Amazon AWS Certified Developer – Associate DVA-C02 Exam Dumps and Practice Test Questions Set 5 Q81-100

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Question 81 

Which AWS service provides serverless orchestration of microservices with workflow visualization?

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 allows developers to coordinate multiple AWS services into serverless workflows. It provides visual workflow diagrams, which makes it easier to design, monitor, and troubleshoot complex application logic. Step Functions supports sequential, parallel, branching, and conditional logic, enabling microservices or serverless functions to interact reliably. By providing state management and built-in error handling, it allows developers to create scalable and maintainable applications without managing underlying infrastructure.

AWS Lambda is a compute service that allows developers to run code in response to events without provisioning or managing servers. While Lambda is excellent for executing individual functions, it does not offer native workflow orchestration or the ability to visually coordinate multiple services. Developers often use Lambda within Step Functions to execute tasks, but Lambda alone cannot manage complex workflows or state transitions.

Amazon EC2 provides resizable virtual servers in the cloud, giving developers control over the operating system and the software stack. EC2 is primarily designed for running applications and services on virtual machines, but it does not include built-in workflow orchestration. Developers using EC2 would need to implement their own orchestration logic or integrate with other services to coordinate multiple tasks.

AWS CodePipeline is a continuous integration and continuous delivery (CI/CD) service that automates building, testing, and deploying applications. It is focused on software release pipelines rather than application-level orchestration of microservices. CodePipeline does not natively provide workflow visualization for application processes or conditional logic for task execution.

Step Functions is the correct choice because it is specifically designed for orchestrating microservices and serverless applications. It combines service integration, error handling, and visualization into a single platform, reducing operational complexity and improving reliability. It allows developers to build event-driven applications that are easier to maintain and scale compared to using Lambda or EC2 alone.

Question 82 

Which AWS service allows storing and retrieving binary or large files in the cloud?

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

Answer:  A) Amazon S3

Explanation:

Amazon S3 is an object storage service designed for storing and retrieving any amount of data, including binary files, images, videos, backups, and large datasets. It provides features such as versioning, encryption, lifecycle management, and cross-region replication, making it highly durable and highly available. S3 is optimized for scalability and allows developers to access data using APIs or SDKs, which makes it suitable for web applications, data lakes, and large file storage.

Amazon DynamoDB is a fully managed NoSQL database designed for low-latency access to structured data. While it supports key-value and document data models, it is not optimized for storing large files or binary objects. Using DynamoDB for large object storage would be inefficient and expensive, as it is intended for quick access to small, structured records rather than bulk file storage.

Amazon RDS is a managed relational database service that handles structured data and transactional workloads. RDS is not designed for large file storage, and storing binary files in a relational database can impact performance and increase storage costs. Relational databases work best for structured records and queries rather than unstructured or large binary objects.

Amazon ElastiCache is an in-memory caching service using Redis or Memcached. It provides fast, low-latency access to data in memory but is not suitable for persistent storage of large files. Data stored in ElastiCache is temporary and will be lost if the cache node is restarted or fails.

Amazon S3 is the correct choice because it is purpose-built for storing and retrieving large objects in a durable and scalable manner. Its features support enterprise-level file management while ensuring high availability, making it the standard choice for cloud file storage.

Question 83 

Which AWS service provides a fully managed message queuing system for decoupling microservices?

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 that allows applications to communicate asynchronously. Producers send messages to a queue, and consumers retrieve them at their own pace, decoupling microservices to improve scalability and reliability. SQS supports standard and FIFO queues, dead-letter queues, retries, and message visibility timeouts, which makes it a robust choice for distributed systems requiring fault tolerance and reliable message delivery.

Amazon SNS is a pub/sub messaging service that pushes notifications to multiple subscribers simultaneously. While SNS is useful for broadcasting messages to multiple endpoints, it does not provide message queuing or persistent storage for delayed consumption. Messages are delivered immediately, and if a subscriber is unavailable, the message could be lost unless additional integration with SQS is used.

AWS Lambda executes code in response to events, such as messages from SQS, SNS, or HTTP requests. It is a compute service, not a messaging or queuing system, and does not provide the persistence or decoupling that a message queue offers. Lambda can work in conjunction with SQS to process messages but is not a replacement for a queue.

Amazon Kinesis is designed for real-time streaming data processing. It captures and processes large streams of data, such as logs or metrics, rather than providing message queuing for asynchronous decoupling. Kinesis is optimized for high-throughput streaming and analytics rather than individual message delivery.

SQS is correct because it provides a reliable, managed queue for asynchronous communication between microservices. Its persistence, scalability, and integration with other AWS services make it the ideal choice for decoupling distributed components while ensuring messages are not lost.

Question 84 

Which AWS service allows developers to deploy machine learning models as scalable endpoints?

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

Answer:  A) Amazon SageMaker

Explanation:

Amazon SageMaker is a fully managed machine learning service that allows developers to build, train, and deploy models at scale. It supports creating endpoints for real-time inference and batch predictions. SageMaker manages infrastructure, scaling, and monitoring automatically, providing an end-to-end ML lifecycle that simplifies deployment, performance optimization, and security for production applications.

AWS Lambda can execute lightweight machine learning models, particularly for inference tasks, but it is not designed for fully managing, deploying, or scaling complex ML models. Lambda is suitable for event-driven predictions but cannot handle high-volume model deployments without additional orchestration.

Amazon EC2 provides raw compute resources that can host ML models, but developers must manage servers, scaling, security, and deployment themselves. While EC2 offers flexibility, it adds significant operational overhead compared to using SageMaker, which automates these processes.

Amazon RDS is a relational database service and does not provide any machine learning deployment or inference capabilities. It is optimized for structured data storage and query execution, making it unsuitable for model hosting or endpoint serving.

SageMaker is the correct choice because it provides a fully managed environment for deploying ML models as scalable, secure endpoints. It reduces the operational complexity of serving models, supports automatic scaling, and integrates easily with other AWS services for production-grade ML workflows.

Question 85 

Which AWS service is best for low-latency, in-memory caching to improve database 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 using Redis or Memcached. It allows applications to store frequently accessed data in memory, reducing database load and improving performance. ElastiCache supports replication, high availability, and automatic failover, ensuring minimal latency while maintaining reliability for read-heavy workloads.

Amazon DynamoDB is a fast and fully managed NoSQL database designed for low-latency access to structured data. While DynamoDB is optimized for high-speed reads and writes, it provides persistent storage rather than an in-memory cache. DynamoDB Accelerator (DAX) can be used for caching, but DynamoDB itself is not an in-memory caching service.

Amazon RDS is a managed relational database service. While it handles structured data efficiently and supports high availability, it cannot provide sub-millisecond access times like an in-memory cache. RDS is ideal for transactional storage but does not replace caching layers.

Amazon S3 is object storage for large files and durable storage of static content. It is not designed for low-latency access or caching and would introduce significant delay if used to replace in-memory caching.

ElastiCache is the correct choice because it enables fast, scalable, and reliable access to frequently used data in memory. It reduces load on underlying databases, improves application responsiveness, and supports distributed caching patterns, making it the ideal solution for performance-critical applications.

Question 86 

Which AWS service allows developers to trigger serverless functions in response to events from other AWS services?

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 allows developers to execute code in response to events without provisioning or managing servers. It can automatically respond to a wide variety of AWS service events, such as changes in an S3 bucket, updates in DynamoDB streams, messages from Kinesis, or HTTP requests via API Gateway. This event-driven model enables developers to build scalable applications that react in real-time, eliminating the overhead of manually managing server instances or scaling infrastructure based on load.

Amazon EC2, on the other hand, is a traditional compute service where users launch virtual servers. While EC2 provides flexibility in running applications, it does not natively respond to events from other AWS services. Any event-driven functionality on EC2 would require additional infrastructure, such as a cron job, polling mechanism, or custom automation, which increases operational complexity and latency. EC2 is not serverless and is not designed for reactive execution in response to AWS events.

AWS Step Functions allow developers to orchestrate multiple AWS services into workflows and manage complex business logic. While Step Functions can call Lambda functions as part of a workflow, they are not designed to respond directly to events themselves. Step Functions are more suited for coordinating multi-step processes and maintaining state across services, rather than executing individual serverless functions triggered by external events.

Amazon S3 can generate events, such as object creation or deletion notifications, which can trigger actions in other services. However, S3 cannot process these events directly; it requires a downstream service like Lambda to handle them. S3 is primarily a storage service and lacks execution capabilities, so it cannot implement event-driven code independently.

The correct answer is Lambda because it directly integrates with AWS events, executes code automatically in response to those events, and supports scaling without server management. Lambda enables developers to implement serverless, event-driven architectures efficiently and reliably.

Question 87 

Which AWS service allows developers to securely store and rotate application 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 provides a secure solution for storing sensitive information such as passwords, API keys, database credentials, and other secrets. It includes built-in automated rotation functionality, allowing credentials to be periodically updated without human intervention. This helps reduce the risk of compromise and ensures compliance with security policies. Secrets Manager also integrates with AWS SDKs, making it easy for applications to retrieve secrets programmatically and securely.

AWS Key Management Service (KMS) focuses on creating, managing, and controlling encryption keys used to encrypt and decrypt data. While KMS is critical for securing data, it does not store or rotate application credentials or API keys. KMS supports encryption but does not provide the higher-level secret management and automatic rotation features that Secrets Manager offers.

AWS Systems Manager Parameter Store can store plain text or encrypted configuration values and secrets. It is suitable for storing application settings or environment variables securely, but it does not provide automatic credential rotation. While Parameter Store is a strong configuration management tool, developers would need to implement additional logic to rotate secrets safely, which Secrets Manager handles natively.

Amazon RDS is a managed relational database service that handles database operations such as scaling, backups, and patching. While RDS can manage database credentials, it is not designed to store application API keys or other sensitive credentials for general use. Secrets Manager is the correct choice because it centralizes secret management, supports automated rotation, and integrates easily with applications, reducing operational overhead while improving security.

Question 88 

Which AWS service allows you to route events between AWS services, SaaS applications, and custom apps?

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

Answer:  A) Amazon EventBridge

Explanation:

Amazon EventBridge is a fully managed event bus that allows developers to route events from AWS services, SaaS applications, and custom sources to multiple targets. EventBridge supports filtering, transformation, and routing rules, which makes it highly flexible for creating event-driven architectures. It enables developers to decouple services, integrate third-party applications, and automate workflows seamlessly without writing custom integration code.

Amazon SNS is a pub/sub messaging service that allows sending notifications to multiple subscribers. While SNS can distribute messages in real-time, it lacks the advanced routing, filtering, and transformation capabilities provided by EventBridge. SNS is best suited for simple fan-out messaging rather than complex event routing.

Amazon SQS is a message queuing service used for decoupling and buffering between microservices or distributed applications. SQS does not provide event routing or filtering capabilities; it simply holds messages until consumers process them. It is not an event bus and does not integrate directly with SaaS applications for event routing.

AWS Lambda is a compute service that executes code in response to events but does not provide event bus functionality on its own. Lambda functions often serve as consumers of events routed through EventBridge or other sources. EventBridge is the correct answer because it provides a centralized, serverless mechanism to route, filter, and deliver events across AWS services, SaaS applications, and custom endpoints.

Question 89 

Which AWS service is used for auditing API calls and user activity in an AWS account?

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

Answer:  A) AWS CloudTrail

Explanation:

AWS CloudTrail provides a comprehensive audit trail for AWS accounts by recording all API calls, including those from the AWS Management Console, SDKs, CLI, and other AWS services. CloudTrail logs contain information about the caller identity, request time, parameters, and responses, making it invaluable for auditing, compliance, and security analysis. Developers and security teams use CloudTrail to investigate suspicious activity, track changes, and ensure accountability.

AWS CloudWatch focuses on monitoring metrics, logs, and events from applications and infrastructure. While it can detect operational issues and trigger alerts, it does not provide detailed audit logs of API calls or user activity. CloudWatch is a monitoring tool, not an auditing solution.

AWS X-Ray helps developers analyze and debug distributed applications by tracing requests across services. It provides insights into latency, bottlenecks, and application performance, but it does not track or log all API calls or provide a comprehensive audit trail for account-level activity. X-Ray is oriented toward observability rather than auditing.

AWS Config tracks resource configuration changes and evaluates compliance with predefined rules. While Config is useful for configuration monitoring and compliance audits, it does not log API calls or user actions. CloudTrail is the correct answer because it captures detailed API-level activity across AWS accounts, ensuring accountability, supporting forensic analysis, and enabling compliance reporting.

Question 90 

Which AWS service allows developers to monitor, collect, and visualize application and infrastructure metrics?

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

Answer:  A) AWS CloudWatch

Explanation:

AWS CloudWatch collects metrics, logs, and events from AWS resources, applications, and on-premises servers. Developers can create dashboards, set alarms, and analyze trends in operational data. CloudWatch provides real-time monitoring, enabling proactive detection of performance issues, automated responses to alarms, and visualization of resource utilization and application behavior. Its integration with other AWS services allows building automated operational workflows.

AWS CloudTrail is primarily an auditing tool that records API calls and user activity. While its logs can provide insights into account operations, CloudTrail is not designed to monitor performance metrics or visualize operational health. It serves compliance and security purposes rather than ongoing monitoring.

AWS X-Ray traces application requests across distributed services, providing detailed insights into request flows, latency, and performance bottlenecks. While X-Ray helps in debugging and understanding application behavior, it does not aggregate general system or application metrics at scale, making it unsuitable as a centralized monitoring solution.

AWS Config tracks changes to resource configurations and helps assess compliance against rules, but it does not provide performance metrics or operational visualizations. CloudWatch is the correct answer because it is the centralized platform for collecting, analyzing, and visualizing both application and infrastructure metrics in real-time, enabling developers to maintain optimal performance and reliability.

Question 91 

Which AWS service allows developers to deploy 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 fully managed container service that abstracts the underlying infrastructure, allowing developers to focus on building and running containerized applications without worrying about provisioning, configuring, or scaling servers. Fargate handles the compute, networking, and isolation for each container automatically, which greatly simplifies deployment workflows and operational overhead. This makes it ideal for developers who want the benefits of containers without the complexity of managing the underlying EC2 instances.

Amazon EKS is AWS’s managed Kubernetes service, which provides Kubernetes orchestration for containerized applications. While it automates cluster management to some extent, it still requires managing worker nodes or integrating with Fargate for serverless execution. EKS is more suitable for teams with existing Kubernetes expertise or applications that require Kubernetes-specific features. It does not fully remove infrastructure management in the same way that ECS with Fargate does.

AWS Lambda can also run container images, but it is primarily designed for serverless function execution with event-driven triggers. Lambda has limitations on execution duration and resource allocation compared to ECS or EKS, making it less suitable for long-running or resource-intensive containerized workloads. Lambda is best for microservices or tasks that run in response to events rather than full-scale container orchestration.

Amazon EC2 provides raw compute infrastructure where containers can be deployed, but developers must manually manage server provisioning, scaling, and networking. This approach gives full control but requires significant operational effort, including patching, monitoring, and managing cluster capacity. In comparison, ECS with Fargate removes these operational burdens while still delivering the flexibility and scalability needed for modern containerized applications. Therefore, ECS with Fargate is the correct choice as it fully abstracts server management while providing seamless container execution, scaling, and isolation.

Question 92 

Which AWS service provides fully managed, serverless APIs for web and mobile applications?

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 that allows developers to create, deploy, and maintain RESTful and WebSocket APIs at scale. It provides built-in features such as request throttling, authentication, authorization, and caching, helping developers expose backend services securely and efficiently to web and mobile clients. API Gateway integrates seamlessly with AWS Lambda and other services to provide a complete serverless API solution without requiring developers to manage infrastructure.

AWS Lambda is primarily a serverless compute service that runs code in response to events. While Lambda functions often serve as backend logic for APIs, Lambda alone does not create or manage API endpoints, handle request routing, or provide security and throttling features for client access. Therefore, Lambda complements API Gateway but does not replace it.

Amazon EC2 provides virtual machines where developers can deploy web servers and host APIs. However, it requires manual provisioning, scaling, and maintenance of the servers. EC2 can be used to host APIs, but it does not provide the serverless features or integrated API management capabilities of API Gateway.

Amazon CloudFront is a content delivery network that accelerates content delivery by caching it closer to end users. While CloudFront can distribute API responses for performance optimization, it does not provide API creation, request handling, or security management. API Gateway is the correct option because it enables fully managed, serverless API creation and integrates easily with backend services, allowing developers to focus on business logic rather than infrastructure.

Question 93 

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

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

Answer:  A) AWS CloudFormation

Explanation:

AWS CloudFormation is a service that allows developers and DevOps teams to define infrastructure resources and configurations in a declarative manner using JSON or YAML templates. By managing infrastructure as code, CloudFormation enables repeatable, version-controlled, and automated deployments, reducing manual errors and ensuring consistency across environments. This approach supports complex architectures and simplifies scaling and maintenance.

AWS CodePipeline is a continuous integration and continuous delivery (CI/CD) service that automates building, testing, and deploying code. While CodePipeline orchestrates deployment workflows, it does not define or provision the underlying infrastructure. It works well in combination with CloudFormation for automated deployment pipelines, but it cannot replace template-driven infrastructure management.

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages. While it is an essential part of CI/CD workflows, CodeBuild does not provision or configure infrastructure resources; it focuses on the build and test stages of the software development lifecycle.

AWS Step Functions is a service for orchestrating workflows across AWS services. Step Functions allows you to coordinate sequences of tasks with conditions and parallel execution. While it can call CloudFormation or CodePipeline tasks, it does not manage infrastructure directly. CloudFormation is the correct choice because it provides a repeatable, automated, and scalable approach to provisioning and managing AWS resources through infrastructure-as-code templates, eliminating the need for manual setup and configuration.

Question 94 

Which AWS service allows developers to process large-scale real-time 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 designed for real-time ingestion and processing of massive streaming datasets, including logs, telemetry data, and clickstreams. It allows developers to build scalable, real-time analytics pipelines, process data as it arrives, and store it for further analysis. Kinesis provides durability, automatic scaling, and integration with AWS analytics and machine learning services, making it ideal for streaming workloads.

Amazon SQS is a message queue service that provides reliable delivery of messages between distributed application components. While SQS supports decoupled communication, it is designed for asynchronous messaging rather than high-throughput streaming analytics. SQS does not process data in real-time at the scale that Kinesis can handle.

Amazon SNS is a publish-subscribe messaging service that enables applications to broadcast messages to multiple subscribers. SNS is suitable for notifications and fan-out architectures, but it is not designed for processing large volumes of streaming data with real-time analytics requirements.

AWS Lambda can process events in real-time and integrate with Kinesis or SQS streams. However, Lambda itself is limited in execution time and throughput, and it does not provide a native high-volume streaming ingestion service. Kinesis Data Streams is correct because it is specifically built for real-time, high-throughput data processing, providing scalable ingestion, retention, and analysis capabilities for streaming applications.

Question 95 

Which AWS service allows for secure storage and retrieval of encryption keys?

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

Answer:  A) AWS KMS

Explanation:

AWS Key Management Service (KMS) is designed to create, store, and manage cryptographic keys used to encrypt data across AWS services. It provides secure key lifecycle management, including key rotation, usage policies, and access control. KMS integrates with S3, RDS, and other services, enabling seamless encryption for data at rest and in transit.

AWS Secrets Manager is used to store sensitive credentials such as API keys, passwords, and tokens. While it provides encryption for stored secrets and integrates with KMS, it is not a full key management service. Secrets Manager relies on KMS to manage the underlying encryption keys but does not allow direct creation and management of cryptographic keys.

AWS Systems Manager Parameter Store allows storage of configuration data and secrets, including encrypted values. While it provides basic encryption support, it is not a dedicated key management service and lacks the full lifecycle management and auditing features of KMS. Parameter Store is better suited for configuration management than enterprise-grade key management.

Amazon S3 is an object storage service that can store encrypted data using KMS-managed keys, but S3 itself does not manage the cryptographic keys. Its role is to store and retrieve objects, while KMS ensures that the keys used for encryption are securely generated, rotated, and audited. AWS KMS is the correct option because it provides centralized, secure key management with full integration into AWS services, ensuring data protection and compliance across applications.

Question 96 

Which AWS service provides distributed tracing for performance monitoring 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 a service designed specifically to trace requests as they travel through distributed applications, such as microservices or serverless architectures. It provides detailed insights into how requests flow between services, allowing developers to pinpoint performance bottlenecks, identify errors, and understand latency issues. With X-Ray, each request is tracked with a trace, and these traces can be aggregated to form a service map. This helps developers visualize how different services interact, where slowdowns occur, and which components may require optimization or scaling adjustments.

AWS CloudWatch is primarily a monitoring service for collecting logs, metrics, and events. While it is excellent for gaining visibility into resource utilization and application performance at a macro level, it does not provide detailed distributed tracing of individual requests. CloudWatch metrics can indicate high latency or errors in an application, but they cannot show how a specific request traveled through a series of microservices or highlight the exact source of the delay.

AWS CloudTrail, on the other hand, is focused on auditing. It records API calls and user activity across AWS accounts, providing an invaluable tool for compliance and security auditing. CloudTrail logs who performed an action, which resources were involved, and when it happened. However, it does not monitor application performance, track request flows, or provide latency analysis. It is not suitable for performance debugging or tracing requests in distributed systems.

AWS Config is used to monitor and track configuration changes of AWS resources over time. It evaluates resource compliance against defined rules and helps organizations maintain governance standards. Config is excellent for identifying configuration drift and ensuring compliance but does not offer insights into how an application handles requests or trace request paths through microservices.

X-Ray is the correct choice because it directly addresses the challenges of performance monitoring in complex, distributed applications. It allows developers to visualize end-to-end request flows, detect and diagnose errors, and optimize application performance. By providing a comprehensive view of latency and dependencies, X-Ray empowers teams to improve reliability and efficiency, making it indispensable for microservice monitoring.

Question 97 

Which AWS service allows developers to execute serverless functions in response to HTTP requests?

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, including HTTP requests. When combined with Amazon API Gateway, Lambda functions can be triggered directly by HTTP requests, allowing developers to build fully serverless backend applications without managing servers. Lambda automatically scales with incoming request volume, ensures fault tolerance, and only charges for compute time consumed during function execution, making it efficient for dynamic workloads.

Amazon EC2 provides virtual servers in the cloud and allows developers to run custom applications. While EC2 can handle HTTP requests by running a web server like Nginx or Apache, it requires manual management of the underlying operating system, scaling, and security configurations. EC2 does not natively provide serverless execution or automatic scaling based on individual HTTP requests.

AWS Step Functions is a service designed to orchestrate workflows and coordinate multiple AWS services. Step Functions can sequence Lambda functions or other services, handle retries, and implement branching logic. However, Step Functions do not directly execute code in response to HTTP requests and are intended for workflow management rather than serving as a direct compute engine.

Amazon S3 is a highly durable object storage service. While S3 can trigger Lambda functions based on object events, such as file uploads, it is not inherently designed to handle HTTP requests as a direct trigger for function execution. S3’s event-driven capabilities are specific to object changes rather than web traffic.

Lambda is the correct option because it enables fully serverless execution of backend logic in response to HTTP requests. It removes the need to manage servers, automatically scales with traffic, and integrates seamlessly with API Gateway to handle web requests. This makes it the ideal solution for building scalable, event-driven web applications.

Question 98 

Which AWS service provides a fully managed document database compatible with MongoDB?

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

Answer:  A) Amazon DocumentDB

Explanation:

Amazon DocumentDB is a fully managed, scalable document database that is compatible with MongoDB workloads. It supports JSON storage, indexing, and querying using MongoDB APIs, allowing developers to migrate applications with minimal code changes. DocumentDB automatically handles backups, scaling, and high availability, making it ideal for applications that require flexible schema and hierarchical data structures.

Amazon DynamoDB is a key-value and document NoSQL database known for low-latency, high-throughput performance. While DynamoDB supports document-like structures and JSON data, it is not compatible with MongoDB APIs. Applications written for MongoDB would require significant refactoring to run on DynamoDB, limiting its direct migration capabilities.

Amazon RDS is a relational database service supporting multiple engines such as MySQL, PostgreSQL, Oracle, and SQL Server. RDS is designed for structured relational data, not document-oriented workloads. While it offers scalability and managed services, it does not natively support MongoDB-compatible queries or JSON document models.

Amazon Aurora is a high-performance relational database compatible with MySQL and PostgreSQL. Aurora is ideal for workloads requiring relational transactions, complex queries, and high availability. However, it is not a document database and cannot directly execute MongoDB workloads.

DocumentDB is correct because it provides a managed MongoDB-compatible document database, enabling developers to migrate existing applications without re-architecting the data layer. Its built-in scalability, backup, and high availability make it a strong choice for modern, document-centric applications.

Question 99 

Which AWS service allows you to automatically scale compute resources based on traffic load?

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 is a service designed to automatically adjust the number of EC2 instances in a fleet based on predefined metrics, such as CPU utilization, memory usage, or network traffic. This dynamic scaling ensures that applications can maintain consistent performance during periods of high demand while avoiding over-provisioning during periods of low traffic. Auto Scaling provides several strategies for managing resources effectively, including dynamic scaling, which reacts in real time to changes in demand, scheduled scaling, which adjusts capacity based on predictable traffic patterns, and predictive scaling, which uses machine learning to forecast future load and proactively adjust resources. By continuously optimizing the number of running instances, EC2 Auto Scaling reduces operational overhead and improves cost efficiency, ensuring that applications remain both responsive and economical.

AWS Lambda also provides automatic scaling, but its model is fundamentally different. Lambda functions scale automatically based on the number of incoming requests, meaning that the service can handle any volume of traffic by running multiple instances of the function in parallel. However, Lambda does not manage EC2 instances or other traditional server-based infrastructure. While it is excellent for serverless workloads, event-driven applications, and microservices, it is not suitable for managing or scaling a fleet of EC2 servers. Lambda’s scaling is function-based, focusing on the execution of individual code snippets rather than infrastructure-level resource management, which makes it conceptually distinct from EC2 Auto Scaling.

AWS Step Functions serves a different purpose altogether. It is a workflow orchestration service that enables developers to coordinate multiple AWS services and tasks into structured workflows. Step Functions allow sequential, parallel, and conditional execution of tasks, making it ideal for automating business processes or handling complex service integrations. However, it does not manage compute resources or adjust the number of EC2 instances in response to traffic. Step Functions focuses on orchestrating processes rather than scaling infrastructure.

Amazon S3 provides highly scalable object storage, automatically expanding capacity to accommodate data growth. While S3 is excellent for storing and retrieving large amounts of data, it does not manage compute resources or adjust EC2 instances based on workload. S3 can integrate with services like Lambda to trigger functions on object events, but it cannot dynamically scale compute resources or handle traffic load on its own.

EC2 Auto Scaling is the correct service for automatically adjusting compute resources based on traffic because it directly manages the number of EC2 instances in a fleet. By dynamically scaling up during traffic spikes and scaling down during idle periods, it ensures applications remain performant, cost-efficient, and resilient. This capability is essential for workloads with variable or unpredictable demand, providing automated resource management and reducing the need for manual intervention.

Question 100 

Which AWS service allows developers to monitor resource configuration changes for compliance purposes?

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

Answer:  A) AWS Config

Explanation:

AWS Config is a service designed to track and record configuration changes across AWS resources. It provides a detailed history of resource configurations, enabling developers and operations teams to understand how resources have evolved over time. By maintaining this historical data, Config allows organizations to audit resource states, investigate changes, and ensure that infrastructure remains aligned with organizational policies. In addition to tracking changes, Config continuously evaluates resources against user-defined rules, allowing teams to detect configuration drift and identify resources that deviate from expected settings. This capability helps ensure compliance with internal policies as well as regulatory requirements, reducing risk and maintaining operational governance. Config also supports automated remediation actions for non-compliant resources, allowing teams to correct issues proactively rather than reactively.

AWS CloudWatch, in contrast, is primarily a monitoring and observability service. It collects metrics, logs, and events from AWS resources and applications, generating alarms and dashboards to provide insight into operational performance. While CloudWatch is highly useful for identifying trends, anomalies, and performance issues, it does not track configuration changes or evaluate compliance against rules. CloudWatch can alert administrators to system failures or unusual activity but does not provide the historical context of resource configurations or compliance status, which is the primary focus of AWS Config.

AWS CloudTrail focuses on auditing API activity and user interactions within an AWS account. It records which user performed a specific action, when the action occurred, and which resources were affected. CloudTrail provides an invaluable audit trail for security investigations and compliance verification. However, it does not monitor or evaluate the configuration state of resources. While CloudTrail can indicate that a change occurred, it does not capture the configuration details before and after the change, nor does it assess whether the change complies with organizational policies.

AWS X-Ray is a distributed tracing service used to monitor application performance, particularly for microservices and serverless applications. It tracks requests as they flow through services, helping developers identify latency, bottlenecks, and errors in application workflows. While X-Ray is excellent for performance troubleshooting and understanding application behavior, it does not provide visibility into resource configurations or assess compliance with policies.

AWS Config is the correct service because it is purpose-built for managing and tracking resource configurations across an AWS environment. It ensures that resources comply with organizational standards, provides historical context for audits, and enables automated remediation of non-compliant resources. By using Config, organizations can proactively manage compliance, reduce operational risk, and maintain governance over complex cloud infrastructures. Its combination of monitoring, historical tracking, rule evaluation, and remediation makes it essential for maintaining secure and compliant AWS environments.