability to show bottlenecks, pinpoint dependencies, and highlight latency sources makes it essential for optimizing microservices environments.

AWS CloudWatch focuses on logs, metrics, alarms, dashboards, and operational monitoring. While CloudWatch offers useful tools such as metric graphs and log insights, it does not perform distributed tracing across multiple services. It provides high-level performance data like CPU usage, memory metrics, throughput, and error rates, but it does not follow individual requests as they move from one microservice to another. CloudWatch Logs can complement X-Ray, but it cannot replace X-Ray’s ability to generate detailed trace maps or analyze request paths.

AWS CloudTrail is designed for governance, auditing, and compliance rather than application performance. CloudTrail records API calls made by users, accounts, or AWS services. It logs events such as who accessed a resource, when they accessed it, and what action was taken. Although CloudTrail helps detect unauthorized actions or security concerns, it does not monitor request latency, trace interactions between microservices, or visualize application architecture. Its purpose is accountability and auditing, not performance tracing.

AWS Config tracks configuration states of AWS resources and monitors changes for compliance. Config is useful for ensuring that infrastructure remains aligned with organizational rules or security guidelines. It identifies drift from expected configurations and helps auditors verify resource histories. However, Config is not designed for debugging microservice performance issues, nor does it track how requests travel through an application. It has no tracing capabilities, no insights into service dependencies, and no latency analysis.

The correct answer is AWS X-Ray because it is the only service purpose-built for distributed tracing across complex architectures. Modern cloud applications often utilize serverless functions, containerized workloads, and multiple AWS-managed services. As systems grow in complexity, identifying which component is responsible for slowdowns or failures becomes increasingly difficult. X-Ray solves this by following individual requests through the entire system, generating a visual service map and detailed trace logs. This enables developers to analyze how long requests spend at each step, where failures occur, and how dependencies interact. CloudWatch provides important metrics and logs but lacks request-level tracing. CloudTrail offers auditing, not performance analysis. AWS Config focuses on configuration compliance rather than runtime behavior. Only X-Ray delivers the complete end-to-end tracing capability needed for microservices monitoring, making it the proper choice for understanding performance, improving reliability, and diagnosing latency problems in distributed applications.

Question 108 

Which AWS service allows developers to store JSON documents with MongoDB compatibility?

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 designed to be compatible with MongoDB APIs. Developers who use MongoDB in their applications can migrate to DocumentDB with minimal changes because it supports the same query language, document model, and indexing approaches. DocumentDB stores data in JSON-like documents, enabling flexible schema and hierarchical nesting. It provides automatic backups, scaling, replication, and high availability without requiring developers to manage infrastructure. By offering MongoDB compatibility, DocumentDB is ideal for organizations that want a managed environment while maintaining their existing MongoDB-based applications, libraries, and drivers.

Amazon DynamoDB is a NoSQL key-value and document store, but it does not provide MongoDB API compatibility. Although DynamoDB allows storing JSON documents and supports document-style access through nested attributes, its interaction model, query features, indexing methods, and API behavior differ significantly from MongoDB. DynamoDB requires designing partitions, access patterns, and indexes in a manner optimized for its unique performance characteristics. It excels at massive scalability and single-digit millisecond response times but is not a substitute for a MongoDB-compatible environment.

Amazon RDS is a relational database service supporting engines such as MySQL, PostgreSQL, Oracle, SQL Server, and MariaDB. Relational databases rely on structured tables, schemas, and SQL queries rather than flexible JSON document structures. Although some RDS engines allow storing JSON fields, this does not make them document databases, nor does it grant MongoDB compatibility. RDS is intended for relational workloads, transactional systems, and SQL-based queries rather than schema-flexible document applications.

Amazon Aurora is also a relational database engine offering MySQL and PostgreSQL compatibility with high performance and distributed storage. Like RDS, Aurora supports relational models and SQL queries. While it provides better scalability and performance than standard RDS engines, it still does not function as a document database nor offer MongoDB-compatible APIs. Developers using Aurora must design applications around tables, rows, indexes, and SQL operations, which differ fundamentally from MongoDB’s document-oriented data model.

The correct answer is Amazon DocumentDB because it is the only service in the list created specifically to support JSON document storage while offering compatibility with MongoDB tools, drivers, and workloads. DocumentDB provides a managed, scalable, fault-tolerant environment without requiring developers to manage replication, patching, cluster setup, or operational overhead. DynamoDB is a powerful NoSQL service but follows a distinct key-value and document model without MongoDB API compatibility. RDS and Aurora are relational and cannot fulfill document-based workloads that require flexible schema or MongoDB features. DocumentDB’s strong alignment with the MongoDB ecosystem makes it the appropriate choice for developers seeking a managed service that supports JSON documents in a MongoDB-compatible environment.

Question 109 

Which AWS service allows developers to monitor, collect, and visualize logs and metrics for applications?

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

Answer:  A) AWS CloudWatch

Explanation:

AWS CloudWatch is the primary monitoring and observability service for AWS applications and infrastructure. It collects metrics from AWS services, custom applications, and on-premises systems. CloudWatch also gathers logs through CloudWatch Logs, allowing developers to centralize log data from Lambda, EC2, ECS, EKS, and other sources. It provides dashboards for visualization, alarms for alerting, and log insights for querying logs. CloudWatch enables developers to monitor performance in real time, detect unusual behavior, respond to operational issues, and gain visibility into application health through a single unified interface.

AWS CloudTrail serves a very different role, focusing on auditing and governance rather than operational monitoring. CloudTrail logs API activity, including who made requests, which services they invoked, and when the actions occurred. While these logs are important for security and intrusion detection, they do not provide metrics, real-time performance data, application logs, or visualization dashboards. CloudTrail is essential for compliance and forensic analysis but is not used to monitor application performance or system behavior.

AWS X-Ray specializes in distributed tracing, allowing developers to follow requests across microservices and identify latency bottlenecks. While X-Ray provides valuable performance insights for debugging distributed systems, it does not replace a comprehensive monitoring solution. X-Ray traces individual requests but does not aggregate system metrics, generate alarms, or provide a complete picture of logs across services. Its purpose is diagnosing service interactions rather than monitoring infrastructure-wide performance.

AWS Config focuses on configuration tracking and compliance auditing. It captures configuration changes, identifies drift from expected baselines, and helps maintain governance across AWS environments. Config is not designed for performance monitoring or log aggregation. It does not track CPU usage, memory utilization, request rates, errors, or application logs. While Config helps ensure infrastructure remains compliant, it does not provide operational awareness.

The correct answer is AWS CloudWatch because it is the only service among the options that centralizes logs, metrics, alarms, dashboards, and event monitoring. CloudWatch provides comprehensive observability for applications and infrastructure, allowing developers to diagnose issues, track performance trends, and visualize system behavior. CloudTrail focuses on auditing, X-Ray traces requests, and Config manages compliance, but none of these offer the complete monitoring capabilities that CloudWatch provides. Developers rely on CloudWatch to ensure applications remain healthy, scalable, and reliable.

Question 110 

Which AWS service enables serverless compute triggered by changes in data, such as DynamoDB Streams or S3 events?

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

Answer:  A) AWS Lambda

Explanation:

AWS Lambda is a serverless compute service that executes code in response to events generated by AWS services or custom applications. Lambda can be triggered automatically when new objects are added to Amazon S3, when updates occur in DynamoDB Streams, when messages arrive in SQS, or when API calls are made through API Gateway. Lambda removes the need to manage servers, scale infrastructure, or provision resources. Developers only upload their code, define triggers, and let Lambda handle scaling and execution. This event-driven behavior makes Lambda ideal for microservices, automation, file processing, real-time transformations, and backend logic for serverless applications.

Amazon EC2 offers virtual machine compute and provides full control over operating systems, applications, and packages. While EC2 can run applications that respond to events, it does not natively integrate with event sources like S3 or DynamoDB Streams. Developers would need to build polling mechanisms or custom event consumers, manage scaling groups, patch servers, and maintain capacity. EC2 is powerful for long-running workloads, custom environments, and high flexibility, but it does not provide serverless event-driven execution.

AWS Step Functions is an orchestration service used to coordinate multiple Lambda functions or AWS services into workflows. Step Functions manages workflow states, transitions, error handling, and branching logic. While Step Functions can invoke Lambda functions or perform parallel processing, it is not responsible for executing standalone code in response to events. Instead, Step Functions relies on event-driven services like Lambda to actually run code. It serves orchestration, not execution.

Amazon S3 is an object storage service and does not execute code. S3 can generate events when objects are uploaded or deleted, but it relies on Lambda to process those events. S3 does not run compute tasks on its own. It integrates with Lambda, EventBridge, and SQS to trigger processing workflows but cannot perform serverless computing directly.

The correct answer is AWS Lambda because it is the only service in this list that executes code automatically in response to events from DynamoDB Streams, S3, API Gateway, CloudWatch Events, and more. EC2 requires manual server management and does not natively integrate with event-driven execution. Step Functions orchestrates tasks rather than executing event-triggered code. S3 generates events but cannot run compute workloads. Lambda provides on-demand execution, scalability, no-server maintenance, and seamless integration with event sources, making it the ideal service for building event-driven applications.

Question 111

Which AWS service allows developers to deploy, run, and scale 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:

The first option, Amazon ECS with Fargate, is a fully managed compute engine for running containers without requiring developers to provision or manage underlying EC2 instances. With Fargate, developers only define their container task definitions, specify CPU and memory requirements, and deploy applications seamlessly. The platform automatically handles server provisioning, cluster management, scaling, isolation, and patching. This serverless container model eliminates the operational overhead associated with managing virtual machines and clusters, allowing development teams to focus exclusively on application logic and container images. ECS with Fargate also integrates deeply with IAM, CloudWatch, Elastic Load Balancing, and VPC networking features, providing granular control while remaining serverless. Because of its simplicity and reduction of infrastructure responsibilities, it is often the preferred choice for teams that want to deploy and scale containerized applications quickly and efficiently.

The second option, Amazon EKS, is a managed Kubernetes service that allows teams to run fully managed Kubernetes control planes on AWS. Although EKS reduces the burden of provisioning and maintaining the Kubernetes control plane, developers still need to manage worker nodes or integrate EKS with Fargate for node-free workloads. This means EKS does not inherently provide a serverless compute model unless combined with additional configuration. Even when EKS launches workloads using Fargate profiles, the Kubernetes environment still requires more operational knowledge and resource management than a fully ECS-native solution. EKS is ideal for organizations that require Kubernetes portability and tooling, but not necessarily the simplest option for serverless container deployment without cluster considerations.

The third option, AWS Lambda, is a serverless compute service designed primarily for running short-lived functions triggered by events. Lambda does support container images up to a certain size, enabling developers to package functions and dependencies inside container formats. However, Lambda is limited by execution duration, memory caps, invocation models, and function semantics. It is not designed for long-running containerized applications, persistent services, or stateful workloads. While Lambda complements container workloads in many architectures, it cannot replace a service dedicated to running continuously operating containers.

The fourth option, Amazon EC2, is the traditional compute service where developers launch virtual machines and manually manage operating systems, container runtimes, scaling groups, patching, networking, and security hardening. Although EC2 can host containers using tools like ECS, Kubernetes, or Docker Swarm, it is not serverless. Developers must manage the infrastructure, which introduces overhead and complexity. EC2 provides maximum control and flexibility, but it contradicts the requirement of deploying containerized applications without managing servers.

Given all these considerations, Amazon ECS with Fargate is the correct answer because it offers a truly serverless environment for deploying, running, and scaling containers. Unlike EC2, it eliminates infrastructure management entirely. Unlike EKS, it does not require understanding or operating Kubernetes clusters. Unlike Lambda, it supports long-running container workloads without function execution limits. ECS with Fargate integrates easily with the broader AWS ecosystem while reducing operational burden, making it the ideal service when developers explicitly need to run containerized applications without managing servers of any kind.

Question 112 

Which AWS service allows developers to define infrastructure as code using declarative templates?

A) AWS CloudFormation
B) AWS CodePipeline
C) AWS CodeDeploy
D) AWS Step Functions

Answer:  A) AWS CloudFormation

Explanation:

The first option, AWS CloudFormation, is a fully managed infrastructure-as-code service that enables developers to define AWS resources and environments using declarative templates written in YAML or JSON. These templates allow teams to model VPCs, subnets, IAM roles, Lambda functions, RDS instances, ECS clusters, S3 buckets, and virtually any AWS service. CloudFormation ensures that infrastructure can be version-controlled, automatically provisioned, tested, and reproduced across multiple environments such as development, staging, and production. By using declarative syntax, developers describe the desired end state, and CloudFormation determines the necessary actions to create, update, or delete resources. This approach minimizes manual configuration errors and ensures consistent deployments across teams and regions.

The second option, AWS CodePipeline, is a continuous integration and continuous delivery orchestration service. Its purpose is to automate build, test, and deployment stages of application delivery. While CodePipeline interacts with infrastructure tools, source control repositories, and deployment systems, it does not define or provision AWS resources. CI/CD automation differs fundamentally from infrastructure as code. CodePipeline can deploy CloudFormation stacks, but it is not the service that creates infrastructure templates or declarative resource definitions. Instead, it focuses on automating the movement of code artifacts through pipelines.

The third option, AWS CodeDeploy, is a deployment automation service that enables application rollouts to EC2 instances, Lambda functions, and on-premises servers. CodeDeploy offers features like blue/green deployments, canary rollouts, and automatic rollback, but it does not serve as an infrastructure provisioning tool. Developers cannot define VPCs, subnets, databases, or AWS networking configurations using CodeDeploy. Instead, it strictly focuses on deploying software to compute environments, not on defining or provisioning those environments themselves.

The fourth option, AWS Step Functions, is an orchestration service that coordinates workflows across multiple AWS services using state machines. It allows developers to build complex sequences, parallel executions, retries, branching logic, and human approval workflows. Although Step Functions can call CloudFormation or other AWS services within workflows, it is not designed to serve as an infrastructure-as-code solution. Its role is orchestration, not resource definition or infrastructure lifecycle management.

Given the nature of these services, AWS CloudFormation is the correct answer because it directly supports defining infrastructure using declarative templates. It is the only option designed specifically to allow developers to model, provision, and manage AWS resources in a consistent, repeatable, and versioned manner. The other services perform important functions—deployment, CI/CD, and orchestration—but do not enable declarative infrastructure modeling. Therefore, CloudFormation best satisfies the requirement of defining infrastructure as code using declarative templates.

Question 113 

Which AWS service allows developers to create, deploy, and manage RESTful APIs securely at scale?

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

Answer:  A) Amazon API Gateway

Explanation:

Amazon API Gateway, the first option, is a fully managed service that enables developers to create, deploy, secure, and manage RESTful APIs, HTTP APIs, and WebSocket APIs at any scale. API Gateway provides built-in features such as authentication and authorization using IAM, Cognito, or Lambda authorizers, along with request throttling, caching, monitoring, access logging, and versioning. Developers can integrate API Gateway with backend services including Lambda, ECS, EKS, EC2, DynamoDB, and other HTTP endpoints. It is specifically designed to manage API traffic, handle routing, and enforce security and governance controls. API Gateway provides endpoint lifecycle management, usage plans, custom domains, and global content delivery when paired with CloudFront. All these features position it as the primary AWS service for building and scaling secure APIs.

AWS Lambda, the second option, is a serverless compute service that executes functions triggered by events. While Lambda is frequently used as a backend for API Gateway, it does not handle API creation, throttling, routing, or access control on its own. Lambda can host logic, but it cannot expose RESTful endpoints or manage their lifecycle without API Gateway or an Application Load Balancer. Lambda functions are a building block of serverless APIs, but they are not an API management service.

Amazon EC2, the third option, provides virtual machines where developers can deploy web servers or API frameworks manually. EC2 requires configuring networking, load balancing, security groups, scaling, and patching. Developers must install API software, manage endpoints, secure them, configure monitoring, and manually handle scaling. While possible, hosting an API on EC2 provides none of the managed capabilities offered by API Gateway. It demands far more operational overhead and lacks built-in API governance features.

Amazon CloudFront, the fourth option, is a content delivery network service that accelerates the distribution of static and dynamic content to users globally. Although CloudFront can sit in front of API Gateway or EC2-based APIs to improve performance, it does not create or manage APIs. It cannot define routes, manage authentication, provide usage plans, or handle request transformations. CloudFront is a content caching and delivery layer rather than an API management platform.

When comparing these services, Amazon API Gateway stands out as the correct answer because it is specifically built to create, deploy, and manage RESTful APIs securely at scale. It provides all the required features for API lifecycle management and integrates seamlessly with backend compute services. The other options support related capabilities—Lambda for backend processing, EC2 for hosting applications, and CloudFront for distribution—but none of them deliver the API management capabilities required for creating secure, scalable RESTful APIs. API Gateway best satisfies all parts of the question.

Question 114 

Which AWS service provides centralized configuration storage for application parameters?

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

Answer:  A) AWS Systems Manager Parameter Store

Explanation:

The first option, AWS Systems Manager Parameter Store, is a fully managed and centralized configuration service that allows developers to store plaintext and encrypted parameters, application settings, environment variables, feature flags, and connection details. Parameter Store integrates with IAM for fine-grained permission control and supports versioning, hierarchical namespaces, and encryption using KMS. Applications running on EC2, ECS, Lambda, and other AWS services can retrieve parameters dynamically at runtime, enabling centralized configuration management across distributed systems. Parameter Store helps reduce hard-coded values in applications, supports automation workflows, and enhances security through structured configuration storage and auditing.

AWS Secrets Manager, the second option, is designed primarily for managing sensitive secrets such as database credentials, API keys, tokens, and authentication information. While Secrets Manager overlaps slightly with Parameter Store, it is more specialized in automatic secret rotation and tighter integration with RDS and other credential-based services. Secrets Manager is not intended for general configuration values like non-sensitive settings, limits, feature flags, or environment variables. It is designed for secrets lifecycle management, not broad application configuration storage.

AWS KMS, the third option, is the Key Management Service used to manage cryptographic keys for encryption and decryption. KMS itself does not store configuration parameters. Instead, it provides the keys that services like Parameter Store and Secrets Manager use for encrypting their values. KMS cannot store application settings or metadata and cannot serve as a configuration repository. It plays a security role in encryption, but not a configuration management role.

Amazon DynamoDB, the fourth option, is a fully managed NoSQL key-value and document database service. Although developers could implement their own configuration repository using DynamoDB, it is not a dedicated configuration management service. It lacks the built-in features that Parameter Store provides, such as automatic versioning, hierarchical paths, encryption mechanisms designed for parameters, easy integration with IAM permission boundaries, and native runtime retrieval mechanisms for application configurations. DynamoDB would require additional logic and operational effort to serve as a configuration store, making it less suitable than a service designed specifically for that purpose.

Considering all these points, AWS Systems Manager Parameter Store is the correct answer because it is the only option built specifically to store application configuration values centrally, securely, and at scale. While Secrets Manager focuses on sensitive secrets, KMS on encryption keys, and DynamoDB on general-purpose data storage, Parameter Store is purpose-built for centralized configuration storage. Its built-in versioning, structure, and security controls make it the appropriate service for the requirement stated in the question.

Question 115 

Which AWS service allows developers to process and analyze streaming data in real time using SQL?

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, the first option, is a fully managed service that allows developers to process and analyze real-time streaming data using SQL. It integrates directly with Kinesis Data Streams and Kinesis Data Firehose, enabling developers to perform continuous queries, detect patterns, generate real-time metrics, and transform data before forwarding it to destinations such as S3, Redshift, Lambda, or Elasticsearch. Kinesis Data Analytics automatically handles scaling, throughput management, state management, and fault tolerance. It is specifically designed for SQL-based analytics on streaming data, providing a serverless approach to real-time data processing without requiring infrastructure provisioning or cluster management.

Amazon SQS, the second option, is a fully managed message queuing service used to decouple and buffer messages between distributed application components. SQS supports standard queues and FIFO queues, but it does not process or analyze data. It merely stores and forwards messages. SQS provides no analytics, no SQL querying capabilities, and no real-time streaming analysis features. Its purpose is reliable message delivery, not data transformation or querying.

Amazon SNS, the third option, is a publish/subscribe messaging service that broadcasts messages to subscribers through HTTP endpoints, Lambda functions, SQS queues, SMS, and email. Like SQS, SNS does not perform analytics or stream processing. It cannot run SQL queries or generate real-time insights. Its purpose is message fan-out and event notification, not continuous analytical processing of streaming data.

AWS Lambda, the fourth option, is a serverless function execution service that processes events in near real time. While Lambda can be triggered by Kinesis Data Streams, it does not provide SQL processing capabilities and cannot run continuous SQL queries on streaming data. Lambda functions run discrete invocations and are limited in duration. They are useful for lightweight transformations, but not for persistent streaming analytics or complex SQL operations.

When comparing these services, Amazon Kinesis Data Analytics clearly stands out as the only service specifically designed for SQL-based real-time data analysis on streaming inputs. It uniquely supports continuous SQL queries, event-time processing, windowing operations, and real-time insights. The other services—SQS, SNS, and Lambda—play important roles in messaging and event-driven architectures but cannot perform SQL analytics on streaming data. Therefore, Amazon Kinesis Data Analytics is the correct answer because it fulfills the requirement of processing and analyzing streaming data in real time using SQL.

Question 116 

Which AWS service allows developers to monitor API usage and audit user activity?

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

Answer:  A) AWS CloudTrail

Explanation:

Option A, AWS CloudTrail, is the AWS service designed specifically for recording API activity and user actions across an AWS environment. It automatically captures details such as API calls, the identity of the caller, the time of the request, source IP address, request parameters, and responses returned by AWS services. CloudTrail plays a critical role in auditing, security analysis, operational troubleshooting, and compliance monitoring. Developers, security engineers, and administrators rely heavily on CloudTrail logs to detect unauthorized actions, investigate changes, and maintain visibility into all interactions occurring within their AWS accounts. CloudTrail can also deliver logs to S3 and integrate with CloudWatch Logs for near real-time detection, making it a comprehensive monitoring and auditing tool.

Option B, AWS CloudWatch, focuses on monitoring logs, metrics, alarms, and events, but it does not provide the ability to track detailed API usage or user-level actions across the environment. CloudWatch is excellent for performance monitoring, resource utilization tracking, and automated responses to operational conditions. It helps observe CPU usage, memory utilization (via custom metrics), log patterns, and application-level performance. However, CloudWatch does not capture who made specific API calls, nor does it record request parameters or identify actions at the API audit level. While CloudWatch and CloudTrail often work together, CloudWatch is not a substitute for CloudTrail when the goal is auditing user activity.

Option C, AWS X-Ray, is a distributed tracing service that helps developers analyze and debug application performance by tracing requests as they pass through microservices and components. X-Ray is valuable for identifying performance bottlenecks, analyzing service maps, visualizing latency issues, and understanding how a request flows through various dependencies. However, X-Ray is not designed to track AWS API usage or record user activity at the AWS service level. Its focus is on application behavior rather than administrative or API-level monitoring within the account.

Option D, AWS Config, tracks configuration changes to AWS resources, ensuring visibility into how resources are created, modified, or deleted over time. Config also evaluates resource compliance against predefined or custom rules. Although Config provides historical tracking for configuration changes, it does not record the detailed API activity associated with those changes. Config might show that a security group rule changed, but CloudTrail shows who made the change and from where.

The correct answer is AWS CloudTrail because it is the only service that records detailed API activity and user actions across all AWS services. It offers a complete audit history that is essential for security investigations, compliance requirements, and monitoring operational activity. CloudWatch, X-Ray, and Config each provide valuable but separate forms of monitoring that do not replace the full API-level auditing capability that CloudTrail delivers.

Question 117 

Which AWS service provides event-driven triggers for serverless compute functions?

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

Answer:  A) AWS Lambda

Explanation:

AWS Lambda is a managed serverless compute service that runs code in response to a wide variety of events. It enables developers to upload functions and have them executed automatically when triggers occur, such as object uploads to Amazon S3, updates to DynamoDB streams, messages from Kinesis, or HTTP requests via API Gateway. Lambda scales automatically and abstracts server management so you focus on code rather than servers.

Amazon EC2 provides virtual machines for general-purpose compute. EC2 instances are long-lived, require provisioning, configuration, and patching, and do not natively provide automatic event-driven invocation of user code. While you can install agents or write custom scripts to poll events, EC2 does not offer the native, pay-per-invocation event model of serverless platforms.

AWS Step Functions coordinate and orchestrate distributed workflows using state machines. Step Functions invoke Lambda functions or other services as steps in a defined process, handling retries, branching, and parallel tasks. They do not themselves execute arbitrary business logic independently but instead coordinate and manage sequences of service calls.

Amazon S3 can emit event notifications when objects are created, removed, or modified. Those notifications are useful as triggers, but S3 does not run code to process events. Instead S3 sends events to Lambda, SNS, or SQS for downstream processing. Because Lambda both receives events directly from many services and runs code without requiring server management, it is the correct service for event-driven serverless compute, and supports multiple language runtimes globally today.

Question 118 

Which AWS service provides fully managed, highly available, and scalable document storage with MongoDB API compatibility?

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

Answer:  A) Amazon DocumentDB

Explanation:

Amazon DocumentDB is a managed document database service compatible with MongoDB drivers and tools. It stores JSON-like documents, supports familiar MongoDB APIs, and handles tasks such as storage scaling, automated backups, and replication. Because it implements MongoDB-compatible interfaces, applications that speak the MongoDB protocol generally require minimal changes to run against DocumentDB, simplifying migration.

Amazon DynamoDB is a fully managed key-value and document database that delivers single-digit millisecond performance at any scale. DynamoDB has its own API and SDKs, design patterns, and data modeling principles. While it can store JSON documents, it is not wire-compatible with MongoDB and therefore cannot be used as a drop-in replacement for MongoDB clients or tools without adaptation.

Amazon RDS provides managed relational databases such as MySQL, PostgreSQL, SQL Server, and Oracle. RDS stores structured, schema-based data rather than document-style, JSON-centric records. Although some engines support JSON columns or extensions, RDS is primarily relational and not intended to offer MongoDB API compatibility.

Amazon Aurora is a high-performance relational database compatible with MySQL and PostgreSQL engines. Like RDS, Aurora is optimized for relational workloads and does not provide native MongoDB protocol compatibility. Because DocumentDB implements MongoDB-compatible APIs while providing managed backups, replication, and scaling, it is the appropriate choice for teams that need a managed document store which integrates with existing MongoDB drivers and tooling. It also simplifies operational overhead, reduces administrative burden, and speeds developer productivity across environments rapidly today.

Question 119 

Which AWS service allows developers to deploy containerized applications without provisioning 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 container execution option for Amazon ECS that allows developers to run containerized tasks and services without provisioning or managing underlying EC2 instances. Fargate handles scheduling, isolation, and compute allocation, letting teams focus on container images, task definitions, and service configuration rather than server maintenance. This model simplifies operations for many container workloads.

Amazon EKS is a managed Kubernetes service that provides a control plane and integrations for running Kubernetes clusters on AWS. Although EKS automates parts of the Kubernetes control plane, users often still manage worker nodes or use managed node groups, and Kubernetes itself requires more operational expertise. EKS is ideal when you need Kubernetes-specific features, portability, or complex orchestration.

AWS Lambda is a serverless compute service that can run small container images as functions, but it enforces execution duration limits and is optimized for short-lived event-driven workloads. Lambda is not a general-purpose long-running container runtime like ECS with Fargate and is constrained by function invocation models and resource limits, making it less suitable for arbitrary containerized services.

Amazon EC2 provides virtual machines with full control over the operating system and runtime. EC2 offers maximum flexibility but requires you to provision, patch, and scale instances, making it a more hands-on option compared with serverless offerings. Because ECS with Fargate removes the need to manage servers while supporting long-running containers and service definitions, it is the correct choice for deploying containerized applications without provisioning servers. It offers predictable networking, task-level IAM, and integrates with service discovery and load balancing.

Question 120 

Which AWS service allows developers to implement automated CI/CD pipelines for serverless applications?

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

Answer:  A) AWS CodePipeline

Explanation:

AWS CodePipeline is a fully managed continuous delivery service that models, visualizes, and automates the steps required to release software changes. It coordinates source retrieval, build, test, and deployment stages, and integrates tightly with services like CodeBuild, CodeDeploy, CloudFormation, and Lambda to support serverless application workflows. CodePipeline provides a clear pipeline view and supports automated gating or manual approvals as part of release processes.

AWS Lambda executes code in response to events and is a core runtime for serverless applications, but it does not orchestrate multi-stage CI/CD workflows by itself. Lambda functions are typically the target of deployments rather than the pipeline manager; they provide compute for application logic rather than pipeline orchestration.

AWS CodeBuild is a managed build service that compiles source code, runs tests, and produces artifacts. While CodeBuild performs the build and test stages within a pipeline, it does not coordinate the entire CI/CD flow across multiple stages and services on its own. CodeBuild is commonly used as a stage within a larger CodePipeline workflow.

AWS CodeDeploy automates application deployments to compute platforms including EC2, Lambda, and on-premises servers. CodeDeploy focuses on deployment strategies and orchestrating updates to targets, but it is not responsible for end-to-end pipeline orchestration from source through build and test to deployment. Because CodePipeline orchestrates and automates the full CI/CD process and integrates with build and deployment services to support serverless application releases end-to-end, it is the correct service for implementing automated CI/CD pipelines for serverless applications. It also supports parallel actions, artifacts, and fine-grained stage transitions for teams.