Amazon AWS Certified Developer – Associate DVA-C02 Exam Dumps and Practice Test Questions Set 8 Q141-160

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Question 141
 

Which AWS service allows developers to monitor and respond to infrastructure metrics with automated actions?

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

Answer:  A) Amazon CloudWatch

Explanation:

Amazon CloudWatch is a monitoring and observability service designed to collect and track metrics, logs, and events from your AWS resources and applications. CloudWatch collects metric data such as CPU utilization, memory and disk usage (via custom metrics), network throughput, and application-level metrics you define. It can also ingest log data and let you search and analyze it, and it provides dashboards to visualize operational health. Crucially for this question, CloudWatch has an alarms facility that evaluates metrics against thresholds or anomaly detection models; when an alarm enters a triggered state it can perform automated actions — for example, sending SNS notifications, invoking Lambda functions, triggering Auto Scaling policies, or creating Systems Manager OpsItems. Those built-in capabilities make CloudWatch the go-to service for real-time monitoring plus automated responses.

AWS CloudTrail is a fundamentally different service focused on auditing and governance. CloudTrail continuously records AWS API calls and management events across an account or organization — who called what API, from which IP, with what parameters, and when. That data is invaluable for forensic investigation, compliance evidence, and tracking changes to the control plane. However, CloudTrail events by themselves are event records; while they can be shipped to CloudWatch Logs or EventBridge for alerting, CloudTrail does not natively provide metric-based alarms or automated operational remediation the way CloudWatch does. In short, CloudTrail answers “who did what” and is essential for security auditing rather than metric-driven automated remediation.

AWS Config is oriented toward tracking and evaluating resource configuration state and compliance. Config records snapshots of resource configurations over time and can evaluate those snapshots against rules (managed or custom) to determine whether resources are compliant with your organization’s policies. When a configuration drift or noncompliance is detected, Config can generate aggregated compliance reports and trigger remediation workflows (for example a Lambda function via Systems Manager or EventBridge). However, Config’s triggers are based on configuration change events and compliance rules rather than continuous performance metrics like CPU or request latency, so it’s not the service developers choose for monitoring operational metrics and responding to performance anomalies.

AWS X-Ray is an application tracing service that collects latency data, service map visualizations, and traces for distributed applications. X-Ray helps developers pinpoint performance bottlenecks by following requests through microservices and showing where time is spent. While X-Ray provides diagnostics for request latency, error rates, and downstream service behavior, it is focused at the application tracing and debugging layer and doesn’t provide the same breadth of metric alarms, dashboards, and operational automation that CloudWatch offers. For automated scaling, alerting, or operational remediation based on metrics across infrastructure and application layers, CloudWatch is the appropriate choice because it was built to aggregate telemetry, create alarms, and trigger automated actions across AWS services.

Putting those pieces together, CloudWatch is the correct answer because it unifies metric collection, log ingestion, visualization, alarms, and automated actions. CloudWatch enables developers and operators to define thresholds and anomaly detectors, launch automated workflows in response to metric states, and integrate alerts with notification and remediation targets. CloudTrail, Config, and X-Ray each have essential, adjacent roles (audit trails, configuration compliance, and tracing respectively), but none match CloudWatch’s intended role as the central hub for monitoring metrics and triggering operational automation.

Question 142 

Which AWS service allows developers to implement serverless backend logic for APIs?

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

Answer:  A) AWS Lambda

Explanation:

AWS Lambda is a serverless compute service that runs code in response to events or direct invocations. Lambda functions can be triggered by many sources — API Gateway HTTP requests, S3 object events, DynamoDB stream records, EventBridge scheduled events, or directly by SDK calls. Lambda abstracts away server management: you supply code, set memory and timeout, and AWS runs it on demand, scaling automatically based on concurrent requests. Because Lambda integrates tightly with API Gateway, developers commonly build serverless backends where API Gateway handles HTTP routing and authentication, and Lambda implements the backend logic, making Lambda the natural service for implementing serverless API business logic.

Amazon EC2 provides virtual machine instances where you control the operating system, networking, and installed software. EC2 is ideal when you need low-level control, long-running processes, or custom system configurations. However, EC2 requires provisioning, patching, scaling, and capacity planning; it does not provide the out-of-the-box serverless invocation model that Lambda does. For small, event-driven API backends where you want to pay per execution and avoid server management, EC2 is an overkill architectural choice and would typically be paired with additional tooling (load balancers, autoscaling groups) to achieve similar elasticity.

Amazon S3 is an object storage service for storing and serving files — static website assets, images, documents, and backups. Although S3 can host a static website and trigger events (like object created) that can invoke Lambda, S3 itself does not execute custom backend logic for API endpoints. It is excellent for storing the objects an API might serve or process, but it cannot replace a compute runtime. S3’s strength is storage durability and simple hosting of static content, not acting as the serverless compute layer for API request handling.

Amazon CloudFront is a global content delivery network that caches and delivers content closer to users to reduce latency. CloudFront can execute lightweight edge logic via Lambda@Edge or CloudFront Functions, but those are specialized for request/response manipulation at the edge and are not the primary mechanism for building full serverless backends. CloudFront accelerates delivery and can protect APIs by caching responses or applying WAF rules, but it doesn’t replace Lambda as the backend compute engine.

Weighing each option, Lambda is the correct answer because it provides the developer-centric facilities needed to implement serverless backend logic for APIs: event-driven invocation, automatic scaling, pay-per-use billing, and native integration with API Gateway and many other AWS services. EC2, S3, and CloudFront are essential AWS building blocks but occupy different roles — managed VMs, storage, and CDN — while Lambda is purpose-built for serverless API logic.

Question 143 

Which AWS service allows developers to store and retrieve large files securely in the cloud?

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

Answer:  A) Amazon S3

Explanation:

Amazon S3 (Simple Storage Service) is an object storage system optimized for storing and retrieving large binary objects such as images, videos, backups, archives, logs, and any other file-based data. S3 provides extremely high durability (designed for 99.999999999% durability), virtually unlimited scalability, and features such as versioning, lifecycle rules for transition to lower-cost storage classes, encryption at rest and in transit, fine-grained access control via IAM and bucket policies, pre-signed URLs for secure temporary access, and integration with CloudFront and other AWS services. For developers needing to store and serve large files securely and durably, S3 is the default and most cost-effective choice.

Amazon DynamoDB is a managed NoSQL key-value and document database that excels at low-latency access to structured items and high request throughput. DynamoDB is not optimized for large binary files; while you can store small binary attributes, using DynamoDB for large files is inefficient and expensive compared to S3. Instead, a common pattern is to store file metadata, object keys, or references to S3 objects in DynamoDB while the actual files remain in S3. DynamoDB’s strengths are predictable performance for queries and single-digit millisecond latency, not large-file storage.

Amazon RDS (Relational Database Service) is a managed relational database platform providing engines like MySQL, PostgreSQL, and SQL Server. RDS is designed for structured, relational data where transactions, SQL queries, and relational constraints matter. Storing large blobs in RDS is possible, but it often increases database storage and backup costs and can complicate performance tuning. For most architectures, storing file contents in S3 and keeping only references in RDS is the recommended pattern to keep databases lean and performant.

Amazon ElastiCache is an in-memory cache service (Redis or Memcached) used to accelerate application performance by caching frequently accessed data. ElastiCache is ideal for transient data requiring very fast reads and writes, but it is not a persistent, durable storage solution and is not suited for large file storage. Its memory-backed nature means that persistence and cost characteristics differ radically from object storage.

S3 is correct because it was designed from the ground up for secure, scalable, and durable storage of large files. The other services play important roles—DynamoDB for fast key-value access, RDS for transactional relational data, and ElastiCache for caching—but none provide the combination of durability, cost efficiency, versioning, lifecycle management, and integration for large object storage that S3 does. For applications that need to store and retrieve files securely in the cloud, S3 is the appropriate choice.

Question 144 

Which AWS service allows developers to automatically scale containerized applications?

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

Answer:  A) Amazon ECS with Fargate

Explanation:

Amazon ECS (Elastic Container Service) with Fargate is a managed container execution option that allows you to run containers without provisioning or managing the underlying EC2 instances. With Fargate, developers specify task definitions (CPU, memory, container images, environment variables) and AWS launches and manages the compute for those containers. ECS integrates with Application Load Balancers, Service Auto Scaling, CloudWatch metrics, and IAM, enabling automated scaling of containerized services based on CPU, memory, request metrics, or custom CloudWatch metrics. Because Fargate abstracts infrastructure, it simplifies operations while enabling predictable scaling for long-running container workloads or microservices.

AWS Lambda is serverless compute for execution of short-lived functions in response to events. Lambda can run container images up to certain size and runtime constraints, but its lifecycle, cold-start profile, and execution time limits (although these have increased over time) still make it less appropriate for many containerized application workloads that require long-running processes, custom networking, or precise control over CPU/memory allocation. For containerized apps that need standard container orchestration features and scaling behavior, ECS (or EKS) is usually the better fit.

Amazon EC2 provides full control over virtual machines and allows you to run container orchestrators yourself or run containers directly on instances. EC2 gives you flexibility but also makes you responsible for capacity planning, patching, scaling groups, and lifecycle management. While you can achieve automatic scaling with EC2 using Auto Scaling Groups and orchestration layers, doing so requires more operational effort than using a managed solution like ECS with Fargate.

Amazon API Gateway is a managed service for creating, deploying, and managing RESTful and WebSocket APIs. API Gateway is not a container execution environment; instead, it routes requests to backends such as Lambda, ECS services behind a load balancer, or HTTP endpoints. Its features include throttling, caching, authorization, and integration with WAF, but API Gateway itself is not responsible for running containerized applications or scaling containers.

Considering these distinctions, Amazon ECS with Fargate is the correct answer because it is explicitly designed to run and scale containerized applications without requiring server provisioning. Fargate gives you the orchestration and scaling benefits of containers while removing the need to manage infrastructure, making it ideal for teams that want to focus on application code and container images rather than servers.

Question 145 

Which AWS service allows developers to track and audit AWS API calls for security and compliance?

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

Answer:  A) AWS CloudTrail

Explanation:

AWS CloudTrail is a governance, compliance, and auditing service that continuously records API calls and related events across AWS accounts. CloudTrail captures who made API calls, when the calls occurred, the IP address of the caller, and the request parameters and responses. These detailed logs are essential for security monitoring, incident response, and forensic investigations because they create an immutable event history of control-plane activity. CloudTrail supports multi-region trails, organization trails across AWS Organizations, and integration with S3 and CloudWatch Logs for analysis and alerting.

Amazon CloudWatch focuses on metrics, logs, and alarms for operational monitoring. CloudWatch collects resource and application telemetry (such as CPU utilization, memory usage via custom metrics, request latency, and error rates) and can drive alarms and automated remediation. While CloudWatch can ingest CloudTrail logs and help trigger notifications, it does not itself serve as the authoritative API audit trail — that is CloudTrail’s role. Thus CloudWatch is complementary to CloudTrail but not a replacement for auditing API calls and user activity.

AWS Config records resource configuration snapshots and changes over time, evaluating those states against compliance rules. Config is useful for proving compliance and tracking configuration drift; it can identify when a resource’s configuration deviates from an expected state and can trigger remediation actions. But Config is centered on configuration state rather than the full set of API call metadata and user-level activity logs that CloudTrail provides, so it is not the primary tool for auditing who did what.

AWS X-Ray helps developers analyze application-level traces, visualize service maps, and find latency bottlenecks in distributed systems. X-Ray is used for debugging and performance analysis of application requests as they traverse services; it is not designed to capture the account-level API call metadata needed for security audits and compliance. X-Ray provides deep insight into request performance but not a comprehensive audit trail of AWS control plane actions.

Putting these capabilities together, CloudTrail is the correct choice because it is explicitly engineered to track and record API calls and user activity for security, compliance, and forensic purposes. CloudWatch, Config, and X-Ray each serve distinct operational roles (monitoring, configuration compliance, and tracing) and can complement CloudTrail in a mature observability and governance posture, but none provide the thorough, account-level audit trail that CloudTrail offers.

Question 146

Which AWS service allows developers to implement feature-rich, serverless APIs for web applications?

A) Amazon API Gateway
B) AWS Lambda
C) Amazon EC2
D) Amazon S3

Answer:  A) Amazon API Gateway

Explanation:

Amazon API Gateway is a fully managed service that allows developers to create, deploy, and manage APIs at scale. It supports RESTful and WebSocket APIs and provides features like request validation, throttling, authentication and authorization (via IAM, Cognito, or custom authorizers), caching, monitoring, and logging. API Gateway abstracts away the infrastructure required to host APIs and provides a consistent endpoint for clients to interact with serverless backends or other AWS services. This makes it ideal for developers who want to implement feature-rich, scalable, and secure APIs without managing servers.

AWS Lambda executes code in response to events or direct invocations. While Lambda often acts as the backend for APIs managed by API Gateway, it does not provide API management features itself. Lambda can process requests, perform business logic, and integrate with databases or other services, but it lacks built-in support for throttling, request validation, caching, or user authentication. Developers usually pair Lambda with API Gateway to provide a complete serverless API solution.

Amazon EC2 is a virtual server service where developers manage the operating system, networking, and software stack. While EC2 instances can host APIs via web servers like Nginx or Apache, scaling, security, and API management must be handled manually. EC2 provides raw compute resources but not the managed API features that API Gateway provides, making it a more complex and operationally intensive choice for modern serverless API architectures.

Amazon S3 is object storage and can serve static websites or host static content for APIs, but it cannot execute server-side logic or provide features for API management. It is useful for storing files or assets that APIs might serve but is not designed to host dynamic or feature-rich API endpoints. API Gateway is correct because it allows developers to build scalable, secure, and fully managed APIs that integrate with Lambda or other backend services, simplifying serverless application development and operational management.

Question 147

Which AWS service allows developers to securely store application secrets with automatic rotation?

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

Answer:  A) AWS Secrets Manager

Explanation:

AWS Secrets Manager is a service specifically designed for securely storing, managing, and rotating sensitive credentials such as database passwords, API keys, and OAuth tokens. It provides automatic rotation capabilities, integration with AWS SDKs, and auditing of access to secrets via CloudTrail. Secrets Manager ensures that credentials are never hardcoded into applications and can be rotated without application downtime, reducing the operational risk of managing secrets manually.

AWS Key Management Service (KMS) is focused on cryptographic key management. KMS allows you to create, store, and manage encryption keys used by AWS services or applications to encrypt data. While KMS can encrypt secrets, it does not provide automated rotation of application credentials or integrated secret management workflows. It is complementary to Secrets Manager but not a full solution for storing and managing secrets for applications.

AWS Systems Manager Parameter Store offers a secure way to store configuration values and secrets in plain text or encrypted form. Parameter Store supports hierarchical parameter organization, versioning, and integration with IAM for access control. However, while it can encrypt secrets using KMS, it does not have built-in automatic rotation for credentials. For organizations needing automated rotation and auditing of sensitive application secrets, Secrets Manager is the more complete solution.

Amazon DynamoDB is a managed NoSQL database for storing structured data. While it could theoretically store secret values, it lacks the security, access controls, auditing, and automatic rotation capabilities of a dedicated secret management service. Using DynamoDB for secrets management would require building a custom solution, increasing operational complexity. Secrets Manager is the correct choice because it provides centralized, secure management of application secrets with built-in rotation, auditing, and access control, helping maintain security and compliance best practices.

Question 148 

Which AWS service allows developers to process 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 a managed service designed for real-time streaming data ingestion and processing. It can handle high-throughput data from sources like IoT devices, clickstreams, logs, and financial transactions. Developers can process data in near real-time with Kinesis Data Analytics, Lambda, or custom consumer applications. Kinesis allows multiple consumers to independently process the same stream in parallel, making it ideal for analytics pipelines, monitoring systems, and event-driven applications where low-latency processing is critical.

Amazon SQS (Simple Queue Service) is a fully managed message queue for asynchronous communication between distributed components. SQS provides reliable delivery and decoupling of producers and consumers but is not designed for high-throughput real-time streaming analytics. Messages in SQS are processed at least once, and ordering is guaranteed only in FIFO queues. SQS is better suited for decoupling workflows and task queues than real-time stream processing.

Amazon SNS (Simple Notification Service) is a pub/sub messaging service for sending notifications to multiple subscribers via email, SMS, or Lambda. SNS enables fan-out messaging but is not designed to handle continuous streams of large data volumes or provide ordered, real-time processing. SNS is ideal for alerts and notifications rather than high-throughput streaming pipelines.

AWS Lambda is a serverless compute service that can process events, including from Kinesis streams. However, Lambda is a compute engine, not a managed streaming platform; it depends on services like Kinesis Data Streams to provide ordered, durable, real-time event ingestion at scale. Kinesis Data Streams is the correct answer because it is explicitly built for ingesting and processing real-time streaming data reliably and at high throughput, enabling developers to build analytics pipelines and event-driven applications.

Question 149 

Which AWS service allows developers to implement event-driven architecture using a centralized event bus?

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

Answer:  A) Amazon EventBridge

Explanation:

Amazon EventBridge is a serverless event bus that allows applications to communicate via events, supporting event routing, filtering, and transformation. EventBridge can consume events from AWS services, SaaS applications, or custom sources and route them to multiple targets, including Lambda, SQS, SNS, and Step Functions. It allows developers to implement decoupled, event-driven architectures where services can respond to changes without tight coupling, increasing scalability, maintainability, and flexibility.

Amazon SNS is a pub/sub messaging service that allows one-to-many message distribution. While SNS can fan out notifications to multiple subscribers, it lacks advanced event filtering, transformation, and routing capabilities. SNS is best suited for notification systems rather than acting as a centralized event bus for complex architectures.

Amazon SQS is a fully managed message queue designed for asynchronous, reliable message delivery between distributed components. SQS supports decoupling producer-consumer workflows but does not natively route events, filter based on content, or broadcast to multiple targets simultaneously. SQS queues are linear pipelines, not a bus for multi-target event routing.

AWS Lambda executes code in response to events but does not act as a centralized event router. Lambda functions can be invoked by EventBridge, SNS, or SQS, but Lambda alone cannot replace the centralized event bus functionality. EventBridge is correct because it enables developers to implement event-driven architectures with centralized event routing, filtering, and multi-target delivery, facilitating loosely coupled application design and operational scalability.

Question 150 

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

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

Answer:  A) Amazon EC2 Auto Scaling

Explanation:

Amazon EC2 Auto Scaling automatically adjusts the number of EC2 instances in a group based on predefined policies, schedules, or predictive scaling. Auto Scaling ensures that applications maintain performance during demand spikes and minimize costs during low-traffic periods. Developers can define scaling policies based on CloudWatch metrics such as CPU utilization, request latency, or custom application metrics. Auto Scaling integrates with load balancers, health checks, and lifecycle hooks to manage EC2 instance availability efficiently.

AWS Lambda automatically scales serverless functions in response to concurrent invocations, eliminating the need for capacity planning. While Lambda provides event-driven scaling, it does not manage EC2 instances and therefore cannot provide the same control over the scaling of traditional virtual machines or server fleets.

AWS Step Functions orchestrates workflows by defining sequences of Lambda functions or other AWS service calls. Step Functions is focused on workflow management and coordinating complex processes but does not directly scale EC2 instances or manage compute capacity. It is complementary to Auto Scaling for orchestrating scaling-related tasks but is not itself a scaling engine.

Amazon CloudFront is a global content delivery network that accelerates delivery of static and dynamic content. CloudFront optimizes latency and bandwidth usage but does not provision or scale compute resources like EC2 instances. It is focused on content delivery rather than resource scaling. EC2 Auto Scaling is the correct choice because it provides automated, policy-driven scaling of EC2 instances to maintain application performance, availability, and cost efficiency without requiring manual intervention.

Question 151 

Which AWS service provides distributed tracing for monitoring microservices performance?

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 specifically designed for distributed tracing of requests through complex applications, particularly microservices architectures. It allows developers to visualize the journey of requests as they travel across various services, providing deep insights into latency, errors, and performance bottlenecks. By collecting trace data, X-Ray helps in identifying which service or component may be causing slowdowns or failures. This visualization is crucial for microservices because it often involves multiple interdependent services that can be challenging to monitor individually. X-Ray also supports sampling strategies and provides detailed service maps to understand the relationship between services and the performance impact of each.

AWS CloudWatch is a monitoring service for AWS resources and applications. It collects metrics, logs, and events, allowing developers to track performance trends and set alarms for specific thresholds. While CloudWatch is powerful for metrics-based monitoring and operational visibility, it does not provide the fine-grained, request-level tracing that X-Ray does. CloudWatch is more focused on the overall health and usage patterns of resources rather than understanding the path of an individual request across multiple services.

AWS CloudTrail is primarily an auditing service. It records API calls and events for governance, compliance, and operational auditing. While CloudTrail helps track who did what in the AWS environment, it does not provide performance metrics or trace requests through an application. It is useful for security and compliance but not for identifying performance bottlenecks or latency issues in microservices.

AWS Config is a configuration management and compliance service. It records configuration changes of AWS resources and evaluates them against rules to ensure compliance. Config provides historical configuration data and drift detection, which is useful for auditing and compliance purposes. However, it does not offer request tracing or performance monitoring of applications.

X-Ray is the correct choice because it focuses on the core challenge of distributed applications: tracing requests end-to-end. By understanding request flow, latency, and errors, developers can optimize service performance, improve troubleshooting, and gain insights into complex serverless and microservices architectures. Unlike CloudWatch, CloudTrail, or Config, X-Ray provides actionable, request-level diagnostic data essential for application reliability

Question 152 

Which AWS service allows developers to store hierarchical configuration data for applications?

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

Answer:  A) AWS Systems Manager Parameter Store

Explanation:

AWS Systems Manager Parameter Store is a centralized service for storing configuration and management data for applications. It allows developers to store parameters in a hierarchical structure, which is useful for managing application configurations across multiple environments or services. Parameter Store supports encryption using AWS KMS, versioning of parameters, and fine-grained access control via IAM policies. This makes it ideal for applications that require dynamic retrieval of configuration values or feature flags without hardcoding sensitive or environment-specific data.

AWS Secrets Manager is focused on managing secrets such as API keys, database credentials, and passwords. It provides automated secret rotation, encryption, and fine-grained access control. While Secrets Manager is excellent for securely storing credentials, it is not intended for general-purpose configuration management or hierarchical storage. Its focus is narrower than Parameter Store.

AWS Key Management Service (KMS) is a cryptographic service used to create, store, and manage encryption keys. KMS provides the foundational encryption needed for services like Parameter Store or Secrets Manager but does not itself store application configuration or secrets. Its purpose is key management, not configuration management.

Amazon DynamoDB is a fully managed NoSQL database that stores data in tables and supports key-value and document models. While DynamoDB can store configuration data, it does not natively support hierarchical structures, automatic versioning, or integration with AWS Systems Manager features for configuration management. Using DynamoDB for this purpose would require additional development overhead.

Parameter Store is the correct choice because it combines hierarchical storage, encryption, access control, and versioning to provide a centralized, secure, and scalable configuration management solution. It integrates seamlessly with serverless and distributed applications, enabling dynamic configuration retrieval, which simplifies application deployment and operational efficiency.

Question 153 

Which AWS service allows developers to build and deploy serverless APIs integrated with Lambda?

A) Amazon API Gateway
B) AWS Lambda
C) Amazon EC2
D) Amazon S3

Answer:  A) Amazon API Gateway

Explanation:

Amazon API Gateway is a fully managed service that allows developers to create, deploy, and manage APIs at scale. It supports RESTful APIs and WebSocket APIs, providing built-in features such as request validation, throttling, caching, authentication, and authorization. API Gateway acts as the front door for applications, routing requests to backend services like AWS Lambda or other HTTP endpoints. Its integration with Lambda allows developers to implement serverless APIs without provisioning or managing servers, providing automatic scaling and high availability.

AWS Lambda is a serverless compute service that executes code in response to events. Lambda is ideal for running backend logic but does not handle HTTP request routing, throttling, or API lifecycle management. Lambda functions are commonly invoked by API Gateway but cannot independently serve as API endpoints.

Amazon EC2 provides virtual servers for running applications. While APIs can be hosted on EC2, developers must manage the servers, scaling, and load balancing manually. EC2 lacks the fully managed serverless API capabilities provided by API Gateway, making it less suitable for rapid, scalable API deployment in a serverless architecture.

Amazon S3 is an object storage service. S3 can host static web content but cannot serve as a backend for dynamic API calls. While S3 triggers Lambda functions for events such as object creation, it does not provide API endpoint management or request routing.

API Gateway is correct because it enables the creation of scalable, secure, and fully managed APIs integrated with Lambda. It abstracts infrastructure concerns, provides robust API management capabilities, and supports serverless architectures efficiently.

Question 154 

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

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

Answer:  A) Amazon DocumentDB

Explanation:

Amazon DocumentDB is a managed document database designed to be compatible with MongoDB APIs. It allows developers to migrate existing MongoDB applications to AWS without changing application code. DocumentDB stores JSON-like documents and provides features such as automatic replication, scaling, backups, and high availability. This allows developers to focus on application logic rather than database management, making it ideal for document-centric applications.

Amazon DynamoDB is a fully managed NoSQL key-value and document database. While DynamoDB can store JSON documents, it does not natively support MongoDB APIs, making migration from MongoDB more complex. DynamoDB uses a different query language and model, which may require significant code changes.

Amazon RDS is a relational database service that supports engines like MySQL, PostgreSQL, SQL Server, and Oracle. RDS is ideal for structured relational data but does not support MongoDB APIs or document storage models. RDS is not suitable for applications designed specifically for MongoDB.

Amazon Aurora is a high-performance relational database compatible with MySQL and PostgreSQL. Aurora supports automatic scaling and high availability but, like RDS, is relational and does not provide MongoDB API compatibility.

DocumentDB is correct because it delivers MongoDB API compatibility with fully managed database features such as replication, backups, scaling, and high availability. It allows developers to run document-based applications efficiently without managing the underlying infrastructure.

Question 155 

Which AWS service allows developers to implement serverless, event-driven compute triggered by S3 or DynamoDB changes?

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

Answer:  A) AWS Lambda

Explanation:

AWS Lambda is a serverless compute service that automatically executes code in response to events. Events can originate from sources like Amazon S3 object uploads, DynamoDB table updates, Kinesis data streams, or API Gateway requests. Lambda abstracts server management, scaling automatically in response to event volume, allowing developers to focus purely on code logic. This makes it ideal for event-driven architectures.

Amazon EC2 provides virtual machines for compute workloads but requires manual provisioning, configuration, and scaling. EC2 cannot natively respond to events like S3 uploads without additional setup such as Lambda integration or custom polling logic.

AWS Step Functions is a workflow orchestration service. Step Functions coordinate multiple AWS services and Lambda functions in a workflow but do not execute code in response to events independently. They are used to build complex processes, not as event-driven compute themselves.

Amazon RDS is a managed relational database service. RDS provides storage and query capabilities but does not execute serverless functions or respond to event triggers from S3 or DynamoDB.

Lambda is correct because it allows serverless, event-driven execution without server management. It scales automatically, integrates with numerous event sources, and provides reliable execution for real-time application processing.

Question 156 

Which AWS service allows developers to implement continuous integration and deployment pipelines?

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

Answer:  A) AWS CodePipeline

Explanation:

AWS CodePipeline is a fully managed continuous integration and continuous delivery (CI/CD) service. It automates the build, test, and deployment stages of application delivery. CodePipeline allows developers to create pipelines that orchestrate the end-to-end software release process, integrating with services like CodeBuild, CodeDeploy, Lambda, and third-party tools. Its automation reduces manual intervention, improves consistency, and accelerates delivery of new features or fixes.

AWS CodeBuild is a managed build service. It compiles source code, runs tests, and produces deployable artifacts. However, CodeBuild alone does not orchestrate the full CI/CD workflow; it focuses on building and testing code within a pipeline.

AWS CodeDeploy automates application deployment to EC2 instances, Lambda functions, or on-premises servers. CodeDeploy is limited to deployment tasks and cannot orchestrate a full CI/CD pipeline.

AWS CloudFormation is an infrastructure-as-code service that provisions and manages AWS resources using templates. While CloudFormation is essential for deploying infrastructure consistently, it does not handle code builds, tests, or full application release workflows.

CodePipeline is correct because it provides orchestration for the entire CI/CD process, integrating building, testing, and deploying applications. It allows automation across AWS services and accelerates delivery cycles while ensuring consistency and repeatability.

Question 157 

Which AWS service allows developers to 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 enables developers to process and analyze streaming data in real time using standard SQL. It connects to data streams from Kinesis Data Streams or Kinesis Firehose, allowing users to filter, aggregate, and transform streaming data without provisioning servers. Developers can write SQL queries against the streaming data and immediately derive insights, making it ideal for real-time dashboards, anomaly detection, or live metrics computation.

Amazon SQS is a message queue service. While SQS reliably stores messages for asynchronous processing, it does not provide SQL-based analytics or real-time stream processing. It is focused on decoupling components in a distributed application rather than analyzing data.

Amazon SNS is a pub/sub notification service. SNS delivers messages to subscribers instantly but does not provide SQL analytics or stream processing capabilities. Its purpose is event notification and broadcasting, not real-time data analytics.

AWS Lambda executes code in response to events. While Lambda can process streaming data, it does not provide native SQL querying capabilities for real-time analytics. Developers would need to implement custom code to achieve similar functionality.

Kinesis Data Analytics is correct because it offers native SQL-based analytics on streaming data. It enables real-time insights without server management, supporting efficient event-driven and streaming application architectures.

Question 158 

Which AWS service allows developers to deploy relational databases that automatically scale for serverless applications?

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

Answer:  A) Amazon Aurora Serverless

Explanation:

Amazon Aurora Serverless is a relational database service that automatically scales compute capacity based on application demand. It is compatible with MySQL and PostgreSQL and allows serverless applications to use relational databases without provisioning or managing database instances. Aurora Serverless scales up during high demand and scales down when unused, helping reduce costs while maintaining high availability.

Amazon RDS for MySQL is a managed relational database service. While it automates tasks such as backups and patching, RDS requires manual instance scaling or use of read replicas for scaling, which is less flexible than Aurora Serverless for variable workloads.

Amazon DynamoDB is a NoSQL database. It automatically scales and is suitable for serverless applications, but it is not a relational database. DynamoDB supports key-value and document models, not relational schemas.

Amazon ElastiCache is a managed in-memory caching service. It provides high-speed caching for applications but is not a database solution and does not support relational data storage or automatic database scaling.

Aurora Serverless is correct because it delivers fully managed, scalable, relational database support ideal for serverless applications. It reduces operational overhead and cost while providing seamless scaling based on workload demand.

Question 159 

Which AWS service allows developers to decouple microservices by sending messages asynchronously?

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

Answer:  A) Amazon SQS

Explanation:

Amazon Simple Queue Service (SQS) is a fully managed message queue that temporarily stores messages sent between distributed services. SQS decouples microservices by enabling producers to send messages without waiting for consumers to process them immediately. This asynchronous communication improves resilience and scalability by allowing services to operate independently and handle varying workloads without failures.

Amazon SNS is a pub/sub messaging service. SNS immediately pushes messages to subscribers but does not persist messages. While SNS is ideal for notifications and broadcasting events, it cannot provide the reliable, persistent queuing needed to decouple microservices fully.

AWS Lambda is a serverless compute service that executes functions in response to events. While Lambda can process messages from SQS or SNS, it does not provide message storage or queuing by itself.

Amazon Kinesis is designed for streaming data ingestion and processing. It handles high-throughput data streams but is more suitable for real-time analytics than for decoupling microservices with reliable asynchronous messaging.

SQS is correct because it allows reliable, asynchronous communication between microservices, supporting scaling and fault tolerance in distributed systems. It provides a persistent, decoupled messaging layer that ensures message delivery even if consumers are temporarily unavailable.

Question 160 

Which AWS service allows developers to monitor configuration changes for compliance and auditing?

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

Answer:  A) AWS Config

Explanation:

AWS Config is a service that records configuration changes of AWS resources, enabling developers and organizations to track compliance and detect drift. Config maintains historical snapshots, evaluates compliance against rules, and provides insights into resource relationships. It is particularly useful for auditing purposes, regulatory compliance, and operational governance. Developers can also trigger notifications when resources deviate from desired configurations.

AWS CloudWatch monitors metrics, collects logs, and triggers alarms based on thresholds. While CloudWatch provides operational insights into resource performance, it does not track configuration changes or evaluate compliance. It is focused on monitoring rather than governance.

AWS CloudTrail records API calls and user activity for auditing purposes. CloudTrail helps determine who made changes and when, but it does not evaluate resource configurations or compliance against rules. It is complementary to Config but not sufficient for configuration monitoring.

AWS X-Ray traces requests through applications to analyze performance and latency. X-Ray is focused on monitoring distributed application performance, not resource configuration or compliance tracking.

Config is correct because it provides continuous assessment of AWS resources’ configurations, evaluates compliance against rules, and maintains a historical record for auditing. It ensures governance, compliance, and operational best practices are applied across all AWS resources.