AWS Certified Developer - Associate Free Sample Questions

In AWS Lambda, CPU power is allocated proportionally to the amount of memory configured. Image processing is a CPU-intensive task. By increasing the memory, the developer also increases the available vCPU, which will speed up the processing of larger images and likely resolve the timeout issue. This is more effective than just increasing the timeout, as it addresses the root cause of the slow performance. AWS Lambda Power Tuning is a tool that can help find the optimal memory setting.

The best practice is to use a single Infrastructure as Code (IaC) template and manage environment-specific configurations separately. AWS CodePipeline's AWS CloudFormation deploy action allows specifying a parameter override file. The developer can create staging-params.json and production-params.json files to define stage-specific variables (like Lambda function names, memory sizes, or environment variables) and reference them in the respective deployment stages of the pipeline.

Using IAM roles for EC2 instances is the most secure and recommended method. The application running on the EC2 instance can automatically retrieve temporary credentials from the EC2 metadata service. The AWS SDKs are designed to automatically look for these credentials. This avoids the need to store any long-term credentials on the instance, simplifying credential management and enhancing security.

Amazon Kinesis Data Streams is designed for real-time processing of streaming data at scale. To maintain the order of events for each user session, the developer can use the user's session ID as the partition key. All events with the same partition key will be sent to the same shard, ensuring in-order processing within that shard.

AWS Lambda integrates natively with Kinesis Data Streams. It can be configured as a consumer to process records from the stream in micro-batches. Lambda scales automatically with the number of shards and the volume of data, making it ideal for handling traffic spikes without manual intervention.

Amazon API Gateway usage plans are the standard way to manage throttling and quotas for your APIs. A developer can create a usage plan, define the rate (requests per second) and burst limits, and then associate this plan with one or more API stages. If the API requires client-specific keys, the plan can be associated with API keys.

This statement is true. For SQS FIFO queues, if a Lambda function invocation returns an error without successfully processing any messages in the batch, all messages in that batch remain in the queue and become visible again after the visibility timeout. The Lambda service ensures that it will attempt to process the same batch of messages again while preserving their original order relative to other messages in the same message group.

Many AWS API operations that return a list of items are paginated. Manually handling the pagination tokens (NextToken, NextContinuationToken, etc.) is error-prone. Boto3 Paginators provide a higher-level abstraction that automatically handles the logic of making multiple service calls to retrieve all results. For listing objects, get_paginator('list_object_versions') should be used for a versioned bucket.

The core X-Ray SDK library is required to create segments, subsegments, and interact with the X-Ray daemon. This is the foundational step for any X-Ray instrumentation in a Node.js application.

To automatically trace downstream calls made with the AWS SDK, the SDK itself must be instrumented. The AWS X-Ray SDK for Node.js provides a captureAWS method that patches the AWS SDK to automatically create subsegments for each API call, including calls to DynamoDB and SNS.

Amazon ElastiCache for Redis is an in-memory data store that provides sub-millisecond latency for reads and writes, making it perfect for session management in high-traffic applications. Redis has built-in support for setting a time-to-live (TTL) on keys, which will automatically expire and evict the session data after the specified duration. This meets all the requirements with high performance and minimal management.

The scenario describes a canary deployment, which is a type of linear deployment in AWS CodeDeploy. The Linear deployment type allows traffic to be shifted in increments with a specified interval between them. The developer would configure Type: Linear, StepPercentage: 10, and Interval: 5 (in minutes) to achieve the initial 10% shift. The Canary deployment type is a predefined linear deployment that shifts traffic in two increments.