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NEW QUESTION 26
The start-up company that you are working for has a batch job application that is currently hosted on an EC2 instance. It is set to process messages from a queue created in SQS with default settings. You configured the application to process the messages once a week. After 2 weeks, you noticed that not all messages are being processed by the application.
What is the root cause of this issue?
- A. Missing permissions in SQS.
- B. Amazon SQS has automatically deleted the messages that have been in a queue for more than the maximum message retention period.
- C. The batch job application is configured to long polling.
- D. The SQS queue is set to short-polling.
Answer: B
Explanation:
Amazon SQS automatically deletes messages that have been in a queue for more than the maximum message retention period. The default message retention period is 4 days. Since the queue is configured to the default settings and the batch job application only processes the messages once a week, the messages that are in the queue for more than 4 days are deleted. This is the root cause of the issue.
To fix this, you can increase the message retention period to a maximum of 14 days using the SetQueueAttributes action.
References:
https://aws.amazon.com/sqs/faqs/
https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-message-lifecy cle.html Check out this Amazon SQS Cheat Sheet:
https://tutorialsdojo.com/amazon-sqs/
NEW QUESTION 27
A company plans to launch an application that tracks the GPS coordinates of delivery trucks in the country. The coordinates are transmitted from each delivery truck every five seconds. You need to design an architecture that will enable real-time processing of these coordinates from multiple consumers. The aggregated data will be analyzed in a separate reporting application.
Which AWS service should you use for this scenario?
- A. Amazon Kinesis
- B. Amazon Simple Queue Service
- C. AWS Data Pipeline
- D. Amazon AppStream
Answer: A
Explanation:
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. It offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application.
With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and responds instantly instead of having to wait until all your data are collected before the processing can begin.
Reference:
https://aws.amazon.com/kinesis/
Check out this Amazon Kinesis Cheat Sheet:
https://tutorialsdojo.com/amazon-kinesis/
NEW QUESTION 28
A company recently launched a variety of new workloads on Amazon EC2 instances in its AWS account. The company needs to create a strategy to access and administer the instances remotely and securely. The company needs to implement a repeatable process that works with native AWS services and follows the AWS Well-Architected Framework.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Create an administrative SSH key pair. Load the public key into each EC2 instance. Deploy a bastion host in a public subnet to provide a tunnel for administration of each instance.
- B. Use the EC2 serial console to directly access the terminal interface of each instance for administration.
- C. Attach the appropriate 1AM role to each existing instance and new instance. Use AWS Systems Manager Session Manager to establish a remote SSH session.
- D. Establish an AWS Site-to-Site VPN connection. Instruct administrators to use their local on-premises machines to connect directly to the instances by using SSH keys across the VPN tunnel.
Answer: C
Explanation:
https://docs.aws.amazon.com/systems-manager/latest/userguide/setup-launch-managed-instance.html
NEW QUESTION 29
A real-time data analytics application is using AWS Lambda to process data and store results in JSON format to an S3 bucket. To speed up the existing workflow, you have to use a service where you can run sophisticated Big Data analytics on your data without moving them into a separate analytics system.
Which of the following group of services can you use to meet this requirement?
- A. Amazon Glue, Glacier Select, Amazon Redshift
- B. S3 Select, Amazon Athena, Amazon Redshift Spectrum
- C. S3 Select, Amazon Neptune, DynamoDB DAX
- D. Amazon X-Ray, Amazon Neptune, DynamoDB
Answer: B
Explanation:
Amazon S3 allows you to run sophisticated Big Data analytics on your data without moving the data into a separate analytics system. In AWS, there is a suite of tools that make analyzing and processing large amounts of data in the cloud faster, including ways to optimize and integrate existing workflows with Amazon S3:
1. S3 Select
Amazon S3 Select is designed to help analyze and process data within an object in Amazon S3 buckets, faster and cheaper. It works by providing the ability to retrieve a subset of data from an object in Amazon S3 using simple SQL expressions. Your applications no longer have to use compute resources to scan and filter the data from an object, potentially increasing query performance by up to 400%, and reducing query costs as much as 80%. You simply change your application to use SELECT instead of GET to take advantage of S3 Select.
2. Amazon Athena
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL expressions. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries you run. Athena is easy to use. Simply point to your data in Amazon S3, define the schema, and start querying using standard SQL expressions. Most results are delivered within seconds.
With Athena, there's no need for complex ETL jobs to prepare your data for analysis. This makes it easy for anyone with SQL skills to quickly analyze large-scale datasets.
3. Amazon Redshift Spectrum
Amazon Redshift also includes Redshift Spectrum, allowing you to directly run SQL queries against exabytes of unstructured data in Amazon S3. No loading or transformation is required, and you can use open data formats, including Avro, CSV, Grok, ORC, Parquet, RCFile, RegexSerDe, SequenceFile, TextFile, and TSV. Redshift Spectrum automatically scales query compute capacity based on the data being retrieved, so queries against Amazon S3 run fast, regardless of data set size.
Reference:
https://aws.amazon.com/s3/features/#Query_in_Place
Amazon Redshift Overview:
https://youtu.be/jlLERNzhHOg
Check out these AWS Cheat Sheets:
https://tutorialsdojo.com/amazon-s3/
https://tutorialsdojo.com/amazon-athena/
https://tutorialsdojo.com/amazon-redshift/
NEW QUESTION 30
A company needs to collect gigabytes of data per second from websites and social media feeds to gain insights into its product offerings and continuously improve the user experience. To meet this design requirement, an application is deployed on an Auto Scaling group of Spot EC2 instances which processes the data and stores the results to DynamoDB and Redshift. The solution should have a built-in enhanced fan-out feature.
Which fully-managed AWS service can you use to collect and process large streams of data records in real-time with the LEAST amount of administrative overhead?
- A. Amazon S3 Access Points
- B. Amazon Managed Streaming for Apache Kafka (Amazon MSK)
- C. AWS Data Exchange
- D. Amazon Kinesis Data Streams
Answer: D
Explanation:
Amazon Kinesis Data Streams is used to collect and process large streams of data records in real-time.
You can use Kinesis Data Streams for rapid and continuous data intake and aggregation. The type of data used includes IT infrastructure log data, application logs, social media, market data feeds, and web clickstream data. Because the response time for the data intake and processing is in real-time, the processing is typically lightweight.
The following diagram illustrates the high-level architecture of Kinesis Data Streams. The producers continually push data to Kinesis Data Streams, and the consumers process the data in real-time.
Consumers (such as a custom application running on Amazon EC2 or an Amazon Kinesis Data Firehose delivery stream) can store their results using an AWS service such as Amazon DynamoDB, Amazon Redshift, or Amazon S3.
Hence, the correct answer is: Amazon Kinesis Data Streams.
Amazon S3 Access Points is incorrect because this is mainly used to manage access of your S3 objects.
Amazon S3 access points are named network endpoints that are attached to buckets that you can use to perform S3 object operations, such as uploading and retrieving objects.
AWS Data Exchange is incorrect because this is just a data marketplace service.
Amazon Managed Streaming for Apache Kafka (Amazon MSK) is incorrect. Although you can process streaming data in real-time with Amazon MSK, this service still entails a lot of administrative overhead, unlike Amazon Kinesis. Moreover, it doesn't have a built-in enhanced fan-out feature as required in the scenario.
References:
https://docs.aws.amazon.com/streams/latest/dev/introduction.html https://aws.amazon.com/kinesis/ Check out this Amazon Kinesis Cheat Sheet:
https://tutorialsdojo.com/amazon-kinesis/
Tutorials Dojo's AWS Certified Solutions Architect Associate Exam Study Guide:
https://tutorialsdojo.com/aws-certified-solutions-architect-associate/
NEW QUESTION 31
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