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A company is using Amazon Aurora PostgreSQL for the backend of its application. The system users are complaining that the responses are slow. A database specialist has determined that the queries to Aurora take longer during peak times. With the Amazon RDS Performance Insights dashboard, the load in the chart for average active sessions is often above the line that denotes maximum CPU usage and the wait state shows that most wait events are IO:XactSync.

What should the company do to resolve these performance issues?

A. Add an Aurora Replica to scale the read traffic.

B. Scale up the DB instance class.

C. Modify applications to commit transactions in batches.

D. Modify applications to avoid conflicts by taking locks.

An online advertising website uses an Amazon DynamoDB table with on-demand capacity mode as its data store. The website also has a DynamoDB Accelerator (DAX) cluster in the same VPC as its web application server. The application needs to perform infrequent writes and many strongly consistent reads from the data store by querying the DAX cluster.

During a performance audit, a systems administrator notices that the application can look up items by using the DAX cluster. However, the QueryCacheHits metric for the DAX cluster consistently shows 0 while the QueryCacheMisses metric continuously keeps growing in Amazon CloudWatch. What is the MOST likely reason for this occurrence?

A. A VPC endpoint was not added to access DynamoDB.

B. Strongly consistent reads are always passed through DAX to DynamoDB.

C. DynamoDB is scaling due to a burst in traffic, resulting in degraded performance.

D. A VPC endpoint was not added to access CloudWatch.

A gaming company is evaluating Amazon ElastiCache as a solution to manage player leaderboards. Millions of players around the world will complete in annual tournaments. The company wants to implement an architecture that is highly available. The company also wants to ensure that maintenance activities have minimal impact on the availability of the gaming platform.

Which combination of steps should the company take to meet these requirements? (Choose two.)

A. Deploy an ElastiCache for Redis cluster with read replicas and Multi-AZ enabled.

B. Deploy an ElastiCache for Memcached global datastore.

C. Deploy a single-node ElastiCache for Redis cluster with automatic backups enabled. In the event of a failure, create a new cluster and restore data from the most recent backup.

D. Use the default maintenance window to apply any required system changes and mandatory updates as soon as they are available.

E. Choose a preferred maintenance window at the time of lowest usage to apply any required changes and mandatory updates.

A company has an application that uses an Amazon DynamoDB table as its data store. During normal business days, the throughput requirements from the application are uniform and consist of 5 standard write calls per second to the DynamoDB table. Each write call has 2 KB of data.

For 1 hour each day, the company runs an additional automated job on the DynamoDB table that makes 20 write requests per second. No other application writes to the DynamoDB table. The DynamoDB table does not have to meet any additional capacity requirements.

How should a database specialist configure the DynamoDB table's capacity to meet these requirements MOST cost-effectively?

A. Use DynamoDB provisioned capacity with 5 WCUs and auto scaling.

B. Use DynamoDB provisioned capacity with 5 WCUs and a write-through cache that DynamoDB Accelerator (DAX) provides.

C. Use DynamoDB provisioned capacity with 10 WCUs and auto scaling.

D. Use DynamoDB provisioned capacity with 10 WCUs and no auto scaling.

A company is concerned about the cost of a large-scale, transactional application using Amazon DynamoDB that only needs to store data for 2 days before it is deleted. In looking at the tables, a Database Specialist notices that much of the data is months old, and goes back to when the application was first deployed.

What can the Database Specialist do to reduce the overall cost?

A. Create a new attribute in each table to track the expiration time and create an AWS Glue transformation to delete entries more than 2 days old.

B. Create a new attribute in each table to track the expiration time and enable DynamoDB Streams on each table.

C. Create a new attribute in each table to track the expiration time and enable time to live (TTL) on each table.

D. Create an Amazon CloudWatch Events event to export the data to Amazon S3 daily using AWS Data Pipeline and then truncate the Amazon DynamoDB table.