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Download Amazon AWS Certified Solutions Architect - Associate (SAA-C03) Exam Exam Dumps

NEW QUESTION 44
A company is using Amazon S3 to store frequently accessed data. The S3 bucket is shared with external users that will upload files regularly. A Solutions Architect needs to implement a solution that will grant the bucket owner full access to all uploaded objects in the S3 bucket.
What action should be done to achieve this task?

A. Enable the Requester Pays feature in the Amazon S3 bucket.B. Create a CORS configuration in the S3 bucket.C. Create a bucket policy that will require the users to set the object's ACL to bucket-owner-full-control.D. Enable server access logging and set up an IAM policy that will require the users to set the object's ACL to bucket-owner-full-control.

Answer: C

Explanation:
Amazon S3 stores data as objects within buckets. An object is a file and any optional metadata that describes the file. To store a file in Amazon S3, you upload it to a bucket. When you upload a file as an object, you can set permissions on the object and any metadata. Buckets are containers for objects. You can have one or more buckets. You can control access for each bucket, deciding who can create, delete, and list objects in it. You can also choose the geographical Region where Amazon S3 will store the bucket and its contents and view access logs for the bucket and its objects.

By default, an S3 object is owned by the AWS account that uploaded it even though the bucket is owned by another account. To get full access to the object, the object owner must explicitly grant the bucket owner access. You can create a bucket policy to require external users to grant bucket-owner-full-control when uploading objects so the bucket owner can have full access to the objects.
Hence, the correct answer is: Create a bucket policy that will require the users to set the object's ACL to bucket-owner-full-control.
The option that says: Enable the Requester Pays feature in the Amazon S3 bucket is incorrect because this option won't grant the bucket owner full access to the uploaded objects in the S3 bucket. With Requester Pays buckets, the requester, instead of the bucket owner, pays the cost of the request and the data download from the bucket.
The option that says: Create a CORS configuration in the S3 bucket is incorrect because this option only allows cross-origin access to your Amazon S3 resources. If you need to grant the bucket owner full control in the uploaded objects, you must create a bucket policy and require external users to grant bucket-owner-full-control when uploading objects.
The option that says: Enable server access logging and set up an IAM policy that will require the users to set the bucket's ACL to bucket-owner-full-control is incorrect because this option only provides detailed records for the requests that are made to a bucket. In addition, the bucket-owner-full-control canned ACL must be associated with the bucket policy and not to an IAM policy. This will require the users to set the object's ACL (not the bucket's) to bucket-owner-full-control.
References:
https://aws.amazon.com/premiumsupport/knowledge-center/s3-bucket-owner-access/
https://aws.amazon.com//premiumsupport/knowledge-center/s3-require-object-ownership/ Check out this Amazon S3 Cheat Sheet:
https://tutorialsdojo.com/amazon-s3/

 

NEW QUESTION 45
A company has a website hosted on AWS The website is behind an Application Load Balancer (ALB) that is configured to handle HTTP and HTTPS separately. The company wants to forward all requests to the website so that the requests will use HTTPS.
What should a solutions architect do to meet this requirement?

A. Create a listener rule on the ALB to redirect HTTP traffic to HTTPS.B. Update the ALB's network ACL to accept only HTTPS trafficC. Replace the ALB with a Network Load Balancer configured to use Server Name Indication (SNI).D. Create a rule that replaces the HTTP in the URL with HTTPS.

Answer: A

Explanation:
Explanation
https://aws.amazon.com/premiumsupport/knowledge-center/elb-redirect-http-to-https-using-alb/ How can I redirect HTTP requests to HTTPS using an Application Load Balancer? Last updated: 2020-10-30 I want to redirect HTTP requests to HTTPS using Application Load Balancer listener rules. How can I do this?
Resolution Reference:
https://aws.amazon.com/premiumsupport/knowledge-center/elb-redirect-http-to-https-using-alb/

 

NEW QUESTION 46
A company needs to implement a solution that will process real-time streaming data of its users across the globe. This will enable them to track and analyze globally-distributed user activity on their website and mobile applications, including clickstream analysis. The solution should process the data in close geographical proximity to their users and respond to user requests at low latencies.
Which of the following is the most suitable solution for this scenario?

A. Integrate CloudFront with Lambda@Edge in order to process the data in close geographical proximity to users and respond to user requests at low latencies. Process real-time streaming data using Amazon Athena and durably store the results to an Amazon S3 bucket.B. Use a CloudFront web distribution and Route 53 with a latency-based routing policy, in order to process the data in close geographical proximity to users and respond to user requests at low latencies.
Process real-time streaming data using Kinesis and durably store the results to an Amazon S3 bucket.C. Use a CloudFront web distribution and Route 53 with a Geoproximity routing policy in order to process the data in close geographical proximity to users and respond to user requests at low latencies. Process real-time streaming data using Kinesis and durably store the results to an Amazon S3 bucket.D. Integrate CloudFront with Lambda@Edge in order to process the data in close geographical proximity to users and respond to user requests at low latencies. Process real-time streaming data using Kinesis and durably store the results to an Amazon S3 bucket.

Answer: D

Explanation:
Lambda@Edge is a feature of Amazon CloudFront that lets you run code closer to users of your application, which improves performance and reduces latency. With Lambda@Edge, you don't have to provision or manage infrastructure in multiple locations around the world. You pay only for the compute time you consume - there is no charge when your code is not running.
With Lambda@Edge, you can enrich your web applications by making them globally distributed and improving their performance - all with zero server administration. Lambda@Edge runs your code in response to events generated by the Amazon CloudFront content delivery network (CDN). Just upload your code to AWS Lambda, which takes care of everything required to run and scale your code with high availability at an AWS location closest to your end user.


By using Lambda@Edge and Kinesis together, you can process real-time streaming data so that you can track and analyze globally-distributed user activity on your website and mobile applications, including clickstream analysis. Hence, the correct answer in this scenario is the option that says: Integrate CloudFront with Lambda@Edge in order to process the data in close geographical proximity to users and respond to user requests at low latencies. Process real-time streaming data using Kinesis and durably store the results to an Amazon S3 bucket.
The options that say: Use a CloudFront web distribution and Route 53 with a latency-based routing policy, in order to process the data in close geographical proximity to users and respond to user requests at low latencies. Process real-time streaming data using Kinesis and durably store the results to an Amazon S3 bucket and Use a CloudFront web distribution and Route 53 with a Geoproximity routing policy in order to process the data in close geographical proximity to users and respond to user requests at low latencies. Process real-time streaming data using Kinesis and durably store the results to an Amazon S3 bucket are both incorrect because you can only route traffic using Route 53 since it does not have any computing capability. This solution would not be able to process and return the data in close geographical proximity to your users since it is not using Lambda@Edge.
The option that says: Integrate CloudFront with Lambda@Edge in order to process the data in close geographical proximity to users and respond to user requests at low latencies. Process real-time streaming data using Amazon Athena and durably store the results to an Amazon S3 bucket is incorrect because although using Lambda@Edge is correct, Amazon Athena is just an interactive query service that enables you to easily analyze data in Amazon S3 using standard SQL. Kinesis should be used to process the streaming data in real-time.
References:
https://aws.amazon.com/lambda/edge/
https://aws.amazon.com/blogs/networking-and-content-delivery/global-data-ingestion-with-amazon-cloudfront-and-lambdaedge/

 

NEW QUESTION 47
A company needs to store data in Amazon S3 and must prevent the data from being changed. The company wants new objects that are uploaded to Amazon S3 to remain unchangeable for a nonspecific amount of time until the company decides to modify the objects. Only specific users in the company's AWS account can have the ability to delete the objects. What should a solutions architect do to meet these requirements?

A. Create an S3 bucket with S3 Object Lock enabled Enable versioning Add a legal hold to the objects Add the s3 PutObjectLegalHold permission to the 1AM policies of users who need to delete the objectsB. Create an S3 Glacier vault Apply a write-once, read-many (WORM) vault lock policy to the objectsC. Create an S3 bucket with S3 Object Lock enabled Enable versioning Set a retention period of 100 years Use governance mode as the S3 bucket's default retention mode for new objectsD. Create an S3 bucket Use AWS CloudTrail to (rack any S3 API events that modify the objects Upon notification, restore the modified objects from any backup versions that the company has

Answer: A

 

NEW QUESTION 48
A manufacturing company launched a new type of IoT sensor. The sensor will be used to collect large streams of data records. You need to create a solution that can ingest and analyze the data in real- time with millisecond response times.
Which of the following is the best option that you should implement in this scenario?

A. Ingest the data using Amazon Simple Queue Service and create an AWS Lambda function to store the data in Amazon Redshift.B. Ingest the data using Amazon Kinesis Data Streams and create an AWS Lambda function to store the data in Amazon DynamoDB.C. Ingest the data using Amazon Kinesis Data Firehose and create an AWS Lambda function to store the data in Amazon DynamoDB.D. Ingest the data using Amazon Kinesis Data Streams and create an AWS Lambda function to store the data in Amazon Redshift.

Answer: B

Explanation:
Amazon Kinesis Data Streams enables you to build custom applications that process or analyze streaming data for specialized needs. You can continuously add various types of data such as clickstreams, application logs, and social media to an Amazon Kinesis data stream from hundreds of thousands of sources. Within seconds, the data will be available for your Amazon Kinesis Applications to read and process from the stream.

Based on the given scenario, the key points are "ingest and analyze the data in real-time" and
"millisecond response times". For the first key point and based on the given options, you can use Amazon Kinesis Data Streams because it can collect and process large streams of data records in real- time. For the second key point, you should use Amazon DynamoDB since it supports single-digit millisecond response times at any scale.
Hence, the correct answer is: Ingest the data using Amazon Kinesis Data Streams and create an AWS Lambda function to store the data in Amazon DynamoDB.
The option that says: Ingest the data using Amazon Kinesis Data Streams and create an AWS Lambda function to store the data in Amazon Redshift is incorrect because Amazon Redshift only delivers sub- second response times. Take note that as per the scenario, the solution must have millisecond response times to meet the requirements. Amazon DynamoDB Accelerator (DAX), which is a fully managed, highly available, in-memory cache for Amazon DynamoDB, can deliver microsecond response times.
The option that says: Ingest the data using Amazon Kinesis Data Firehose and create an AWS Lambda function to store the data in Amazon DynamoDB is incorrect. Amazon Kinesis Data Firehose only supports Amazon S3, Amazon Redshift, Amazon Elasticsearch, and an HTTP endpoint as the destination.
The option that says: Ingest the data using Amazon Simple Queue Service and create an AWS Lambda function to store the data in Amazon Redshift is incorrect because Amazon SQS can't analyze data in real-time. You have to use an Amazon Kinesis Data Stream to process the data in near-real-time.
References:
https://aws.amazon.com/kinesis/data-streams/faqs/
https://aws.amazon.com/dynamodb/
Check out this Amazon Kinesis Cheat Sheet:
https://tutorialsdojo.com/amazon-kinesis/

 

NEW QUESTION 49
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