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

Reference / correct answer:

Most accepted answer: C. Configure an EC2 Auto Scaling scheduled scaling policy based on the monthly schedule.

Community votes: C=5

Selected Answer: C 'On the first day of every month at midnight' = Scheduled scaling policy upvoted 6 times

Selected Answer: C By configuring a scheduled scaling policy, the EC2 Auto Scaling group can proactively launch additional EC2 instances before the CPU utilization peaks to 100%. This will ensure that the application can handle the workload during the month-end financial calculation batch, and avoid any disruption or downtime. Configuring a simple scaling policy based on CPU utilization or adding Amazon CloudFront distribution or Amazon ElastiCache will not directly address the issue of handling the monthly peak workload. upvoted 4 times

Selected Answer: C If the scaling were based on CPU or memory, it requires a certain amount of time above that threshhold, 5 minutes for example. That would mean the CPU would be at 100% for five minutes. upvoted 4 times

Selected Answer: C C: Configure an EC2 Auto Scaling scheduled scaling policy based on the monthly schedule is the best option because it allows for the proactive scaling of the EC2 instances before the monthly batch run begins. This will ensure that the application is able to handle the increased workload without experiencing downtime. The scheduled scaling policy can be configured to increase the number of instances in the Auto Scaling group a few hours before the batch run and then decrease the number of instances after the batch run is complete. This will ensure that the resources are available when needed and not wasted when not needed. The most appropriate solution to handle the increased workload during the monthly batch run and avoid downtime would be to configure an EC2 Auto Scaling scheduled scaling policy based on the monthly schedule. upvoted 3 times LuckyAro 2 years, 5 months ago Scheduled scaling policies allow you to schedule EC2 instance scaling events in advance based on a specified time and date. You can use this feature to plan for anticipated traffic spikes or seasonal changes in demand. By setting up scheduled scaling policies, you can ensure that you have the right number of instances running at the right time, thereby optimizing performance and reducing costs. To set up a scheduled scaling policy in EC2 Auto Scaling, you need to specify the following: Start time and date: The date and time when the scaling event should begin. Desired capacity: The number of instances that you want to have running after the scaling event. Recurrence: The frequency with which the scaling event should occur. This can be a one-time event or a recurring event, such as daily or weekly. upvoted 2 times ...

Scheduled scaling policies allow you to schedule EC2 instance scaling events in advance based on a specified time and date. You can use this feature to plan for anticipated traffic spikes or seasonal changes in demand. By setting up scheduled scaling policies, you can ensure that you have the right number of instances running at the right time, thereby optimizing performance and reducing costs. To set up a scheduled scaling policy in EC2 Auto Scaling, you need to specify the following: Start time and date: The date and time when the scaling event should begin. Desired capacity: The number of instances that you want to have running after the scaling event. Recurrence: The frequency with which the scaling event should occur. This can be a one-time event or a recurring event, such as daily or weekly. upvoted 2 times

Reference / correct answer:

Most accepted answer: A. Create a new AWS Key Management Service (AWS KMS) encryption key. Use AWS Secrets Manager to create a new secret that uses the KMS key with the appropriate credentials. Associate the secret with the Aurora DB cluster. Configure a custom rotation period of 14 days.

Community votes: A=8

Selected Answer: A AWS Secrets Manager allows you to easily rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. With this service, you can automate the rotation of secrets, such as database credentials, on a schedule that you choose. The solution allows you to create a new secret with the appropriate credentials and associate it with the Aurora DB cluster. You can then configure a custom rotation period of 14 days to ensure that the credentials are automatically rotated every two weeks, as required by the IT security guidelines. This approach requires the least amount of operational effort as it allows you to manage secrets centrally without modifying your application code or infrastructure. upvoted 7 times

Answer is A To implement password rotation lifecycles, use AWS Secrets Manager. You can rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle using Secrets Manager. https://aws.amazon.com/blogs/security/how-to-use-aws-secrets-manager-rotate-credentials-amazon-rds-database-types-oracle/ upvoted 5 times

Selected Answer: A Most questions related to rotating credentials - AWS Secrets Manager AWS System Parameter Store CANNOT rotate credentials. upvoted 1 times

Selected Answer: A Well... I would simply rule out all the options with "Implement an AWS Lambda function" given that we need a solution with least operational effort. Why manually compose codes to rotate when you can achieve the same goal automatically? upvoted 1 times

Selected Answer: A Create a new AWS Key Management Service (AWS KMS) encryption key. Use AWS Secrets Manager to create a new secret that uses the KMS key with the appropriate credentials. Associate the secret with the Aurora DB cluster. Configure a custom rotation period of 14 days upvoted 3 times

Reference / correct answer:

Most accepted answer: D. Use a Lake Formation blueprint to ingest the data from the database to the S3 data lake. Use Lake Formation to enforce column-level access control for the QuickSight users. Use Amazon Athena as the data source in QuickSight.

Community votes: D=6

Selected Answer: D This solution leverages AWS Lake Formation to ingest data from the Aurora MySQL database into the S3 data lake, while enforcing column-level access control for QuickSight users. Lake Formation can be used to create and manage the data lake's metadata and enforce security and governance policies, including column-level access control. This solution then uses Amazon Athena as the data source in QuickSight to query the data in the S3 data lake. This solution minimizes operational overhead by leveraging AWS services to manage and secure the data, and by using a standard query service (Amazon Athena) to provide a SQL interface to the data. upvoted 15 times

Answer - D https://aws.amazon.com/blogs/big-data/enforce-column-level-authorization-with-amazon-quicksight-and-aws-lake-formation/ upvoted 10 times

Selected Answer: D Option A require operationnal overhead, B & D does not mention Athena to connect to QuickSight which is required. So option D upvoted 1 times

Selected Answer: D https://docs.aws.amazon.com/lake-formation/latest/dg/workflows-about.html upvoted 2 times

Selected Answer: D Use a Lake Formation blueprint to ingest data from the Aurora database into the S3 data lake Leverage Lake Formation to enforce column-level access control for the marketing team Use Amazon Athena as the data source in QuickSight The key points: Need to join S3 data lake data with Aurora MySQL data Require column-level access controls for marketing team in QuickSight Minimize operational overhead upvoted 4 times