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Amazon AWS Certified Developer - Associate Sample Questions (Q262-Q267):

NEW QUESTION # 262
An application is processing clickstream data using Amazon Kinesis. The clickstream data feed into Kinesis experiences periodic spikes. The PutRecords API call occasionally fails and the logs show that the failed call returns the response shown below:

Which techniques will help mitigate this exception? (Choose two.)

A. Reduce the number of KCL consumers.
B. Reduce the frequency and/or size of the requests.
C. Implement retries with exponential backoff.
D. Use Amazon SNS instead of Kinesis.
E. Use a PutRecord API instead of PutRecords.

Answer: B,C

Explanation:
The response from the API call indicates that the ProvisionedThroughputExceededException exception has occurred. This exception means that the rate of incoming requests exceeds the throughput limit for one or more shards in a stream. To mitigate this exception, the developer can use one or more of the following techniques:
* Implement retries with exponential backoff. This will introduce randomness in the retry intervals and avoid overwhelming the shards with retries.
* Reduce the frequency and/or size of the requests. This will reduce the load on the shards and avoid throttling errors.
* Increase the number of shards in the stream. This will increase the throughput capacity of the stream and accommodate higher request rates.
* Use a PutRecord API instead of PutRecords. This will reduce the number of records per request and avoid exceeding the payload limit.
References:
* [ProvisionedThroughputExceededException - Amazon Kinesis Data Streams Service API Reference]
* [Best Practices for Handling Kinesis Data Streams Errors]

NEW QUESTION # 263
A developer at a company needs to create a small application that makes the same API call once each day at a designated time. The company does not have infrastructure in the AWS Cloud yet, but the company wants to implement this functionality on AWS.
Which solution meets these requirements in the MOST operationally efficient manner?

A. Use an Amazon Linux crontab scheduled job that runs on Amazon EC2.
B. Use an AWS Batch job that is submitted to an AWS Batch job queue.
C. Use a Kubernetes cron job that runs on Amazon Elastic Kubernetes Service (Amazon EKS).
D. Use an AWS Lambda function that is invoked by an Amazon EventBridge scheduled event.

Answer: D

Explanation:
The correct answer is C) Use an AWS Lambda function that is invoked by an Amazon EventBridge scheduled event.
C) Use an AWS Lambda function that is invoked by an Amazon EventBridge scheduled event. This is correct. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. Lambda runs your code on a high-availability compute infrastructure and performs all of the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, and logging1. Amazon EventBridge is a serverless event bus service that enables you to connect your applications with data from a variety of sources2. EventBridge can create rules that run on a schedule, either at regular intervals or at specific times and dates, and invoke targets such as Lambda functions3. This solution meets the requirements of creating a small application that makes the same API call once each day at a designated time, without requiring any infrastructure in the AWS Cloud or any operational overhead.
A) Use a Kubernetes cron job that runs on Amazon Elastic Kubernetes Service (Amazon EKS). This is incorrect. Amazon EKS is a fully managed Kubernetes service that allows you to run containerized applications on AWS4. Kubernetes cron jobs are tasks that run periodically on a given schedule5. This solution could meet the functional requirements of creating a small application that makes the same API call once each day at a designated time, but it would not be the most operationally efficient manner. The company would need to provision and manage an EKS cluster, which would incur additional costs and complexity.
B) Use an Amazon Linux crontab scheduled job that runs on Amazon EC2. This is incorrect. Amazon EC2 is a web service that provides secure, resizable compute capacity in the cloud6. Crontab is a Linux utility that allows you to schedule commands or scripts to run automatically at a specified time or date7. This solution could meet the functional requirements of creating a small application that makes the same API call once each day at a designated time, but it would not be the most operationally efficient manner. The company would need to provision and manage an EC2 instance, which would incur additional costs and complexity.
D) Use an AWS Batch job that is submitted to an AWS Batch job queue. This is incorrect. AWS Batch enables you to run batch computing workloads on the AWS Cloud8. Batch jobs are units of work that can be submitted to job queues, where they are executed in parallel or sequentially on compute environments9. This solution could meet the functional requirements of creating a small application that makes the same API call once each day at a designated time, but it would not be the most operationally efficient manner. The company would need to configure and manage an AWS Batch environment, which would incur additional costs and complexity.
Reference:
1: What is AWS Lambda? - AWS Lambda
2: What is Amazon EventBridge? - Amazon EventBridge
3: Creating an Amazon EventBridge rule that runs on a schedule - Amazon EventBridge
4: What is Amazon EKS? - Amazon EKS
5: CronJob - Kubernetes
6: What is Amazon EC2? - Amazon EC2
7: Crontab in Linux with 20 Useful Examples to Schedule Jobs - Tecmint
8: What is AWS Batch? - AWS Batch
9: Jobs - AWS Batch

NEW QUESTION # 264
A developer is working on an ecommerce website The developer wants to review server logs without logging in to each of the application servers individually. The website runs on multiple Amazon EC2 instances, is written in Python, and needs to be highly available How can the developer update the application to meet these requirements with MINIMUM changes?

A. Scale down the application to one larger EC2 instance where only one instance is recording logs
B. Rewrite the application to be cloud native and to run on AWS Lambda, where the logs can be reviewed in Amazon CloudWatch
C. Install the unified Amazon CloudWatch agent on the EC2 instances Configure the agent to push the application logs to CloudWatch
D. Set up centralized logging by using Amazon OpenSearch Service, Logstash, and OpenSearch Dashboards

Answer: C

Explanation:
* Centralized Logging Benefits: Centralized logging is essential for operational visibility in scalable systems, especially those using multiple EC2 instances like our e-commerce website. CloudWatch provides this capability, along with other monitoring features.
* CloudWatch Agent: This is the best way to send custom application logs from EC2 instances to CloudWatch. Here's the process:
* Install the CloudWatch agent on each EC2 instance.
* Configure the agent with a configuration file, specifying:
* Which log files to collect.
* The format in which to send logs to CloudWatch (e.g., JSON).
* The specific CloudWatch Logs log group and log stream for these logs.
* Viewing and Analyzing Logs: Once the agent is pushing logs, use the CloudWatch Logs console or API:
* View and search the logs across all instances.
* Set up alarms based on log events.
* Use CloudWatch Logs Insights for sophisticated queries and analysis.
References:
* Amazon CloudWatch Logs: https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs
/WhatIsCloudWatchLogs.html
* Unified CloudWatch Agent: https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs
/AgentReference.html
* CloudWatch Logs Insights: https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs
/AnalyzingLogData.html

NEW QUESTION # 265
A developer is building an application to process a stream of customer orders. The application sends processed orders to an Amazon Aurora MySQL database. The application needs to process the orders in batches.
The developer needs to configure a workflow that ensures each record is processed before the application sends each order to the database.
Options:

A. Use Amazon DynamoDB Streams to stream the orders. Use an Amazon ECS cluster on AWS Fargate to process the orders. Configure an event source mapping for the cluster, and set the BatchSize setting to 1.
B. Use Amazon Kinesis Data Streams to stream the orders. Use an AWS Lambda function to process the orders. Configure an event source mapping for the Lambda function, and set the MaximumBatchingWindowInSeconds setting to 300.
C. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to stream the orders. Use an Amazon EC2 instance to process the orders. Configure an event source mapping for the EC2 instance, and increase the payload size limit to 36 MB.
D. Use Amazon SQS to stream the orders. Use an AWS Lambda function to process the orders. Configure an event source mapping for the Lambda function, and set the MaximumBatchingWindowInSeconds setting to 0.

Answer: B

Explanation:
Step 1: Understanding the Problem
Processing in Batches: The application must process records in groups.
Sequential Processing: Each record in the batch must be processed before writing to Aurora.
Solution Goals: Use services that support ordered, batched processing and integrate with Aurora.
Step 2: Solution Analysis
Option A:
Amazon Kinesis Data Streams supports ordered data processing.
AWS Lambda can process batches of records via event source mapping with MaximumBatchingWindowInSeconds for timing control.
Configuring the batching window ensures efficient processing and compliance with the workflow.
Correct Option.
Option B:
Amazon SQS is not designed for streaming; it provides reliable, unordered message delivery.
Setting MaximumBatchingWindowInSeconds to 0 disables batching, which is contrary to the requirement.
Not suitable.
Option C:
Amazon MSK provides Kafka-based streaming but requires custom EC2-based processing.
This increases system complexity and operational overhead.
Not ideal for serverless requirements.
Option D:
DynamoDB Streams is event-driven but lacks strong native integration for batch ordering.
Using ECS adds unnecessary complexity.
Not suitable.
Step 3: Implementation Steps for Option A
Set up Kinesis Data Stream:
Configure shards based on the expected throughput.
Configure Lambda with Event Source Mapping:
Enable Kinesis as the event source for Lambda.
Set MaximumBatchingWindowInSeconds to 300 to accumulate data for processing.
Example:
{
"EventSourceArn": "arn:aws:kinesis:region:account-id:stream/stream-name",
"BatchSize": 100,
"MaximumBatchingWindowInSeconds": 300
}
Write Processed Data to Aurora:
Use AWS RDS Data API for efficient database operations from Lambda.
AWS Developer Reference:
Amazon Kinesis Data Streams Developer Guide
AWS Lambda Event Source Mapping
Batch Processing with Lambda

NEW QUESTION # 266
A developer is managing an application that uploads user files to an Amazon S3 bucket named companybucket. The company wants to maintain copies of all the files uploaded by users for compliance purposes, while ensuring users still have access to the data through the application.
Which IAM permissions should be applied to users to ensure they can create but not remove files from the bucket?

A. json
Copy code
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "statement1",
"Effect": "Allow",
"Action": ["s3:GetObject", "s3:PutObject", "s3:DeleteObject"],
"Resource": ["arn:aws:s3:::companybucket"]
}
]
}
B. json
Copy code
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "statement1",
"Effect": "Allow",
"Action": ["s3:GetObject", "s3:PutObject"],
"Resource": ["arn:aws:s3:::companybucket"]
}
]
}
C. json
Copy code
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "statement1",
"Effect": "Allow",
"Action": ["s3:GetObject", "s3:PutObject", "s3:DeleteObject", "s3:PutObjectRetention"],
"Resource": "arn:aws:s3:::companybucket"
}
]
}
D. json
Copy code
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "statement1",
"Effect": "Allow",
"Action": ["s3:CreateBucket", "s3:GetBucketLocation"],
"Resource": "arn:aws:s3:::companybucket"
}
]
}

Answer: B

Explanation:
To meet the requirement:
Users must be able to upload (PutObject) and read (GetObject) files but not delete them.
Option D ensures users cannot delete files by omitting the s3:DeleteObject action while allowing s3:GetObject and s3:PutObject.
Option A: Includes s3:DeleteObject, which allows users to delete files and does not meet the requirement.
Option B: Contains unrelated actions like CreateBucket, which is not relevant here.
Option C: Adds s3:PutObjectRetention, which is unnecessary and does not restrict DeleteObject.
Reference:
AWS S3 Permissions Documentation
Reference:
AWS S3 Permissions Documentation

NEW QUESTION # 267
......

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Amazon AWS Certified Developer - Associate Sample Questions (Q262-Q267):

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