Amazon AWS Certified Developer — Associate DVA-C02 Exam Dumps and Practice Test Questions Set9 Q121-135

Visit here for our full Amazon AWS Certified Developer — Associate DVA-C02 exam dumps and practice test questions.

Question 121: 

What is the purpose of Amazon DynamoDB Accelerator (DAX)?

A) To replace DynamoDB tables

B) To provide an in-memory cache for DynamoDB

C) To backup DynamoDB data

D) To migrate data to DynamoDB

Answer: B

Explanation:

Amazon DynamoDB Accelerator, or DAX, is designed to provide an in-memory cache for DynamoDB, making this the correct answer. DAX is a fully managed, highly available caching service that delivers up to 10x performance improvement, reducing response times from milliseconds to microseconds for read-heavy workloads. DAX sits in front of your DynamoDB tables and caches the results of GetItem and Query operations, serving subsequent requests for the same data from memory. DAX is write-through, meaning writes go through DAX to DynamoDB, keeping the cache consistent. This caching layer requires minimal application code changes since DAX uses a DynamoDB-compatible API, allowing you to add caching by simply changing the endpoint your application uses. DAX is ideal for read-intensive applications that require the fastest possible response times.

Replacing DynamoDB tables is incorrect because DAX does not replace DynamoDB; it complements it by adding a caching layer. DynamoDB remains the underlying data store, handling all writes and serving as the source of truth for your data. DAX caches frequently accessed data to improve read performance, but all data ultimately resides in DynamoDB tables. DAX does not provide the persistence, durability, or full feature set of DynamoDB. You use DAX in addition to DynamoDB, not instead of it, to accelerate read operations for specific use cases where sub-millisecond latency is required or where reducing read load on DynamoDB tables provides cost or performance benefits.

Backing up DynamoDB data is not the purpose of DAX. DynamoDB provides built-in backup and restore features, including on-demand backups and continuous backups with point-in-time recovery, which are completely separate from DAX functionality. While DAX maintains copies of data in memory for caching purposes, these copies are temporary and not designed for backup or disaster recovery. DAX focuses purely on performance acceleration through caching, not on data protection or recovery. For backup requirements, you should use DynamoDB’s native backup features or export data to S3, not rely on DAX which is a performance enhancement tool.

Migrating data to DynamoDB is incorrect because DAX does not provide data migration capabilities. Data migration to DynamoDB is typically handled using AWS Database Migration Service, custom scripts, or bulk loading tools. DAX is purely a caching layer that works with existing DynamoDB tables to improve read performance. DAX does not move data between systems or help with initial data loading. If you need to migrate data from other databases to DynamoDB, you would use migration tools first, then optionally add DAX later if you need caching to improve read performance for your workload.

Question 122: 

Which AWS service allows you to run code at AWS edge locations in response to CloudFront events?

A) AWS Lambda

B) Lambda@Edge

C) AWS Outposts

D) AWS Local Zones

Answer: B

Explanation:

Lambda@Edge is the correct service that allows you to run code at AWS edge locations in response to CloudFront events. Lambda@Edge lets you execute Lambda functions closer to your users by running them at CloudFront edge locations worldwide, reducing latency for compute-intensive operations. Your functions can run in response to four CloudFront events: viewer request (when CloudFront receives a request from a viewer), viewer response (before CloudFront returns the response to the viewer), origin request (before CloudFront forwards the request to the origin), and origin response (after CloudFront receives the response from the origin). Lambda@Edge is ideal for customizing content based on user location, A/B testing, authentication, URL rewriting, header manipulation, and other edge computing scenarios that benefit from execution close to end users.

AWS Lambda is incorrect because while Lambda@Edge uses Lambda functions, standard Lambda functions run in AWS regions, not at CloudFront edge locations. Regular Lambda functions are invoked in the region where they are defined and do not provide the global edge execution capability that Lambda@Edge offers. Lambda is perfect for many serverless computing scenarios within regions, but it does not run at edge locations to intercept and modify CloudFront requests and responses. Lambda@Edge is a specific variant of Lambda designed for edge computing with CloudFront, with some differences in configuration, runtime support, and execution limits compared to regional Lambda.

AWS Outposts is not the right answer because Outposts brings AWS infrastructure and services to your on-premises data center, not to CloudFront edge locations. Outposts allows you to run AWS services like EC2, EBS, and RDS on hardware installed in your own facility for low-latency access to on-premises systems or data residency requirements. This is fundamentally different from edge computing with CloudFront. Outposts extends AWS infrastructure to your location, while Lambda@Edge distributes your code to AWS’s global edge network to serve users worldwide with low latency. These are different deployment models serving different use cases.

AWS Local Zones are incorrect because they are infrastructure deployments in major metropolitan areas that provide single-digit millisecond latency to end users in those specific locations. Local Zones are not the same as CloudFront edge locations, and they do not run code in response to CloudFront events. Local Zones allow you to run latency-sensitive applications closer to specific populations, but they are separate AWS infrastructure zones, not edge computing integrated with CloudFront’s content delivery network. Lambda@Edge specifically integrates with CloudFront to run code at hundreds of edge locations globally in response to content delivery events.

Question 123: 

What is the minimum duration for which an Amazon EC2 Reserved Instance can be purchased?

A) 1 month

B) 6 months

C) 1 year

D) 3 years

Answer: C

Explanation:

The minimum duration for which an Amazon EC2 Reserved Instance can be purchased is 1 year, making this the correct answer. Reserved Instances are a pricing model that provides significant discounts compared to On-Demand pricing in exchange for committing to use EC2 instances for a one-year or three-year term. These are the only two term options available for Reserved Instances. By committing to a one-year or three-year reservation, you can save up to 72 percent compared to On-Demand prices, with three-year terms typically offering deeper discounts than one-year terms. Reserved Instances are ideal for applications with steady-state usage or predictable capacity requirements where you can confidently commit to using instances for at least a year.

1 month is incorrect as AWS does not offer monthly Reserved Instance commitments. While you can purchase Savings Plans which provide some flexibility, traditional EC2 Reserved Instances require a minimum commitment of one full year. If you need EC2 capacity for shorter periods or have variable workloads, you should use On-Demand instances or Spot instances instead. On-Demand provides the flexibility to start and stop instances as needed without commitments, while Spot instances offer steep discounts for interruptible workloads. Reserved Instances are specifically designed for long-term, predictable workloads that justify the commitment period.

6 months is not a valid Reserved Instance term option. AWS offers only one-year and three-year terms for Reserved Instances to encourage meaningful commitments that enable them to offer substantial discounts. If you need capacity for six months, you would need to either use On-Demand pricing or purchase a one-year Reserved Instance and potentially sell the remaining term on the Reserved Instance Marketplace if your needs change. The marketplace allows you to sell unused Reserved Instance term to other AWS customers, providing some flexibility even with the longer commitment periods.

3 years is incorrect as the answer to the minimum duration question, though it is a valid Reserved Instance term. While you can purchase Reserved Instances for three years and receive even deeper discounts than one-year terms, the question asks for the minimum duration. The minimum commitment period is one year, not three years. Three-year Reserved Instances are excellent for workloads with long-term, stable capacity requirements where the extended commitment is acceptable in exchange for maximum cost savings on compute resources.

Question 124: 

Which DynamoDB API operation should you use when you want to update only specific attributes of an item?

A) PutItem

B) UpdateItem

C) GetItem

D) DeleteItem

Answer: B

Explanation:

UpdateItem is the correct DynamoDB API operation for updating only specific attributes of an item without affecting other attributes. UpdateItem allows you to add, modify, or remove individual attributes from an item using update expressions. You specify which attributes to update and provide the new values or operations to perform, such as incrementing a numeric value or adding elements to a list. UpdateItem is efficient because it only modifies the attributes you specify while leaving all other attributes unchanged. This operation also supports conditional updates, allowing you to specify conditions that must be met for the update to succeed, which helps prevent race conditions and ensures data consistency in concurrent access scenarios.

PutItem is incorrect because it replaces the entire item with the new data you provide in the request. If an item with the specified primary key already exists, PutItem overwrites the entire item, removing any attributes that are not included in the new data. This means you would lose any existing attributes that you did not include in the PutItem request. PutItem is appropriate when you want to create a new item or completely replace an existing item, but not when you want to modify only certain attributes while preserving others. For partial updates that modify specific attributes, UpdateItem is the appropriate operation to use.

GetItem is incorrect because it retrieves an item from DynamoDB but does not modify data. GetItem returns the attributes of an item based on its primary key, allowing you to read data from the table. To update attributes, you would need to use UpdateItem after retrieving the item, or use UpdateItem directly without first retrieving if you know which attributes to modify. GetItem is strictly a read operation and cannot make changes to items in the table. Understanding the distinction between read operations like GetItem and write operations like UpdateItem is fundamental to working effectively with DynamoDB.

DeleteItem is incorrect because it removes an entire item from the table based on its primary key. DeleteItem does not update attributes; it completely removes the item from DynamoDB. If you want to remove specific attributes from an item while keeping the item itself and other attributes, you would use UpdateItem with a REMOVE action in the update expression. DeleteItem is only appropriate when you want to completely remove an item from the table, not when you want to modify it by updating, adding, or removing individual attributes while preserving the item.

Question 125: 

What is the purpose of the AWS SDK for JavaScript?

A) To provide a graphical interface for AWS

B) To allow programmatic access to AWS services from JavaScript applications

C) To deploy JavaScript applications to AWS

D) To monitor JavaScript application performance

Answer: B

Explanation:

The AWS SDK for JavaScript provides programmatic access to AWS services from JavaScript applications, making this the correct answer. The SDK allows developers to interact with AWS services using JavaScript code in both Node.js backend applications and browser-based frontend applications. It provides JavaScript APIs for services like S3, DynamoDB, Lambda, SQS, SNS, and many others, handling authentication, request signing, error handling, and retry logic automatically. The SDK simplifies calling AWS service APIs by providing intuitive JavaScript objects and methods rather than requiring developers to construct raw HTTP requests. This enables JavaScript developers to build applications that leverage AWS services using familiar programming patterns and tools.

Providing a graphical interface for AWS is incorrect because that is the purpose of the AWS Management Console, not the SDK. The console is a web-based GUI that allows you to manage AWS resources through point-and-click interactions without writing code. The SDK, in contrast, is a set of libraries and tools for programmatic access through code. While both the console and SDK can accomplish similar tasks, the SDK is designed for automation, application integration, and building software that interacts with AWS, whereas the console is for manual management and configuration through a visual interface. The SDK has no graphical components; it is purely code-based.

Deploying JavaScript applications to AWS is not the primary purpose of the SDK, though the SDK might be used by deployment tools. Services like AWS Amplify, Elastic Beanstalk, or CodeDeploy handle application deployment, while the SDK provides libraries for your application code to interact with AWS services during runtime. The SDK allows your JavaScript application to call AWS APIs, such as storing data in S3 or querying DynamoDB, but deployment is a separate concern handled by other tools and services. You might use the SDK to build deployment automation scripts, but its primary purpose is enabling applications to use AWS services, not deploying the applications themselves.

Monitoring JavaScript application performance is incorrect because that is the function of services like CloudWatch, X-Ray, or third-party application performance monitoring tools. While the SDK includes features like request metrics and integration with X-Ray for distributed tracing, its primary purpose is not monitoring but rather providing access to AWS services. Monitoring tools observe and analyze your application’s behavior, while the SDK enables your application to interact with AWS infrastructure and services. You would use the SDK within your application code to perform business logic with AWS services, and separately use monitoring services to track performance and health.

Question 126: 

Which Amazon S3 feature automatically transitions objects between storage classes based on access patterns?

A) S3 Versioning

B) S3 Lifecycle Policies

C) S3 Replication

D) S3 Inventory

Answer: B

Explanation:

S3 Lifecycle Policies are the correct feature for automatically transitioning objects between storage classes based on rules you define. Lifecycle policies allow you to define actions that S3 performs on objects during their lifetime, including transitioning objects to different storage classes after specified time periods or deleting objects after they reach a certain age. For example, you might configure a policy to move objects to S3 Standard-IA after 30 days, then to S3 Glacier after 90 days, and finally delete them after one year. Lifecycle policies help optimize storage costs by automatically moving data to more cost-effective storage classes as it ages and becomes less frequently accessed. You can apply policies to all objects in a bucket or filter by prefix or tags to target specific object sets.

S3 Versioning is incorrect because it maintains multiple versions of objects in your bucket to protect against accidental deletion and overwrites, not for transitioning between storage classes. When versioning is enabled, S3 keeps every version of an object, including all writes and even deletions which become delete markers. Versioning helps with data protection and allows you to restore previous versions of objects, but it does not automatically move objects between storage classes based on age or access patterns. However, lifecycle policies can work with versioning to transition or expire non-current versions of objects to manage storage costs for versioned buckets.

S3 Replication is not the right answer because it copies objects from one bucket to another, either in the same region or across regions, but does not transition objects between storage classes within a bucket. Replication is used for disaster recovery, compliance requirements, or reducing latency by keeping copies closer to users in different geographic locations. While you can specify a different storage class for replicated objects in the destination bucket, replication is about copying objects to different buckets, not managing the storage class lifecycle of objects within a bucket. Lifecycle policies handle storage class transitions for cost optimization.

S3 Inventory is incorrect because it provides scheduled reports about your objects and their metadata, not automatic transitions between storage classes. Inventory generates CSV, ORC, or Parquet files listing your objects and their properties such as storage class, encryption status, and replication status. Inventory helps you understand what objects you have and their characteristics, which can inform decisions about lifecycle policies, but inventory itself does not perform any actions on objects. Inventory is for reporting and analysis, while lifecycle policies are for automating object management including storage class transitions and expiration.

Question 127: 

What is the purpose of AWS Secrets Manager?

A) To manage API Gateway APIs

B) To store and rotate secrets such as database credentials

C) To encrypt S3 objects

D) To manage IAM policies

Answer: B

Explanation:

AWS Secrets Manager is designed to store and rotate secrets such as database credentials, API keys, and other sensitive information, making this the correct answer. Secrets Manager enables you to centrally manage secrets used by your applications, eliminating the need to hard-code credentials in application code or configuration files. The service provides automatic rotation of secrets on a schedule you define, using Lambda functions to update credentials in both Secrets Manager and the resource using those credentials. Secrets Manager encrypts secrets at rest using AWS KMS and provides fine-grained access control through IAM policies. The service integrates with RDS, Redshift, DocumentDB, and other AWS services to simplify credential management and improve security through automatic rotation and centralized secret storage.

Managing API Gateway APIs is incorrect because that is handled through the API Gateway service itself or through infrastructure as code tools like CloudFormation or Terraform. While you might store API keys used with API Gateway in Secrets Manager, the service is not for managing the APIs themselves. API Gateway management involves defining resources, methods, integrations, and deployment stages, which are configuration tasks separate from secret storage. Secrets Manager focuses specifically on storing, retrieving, and rotating sensitive information like credentials and keys, not on managing API definitions or configurations.

Encrypting S3 objects is not the purpose of Secrets Manager; S3 encryption is handled by S3’s built-in encryption features using SSE-S3, SSE-KMS, or SSE-C. S3 provides server-side encryption that automatically encrypts objects as they are written to buckets and decrypts them when accessed. While both Secrets Manager and S3 encryption use KMS for key management in some configurations, Secrets Manager is specifically for storing and managing access to secret values like passwords and API keys, not for encrypting files or objects stored in S3. The encryption mechanisms serve different purposes and operate independently.

Managing IAM policies is incorrect because IAM policy management is handled through AWS IAM itself. IAM allows you to create, attach, and manage policies that define permissions for AWS resources and actions. While Secrets Manager uses IAM policies to control who can access which secrets, it does not provide functionality for managing IAM policies generally. Secrets Manager is specifically designed for secret storage and rotation, not for general IAM administration. You use IAM to define who can access Secrets Manager and what they can do with secrets, but Secrets Manager’s purpose is managing the secrets themselves, not the access policies.

Question 128: 

Which AWS service provides a managed Apache Kafka-compatible event streaming platform?

A) Amazon Kinesis Data Streams

B) Amazon MSK

C) Amazon SQS

D) Amazon EventBridge

Answer: B

Explanation:

Amazon MSK, which stands for Managed Streaming for Apache Kafka, is the correct service that provides a fully managed Apache Kafka-compatible event streaming platform. MSK makes it easy to build and run applications that use Apache Kafka to process streaming data without needing to manage Kafka clusters yourself. MSK handles cluster setup, configuration, patching, and failure recovery automatically while providing open-source Apache Kafka APIs, so your existing Kafka applications and tools work without modification. MSK integrates with other AWS services for monitoring, security, and data processing, and it provides options for different levels of availability, throughput, and storage to match your requirements and budget.

Amazon Kinesis Data Streams is incorrect because while it is a real-time data streaming service, it is not Apache Kafka-compatible. Kinesis Data Streams provides its own proprietary API and architecture for ingesting and processing streaming data at scale. While Kinesis and Kafka serve similar purposes in building streaming data applications, they are different technologies with different APIs and operational characteristics. If you need Apache Kafka specifically for compatibility with existing Kafka applications or ecosystems, you must use Amazon MSK. Kinesis is an excellent alternative for new streaming applications but does not provide Kafka API compatibility.

Amazon SQS is not the right answer because it is a message queue service, not a streaming platform. While SQS can handle message passing between distributed application components, it does not provide the streaming data capabilities or Kafka compatibility that MSK offers. SQS is designed for discrete messages with queue semantics like visibility timeouts and message deletion, whereas Kafka and MSK are designed for continuous streams of events with log-based storage and replay capabilities. SQS and MSK serve different use cases and have fundamentally different architectural patterns for data processing and distribution.

Amazon EventBridge is incorrect because it is a serverless event bus service for connecting applications using events from AWS services, SaaS applications, and custom sources. While EventBridge handles events, it is not an Apache Kafka-compatible streaming platform. EventBridge routes events to targets based on rules and patterns, providing an event-driven architecture foundation. EventBridge is excellent for event-driven workflows and integrating AWS services, but it does not provide Kafka APIs or the streaming data processing capabilities of MSK. If you need Apache Kafka specifically, MSK is the appropriate service.

Question 129: 

What is the maximum size of a deployment package for an AWS Lambda function when uploaded directly?

A) 10 MB

B) 50 MB

C) 100 MB

D) 250 MB

Answer: B

Explanation:

The maximum size of a deployment package for AWS Lambda when uploaded directly is 50 MB compressed, making this the correct answer. This limit applies when you upload your deployment package as a ZIP file directly through the Lambda console, CLI, or API. The ZIP file can contain your function code and any dependencies, and when uncompressed, the total size must not exceed 250 MB. This 50 MB compressed upload limit is important to consider when packaging Lambda functions with large dependencies. If your deployment package exceeds 50 MB compressed, you must upload it to Amazon S3 and provide the S3 location to Lambda instead of uploading directly, which allows packages up to 250 MB uncompressed.

10 MB is incorrect as it is too small to be the Lambda direct upload limit. While 10 MB might be sufficient for simple Lambda functions with minimal dependencies, AWS allows up to 50 MB for compressed deployment packages uploaded directly. Many modern applications with frameworks and libraries can easily exceed 10 MB, so the higher 50 MB limit provides more flexibility. If your package is under 50 MB compressed, you can upload it directly without needing to use S3 as an intermediary, simplifying the deployment process for functions that fit within this size constraint.

100 MB is not correct for direct uploads, though it might seem like a reasonable limit. The actual compressed upload limit is 50 MB for direct uploads to Lambda. However, the uncompressed size limit is larger at 250 MB, which might be where confusion arises. Understanding both limits is important: your ZIP file must be under 50 MB to upload directly, and when that ZIP file is uncompressed, the total content must be under 250 MB. For larger packages, you must use the S3 upload method instead of direct upload through the Lambda API or console.

250 MB is the uncompressed size limit, not the compressed direct upload limit. While your deployment package can be up to 250 MB when uncompressed, the compressed ZIP file must be 50 MB or less to upload directly to Lambda. The 250 MB limit applies to the uncompressed contents of your deployment package, including all code and dependencies. If your compressed package exceeds 50 MB but uncompressed size is under 250 MB, you must upload the ZIP file to S3 first and then create or update your Lambda function referencing the S3 bucket and key.

Question 130: 

Which CloudWatch Logs feature allows you to extract fields from log events and create custom metrics?

A) Log Groups

B) Metric Filters

C) Log Streams

D) Subscription Filters

Answer: B

Explanation:

Metric Filters are the correct CloudWatch Logs feature for extracting fields from log events and creating custom metrics. Metric filters allow you to define patterns that CloudWatch Logs uses to search log events and extract numeric values to publish as CloudWatch metrics. For example, you can create a metric filter to count error messages, measure response times, or track any numeric value that appears in your logs. Once you create metrics from log data, you can set CloudWatch Alarms on those metrics to trigger notifications or automated actions. Metric filters are powerful for turning unstructured log data into quantitative metrics that you can monitor, graph, and alert on, enabling proactive monitoring of application-specific indicators extracted from logs.

Log Groups are incorrect because they are organizational containers for log streams sharing the same retention, monitoring, and access control settings, not a feature for creating metrics. Log groups help you organize related log streams from the same application or resource, and you configure retention policies and access permissions at the log group level. While you apply metric filters to log groups, the log group itself is just a collection mechanism. The metric filter is what actually extracts data and creates metrics from the log events within the log group.

Log Streams are not the right answer because they represent sequences of log events from a single source, such as a specific EC2 instance or Lambda function execution. Log streams are within log groups and contain the actual log event data in chronological order. While log streams hold the data that metric filters analyze, streams themselves do not create metrics. Log streams are about organizing and storing log events, while metric filters are about extracting meaningful numeric data from those events to create monitorable metrics.

Subscription Filters are incorrect because they allow you to stream log data to other services like Kinesis Data Streams, Kinesis Data Firehose, or Lambda for custom processing, not for creating CloudWatch metrics. Subscription filters are useful when you want to process log data in real time with custom logic, send logs to a centralized logging system, or analyze logs with external tools. While subscription filters can forward data for analysis that might eventually create metrics elsewhere, they do not directly create CloudWatch metrics from log data. Metric filters are specifically designed for extracting data from logs and publishing it as CloudWatch metrics.

Question 131: 

What is the purpose of Amazon EventBridge rules?

A) To store events permanently

B) To route events to targets based on patterns

C) To encrypt event data

D) To compress event payloads

Answer: B

Explanation:

Amazon EventBridge rules are designed to route events to targets based on event patterns you define, making this the correct answer. Rules match incoming events against patterns that specify which events should trigger the rule. When an event matches a rule’s pattern, EventBridge routes that event to one or more configured targets such as Lambda functions, Step Functions state machines, SNS topics, SQS queues, or other AWS services. Rules allow you to build event-driven architectures where different events trigger different actions based on their content. You can define patterns that match on event source, detail type, specific field values, or complex combinations of criteria, providing flexible event routing for building responsive, decoupled applications that react to changes in your systems.

Storing events permanently is incorrect because EventBridge does not provide long-term event storage. EventBridge is an event routing service that delivers events to configured targets in real time or near real time. Events pass through EventBridge to their destinations but are not retained for long periods. If you need to store events for historical analysis or compliance, you should configure EventBridge rules to send events to services designed for storage, such as S3 for archival, CloudWatch Logs for searchable storage, or databases for structured querying. EventBridge focuses on event routing and delivery, not storage and retention.

Encrypting event data is not the primary purpose of EventBridge rules. While EventBridge does encrypt events at rest and in transit using AWS encryption services, this encryption is a security feature of the EventBridge service itself, not something that rules configure or control. Rules are specifically about matching event patterns and routing events to appropriate targets based on those patterns. Encryption happens automatically as a background service feature to protect event data, but it is not configured or managed through rules. Rules define event matching logic and target routing, not encryption settings.

Compressing event payloads is incorrect because EventBridge rules do not perform compression on events. EventBridge delivers events to targets in their original format, potentially transforming the JSON structure using input transformers if configured, but not compressing the data. Compression of event data, if needed, would be handled by the target service or application receiving the event, not by EventBridge rules. EventBridge rules are concerned with event pattern matching and routing decisions, not with optimizing event size through compression. The rules evaluate event content to determine routing, they do not modify the size or encoding of event payloads.

Question 132: 

Which AWS service allows you to search and analyze large volumes of log data using SQL-like queries?

A) Amazon S3 Select

B) Amazon Athena

C) Amazon CloudWatch Logs Insights

D) AWS Glue

Answer: C

Explanation:

Amazon CloudWatch Logs Insights is the correct service for searching and analyzing large volumes of log data using SQL-like queries. Logs Insights provides a purpose-built query language designed specifically for log data analysis, allowing you to search through millions of log events across multiple log groups quickly. The query language supports filtering, aggregation, sorting, and statistical operations that help you troubleshoot issues, identify trends, and extract insights from your logs. Logs Insights automatically discovers fields in JSON logs and common log formats, provides query suggestions and autocomplete, and visualizes query results with charts and statistics. The service is ideal for operational troubleshooting, security analysis, and performance investigation using log data from your applications and AWS services.

Amazon S3 Select is incorrect because it allows you to retrieve subsets of data from S3 objects using SQL expressions, but it is not designed for searching and analyzing log data specifically. S3 Select works on individual objects stored in S3, allowing you to filter and project data from CSV, JSON, or Parquet files without retrieving entire objects. While you could store logs in S3 and use S3 Select to query them, this is not the primary or most efficient way to analyze logs. CloudWatch Logs Insights is purpose-built for log analysis with features specifically designed for the characteristics and use cases of log data.

Amazon Athena is not the best answer for this specific use case, though it does allow SQL queries on data in S3. Athena is a serverless query service that analyzes data stored in S3 using standard SQL, and you could export CloudWatch Logs to S3 and query them with Athena. However, CloudWatch Logs Insights is specifically designed for log analysis with a query language optimized for log data patterns and provides a more direct path for analyzing logs already in CloudWatch Logs. Athena is better suited for ad hoc SQL analysis of structured data in S3, while Logs Insights is optimized for log-specific queries and troubleshooting workflows.

AWS Glue is incorrect because it is a serverless data integration service for ETL (extract, transform, load) operations, not specifically for querying and analyzing logs. Glue crawls data sources to create metadata catalogs, runs ETL jobs to transform data, and prepares data for analytics, but it does not provide an interactive query interface for log analysis. While Glue could be part of a pipeline that processes log data, CloudWatch Logs Insights is the service specifically designed for searching and analyzing logs with SQL-like queries in real time for operational and troubleshooting purposes.

Question 133: 

What is the default behavior of an Amazon SQS queue when the message retention period expires?

A) Messages are moved to a dead-letter queue

B) Messages are automatically deleted

C) Messages are archived to S3

D) Messages remain in the queue indefinitely

Answer: B

Explanation:

When the message retention period expires for an Amazon SQS queue, messages are automatically deleted, making this the correct answer. The message retention period defines how long SQS keeps a message in the queue if it is not deleted by a consumer. The default retention period is 4 days, and you can configure it to any value between 1 minute and 14 days. After a message has been in the queue for the configured retention period, SQS automatically deletes it to prevent the queue from growing indefinitely with unprocessed messages. This automatic deletion helps manage queue size and storage costs, ensuring that old, potentially irrelevant messages do not accumulate. If messages are important, you must ensure they are processed and deleted by consumers before the retention period expires.

Messages are moved to a dead-letter queue is incorrect because dead-letter queues are used for messages that fail processing repeatedly, not for messages that simply age out. A dead-letter queue receives messages from a source queue when those messages exceed the maximum receive count, indicating they cannot be processed successfully. The retention period expiration is a separate mechanism that affects all messages regardless of processing attempts. While you can configure dead-letter queues to handle problematic messages, normal retention period expiration results in deletion, not movement to a dead-letter queue. Dead-letter queues must be explicitly configured and are triggered by receive count, not retention time.

Messages are archived to S3 is not correct because SQS does not automatically archive messages to S3 when they reach the retention period. SQS simply deletes expired messages from the queue. If you need to preserve messages long-term, you must implement your own solution to consume messages from SQS and store them in S3 or another storage service before they expire. Some organizations build pipelines that archive all messages to S3 for compliance or analysis purposes, but this requires custom implementation using Lambda, Kinesis Data Firehose, or other integration tools. SQS itself only deletes expired messages, it does not preserve them.

Messages remain in the queue indefinitely is incorrect because SQS enforces the message retention period and will delete messages after that time. Unlike some other messaging systems, SQS does not keep messages forever; there is always a retention period with a maximum of 14 days. This design ensures that queues do not grow without bounds and that old messages are cleaned up automatically. If you need longer retention, you should process messages and store them in a more appropriate service like S3, DynamoDB, or a database designed for long-term data storage rather than message queuing.

Question 134: 

Which AWS Lambda feature allows you to allocate up to 10 GB of memory to a function?

A) Memory configuration

B) Lambda Layers

C) Provisioned Concurrency

D) Reserved Concurrency

Answer: A

Explanation:

Memory configuration is the correct Lambda feature that allows you to allocate up to 10,240 MB (10 GB) of memory to a function. When you configure a Lambda function, you specify the amount of memory allocated, which ranges from 128 MB to 10,240 MB in 1 MB increments. The memory setting directly affects the function’s performance because Lambda allocates CPU power, network bandwidth, and disk I/O proportionally to the memory configured. Functions with higher memory allocations execute faster for compute-intensive tasks and have access to more CPU cycles. You pay for the memory allocated multiplied by the execution time, so choosing the right memory size balances performance and cost. Testing different memory configurations helps identify the optimal setting for your workload.

Lambda Layers are incorrect because they allow you to package and share code and dependencies across multiple functions, not configure function memory. Layers help you manage common code, libraries, or configuration files that multiple functions need, reducing duplication and deployment package sizes. While layers are valuable for code organization and reuse, they do not affect the memory, CPU, or other compute resources allocated to function execution. Memory configuration is a separate setting that determines the computational resources available when your function runs, including code from layers.

Provisioned Concurrency is not the right answer because it keeps function instances initialized and ready to respond immediately, reducing cold start latency, but it does not control memory allocation. Provisioned Concurrency ensures a specified number of function instances are always warm and ready to handle requests, improving response times for latency-sensitive applications. While provisioned instances still use the memory configuration you specify for the function, Provisioned Concurrency itself is about availability and responsiveness, not about the amount of memory or CPU allocated to each individual execution. Memory configuration and Provisioned Concurrency address different performance aspects.

Reserved Concurrency is incorrect because it limits the maximum number of concurrent executions for a function to prevent it from consuming all available account-level concurrency, not a feature for allocating memory. Reserved Concurrency ensures that a function always has a guaranteed number of concurrent executions available and prevents it from scaling beyond that limit. This helps protect other functions from being starved of concurrency and helps manage costs for functions that might otherwise scale excessively. Like Provisioned Concurrency, Reserved Concurrency does not affect the memory allocated to each function execution, which is controlled separately through the memory configuration setting.

Question 135: 

What is the purpose of Amazon API Gateway usage plans?

A) To design API resources and methods

B) To throttle and quota API requests for different customer tiers

C) To deploy APIs to stages

D) To monitor API performance

Answer: B

Explanation:

Amazon API Gateway usage plans allow you to throttle and apply quotas to API requests for different customer tiers or API keys, making this the correct answer. Usage plans enable you to define rate limits and burst limits that control how frequently clients can call your API, as well as quotas that limit the total number of requests allowed within a specified time period like per day or per month. You can create different usage plans for different customer segments, such as free tier, basic, and premium tiers, each with different throttle and quota limits. Usage plans work with API keys to identify clients and enforce the appropriate limits, helping you monetize APIs, manage capacity, prevent abuse, and provide differentiated service levels to various customer groups.

Designing API resources and methods is incorrect because that is part of API definition and modeling, not the function of usage plans. You design your API structure, including resources, methods, request/response models, and integrations, when building the API itself using API Gateway’s API design features. Usage plans are applied after you have designed and deployed your API to control access patterns and enforce limits on how clients can use the already-designed API. The API structure defines what the API does, while usage plans control how much clients can use it.

Deploying APIs to stages is not the purpose of usage plans. API deployment involves creating a deployment and associating it with a stage, which represents different environments like development, testing, or production. Stages allow you to manage different versions of your API and configure stage-specific settings like caching, logging, and stage variables. While you associate usage plans with specific API stages, the usage plan itself does not handle deployment. Deployment makes your API available, while usage plans control access limits for clients using that deployed API.

Monitoring API performance is incorrect because that is the function of CloudWatch and X-Ray integration with API Gateway. CloudWatch provides metrics like request count, latency, and error rates, while X-Ray provides detailed tracing of requests through your API and backend services. Usage plans do not monitor performance; they enforce request limits and quotas. While the enforcement of throttling limits might indirectly affect performance by rejecting excess requests, usage plans are about access control and request limiting, not about measuring or analyzing API performance characteristics.