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NEW QUESTION 40
Amazon Neptune is being used by a corporation as the graph database for one of its products. During an ETL procedure, the company's data science team produced enormous volumes of temporary data by unintentionally. The Neptune DB cluster extended its storage capacity automatically to handle the added data, but the data science team erased the superfluous data.
What should a database professional do to prevent incurring extra expenditures for cluster volume space that is not being used?
Answer: D
Explanation:
Explanation
The only way to shrink the storage space used by your DB cluster when you have a large amount of unused allocated space is to export all the data in your graph and then reload it into a new DB cluster. Creating and restoring a snapshot does not reduce the amount of storage allocated for your DB cluster, because a snapshot retains the original image of the cluster's underlying storage.
NEW QUESTION 41
A company is using a Single-AZ Amazon RDS for MySQL DB instance for development. The DB instance is experiencing slow performance when queries are executed. Amazon CloudWatch metrics indicate that the instance requires more I/O capacity.
Which actions can a database specialist perform to resolve this issue? (Choose two.)
Answer: C,E
Explanation:
Explanation
https://aws.amazon.com/blogs/database/best-storage-practices-for-running-production-workloads-on-hosted-data
"If you find the pattern of IOPS usage consistently going beyond more than 16,000, you should modify the DB instance and change the storage type from gp2 to io1.
NEW QUESTION 42
A company runs hundreds of Microsoft SQL Server databases on Windows servers in its on-premises data center. A database specialist needs to migrate these databases to Linux on AWS.
Which combination of steps should the database specialist take to meet this requirement? (Choose three.)
Answer: A,B,F
Explanation:
https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/replatform-sql-server.html
https://d1.awsstatic.com/events/reinvent/2019/REPEAT_1_Leverage_automation_to_re-platform_SQL_Server_to_Linux_WIN322-R1.pdf
NEW QUESTION 43
A company is running its customer feedback application on Amazon Aurora MySQL. The company runs a report every day to extract customer feedback, and a team reads the feedback to determine if the customer comments are positive or negative. It sometimes takes days before the company can contact unhappy customers and take corrective measures. The company wants to use machine learning to automate this workflow.
Which solution meets this requirement with the LEAST amount of effort?
Answer: D
Explanation:
Explanation
For details about using Aurora and Amazon Comprehend together, see Using Amazon Comprehend for sentiment detection. Aurora machine learning uses a highly optimized integration between the Aurora database and the AWS machine learning (ML) services SageMaker and Amazon Comprehend.
https://www.stackovercloud.com/2019/11/27/new-for-amazon-aurora-use-machine-learning-directly-from-your-d
NEW QUESTION 44
An ecommerce company uses Amazon DynamoDB as the backend for its payments system. A new regulation requires the company to log all data access requests for financial audits. For this purpose, the company plans to use AWS logging and save logs to Amazon S3 How can a database specialist activate logging on the database?
Answer: B
Explanation:
https://aws.amazon.com/about-aws/whats-new/2021/04/you-now-can-use-aws-cloudtrail-to-log-amazon-dynamodb-streams-da/
NEW QUESTION 45
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