Amazon AWS Certified Developer — Associate DVA-C02 Exam Dumps and Practice Test Questions Set10 Q136-150
Visit here for our full Amazon AWS Certified Developer — Associate DVA-C02 exam dumps and practice test questions.
Question 136:
Which AWS service provides managed Redis or Memcached compatible in-memory data stores?
A) Amazon RDS
B) Amazon DynamoDB
C) Amazon ElastiCache
D) Amazon Redshift
Answer: C
Explanation:
Amazon ElastiCache is the correct service that provides managed Redis or Memcached compatible in-memory data stores. ElastiCache is a fully managed caching service that supports two popular open-source in-memory engines: Redis and Memcached. ElastiCache automates administrative tasks such as hardware provisioning, software patching, setup, configuration, monitoring, failure recovery, and backups for your cache environment. Redis on ElastiCache provides advanced features like persistence, replication, automatic failover, and support for complex data structures, while Memcached offers a simple, high-performance distributed memory caching system. ElastiCache improves application performance by reducing database load and providing sub-millisecond response times for frequently accessed data.
Amazon RDS is incorrect because it provides managed relational databases, not in-memory caching. RDS supports database engines like MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, and Amazon Aurora, all of which store data persistently on disk rather than primarily in memory. While RDS databases use memory for caching internally to improve performance, RDS is fundamentally a persistent database service, not an in-memory caching layer. For caching to improve RDS performance, you would use ElastiCache in front of RDS to cache query results and reduce database load.
Amazon DynamoDB is not correct for this question because while it is a fast NoSQL database that provides single-digit millisecond performance, it is not specifically a Redis or Memcached compatible in-memory data store. DynamoDB stores data persistently and provides its own API, not Redis or Memcached APIs. Although DynamoDB Accelerator (DAX) provides in-memory caching for DynamoDB, it is DynamoDB-specific and not Redis or Memcached compatible. For Redis or Memcached compatibility, you must use ElastiCache.
Amazon Redshift is incorrect because it is a data warehousing service for analytical queries on large datasets, not an in-memory caching service. Redshift uses columnar storage on disk and is optimized for complex queries across petabytes of data, providing fast query performance through parallel processing and compression. Redshift is designed for business intelligence and analytics workloads, not for the sub-millisecond caching use cases that ElastiCache addresses. Redshift stores data persistently and does not provide Redis or Memcached compatibility.
Question 137:
What is the maximum number of concurrent executions for AWS Lambda functions per region by default?
A) 100
B) 500
C) 1,000
D) 10,000
Answer: C
Explanation:
The default maximum number of concurrent executions for AWS Lambda functions per region is 1,000, making this the correct answer. This limit, called the concurrent execution limit or account-level concurrency, applies across all functions in a region. Concurrent executions represent the number of function instances processing events at any given moment. When you exceed this limit, Lambda throttles additional invocations, returning errors to synchronous callers or retrying asynchronous invocations. This limit protects your account from unexpected scaling and associated costs. You can request increases to this limit through AWS Support if your applications require higher concurrency. Understanding this limit is important for capacity planning and for designing applications that handle throttling appropriately.
100 concurrent executions is incorrect as it is too low to be the default account limit. While 100 might be sufficient for small applications or specific functions, AWS provides a default concurrent execution limit of 1,000 per region to accommodate most workloads without requiring limit increases. However, you can set reserved concurrency at the function level to allocate a portion of your account concurrency to specific functions and ensure they always have capacity available, and these function-level reservations could be set to 100 or any value up to your account limit.
500 concurrent executions is not correct, though it represents half of the actual default limit. The default concurrency limit is 1,000, not 500. You might see 500 in various contexts if you have set function-level reserved concurrency allocations or if you are discussing partial usage of the account limit, but the total default concurrency available to your account in a region is 1,000 concurrent executions. For higher limits, you must request a quota increase from AWS Support with justification for your use case.
10,000 concurrent executions is incorrect as it exceeds the default limit by an order of magnitude. While some AWS accounts with approved quota increases might have limits this high or higher, the default starting limit for new accounts is 1,000 concurrent executions per region. If your applications require 10,000 concurrent executions, you would need to request a service limit increase from AWS Support, explaining your use case and expected traffic patterns. AWS can grant higher limits, but they are not the default.
Question 138:
Which Amazon S3 feature provides an audit trail of all requests made to your bucket?
A) S3 Versioning
B) S3 Inventory
C) S3 Access Logs
D) S3 Lifecycle Policies
Answer: C
Explanation:
S3 Access Logs provide an audit trail of all requests made to your bucket, making this the correct answer. When you enable server access logging for a bucket, S3 records detailed information about every request made to that bucket, including the request type, resources accessed, request time, response status, and error codes if any. These log files are delivered to a target bucket you specify, where they can be analyzed for security auditing, access pattern analysis, or troubleshooting. Access logs help you understand who is accessing your data, when they access it, and what operations they perform, which is essential for security, compliance, and operational visibility into bucket usage.
S3 Versioning is incorrect because it maintains multiple versions of objects to protect against accidental deletion and overwrites, not to provide an audit trail of requests. Versioning keeps a history of object versions, allowing you to retrieve or restore previous versions, but it does not record who made requests, when they were made, or what actions were performed. Versioning is about data protection and recovery, while access logging is about auditing and monitoring bucket access patterns. These features serve different purposes and are often used together for comprehensive data protection and security monitoring.
S3 Inventory is not the right answer because it provides scheduled reports listing your objects and their metadata, not detailed request logs. Inventory generates files containing information about object storage class, encryption status, replication status, and other object metadata, which helps with storage management and analytics. However, inventory reports do not track access requests or provide an audit trail of who accessed objects and when. Inventory is useful for understanding what you have stored, while access logs tell you how your stored data is being used.
S3 Lifecycle Policies are incorrect because they define automated actions to transition objects between storage classes or delete objects after specified time periods, not to audit requests. Lifecycle policies help optimize storage costs by automatically managing object lifecycle based on age or other criteria. These policies do not record access requests or create audit trails. Lifecycle policies are about automated storage management, while access logs provide visibility into bucket access patterns for security and compliance purposes. These features address different aspects of S3 bucket management.
Question 139:
What AWS CodeBuild feature allows you to cache dependencies between builds to reduce build time?
A) Build artifacts
B) Build cache
C) Build projects
D) Build environments
Answer: B
Explanation:
Build cache is the correct CodeBuild feature that allows you to cache dependencies between builds to reduce build time and improve build performance. When you configure caching for a CodeBuild project, CodeBuild saves specified files or directories to an S3 bucket after the first build. In subsequent builds, CodeBuild retrieves these cached items before starting the build, eliminating the need to download the same dependencies repeatedly. This is particularly useful for caching package manager dependencies like npm modules, pip packages, or Maven artifacts that do not change frequently. Build cache significantly reduces build time for projects with large dependency sets and helps reduce network transfer costs by avoiding redundant downloads of the same dependencies in every build.
Build artifacts are incorrect because they are the output files produced by your build process, not a caching mechanism for dependencies. Artifacts are the compiled code, packaged applications, or other files that your build generates and that you want to save or deploy. CodeBuild stores artifacts in S3 buckets you specify, making them available for deployment or further processing. While artifacts are important outputs of the build process, they do not provide dependency caching between builds. Artifacts represent build results, whereas build cache stores inputs like dependencies to speed up subsequent builds.
Build projects are not the feature for caching dependencies; a build project is the overall configuration that defines how CodeBuild runs your builds. A build project includes the source code location, build environment, build commands, output artifacts location, and other settings including cache configuration. The build project is the container for all build settings, but the specific feature that caches dependencies is the build cache configuration within that project. Build projects define what to build and how, while build cache is one specific feature within projects that improves build performance.
Build environments are incorrect because they define the Docker image, compute type, and runtime that CodeBuild uses to run your builds, not dependency caching. Build environments specify the operating system, programming language runtimes, and tools available during the build. You can use managed images provided by CodeBuild or custom Docker images. While the build environment affects what tools are available for your build, it does not provide caching of dependencies between builds. Build cache is a separate feature that works within any build environment to store and retrieve dependencies across build executions.
Question 140:
Which DynamoDB feature allows you to perform read operations without consuming read capacity units?
A) Eventually Consistent Reads
B) Strongly Consistent Reads
C) Global Secondary Index
D) None, all reads consume capacity
Answer: A
Explanation:
This is a trick question — all DynamoDB read operations consume read capacity units, but Eventually Consistent Reads consume half the read capacity of Strongly Consistent Reads, making D the technically correct answer. However, if forced to choose the option that consumes the least capacity, Eventually Consistent Reads would be A. Eventually Consistent Reads may not reflect the results of a recently completed write operation and typically return data within one second of the write. They consume half the read capacity units compared to Strongly Consistent Reads for the same data. This makes Eventually Consistent Reads more cost-effective for scenarios where slight staleness is acceptable, such as reading user profiles, product catalogs, or other data where immediate consistency after writes is not critical.
Strongly Consistent Reads are incorrect if looking for capacity savings because they consume more read capacity units than Eventually Consistent Reads. Strongly Consistent Reads return data that reflects all successful write operations that completed before the read, guaranteeing you receive the most up-to-date data. This guarantee requires additional work by DynamoDB and costs twice as many read capacity units compared to Eventually Consistent Reads. Use Strongly Consistent Reads when your application requires reading the latest data immediately after writes, such as financial transactions or inventory systems where stale data could cause problems.
Global Secondary Index is not related to read capacity consumption models; GSIs are alternate key structures that allow querying on non-primary-key attributes. When you query a Global Secondary Index, you still consume read capacity units based on whether you specify consistent or eventually consistent reads for the query, just like reading from the base table. GSIs themselves have their own provisioned or on-demand capacity settings separate from the base table, and reads against GSIs consume capacity from the index’s capacity, not from avoiding capacity consumption altogether.
None, all reads consume capacity is actually the correct technical answer because DynamoDB has no free read operations. Every read operation, whether Eventually Consistent or Strongly Consistent, whether from the base table or from indexes, consumes read capacity units. The difference is in how much capacity is consumed: Eventually Consistent Reads consume half the capacity of Strongly Consistent Reads. Understanding capacity consumption for different read types is essential for optimizing costs and performance when designing DynamoDB applications.
Question 141:
What is the purpose of Amazon CloudWatch composite alarms?
A) To monitor multiple metrics with a single alarm
B) To combine multiple alarms using logical operators
C) To automatically remediate issues
D) To store alarm history
Answer: B
Explanation:
Composite alarms in Amazon CloudWatch allow you to combine multiple alarms using logical operators such as AND, OR, and NOT, making this the correct answer. Composite alarms reduce alarm noise by allowing you to create higher-level alarms that trigger only when specific combinations of underlying alarms are in the ALARM state. For example, you might create a composite alarm that triggers only when both CPU utilization is high AND network throughput is low, indicating a specific problematic condition rather than reacting to each metric individually. Composite alarms help implement more sophisticated monitoring logic that better represents actual problem conditions, reducing false positives and enabling more targeted responses to issues in complex systems with multiple interdependent metrics.
Monitoring multiple metrics with a single alarm is not quite accurate, though it sounds similar to the correct answer. While composite alarms do relate to multiple alarms, they do not directly monitor metrics themselves. Instead, composite alarms monitor the state of other alarms that each monitor individual metrics. Each underlying alarm has its own metric and threshold, and the composite alarm evaluates the combined state of these alarms using logical expressions. This distinction is important: composite alarms work at the alarm level, not the metric level, allowing you to build hierarchical monitoring structures.
Automatically remediating issues is incorrect because CloudWatch alarms, including composite alarms, detect problems and trigger actions but do not remediate issues themselves. When a composite alarm enters the ALARM state, it can trigger actions like sending SNS notifications, executing Auto Scaling policies, or invoking Systems Manager automations, but the alarm is the detection mechanism, not the remediation. Remediation happens through the actions configured for the alarm, such as Lambda functions or Systems Manager automation documents that actually fix the detected problems.
Storing alarm history is not the purpose of composite alarms, though CloudWatch does maintain alarm history. All CloudWatch alarms, including standard and composite alarms, automatically record state changes and history, which you can view in the console or retrieve via API. However, this history storage is a general feature of all alarms, not the specific purpose of composite alarms. Composite alarms are specifically designed to combine multiple alarms with logical operators to create more sophisticated alert conditions, not for history management.
Question 142:
Which AWS Lambda runtime supports custom runtimes using runtime API?
A) Provided runtime
B) Node.js runtime
C) Python runtime
D) Java runtime
Answer: A
Explanation:
The Provided runtime (provided.al2 or provided.al2023) is the correct Lambda runtime that supports custom runtimes using the Lambda Runtime API, allowing you to use any programming language or runtime version. The provided runtime gives you a minimal Amazon Linux environment where you can include a custom runtime implementation or bootstrap script that knows how to execute your code. You implement the Runtime API interface to receive invocation events from Lambda and return responses. This flexibility enables you to use languages like Rust, C++, Cobol, or any other language by providing the necessary runtime components in your deployment package or Lambda layer. Custom runtimes are ideal when your preferred language is not natively supported or when you need specific runtime versions or configurations.
Node.js runtime is incorrect because it is a managed runtime provided by AWS that supports JavaScript and TypeScript code without requiring custom runtime implementation. Lambda manages the Node.js environment, including the language runtime, libraries, and execution context. While you can customize your Node.js function with layers and dependencies, you do not need to implement the Runtime API yourself. Node.js is one of the native runtimes that Lambda supports out of the box, whereas the provided runtime is specifically for implementing custom runtimes for unsupported languages.
Python runtime is not correct as it is also a managed runtime provided by AWS for Python code. Lambda handles the Python environment and runtime details, allowing you to simply write Python functions without implementing runtime interfaces. Like Node.js, Python is natively supported with multiple versions available as managed runtimes. You use the Python runtime when you write Lambda functions in Python, but if you want to use a language that Lambda does not natively support, you would use the provided runtime to create a custom runtime implementation.
Java runtime is incorrect because it is another managed runtime that AWS provides for Java applications. Lambda supports Java with specific runtime versions that include the Java Virtual Machine and necessary libraries. Java functions run in the managed Java runtime without requiring custom Runtime API implementation. The provided runtime is specifically for cases where you need a language or runtime configuration that AWS does not offer as a managed runtime, allowing you to bring your own runtime through the Runtime API.
Question 143:
What is the purpose of AWS CodePipeline stages?
A) To store source code
B) To organize pipeline actions into logical groups
C) To compile application code
D) To monitor pipeline execution
Answer: B
Explanation:
AWS CodePipeline stages organize pipeline actions into logical groups representing phases of your software release process, making this the correct answer. A stage is a logical grouping of actions that are performed as part of your continuous delivery workflow. Common stages include Source (retrieving code from a repository), Build (compiling and testing code), and Deploy (deploying to environments like staging or production). Each stage contains one or more actions that run sequentially or in parallel depending on configuration. Stages execute in the order you define them, with each stage completing before the next stage begins. This organization provides clear visibility into where your code is in the release process and allows you to control the flow from source code to production deployment.
Storing source code is incorrect because that is the function of source code repositories like AWS CodeCommit, GitHub, or Bitbucket, not CodePipeline stages. CodePipeline integrates with source repositories through source actions within a Source stage, but the pipeline does not store the code itself. The Source stage contains actions that detect changes in source repositories and retrieve the latest code to pass to subsequent stages, but storage happens in the dedicated version control system, not in CodePipeline. CodePipeline orchestrates the workflow, while repositories store the actual code.
Compiling application code is not the purpose of stages themselves; that is the function of build actions within a Build stage. A build action typically uses AWS CodeBuild or another build service to compile code, run tests, and produce artifacts. The stage is the organizational container that groups related actions together, while the actions perform the actual work. A Build stage might contain actions for compiling code, running unit tests, performing static analysis, and packaging artifacts, but the stage itself is just the logical grouping, not the compilation process.
Monitoring pipeline execution is incorrect because while CodePipeline provides monitoring and visualization of pipeline execution, this is a feature of the pipeline service itself, not the purpose of stages. You can monitor stage execution status, view execution history, and receive notifications about stage transitions, but stages are not monitoring tools. Stages organize workflow actions, and the CodePipeline service provides monitoring of how those stages execute. CloudWatch and CodePipeline’s built-in visualization tools handle monitoring, while stages structure the release workflow.
Question 144:
Which Amazon S3 Glacier retrieval option provides access to data within 1-5 minutes?
A) Expedited
B) Standard
C) Bulk
D) Instant
Answer: A
Explanation:
Expedited retrieval is the correct Glacier retrieval option that provides access to archived data within 1-5 minutes for most archives. Expedited retrievals allow you to quickly access your data when you have urgent requests for a subset of archives. This option is the fastest but also the most expensive Glacier retrieval tier. Expedited retrievals work well for occasional urgent data access needs, such as restoring specific customer data on demand or accessing archived files for time-sensitive operations. You can provision retrieval capacity to guarantee that expedited retrievals are always available, ensuring you can access critical archived data quickly even during high-demand periods.
Standard retrieval is incorrect because it takes 3-5 hours to access data, not 1-5 minutes. Standard is the default Glacier retrieval option that provides a balance between cost and retrieval time. It is suitable for scenarios where you need archived data but do not have urgent timing requirements, such as disaster recovery operations or periodic data analysis. Standard retrieval costs less than Expedited but more than Bulk. For most non-urgent archive access, Standard retrieval provides adequate performance at a reasonable cost, but it does not meet the 1-5 minute requirement mentioned in the question.
Bulk retrieval is not correct as it is the slowest and lowest-cost option, taking 5-12 hours to retrieve data. Bulk retrievals are designed for large amounts of data where retrieval time is not critical, such as retrieving petabytes of archived data for batch processing or annual compliance reporting. Bulk is the most cost-effective option for accessing large volumes of archived data when you can plan ahead and wait several hours. For time-sensitive access requiring retrieval in minutes, Expedited is the only appropriate option among Glacier’s retrieval tiers.
Instant is not a valid Glacier retrieval option, though S3 Glacier Instant Retrieval is a separate storage class. Glacier Instant Retrieval is a storage class that provides millisecond retrieval times similar to S3 Standard but at lower storage costs for rarely accessed data. However, for the original S3 Glacier storage class (now called S3 Glacier Flexible Retrieval), the fastest retrieval option is Expedited, which provides 1-5 minute access. Understanding the distinction between Glacier storage classes and retrieval options is important for designing appropriate archival strategies.
Question 145:
What is the purpose of AWS X-Ray sampling?
A) To reduce the cost and performance impact of tracing
B) To increase trace data accuracy
C) To encrypt trace data
D) To compress trace data
Answer: A
Explanation:
AWS X-Ray sampling is designed to reduce the cost and performance impact of tracing by recording trace data for only a subset of requests rather than every single request, making this the correct answer. Sampling allows you to collect representative trace data without the overhead of tracing 100 percent of traffic. X-Ray uses sampling rules that determine which requests to trace based on criteria like service name, HTTP method, URL path, and a sampling rate. By default, X-Ray records the first request each second and five percent of additional requests, providing enough data for analysis while minimizing impact on application performance and reducing costs associated with trace storage and analysis. You can customize sampling rules to increase or decrease sampling rates for specific services or requests based on your monitoring needs.
Increasing trace data accuracy is incorrect because sampling actually reduces the amount of trace data collected, not increases accuracy. Sampling provides a statistical representation of your traffic rather than complete data for every request. While well-designed sampling can provide sufficiently accurate insights into application behavior, sampling by definition means you are not capturing every request. The accuracy of insights depends on having representative samples, but the purpose of sampling is to reduce overhead and cost while maintaining useful visibility, not to improve accuracy compared to full tracing.
Encrypting trace data is not the purpose of sampling; X-Ray encrypts trace data automatically as a security feature separate from sampling. X-Ray encrypts all trace data at rest using AWS KMS encryption, protecting sensitive information in your traces. This encryption happens regardless of sampling configuration and is a built-in security measure. Sampling is about controlling the volume of trace data collected for cost and performance reasons, while encryption is about protecting the confidentiality of trace data. These are independent features serving different purposes in the X-Ray service.
Compressing trace data is incorrect because X-Ray automatically compresses trace data for storage efficiency, which is separate from sampling. Compression reduces the storage space required for trace data, while sampling reduces the number of requests that generate trace data in the first place. X-Ray applies compression to all stored traces to optimize storage costs, but this is a backend storage optimization, not related to sampling decisions. Sampling happens during request processing to decide whether to create trace data, while compression happens after trace data is created to store it efficiently.
Question 146:
Which AWS service provides a fully managed GraphQL API and real-time data synchronization?
A) Amazon API Gateway
B) AWS AppSync
C) Amazon DynamoDB
D) AWS Amplify
Answer: B
Explanation:
AWS AppSync is the correct service that provides a fully managed GraphQL API with real-time data synchronization capabilities. AppSync makes it easy to build scalable GraphQL APIs that securely access, manipulate, and combine data from multiple sources including DynamoDB, Lambda, RDS, and HTTP APIs. AppSync handles GraphQL schema definition, resolver logic, real-time subscriptions, and offline data synchronization for mobile and web applications. The service provides built-in features for authentication, authorization, caching, and real-time updates through WebSocket connections. AppSync is ideal for building applications that need real-time collaboration features, offline support, or unified access to multiple backend data sources through a single GraphQL endpoint.
Amazon API Gateway is incorrect because while it can proxy GraphQL requests to backend services, it does not provide managed GraphQL API functionality with built-in resolvers and real-time subscriptions like AppSync. API Gateway is a general-purpose API management service that supports REST, HTTP, and WebSocket APIs. You could build a GraphQL API using API Gateway with Lambda functions handling GraphQL resolution, but this requires more manual implementation compared to AppSync’s managed GraphQL features. AppSync was specifically designed for GraphQL APIs with native support for queries, mutations, subscriptions, and data source integrations.
Amazon DynamoDB is not correct because it is a NoSQL database service, not a GraphQL API service. While AppSync commonly uses DynamoDB as a data source, DynamoDB itself does not provide GraphQL APIs or real-time synchronization features. DynamoDB stores data and provides fast key-value and document access through its own APIs. You would use AppSync to create a GraphQL layer on top of DynamoDB data, with AppSync handling the GraphQL API and DynamoDB providing the underlying data storage. These services work together but serve different purposes.
AWS Amplify is incorrect because while it is a development platform that includes tools for building mobile and web applications, it is not specifically a managed GraphQL API service. Amplify provides client libraries, CLI tools, and hosting services for full-stack applications, and it integrates with AppSync for GraphQL APIs. However, the underlying managed GraphQL service is AppSync, not Amplify. Amplify simplifies using AppSync and other AWS services, but AppSync is the service that actually provides the managed GraphQL API functionality. Amplify is the broader development framework, while AppSync is the specific GraphQL API service.
Question 147:
What is the maximum timeout for an AWS Step Functions execution?
A) 15 minutes
B) 1 hour
C) 1 day
D) 1 year
Answer: D
Explanation:
The maximum timeout for an AWS Step Functions execution is 1 year, making this the correct answer. Step Functions Standard Workflows can run for up to one year, allowing you to orchestrate very long-running processes such as order fulfillment workflows, approval processes with extended wait periods, or complex data processing pipelines. This extended execution time makes Step Functions suitable for workflows that might pause for human approvals, wait for external events, or coordinate processes that span days, weeks, or months. Express Workflows have a much shorter maximum duration of 5 minutes and are designed for high-volume, short-duration workloads. Understanding these limits helps you choose the appropriate workflow type for your use case.
15 minutes is incorrect and actually represents the maximum execution time for AWS Lambda functions, not Step Functions. While Lambda functions that Step Functions orchestrates are limited to 15 minutes each, the Step Functions workflow itself can run far longer by coordinating multiple Lambda invocations or other service integrations over time. Step Functions was designed specifically to overcome Lambda’s execution time limit by providing durable workflow orchestration. The workflow can pause between steps, wait for events, and continue execution across time periods far exceeding what any single Lambda function could achieve.
1 hour is not correct as it is far too short to be the Step Functions execution limit. While 1 hour might be appropriate for many workflows, Step Functions supports much longer execution times to accommodate processes that might wait for human approvals, scheduled events, or long-running tasks. The 1-year limit provides flexibility for complex business processes that naturally span extended time periods. If Step Functions were limited to 1 hour, it would not be suitable for many real-world workflow orchestration scenarios that require days or weeks to complete.
1 day is also incorrect, representing only a fraction of the actual limit. While 24 hours would accommodate many workflows, Step Functions’ 1-year maximum execution time enables scenarios like annual processing cycles, extended approval workflows, or other processes that naturally span weeks or months. This generous time limit is one of Step Functions’ key advantages, allowing you to model long-running business processes without worrying about artificial time constraints. The state machine can wait for external events, pause for scheduled times, and coordinate activities across extended periods without timeout concerns.
Question 148:
Which Amazon DynamoDB feature allows you to restore a table to any point in time within the last 35 days?
A) DynamoDB Backups
B) Point-in-Time Recovery
C) DynamoDB Streams
D) Global Tables
Answer: B
Explanation:
Point-in-Time Recovery (PITR) is the correct DynamoDB feature that allows you to restore a table to any point in time within the last 35 days. When you enable PITR for a table, DynamoDB continuously backs up your table data with per-second granularity, allowing you to restore to any second within the retention period. This protects against accidental write or delete operations, providing a safety net for operational mistakes. PITR does not affect table performance or consume provisioned throughput, and it works independently of DynamoDB Streams or on-demand backups. PITR is essential for compliance requirements or scenarios where you need fine-grained recovery capabilities for critical data that might be accidentally modified or deleted.
DynamoDB Backups are incorrect because while they do create full backups of your table, they are point-in-time snapshots rather than continuous backups allowing restoration to any second. On-demand backups create a full copy of your table at the time you initiate the backup, which you can restore later, but you can only restore to the exact moment when that backup was taken. If you want to restore to a time between backups, on-demand backups cannot help. Point-in-Time Recovery provides much more flexibility by enabling restoration to any second within the retention window, not just to specific backup times.
DynamoDB Streams is not correct because it captures item-level changes in your table for real-time processing, not for point-in-time recovery. Streams provide a time-ordered sequence of modifications to table items, which you can process with Lambda functions or other stream consumers. While you could theoretically build a custom recovery solution using Streams data, this is not the same as the managed Point-in-Time Recovery feature. Streams are designed for triggering workflows and maintaining materialized views, not for table-level recovery to arbitrary points in time.
Global Tables are incorrect because they provide multi-region replication for high availability and disaster recovery, not point-in-time restoration. Global Tables replicate your data across multiple AWS Regions automatically, enabling low-latency access worldwide and providing region-level resilience. While Global Tables protect against regional failures, they do not protect against accidental data deletion or modification because changes replicate across all regions. If you accidentally delete data, that deletion replicates globally. Point-in-Time Recovery works with Global Tables to provide time-based recovery in addition to geographic replication.
Question 149:
What is the purpose of Amazon CloudWatch Logs metric filters?
A) To delete old log events
B) To extract metrics from log data and publish to CloudWatch
C) To compress log files
D) To replicate logs across regions
Answer: B
Explanation:
Amazon CloudWatch Logs metric filters extract metrics from log data and publish them to CloudWatch as custom metrics, making this the correct answer. Metric filters define patterns that CloudWatch Logs uses to search log events for specific text patterns or numeric values. When log events match the filter pattern, CloudWatch publishes data points to a CloudWatch metric you specify. For example, you can create a metric filter to count error messages in application logs, extract response times from web server logs, or track any numeric value that appears in your logs. Once you have metrics from log data, you can create alarms, build dashboards, and perform analysis just like any other CloudWatch metric. This transforms unstructured log data into quantifiable metrics for monitoring and alerting.
Deleting old log events is incorrect because that is handled by log retention settings, not metric filters. You configure retention periods at the log group level to specify how long CloudWatch Logs keeps log events before automatically deleting them. Retention can range from 1 day to 10 years, or you can choose indefinite retention. This retention policy automatically manages log lifecycle and storage costs, but it is completely separate from metric filters. Metric filters analyze log content to create metrics, while retention settings determine how long logs are stored.
Compressing log files is not the purpose of metric filters, though CloudWatch Logs does compress log data for storage efficiency automatically. Log compression is a backend storage optimization that CloudWatch Logs applies to all stored log events to reduce storage costs and improve efficiency. This compression happens transparently and is not related to metric filters. Metric filters focus on analyzing log content to extract meaningful metrics, not on optimizing log storage. The compression is automatic and requires no configuration, while metric filters must be explicitly defined with patterns and metric specifications.
Replicating logs across regions is incorrect because that is not a function of metric filters. While you can use subscription filters to stream log data to other services that might forward logs to different regions, this is different from metric filters. Metric filters analyze log data and publish metrics to CloudWatch within the same region, not replicate the logs themselves. For cross-region log replication or centralization, you would use subscription filters to stream logs to Kinesis or Lambda, which could then forward logs to other regions, but this is separate from the metric extraction purpose of metric filters.
Question 150:
Which AWS service allows you to run containerized applications without managing servers or clusters?
A) Amazon ECS with EC2
B) Amazon EKS
C) AWS Fargate
D) AWS Lambda
Answer: C
Explanation:
AWS Fargate is the correct service that allows you to run containerized applications without managing servers or clusters. Fargate is a serverless compute engine for containers that works with both Amazon ECS and Amazon EKS, eliminating the need to provision, configure, or scale EC2 instances for your containers. With Fargate, you simply define your application requirements including CPU, memory, and networking, and Fargate handles all infrastructure management. You pay only for the compute and memory resources your containers use, with no need to manage underlying instances. Fargate is ideal for teams that want to focus on building applications rather than managing infrastructure, providing true serverless container execution with automatic scaling and built-in security isolation.
Amazon ECS with EC2 is incorrect because when you use ECS with EC2 launch type, you must manage the underlying EC2 instances that run your containers. You are responsible for choosing instance types, scaling the cluster, patching operating systems, and managing instance lifecycle. While ECS simplifies container orchestration compared to manually managing containers on EC2, you still manage the server infrastructure when using the EC2 launch type. For truly serverless container execution without server management, you need to use ECS with the Fargate launch type, not EC2 launch type.
Amazon EKS is not the best answer because while EKS can use Fargate for serverless execution, EKS by itself is a managed Kubernetes service that often requires managing EC2 nodes. EKS manages the Kubernetes control plane, but you typically still manage worker nodes unless you specifically use EKS with Fargate. The question asks about running containers without managing servers, and while EKS with Fargate provides this, Fargate is the specific component that eliminates server management. Fargate can be used with both ECS and EKS, so it is the more direct answer to the question about serverless container execution.
AWS Lambda is incorrect because while it is a serverless compute service, it is designed for running functions, not containerized applications in the traditional sense. Lambda supports container images as a packaging format, but these container images must implement the Lambda runtime API and are limited by Lambda’s constraints like execution timeout and resource limits. Lambda is not a general-purpose container runtime like Fargate; it is a function execution environment that can use container images for packaging. For running standard containerized applications without server management, Fargate is the appropriate service.