Amazon DBS-C01 Practice Test - Questions Answers, Page 28

General Purpose SSD storage is a cost-effective storage option that is ideal for a broad range of workloads running on medium-sized DB instances1.General Purpose storage is best suited for development and testing environments1.Since the database is primarily used for nightly batch processing, it does not require high I/O performance or low latency that Provisioned IOPS storage offers12.Magnetic storage and Throughput Optimized HDD are not recommended for new storage needs, and they have lower storage limits than General Purpose SSD and Provisioned IOPS SSD1. Therefore, General Purpose SSD storage meets the requirements most cost-effectively.

Create a new DAX cluster using a larger node type. ChangeExplanation from Amazon documents:The cache miss rate is the percentage of read requests that are not satisfied by the DAX cache and have to be forwarded to DynamoDB1. A high cache miss rate indicates that the DAX cluster does not have enough memory to store all the frequently accessed items. Increasing the number of nodes in the existing DAX cluster (option A) or creating a new DAX cluster with more nodes (option B) will not increase the total memory available for caching, because DAX uses a partitioned cache model, where each node is responsible for caching a subset of the data2. Modifying the node type in the existing DAX cluster (option D) will cause downtime and data loss, because DAX does not support online resizing of clusters3. Therefore, the best option is to create a new DAX cluster using a larger node type (option C), which will provide more memory per node and allow more items to be cached. The application will need to change the DAX endpoint to point to the new cluster, which can be done with minimal disruption by using DNS aliasing or load balancing3.

Answer:: BExplanation from Amazon documents:Amazon Quantum Ledger Database (Amazon QLDB) is a fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log of all your application changes. Amazon QLDB tracks each and every application data change and maintains a complete and verifiable history of changes over time1. This makes it ideal for storing historical records that need to be tamper-proof and auditable. Amazon QLDB also supports PartiQL, a SQL-compatible query language that lets you query data using familiar SQL operators2.Amazon OpenSearch Service (successor to Amazon Elasticsearch Service) is a fully managed service that makes it easy to deploy, operate, and scale OpenSearch, an open source search and analytics engine. Amazon OpenSearch Service lets you store, search, and analyze large volumes of data quickly and at low cost3. You can use Amazon OpenSearch Service to index and query your employee record history data stored in Amazon QLDB using the QLDB Streams feature. This way, you can leverage the powerful search and analytics capabilities of OpenSearch Service to run fast and flexible queries on your historical data.Therefore, option B is the best solution that meets the requirements of preventing modification of the historical records and maximizing the speed of the queries. Option A is not suitable because DynamoDB is a key-value and document database that does not provide a ledger-like transaction log. Option C is not suitable because Aurora PostgreSQL is a relational database that does not guarantee immutability of the historical records. Option D is not suitable because Redshift is a data warehouse that is optimized for analytical queries on large datasets, not for storing and querying individual records.

Answer:: B, D, EExplanation from Amazon documents:To share an encrypted Amazon RDS snapshot with another account, you need to do the following123:Create a manual snapshot of the production database. You can't share an automated snapshot directly, but you can copy it to a manual snapshot and then share it1.Use a custom KMS encryption key for the manual snapshot. You can't share a snapshot that is encrypted using the default KMS key of the source account1.Share the snapshot with the development account by specifying the account ID of the target account1.Allow the development account to access the custom KMS key of the source account by adding the target account ID to the key policy of the source account2.Copy the shared snapshot to the development account by using a KMS key of the target account2.Therefore, option B, D, and E are the correct steps to meet the requirements. Option A is incorrect because you can't share an automated snapshot. Option C is incorrect because you can't share a snapshot that is encrypted using the default KMS key. Option F is unnecessary because the production account does not need to access the development account KMS key.

Explanation from Amazon documents:Amazon S3 Standard-Infrequent Access (S3 Standard-IA) is a storage class for data that is accessed less frequently, but requires rapid access when needed. S3 Standard-IA offers the high durability, throughput, and low latency of S3 Standard, with a low per GB storage price and per GB retrieval fee1. S3 Standard-IA is designed for long-lived and infrequently accessed data. Examples include disaster recovery, backups, and long-term data retention1.Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run2. Athena scales automatically---executing queries in parallel---so results are fast, even with large datasets and complex queries2.The news portal can use S3 Standard-IA to store its metadata about posts and comments, which are not frequently looked up or updated. This way, the portal can benefit from the low storage cost of S3 Standard-IA ($0.0125 per GB per month) and the high durability and availability of S31. The portal can also use Athena to query the data stored in S3 using SQL, without having to set up any servers or databases. The portal only pays for the amount of data scanned by each query ($5 per TB scanned) and can optimize the query cost by partitioning, compressing, and converting the data into columnar formats2.Therefore, option C is the most cost-effective solution for the news portal's use case. Option A is not cost-effective because DynamoDB on-demand capacity mode charges for read and write requests ($1.25 per million read requests and $1.25 per million write requests), regardless of how frequently the data is accessed3. Purchasing reserved capacity can reduce the cost, but it requires a minimum commitment of 100 units per region. Option B is not suitable because ElastiCache for Redis is an in-memory data store that provides sub-millisecond latency, but it is more expensive than S3 Standard-IA ($0.046 per GB per hour for cache.t2.micro node type). ElastiCache for Redis is also not designed for long-term data storage, but for caching frequently accessed data. Option D is not available because DynamoDB Standard-Infrequent Access (DynamoDB Standard-IA) is not a valid table class for DynamoDB. The only table classes for DynamoDB are On-Demand and Provisioned.

Answer:: A, C, EExplanation 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.