Free AWS-CERTIFIED-SOLUTIONS-ARCHITECT-ASSOCIATE-SAA-C03 Amazon AWS-CERTIFIED-SOLUTIONS-ARCHITECT-ASSOCIATE-SAA-C03 Practice Test Question - Page 3

Question 7 (Topic 1)

A company runs an on-premises application that is powered by a MySQL database. The company is migrating the application to AWS to increase the application's elasticity and availability. The current architecture shows heavy read activity on the database during times of normal operation. Every 4 hours, the company's development team pulls a full export of the production database to populate a database in the staging environment. During this period, users experience unacceptable application latency. The development team is unable to use the staging environment until the procedure completes. A solutions architect must recommend replacement architecture that alleviates the application latency issue. The replacement architecture also must give the development team the ability to continue using the staging environment without delay. Which solution meets these requirements?

Select an option, then click Submit answer.

○ Use Amazon Aurora MySQL with Multi-AZ Aurora Replicas for production. Populate the staging database by implementing a backup and restore process that uses the mysqldump utility.
○ Use Amazon Aurora MySQL with Multi-AZ Aurora Replicas for production. Use database cloning to create the staging database on-demand.
○ Use Amazon RDS for MySQL with a Multi-AZ deployment and read replicas for production. Use the standby instance for the staging database.
○ Use Amazon RDS for MySQL with a Multi-AZ deployment and read replicas for production. Populate the staging database by implementing a backup and restore process that uses the mysqldump utility.

Reference / correct answer:

Use Amazon Aurora MySQL with Multi-AZ Aurora Replicas for production. Use database cloning to create the staging database on-demand.

Most accepted answer: B. Use Amazon Aurora MySQL with Multi-AZ Aurora Replicas for production. Use database cloning to create the staging database on-demand.

Community votes: B=12, C=1

Selected Answer: B The recommended solution is Option B: Use Amazon Aurora MySQL with Multi-AZ Aurora Replicas for production. Use database cloning to create the staging database on-demand. To alleviate the application latency issue, the recommended solution is to use Amazon Aurora MySQL with Multi-AZ Aurora Replicas for production, and use database cloning to create the staging database on-demand. This allows the development team to continue using the staging environment without delay, while also providing elasticity and availability for the production application. Therefore, Options A, C, and D are not recommended upvoted 25 times Buruguduystunstugudunstuy 3 years, 1 month ago Option A: Use Amazon Aurora MySQL with Multi-AZ Aurora Replicas for production. Populating the staging database by implementing a backup and restore process that uses the mysqldump utility is not the recommended solution because it involves taking a full export of the production database, which can cause unacceptable application latency. Option C: Use Amazon RDS for MySQL with a Multi-AZ deployment and read replicas for production. Using the standby instance for the staging database is not the recommended solution because it does not give the development team the ability to continue using the staging environment without delay. The standby instance is used for failover in case of a production instance failure, and it is not intended for use as a staging environment. upvoted 20 times Buruguduystunstugudunstuy 3 years, 1 month ago Option D: Use Amazon RDS for MySQL with a Multi-AZ deployment and read replicas for production. Populating the staging database by implementing a backup and restore process that uses the mysqqldump utility is not the recommended solution because it involves taking a full export of the production database, which can cause unacceptable application latency. upvoted 9 times ... ... MutiverseAgent 2 years, 6 months ago Agree, solution it seems to be the B) 1) Because the company wants "elasticity and availability" as the question mentioned, so I think this leaves us in the two questions related to Aurora discarding the RDS Mysql solution. 2) Accoding AWS documentation (https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Managing.Clone.html) "Aurora cloning is especially useful for quickly setting up test environments using your production data, without risking data corruption" upvoted 9 times ...

Option A: Use Amazon Aurora MySQL with Multi-AZ Aurora Replicas for production. Populating the staging database by implementing a backup and restore process that uses the mysqldump utility is not the recommended solution because it involves taking a full export of the production database, which can cause unacceptable application latency. Option C: Use Amazon RDS for MySQL with a Multi-AZ deployment and read replicas for production. Using the standby instance for the staging database is not the recommended solution because it does not give the development team the ability to continue using the staging environment without delay. The standby instance is used for failover in case of a production instance failure, and it is not intended for use as a staging environment. upvoted 20 times Buruguduystunstugudunstuy 3 years, 1 month ago Option D: Use Amazon RDS for MySQL with a Multi-AZ deployment and read replicas for production. Populating the staging database by implementing a backup and restore process that uses the mysqqldump utility is not the recommended solution because it involves taking a full export of the production database, which can cause unacceptable application latency. upvoted 9 times ...

Option D: Use Amazon RDS for MySQL with a Multi-AZ deployment and read replicas for production. Populating the staging database by implementing a backup and restore process that uses the mysqqldump utility is not the recommended solution because it involves taking a full export of the production database, which can cause unacceptable application latency. upvoted 9 times

Agree, solution it seems to be the B) 1) Because the company wants "elasticity and availability" as the question mentioned, so I think this leaves us in the two questions related to Aurora discarding the RDS Mysql solution. 2) Accoding AWS documentation (https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Managing.Clone.html) "Aurora cloning is especially useful for quickly setting up test environments using your production data, without risking data corruption" upvoted 9 times

Selected Answer: B Aura MySQL is very fast in comparison to RDS for creating a clone of DB, you can create a even clone of a clone while you still work on your own clone, this will allow the dev team continue working during cloning step. https://aws.amazon.com/blogs/aws/amazon-aurora-fast-database-cloning/ upvoted 12 times

Question 8 (Topic 1)

A company is designing an application where users upload small files into Amazon S3. After a user uploads a file, the file requires one-time simple processing to transform the data and save the data in JSON format for later analysis. Each file must be processed as quickly as possible after it is uploaded. Demand will vary. On some days, users will upload a high number of files. On other days, users will upload a few files or no files. Which solution meets these requirements with the LEAST operational overhead?

Select an option, then click Submit answer.

○ Configure Amazon EMR to read text files from Amazon S3. Run processing scripts to transform the data. Store the resulting JSON file in an Amazon Aurora DB cluster.
○ Configure Amazon S3 to send an event notification to an Amazon Simple Queue Service (Amazon SQS) queue. Use Amazon EC2 instances to read from the queue and process the data. Store the resulting JSON file in Amazon DynamoDB.
○ Configure Amazon S3 to send an event notification to an Amazon Simple Queue Service (Amazon SQS) queue. Use an AWS Lambda function to read from the queue and process the data. Store the resulting JSON file in Amazon DynamoDB.
○ Configure Amazon EventBridge (Amazon CloudWatch Events) to send an event to Amazon Kinesis Data Streams when a new file is uploaded. Use an AWS Lambda function to consume the event from the stream and process the data. Store the resulting JSON file in an Amazon Aurora DB cluster.

Reference / correct answer:

Configure Amazon S3 to send an event notification to an Amazon Simple Queue Service (Amazon SQS) queue. Use an AWS Lambda function to read from the queue and process the data. Store the resulting JSON file in Amazon DynamoDB.

Most accepted answer: C. Configure Amazon S3 to send an event notification to an Amazon Simple Queue Service (Amazon SQS) queue. Use an AWS Lambda function to read from the queue and process the data. Store the resulting JSON file in Amazon DynamoDB.

Community votes: C=14

Option C Dynamo DB is a NoSQL-JSON supported upvoted 17 times rjam 3 years, 9 months ago also Use an AWS Lambda - serverless - less operational overhead upvoted 12 times ...

also Use an AWS Lambda - serverless - less operational overhead upvoted 12 times

Selected Answer: C A. Configuring EMR and an Aurora DB cluster for this use case would introduce unnecessary complexity and operational overhead. EMR is typically used for processing large datasets and running big data frameworks like Apache Spark or Hadoop. B. While using S3 event notifications and SQS for decoupling is a good approach, using EC2 to process the data would introduce operational overhead in terms of managing and scaling the EC2. D. Using EventBridge and Kinesis Data Streams for this use case would introduce additional complexity and operational overhead compared to the other options. EventBridge and Kinesis are typically used for real-time streaming and processing of large volumes of data. In summary, option C is the recommended solution as it provides a serverless and scalable approach for processing uploaded files using S3 event notifications, SQS, and Lambda. It offers low operational overhead, automatic scaling, and efficient handling of varying demand. Storing the resulting JSON file in DynamoDB aligns with the requirement of saving the data for later analysis. upvoted 10 times

Selected Answer: C C is the simplest and most widely used architecture, for example, processing photos to generate thumbnails. upvoted 1 times

Selected Answer: C C is most suitable here as we need DynamoDB for low latency, AWS Lambda for scaling as per the demand. upvoted 1 times

Question 9 (Topic 1)

A company has an AWS Glue extract, transform, and load (ETL) job that runs every day at the same time. The job processes XML data that is in an Amazon S3 bucket. New data is added to the S3 bucket every day. A solutions architect notices that AWS Glue is processing all the data during each run. What should the solutions architect do to prevent AWS Glue from reprocessing old data?

Select an option, then click Submit answer.

○ Edit the job to use job bookmarks.
○ Edit the job to delete data after the data is processed.
○ Edit the job by setting the NumberOfWorkers field to 1.
○ Use a FindMatches machine learning (ML) transform.

Reference / correct answer:

Edit the job to use job bookmarks.

Most accepted answer: A. Edit the job to use job bookmarks.

Community votes: A=12

Selected Answer: A This is the purpose of bookmarks: "AWS Glue tracks data that has already been processed during a previous run of an ETL job by persisting state information from the job run. This persisted state information is called a job bookmark. Job bookmarks help AWS Glue maintain state information and prevent the reprocessing of old data." https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html upvoted 51 times

Selected Answer: A A. Job bookmarks in Glue allow you to track the last-processed data in a job. By enabling job bookmarks, Glue keeps track of the processed data and automatically resumes processing from where it left off in subsequent job runs. B. Results in the permanent removal of the data from the S3, making it unavailable for future job runs. This is not desirable if the data needs to be retained or used for subsequent analysis. C.It would only affect the parallelism of the job but would not address the issue of reprocessing old data. It does not provide a mechanism to track the processed data or skip already processed data. D. It is not directly related to preventing Glue from reprocessing old data. The FindMatches transform is used for identifying and matching duplicate or matching records in a dataset. While it can be used in data processing pipelines, it does not address the specific requirement of avoiding reprocessing old data in this scenario. upvoted 12 times

Selected Answer: A B: Glue can delete DataSet but this option is too vague to consider or too open to mean anything C: Won't help with repeated ETL. This property affects parallelism D: Too vague upvoted 1 times

https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html upvoted 2 times

Selected Answer: A The best solution is to edit the AWS Glue job to use job bookmarks. Job bookmarks allow AWS Glue ETL jobs to track which data has already been processed during previous runs. This prevents reprocessing of old data. Deleting the data after processing would cause the data to be lost and unavailable for future processing. Reducing the number of workers may improve performance but does not prevent reprocessing of old data. Using a FindMatches ML transform is used for record matching, not preventing reprocessing. So the solutions architect should enable job bookmarks in the AWS Glue job configuration. This will allow the ETL job to keep track of processed data and only transform the new data added since the last run. upvoted 1 times

Free AWS-CERTIFIED-SOLUTIONS-ARCHITECT-ASSOCIATE-SAA-C03 – Amazon AWS-CERTIFIED-SOLUTIONS-ARCHITECT-ASSOCIATE-SAA-C03 Practice Test Question