Get Amazon DBS-C01 Exam Dumps

Answer : A

Correct Answer: A

Explanation from Amazon documents:

Performance Insights is a feature of Amazon Aurora MySQL that helps you quickly assess the load on your database and determine when and where to take action. Performance Insights displays a dashboard that shows the database load in terms of average active sessions (AAS), which is the average number of sessions that are actively running SQL statements at any given time. Performance Insights also shows the top SQL statements, waits, hosts, and users that are contributing to the database load.

Amazon DevOps Guru is a fully managed service that helps you improve the operational performance and availability of your applications by detecting operational issues and recommending specific actions for remediation. Amazon DevOps Guru applies machine learning to automatically analyze data such as application metrics, logs, events, and traces for behaviors that deviate from normal operating patterns. Amazon DevOps Guru supports Amazon RDS as a resource type and can monitor the performance and availability of your RDS databases.

By turning on Performance Insights for the Aurora MySQL database and configuring and turning on Amazon DevOps Guru for RDS, the database specialist can automatically detect and diagnose database performance issues and resolve bottlenecks faster. This solution will allow the database specialist to monitor the database load and identify the root causes of performance problems using Performance Insights, and receive actionable insights and recommendations from Amazon DevOps Guru to improve the operational performance and availability of the database.

Therefore, option A is the correct solution to meet the requirements. Option B is not sufficient because creating a CPU usage alarm will only notify the database specialist when the CPU utilization is high, but it will not help diagnose or resolve the database performance issues. Option C is not efficient because using the Amazon RDS query editor to get the process ID of the query that is causing the database to lock and running a command to end the process will require manual intervention and may cause data loss or inconsistency. Option D is not efficient because using the SELECT INTO OUTFILE S3 statement to query data from the database and saving the data directly to an Amazon S3 bucket will incur additional time and cost, and using Amazon Athena to analyze the files for long-running queries will not help prevent or resolve locking contention on the database.

Answer : B, D, E

Correct Answer: B, D, E

Explanation from Amazon documents:

To allow an existing Redshift cluster to access data from other Redshift clusters, Amazon RDS for PostgreSQL databases, and AWS Glue Data Catalog tables, the database specialist should use the following features123:

Create external tables in the existing Redshift database to connect to the AWS Glue Data Catalog tables. This feature allows you to query data stored in Amazon S3 using the AWS Glue Data Catalog as the metadata store. You can create external tables in your Redshift database that reference the data catalog tables and use SQL to query the data in S3. This feature is operationally efficient because it does not require moving or copying the data from S3 to Redshift1.

Use federated queries to access data in Amazon RDS. This feature allows you to query and join data from one or more Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL databases with data already in your Amazon Redshift cluster. You can use SQL to query the RDS databases directly from your Redshift cluster without having to load or unload any data. This feature is operationally efficient because it reduces data movement and storage costs, and simplifies data access and analysis2.

Use data sharing to access data from the other Redshift clusters. This feature allows you to securely share live data across different Redshift clusters without the complexity and delays associated with data copies and data movement. You can share data within or across AWS accounts using a consumer-producer model. The producer cluster grants privileges on one or more schemas, called datashares, to the consumer clusters. The consumer clusters can then query the shared data in the producer cluster as if it were local tables. This feature is operationally efficient because it enables real-time and transactionally consistent data access, and eliminates data duplication and stale data issues3.

Therefore, option B, D, and E are the correct steps to meet the requirements with the most operational efficiency. Option A is not efficient because it involves taking and restoring snapshots, which can be time-consuming and costly. Option C is not efficient because it involves unloading and loading data between S3 and Redshift, which can also incur additional time and cost. Option F is not necessary because it involves transferring the AWS Glue Data Catalog tables into S3, which can be avoided by using external tables to connect to the data catalog tables directly.

Answer : B, E, F

Correct Answer: B, E, F

Explanation from Amazon documents:

To upload data faster to a Neptune DB instance from a .csv file stored in Amazon S3, the database specialist should use the Neptune Bulk Loader, which is a feature that allows you to load data from external files directly into a Neptune DB instance1. The Neptune Bulk Loader is faster and has less overhead than the API calls, such as SPARQL INSERT statements or Gremlin addV and addE steps2. The Neptune Bulk Loader supports both RDF and Gremlin data formats1.

To use the Neptune Bulk Loader, the database specialist needs to do the following13:

Ensure the vertices and edges are specified in different .csv files with proper header column formatting. This is required for the Gremlin data format, which uses two .csv files: one for vertices and one for edges. The first row of each file must contain the column names, which must match the property names of the graph elements. The files must also have a column named ~id for vertices and ~from and ~to for edges, which specify the unique identifiers of the graph elements1.

Ensure an IAM role for the Neptune DB instance is configured with the appropriate permissions to allow access to the file in the S3 bucket. This is required for the Neptune DB instance to read the data from the S3 bucket. The IAM role must have a trust policy that allows Neptune to assume the role, and a permissions policy that allows access to the S3 bucket and objects3.

Create an S3 VPC endpoint and issue an HTTP POST to the database's loader endpoint. This is required for the Neptune DB instance to communicate with the S3 bucket without going through the public internet. The S3 VPC endpoint must be in the same VPC as the Neptune DB instance. The HTTP POST request must specify the source parameter as the S3 URI of the .csv file, and optionally other parameters such as format, failOnError, parallelism, etc1.

Therefore, option B, E, and F are the correct steps to upload the data faster. Option A is not necessary because Amazon Cognito is not used for authenticating the Neptune DB instance to the S3 bucket. Option C is not suitable because AWS DMS is not designed for loading graph data into Neptune. Option D is not efficient because curling the S3 URI and running the addVertex or addEdge commands will be slower and more costly than using the Neptune Bulk Loader.

Answer : B

Answer : A, C, E

Correct Answer: A, C, E

Explanation from Amazon documents:

To enable CDC mode for RDS for PostgreSQL database, the database architect needs to configure the following parameters12:

Set wal_level = logical. This parameter determines how much information is written to the write-ahead log (WAL). For CDC mode, the wal_level must be set to logical, which enables logical decoding of the WAL and allows AWS DMS to read changes from the source database1.

Set max_replication_slots to 1 or more, depending on the number of DMS tasks. This parameter specifies the maximum number of replication slots that the source database can support. A replication slot is a data structure that records the state of a replication stream. AWS DMS uses replication slots to set up logical replication and track changes in the source database. The max_replication_slots parameter must be equal to or greater than the number of DMS tasks that use CDC mode for the source database1.

Set wal_sender_timeout to 20,000 milliseconds. This parameter specifies the amount of time that a WAL sender process waits for feedback from a WAL receiver process before terminating the connection. A WAL sender process is a background process that streams WAL data from the source database to AWS DMS. A WAL receiver process is a background process that receives WAL data from a WAL sender process and writes it to a local file. The wal_sender_timeout parameter must be set to a value greater than 10,000 milliseconds (10 seconds) to prevent connection timeouts during CDC mode2.

Therefore, option A, C, and E are the correct parameters to support CDC mode for RDS for PostgreSQL database. Option B is incorrect because wal_level = replica is not sufficient for logical decoding and CDC mode. Option D is incorrect because max_replication_slots must be a positive integer, not zero. Option F is incorrect because wal_sender_timeout = 5,000 milliseconds is too low and may cause connection timeouts during CDC mode.