Amazon AWS Certified Developer — Associate DVA-C02 Exam Dumps and Practice Test Questions Set6 Q76-90
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Question 76:
Which AWS service provides a fully managed NoSQL database with single-digit millisecond performance at any scale?
A) Amazon RDS
B) Amazon DynamoDB
C) Amazon Redshift
D) Amazon Aurora
Answer: B
Explanation:
Amazon DynamoDB is the correct answer as it is a fully managed NoSQL database service that delivers consistent single-digit millisecond performance at any scale. DynamoDB is designed to handle massive workloads with seamless scalability, making it ideal for applications requiring high performance and low latency. The service automatically scales throughput capacity based on application demands and maintains performance even as data volumes grow. DynamoDB supports both key-value and document data models, providing flexibility for various application architectures. It offers features like auto-scaling, on-demand capacity mode, and global tables for multi-region deployments.
Amazon RDS is incorrect because it is a managed relational database service that supports multiple database engines like MySQL, PostgreSQL, and Oracle. While RDS provides good performance, it is not specifically optimized for single-digit millisecond latency like DynamoDB. RDS is better suited for traditional relational database workloads that require ACID transactions and complex queries using SQL. The performance characteristics of RDS depend on the chosen instance type and storage configuration, and it typically cannot match the consistent low latency that DynamoDB provides.
Amazon Redshift is not the right choice as it is a data warehousing service designed for analytical queries on large datasets. Redshift uses columnar storage and parallel query execution to analyze petabytes of data, but it is not optimized for transactional workloads requiring millisecond response times. The service is intended for business intelligence and reporting applications rather than operational database needs. Redshift typically handles queries that may take seconds to minutes rather than milliseconds, making it unsuitable for low-latency application requirements.
Amazon Aurora is incorrect because although it is a high-performance relational database compatible with MySQL and PostgreSQL, it does not provide the same consistent single-digit millisecond performance as DynamoDB. Aurora is designed for relational workloads and offers better performance than standard RDS instances, but it still operates within the constraints of relational database architectures. Aurora is excellent for applications requiring SQL compatibility and relational data models but not for use cases demanding the extreme low latency that DynamoDB provides for NoSQL workloads. Understanding these differences is essential for the AWS Certified Developer Associate exam.
Question 77:
What is the maximum execution duration for an AWS Lambda function?
A) 5 minutes
B) 10 minutes
C) 15 minutes
D) 30 minutes
Answer: C
Explanation:
The maximum execution duration for an AWS Lambda function is 15 minutes, making this the correct answer. This timeout limit applies to all Lambda functions regardless of the runtime or memory configuration. When you create or update a Lambda function, you can specify a timeout value between 1 second and 900 seconds, which equals 15 minutes. If your function exceeds this duration, Lambda automatically terminates the execution and returns a timeout error. This limitation is important to consider when designing serverless applications, as long-running processes may need to be broken into smaller chunks or handled by alternative services like AWS Step Functions or container-based solutions.
The 5-minute option is incorrect as it represents an outdated limit. In the past, Lambda functions had shorter maximum execution times, but AWS has progressively increased this limit to accommodate more complex workloads. While 5 minutes might be sufficient for many use cases, it is not the current maximum allowed duration. Developers who need longer execution times can take advantage of the extended 15-minute limit for their applications. Understanding the current limits is crucial for proper application design and architecture decisions.
The 10-minute option is also incorrect, though it might seem like a reasonable middle ground. This duration does not correspond to any official Lambda limit. If your application requires processing that might exceed a few minutes, it is essential to design with the actual 15-minute maximum in mind. For processes that genuinely need more than 15 minutes, you should consider alternative AWS services such as AWS Fargate, Amazon ECS, or AWS Step Functions to orchestrate multiple Lambda invocations for complex workflows.
The 30-minute option is incorrect as it exceeds the current Lambda execution time limit. No Lambda function can run for 30 minutes continuously. If you have workloads requiring this much time, you need to architect your solution differently, perhaps using AWS Step Functions to chain multiple Lambda functions together or migrating to container-based services like ECS or Fargate. Understanding these limits is crucial for the AWS Certified Developer Associate exam and for designing effective serverless applications in production environments that meet business requirements.
Question 78:
Which service should you use to distribute traffic across multiple Amazon EC2 instances in different Availability Zones?
A) Amazon CloudFront
B) AWS Global Accelerator
C) Elastic Load Balancing
D) Amazon Route 53
Answer: C
Explanation:
Elastic Load Balancing is the correct service for distributing traffic across multiple EC2 instances in different Availability Zones within a region. ELB automatically distributes incoming application traffic across multiple targets such as EC2 instances, containers, and IP addresses in one or more Availability Zones. There are three types of load balancers: Application Load Balancer for HTTP/HTTPS traffic with advanced routing capabilities, Network Load Balancer for TCP/UDP traffic requiring extreme performance, and Gateway Load Balancer for third-party virtual appliances. ELB performs health checks on registered targets and routes traffic only to healthy instances, ensuring high availability and fault tolerance for your applications across multiple zones.
Amazon CloudFront is incorrect because it is a content delivery network service that caches content at edge locations worldwide to reduce latency for end users. While CloudFront can work with EC2 instances as origins, it is primarily designed for content distribution rather than load balancing across instances within a region. CloudFront focuses on delivering static and dynamic content quickly to users based on their geographic location. It does not provide the same instance-level load balancing and health checking capabilities as Elastic Load Balancing for distributing traffic among backend resources.
AWS Global Accelerator is not the right choice for this scenario because while it does improve application availability and performance, it works at the global level using the AWS global network. Global Accelerator provides static IP addresses that act as a fixed entry point to your application hosted in one or more AWS Regions. It is designed to route traffic to optimal endpoints based on health, geography, and routing policies across multiple regions, not specifically for distributing traffic among instances within Availability Zones in a single region like ELB does.
Amazon Route 53 is incorrect as it is primarily a DNS web service that translates domain names into IP addresses. While Route 53 can perform some traffic routing through DNS-based load balancing using routing policies like weighted or latency-based routing, it does not provide the application-level load balancing, detailed health checks, and automatic traffic distribution that Elastic Load Balancing offers. Route 53 is better suited for DNS management and global traffic routing between regions rather than instance-level load distribution within a region.
Question 79:
Which DynamoDB feature allows you to automatically delete expired items from your table?
A) DynamoDB Streams
B) Time to Live
C) Global Secondary Index
D) Conditional Writes
Answer: B
Explanation:
Time to Live, commonly known as TTL, is the correct DynamoDB feature that enables automatic deletion of expired items from your table. TTL allows you to define a timestamp attribute in your items that indicates when each item should be considered expired. DynamoDB automatically deletes expired items in the background without consuming write throughput or incurring additional costs. This feature is particularly useful for managing data that has a limited useful lifespan, such as session data, event logs, temporary records, or time-sensitive application data. By implementing TTL, you can reduce storage costs and maintain cleaner tables without manual intervention or custom deletion logic that would consume resources.
DynamoDB Streams is incorrect because it captures a time-ordered sequence of item-level modifications in a DynamoDB table. Streams allow you to track changes such as inserts, updates, and deletes in near real-time, enabling you to build applications that react to data changes. While Streams can notify you of deletions that occur, they do not automatically delete items based on expiration criteria. Streams are typically used for data replication, triggering Lambda functions, maintaining materialized views, or creating audit logs rather than managing item lifecycle and cleanup operations.
Global Secondary Index is not the right answer as it is a feature that allows you to query data using alternate key attributes beyond the primary key. A GSI provides a different partition key and optional sort key, enabling more flexible query patterns for your application needs. While GSIs are powerful for data access patterns and improving query performance, they do not have any functionality related to automatically deleting expired items. GSIs are about improving query capabilities and access patterns rather than managing data lifecycle or cleanup operations in your tables.
Conditional Writes are incorrect because they allow you to specify conditions that must be met for a write operation to succeed. Conditional writes help ensure data consistency by preventing conflicts when multiple processes attempt to modify the same item simultaneously. While you could theoretically use conditional writes as part of a custom deletion process, they do not provide automatic expiration functionality. You would still need to implement logic to identify and delete expired items, which would consume write capacity units unlike the TTL feature that handles deletions without throughput costs.
Question 80:
What AWS service can you use to deploy and manage applications without worrying about the underlying infrastructure?
A) Amazon EC2
B) AWS Elastic Beanstalk
C) Amazon ECS
D) AWS CloudFormation
Answer: B
Explanation:
AWS Elastic Beanstalk is the correct service for deploying and managing applications without worrying about the underlying infrastructure. Elastic Beanstalk is a Platform as a Service offering that automatically handles deployment, capacity provisioning, load balancing, auto-scaling, and application health monitoring. Developers simply upload their application code, and Elastic Beanstalk automatically handles all the infrastructure details. It supports multiple programming languages and platforms including Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker. Elastic Beanstalk maintains full control and visibility of the resources powering your application while abstracting the complexity of infrastructure management, making it ideal for developers who want to focus on code.
Amazon EC2 is incorrect because it provides virtual servers in the cloud where you have complete control over the computing resources. With EC2, you are responsible for managing the operating system, middleware, runtime environment, and application stack. You must handle patching, scaling, load balancing, and infrastructure configuration yourself. While EC2 offers maximum flexibility and control, it does not abstract infrastructure management in the way Elastic Beanstalk does. EC2 is an Infrastructure as a Service offering rather than a Platform as a Service, requiring more operational overhead and infrastructure knowledge.
Amazon ECS, which stands for Elastic Container Service, is not the best answer because while it simplifies container orchestration, you still need to manage the underlying EC2 instances or configure Fargate tasks. ECS requires more infrastructure knowledge compared to Elastic Beanstalk, including understanding of container concepts, task definitions, cluster management, and networking configurations. Although ECS reduces some operational overhead compared to managing containers manually, it does not provide the same level of abstraction as Elastic Beanstalk for traditional application deployments where infrastructure details are completely hidden.
AWS CloudFormation is incorrect because it is an infrastructure as code service that allows you to model and provision AWS resources using templates written in JSON or YAML. CloudFormation helps you manage infrastructure through code, but you must still define and understand all the resources your application needs. You are responsible for specifying every component from networking to compute resources. While CloudFormation automates resource provisioning and management, it does not abstract infrastructure concerns the way Elastic Beanstalk does for application deployment.
Question 81:
Which AWS service provides a Git-based repository for storing and managing source code?
A) AWS CodeCommit
B) AWS CodeBuild
C) AWS CodeDeploy
D) AWS CodePipeline
Answer: A
Explanation:
AWS CodeCommit is the correct service as it provides fully managed Git-based repositories for storing and managing source code securely. CodeCommit eliminates the need to operate your own source control system or worry about scaling its infrastructure. It supports standard Git functionality, making it easy for development teams to collaborate on code through pull requests, branching, and merging workflows. CodeCommit repositories are encrypted at rest and in transit, providing secure storage for your code and intellectual property. The service integrates seamlessly with other AWS developer tools like CodeBuild, CodeDeploy, and CodePipeline, as well as third-party tools, making it an excellent choice for version control in AWS environments.
AWS CodeBuild is incorrect because it is a fully managed continuous integration service that compiles source code, runs tests, and produces software packages ready for deployment. CodeBuild does not store source code repositories; instead, it retrieves code from repositories like CodeCommit, GitHub, or Bitbucket to perform build operations. CodeBuild scales automatically to process multiple builds concurrently and charges only for the compute time consumed during builds. While CodeBuild is essential for CI/CD pipelines and automating the build process, it is not a version control or repository service for storing source code.
AWS CodeDeploy is not the right answer as it is a deployment service that automates application deployments to various compute services such as EC2, Fargate, Lambda, and on-premises servers. CodeDeploy handles the complexity of updating applications while maintaining availability and minimizing downtime during deployments. It does not provide repository functionality for storing source code but rather focuses on the deployment phase of the software development lifecycle. CodeDeploy retrieves application artifacts from sources like S3 or GitHub but does not host Git repositories itself for version control purposes.
AWS CodePipeline is incorrect because it is a continuous delivery service that automates the build, test, and deploy phases of your release process every time there is a code change. CodePipeline orchestrates the different stages of your software release workflow by integrating with source control systems, build services, and deployment tools. While CodePipeline can work with CodeCommit as a source stage, it does not provide repository hosting capabilities. CodePipeline focuses on workflow automation and orchestration rather than source code storage and version control management.
Question 82:
What is the purpose of an Amazon S3 presigned URL?
A) To make an S3 bucket public permanently
B) To grant temporary access to a private S3 object
C) To encrypt data stored in S3
D) To create a backup of S3 data
Answer: B
Explanation:
A presigned URL is used to grant temporary access to a private S3 object without requiring the user to have AWS credentials or permissions. When you create a presigned URL, you use your own security credentials to sign the URL with an expiration time that you specify. Anyone with the presigned URL can perform the action embedded in the URL, such as downloading or uploading an object, until the URL expires. This mechanism is particularly useful for allowing users to access private content temporarily, such as sharing documents, enabling file uploads from web applications, or providing time-limited access to media files. Presigned URLs maintain security by ensuring access is temporary and controlled while avoiding the need to manage user credentials.
Making an S3 bucket public permanently is incorrect because presigned URLs do not change the bucket’s access control settings or policies. A presigned URL provides temporary access to specific objects without altering the overall bucket policy or Access Control Lists. If you wanted to make a bucket public permanently, you would modify the bucket policy or ACL directly through bucket configuration, which is a different mechanism from presigned URLs. Presigned URLs are about temporary, object-level access rather than permanent bucket-level configuration changes that affect all objects.
Encrypting data stored in S3 is not related to presigned URLs. Encryption in S3 is handled through server-side encryption options such as SSE-S3, SSE-KMS, or SSE-C, or through client-side encryption before uploading data to the bucket. Presigned URLs work with encrypted objects but do not perform encryption themselves. When you create a presigned URL for an encrypted object, the encryption and decryption happen transparently on the S3 side, but the presigned URL mechanism itself is purely about access control and authentication, not data encryption or security at rest.
Creating a backup of S3 data is incorrect because presigned URLs do not have any backup functionality. Backups in S3 can be achieved through various methods such as cross-region replication, versioning, or copying objects to different buckets or storage classes for redundancy. Presigned URLs are simply signed HTTP requests that grant temporary access to objects. They do not copy, replicate, or backup data in any way. The purpose of presigned URLs is strictly to provide secure, temporary access to objects that would otherwise be private and inaccessible.
Question 83:
Which AWS service helps you monitor and troubleshoot applications by collecting and analyzing log files?
A) Amazon CloudWatch Logs
B) AWS X-Ray
C) AWS CloudTrail
D) Amazon Inspector
Answer: A
Explanation:
Amazon CloudWatch Logs is the correct service for monitoring and troubleshooting applications through log file collection and analysis. CloudWatch Logs enables you to centralize logs from all your systems, applications, and AWS services in a single, highly scalable service. You can query logs using CloudWatch Logs Insights, create metrics from log data through metric filters, and set up alarms based on specific patterns in your logs. CloudWatch Logs supports real-time monitoring of log data and can trigger Lambda functions or send notifications when certain patterns appear in the logs. The service retains log data for as long as you need with configurable retention periods and provides powerful filtering and search capabilities.
AWS X-Ray is incorrect because while it is a monitoring service, it focuses on distributed tracing rather than log file collection and analysis. X-Ray helps you understand how your application and its underlying services are performing by providing an end-to-end view of requests as they travel through your application. X-Ray creates a service map showing the architecture of your application and traces individual requests to identify performance bottlenecks, errors, and latency issues. Although X-Ray is valuable for troubleshooting distributed applications and microservices, it does not collect or analyze log files in the same way CloudWatch Logs does.
AWS CloudTrail is not the right answer because it records API calls made in your AWS account for governance, compliance, and auditing purposes. CloudTrail logs provide details about who made requests, from which IP address, what time, and what resources were affected by those API calls. While CloudTrail is essential for security analysis, compliance auditing, and tracking account activity, it is not designed for application log monitoring and troubleshooting. CloudTrail focuses on AWS account activity and API usage rather than application-level logs from your software running on AWS services.
Amazon Inspector is incorrect as it is an automated security assessment service that helps improve the security and compliance of applications deployed on AWS. Inspector automatically assesses applications for vulnerabilities, deviations from best practices, and security issues in your EC2 instances and container images. It performs network assessments and host assessments to identify security findings. Inspector does not collect or analyze application log files for troubleshooting purposes. It focuses on security findings and recommendations rather than operational monitoring through logs. For application logging and troubleshooting operational issues, CloudWatch Logs is the appropriate service.
Question 84:
What type of load balancer operates at Layer 7 of the OSI model and can route traffic based on URL paths?
A) Classic Load Balancer
B) Application Load Balancer
C) Network Load Balancer
D) Gateway Load Balancer
Answer: B
Explanation:
Application Load Balancer operates at Layer 7, the application layer of the OSI model, and provides advanced routing capabilities including URL path-based routing. ALB can examine the content of HTTP/HTTPS requests and make routing decisions based on various factors such as URL paths, hostnames, HTTP headers, HTTP methods, and query parameters. This content-based routing enables sophisticated traffic management for modern applications, including microservices and container-based architectures. ALB also supports WebSocket and HTTP/2 protocols, target groups for different types of resources, and integration with AWS Web Application Firewall for security. The ability to route based on application-level data makes ALB ideal for complex web applications requiring intelligent traffic distribution.
Classic Load Balancer is incorrect because it operates at both Layer 4 and Layer 7 but provides basic load balancing features without advanced routing capabilities like path-based or host-based routing. Classic Load Balancer was the original ELB offering and is now considered a legacy service. It can distribute traffic based on network information and some application protocol information, but it does not support the sophisticated routing rules that Application Load Balancer offers. AWS recommends using Application Load Balancer or Network Load Balancer for new applications as they provide better features, performance, and flexibility.
Network Load Balancer is not the right answer because it operates at Layer 4, the transport layer of the OSI model. NLB routes traffic based on IP protocol data and is designed for extreme performance, handling millions of requests per second with ultra-low latency. Network Load Balancer is ideal for TCP and UDP traffic where you need high throughput, low latency, and the ability to handle volatile workloads. However, it cannot inspect application-layer data like HTTP headers or URL paths. NLB makes routing decisions based on network and transport layer information only, not application content.
Gateway Load Balancer is incorrect as it operates at Layer 3, the network layer, and is designed for deploying, scaling, and managing third-party virtual appliances such as firewalls, intrusion detection and prevention systems, and deep packet inspection systems. Gateway Load Balancer is not used for typical application load balancing and does not route traffic based on URL paths or other application-layer information. It is specifically designed for network security and traffic inspection use cases rather than distributing application traffic across backend servers.
Question 85:
Which feature of Amazon RDS automates database backups and allows point-in-time recovery?
A) Read Replicas
B) Multi-AZ Deployment
C) Automated Backups
D) Manual Snapshots
Answer: C
Explanation:
Automated Backups is the correct RDS feature that automatically creates backups of your database and enables point-in-time recovery capabilities. When you enable automated backups, RDS automatically performs a full daily snapshot of your database and captures transaction logs throughout the day. This combination allows you to restore your database to any point in time within your backup retention period, which can be configured from 1 to 35 days. Automated backups are stored in Amazon S3 and are taken during a backup window that you specify or during a default window if not specified. The point-in-time recovery feature is invaluable for recovering from accidental data deletion, corruption, or other data-related issues.
Read Replicas are incorrect because they are used to improve read performance and provide read scalability for your database workloads. Read replicas are asynchronous copies of your primary database that can serve read traffic, allowing you to offload read queries from the primary instance. While read replicas can provide some level of data redundancy, they are not specifically designed for backup and recovery purposes. If data is deleted or corrupted on the primary database, those changes will replicate to the read replicas, so they do not protect against data loss or provide point-in-time recovery capabilities.
Multi-AZ Deployment is not the right answer because it provides high availability and automatic failover rather than backup and recovery functionality. In a Multi-AZ deployment, RDS automatically maintains a synchronous standby replica in a different Availability Zone. If the primary database fails, RDS automatically fails over to the standby replica with minimal downtime. While Multi-AZ provides disaster recovery for infrastructure failures, it does not protect against data corruption or accidental deletion, and it does not provide point-in-time recovery. Multi-AZ focuses on availability rather than backup capabilities.
Manual Snapshots are incorrect because while they do create backups of your database, they do not provide automated backup functionality or point-in-time recovery. Manual snapshots are user-initiated backups that you create explicitly and retain until you explicitly delete them. They are useful for creating backups before major changes or for long-term retention beyond the automated backup retention period. However, manual snapshots only allow you to restore to the exact time the snapshot was taken, not to any arbitrary point in time like automated backups with transaction log capture enable.
Question 86:
Which AWS CLI command parameter allows you to filter the output of a command?
A) —output
B) —query
C) —filter
D) —select
Answer: B
Explanation:
The —query parameter is the correct answer as it allows you to filter and manipulate the output of AWS CLI commands using JMESPath query language. The query parameter enables you to extract specific data from the JSON response returned by AWS services, making it easier to parse and use the information you need. You can use —query to select specific fields, filter arrays based on conditions, project new data structures, and perform various transformations on the output. This is particularly useful when dealing with large responses where you only need specific information, or when scripting and automation require precise data extraction from AWS CLI commands.
The —output parameter is incorrect because it only controls the format in which the CLI displays results, not which data is displayed. The —output parameter accepts values like json, text, table, or yaml, which determine how the information is formatted for display. While changing the output format can make results easier to read, it does not filter or reduce the amount of data returned. Using —output will present all the data from the response in the specified format, whereas —query actually filters and selects specific data elements from the response.
The —filter parameter is not a standard AWS CLI parameter. While some individual AWS service API operations accept filter parameters as part of their specific command syntax, there is no universal —filter parameter that works across all AWS CLI commands. Filtering in AWS CLI is primarily accomplished through the —query parameter, which provides a consistent and powerful way to filter data across all AWS services. Some commands do have service-specific filtering options, but these are passed as regular command parameters rather than a universal —filter option.
The —select parameter is incorrect because it does not exist as a standard AWS CLI parameter. This option might seem logical given SQL-like syntax where SELECT is used to choose specific columns, but the AWS CLI uses the —query parameter with JMESPath syntax instead. JMESPath provides a powerful and flexible way to query JSON documents, which is more suitable for the nested and complex data structures returned by AWS APIs. Understanding the correct parameter names and their functions is essential for effectively using the AWS CLI in development and automation tasks.
Question 87:
What is the primary purpose of Amazon ElastiCache?
A) To provide object storage for files
B) To improve application performance with in-memory caching
C) To manage relational databases
D) To process streaming data
Answer: B
Explanation:
Amazon ElastiCache is designed to improve application performance through in-memory caching, making this the correct answer. ElastiCache is a fully managed service that supports two open-source in-memory caching engines: Redis and Memcached. By caching frequently accessed data in memory, ElastiCache reduces the load on databases and improves response times for read-heavy application workloads. The service is particularly effective for scenarios like database query result caching, session storage, and caching computed data. ElastiCache provides sub-millisecond latency for cached data, which can significantly improve user experience and reduce costs by decreasing database read operations and compute requirements.
Providing object storage for files is incorrect because that is the primary purpose of Amazon S3, not ElastiCache. S3 is designed to store and retrieve any amount of data as objects, including documents, images, videos, and backups. ElastiCache, in contrast, is specifically designed for temporary, in-memory storage of frequently accessed data to speed up application performance. While both services involve data storage, they serve completely different purposes and use cases. ElastiCache stores data in volatile memory for fast access, while S3 provides durable, persistent object storage on disk.
Managing relational databases is not the purpose of ElastiCache; that function belongs to Amazon RDS. RDS provides managed relational database services for engines like MySQL, PostgreSQL, Oracle, and SQL Server. While ElastiCache is often used in conjunction with relational databases to cache query results and reduce database load, it does not manage or host the databases themselves. ElastiCache complements database services by providing a caching layer that sits in front of the database to improve performance and scalability for read-intensive workloads.
Processing streaming data is incorrect because that is the function of services like Amazon Kinesis and Amazon Managed Streaming for Apache Kafka. These services are designed to collect, process, and analyze real-time streaming data from various sources. ElastiCache, while it can store session data and other real-time information, is not designed for stream processing. Its primary role is to cache data in memory to improve application performance through faster data retrieval, not to process continuous streams of data or perform real-time analytics on streaming information.
Question 88:
Which DynamoDB capacity mode automatically scales read and write throughput based on application traffic?
A) Provisioned Capacity
B) Reserved Capacity
C) On-Demand Capacity
D) Burst Capacity
Answer: C
Explanation:
On-Demand Capacity mode is the correct answer as it automatically scales read and write throughput based on your application’s traffic patterns without requiring capacity planning. With on-demand mode, DynamoDB instantly accommodates workload increases or decreases, charging you only for the reads and writes your application actually performs. This mode is ideal for applications with unpredictable traffic, new tables where traffic patterns are unknown, or workloads with large spikes in usage. On-demand mode eliminates the need to specify read and write capacity units in advance, making it easier to manage and preventing throttling due to unexpected traffic increases.
Provisioned Capacity mode is incorrect because it requires you to specify the number of read and write capacity units your application needs in advance. While provisioned mode can use auto-scaling to adjust capacity within configured minimum and maximum limits, it does not automatically scale as seamlessly as on-demand mode. With provisioned capacity, you must predict your traffic patterns and configure appropriate capacity settings, though auto-scaling can help adjust within boundaries. This mode is more cost-effective for predictable workloads with steady traffic, but it requires more planning and management compared to on-demand capacity.
Reserved Capacity is not a DynamoDB capacity mode but rather a pricing option for provisioned capacity. Reserved capacity allows you to commit to a minimum level of provisioned capacity usage over a one-year or three-year term in exchange for discounted rates. This is a cost optimization strategy for provisioned capacity mode when you have predictable baseline traffic. Reserved capacity does not provide automatic scaling functionality; it is simply a way to reduce costs for committed usage levels. You still need to manage capacity units separately from your reserved capacity commitment.
Burst Capacity is incorrect because while DynamoDB does provide some burst capacity to accommodate occasional spikes, this is not a capacity mode you can select or configure. Burst capacity is a feature of provisioned capacity mode where DynamoDB retains unused capacity for up to five minutes and makes it available to handle sudden increases in traffic. However, this is limited and not designed for sustained traffic increases. Burst capacity is a buffer mechanism rather than a scaling mode, and you cannot rely on it for consistent performance during traffic spikes.
Question 89:
What AWS service enables you to run code in response to events without provisioning servers?
A) Amazon EC2
B) AWS Lambda
C) AWS Batch
D) Amazon ECS
Answer: B
Explanation:
AWS Lambda is the correct service that enables you to run code in response to events without provisioning or managing servers. Lambda is a serverless compute service that automatically runs your code in response to triggers such as changes to data in Amazon S3, updates to DynamoDB tables, HTTP requests via API Gateway, or custom events from your applications. You only pay for the compute time consumed when your code is executing, with no charges when your code is not running. Lambda automatically scales your application by running code in response to each trigger, handling everything from a few requests per day to thousands per second without any infrastructure management.
Amazon EC2 is incorrect because it provides virtual servers that require you to provision, configure, and manage the underlying infrastructure. With EC2, you select instance types, configure operating systems, manage scaling, and handle all aspects of server administration. While EC2 offers maximum control and flexibility, it requires significant operational overhead compared to Lambda. EC2 is Infrastructure as a Service rather than serverless computing, meaning you are responsible for server management, patching, and capacity planning. Lambda abstracts all these concerns away, allowing you to focus solely on your code.
AWS Batch is not the right answer because while it is a fully managed service for running batch computing workloads, it still requires you to provision compute resources. Batch is designed for long-running batch jobs that process large volumes of data and can run for hours. It manages job scheduling and compute resource provisioning based on your job requirements, but it uses EC2 instances or Fargate under the hood. Unlike Lambda, which is event-driven and executes quickly in response to triggers, Batch is optimized for scheduled or queued batch processing jobs.
Amazon ECS, or Elastic Container Service, is incorrect because it is a container orchestration service that requires you to manage the underlying compute resources, whether EC2 instances or Fargate tasks. While ECS simplifies container management, it still involves infrastructure considerations like cluster configuration, task definitions, and capacity planning. ECS is excellent for running containerized applications, but it does not provide the same event-driven, fully serverless experience as Lambda. With Lambda, you write functions that respond to events without any container or infrastructure management.
Question 90:
Which AWS service provides a managed message queue for decoupling application components?
A) Amazon SNS
B) Amazon SQS
C) Amazon Kinesis
D) AWS Step Functions
Answer: B
Explanation:
Amazon SQS, which stands for Simple Queue Service, is the correct answer as it provides a fully managed message queuing service for decoupling application components. SQS enables you to send, store, and receive messages between software components at any volume without losing messages or requiring other services to be available. The service offers two types of queues: Standard queues provide maximum throughput with at-least-once delivery, while FIFO queues guarantee exactly-once processing and preserve the order of messages. SQS helps build resilient, scalable applications by allowing components to communicate asynchronously, so if one component fails or slows down, it does not affect other components.
Amazon SNS, or Simple Notification Service, is incorrect because it is a pub/sub messaging service rather than a message queue. SNS is designed for distributing messages to multiple subscribers simultaneously through various protocols like email, SMS, HTTP endpoints, Lambda functions, or SQS queues. While SNS is excellent for broadcasting messages to many recipients, it does not store messages or provide the same queuing and buffering capabilities as SQS. SNS delivers messages immediately to available subscribers, whereas SQS stores messages until they are explicitly processed and deleted by consumers.
Amazon Kinesis is not the right answer because it is a streaming data platform designed for real-time processing of continuous data streams rather than message queuing. Kinesis is ideal for scenarios where you need to collect, process, and analyze streaming data such as application logs, clickstreams, or IoT telemetry in real time. While Kinesis can decouple producers and consumers of streaming data, it is optimized for high-throughput, ordered data streams rather than discrete messages between application components. The retention and processing model of Kinesis differs significantly from the job queue pattern that SQS provides.
AWS Step Functions is incorrect because it is a workflow orchestration service that coordinates multiple AWS services into serverless workflows, not a message queue. Step Functions allows you to build visual workflows that define the steps and decision logic in your application. While Step Functions can help decouple components by managing the flow between them, it does not provide message storage and delivery like SQS. Step Functions focuses on orchestrating complex, multi-step processes with error handling and retry logic, whereas SQS focuses on reliable message delivery between components.