Amazon DBS-C01 Exam Actual Questions

Correct Answer: B. Create a new AWS Key Management Service (AWS KMS)

Explanation from Amazon documents:

Amazon Redshift supports encryption at rest using AWS Key Management Service (AWS KMS) customer master keys (CMKs). To copy encrypted snapshots across Regions, you need to create a snapshot copy grant in the destination Region and specify a CMK in that Region. You also need to configure cross-Region snapshots in the source Region and provide the destination Region, the snapshot copy grant, and retention periods for the snapshots. This way, you can automate the process of backing up the cluster data in compliance with the corporate policies.

Option A is incorrect because you cannot copy a CMK from one Region to another. You can only import key material from an external source into a CMK in a specific Region. Option C is incorrect because it involves unnecessary steps of copying snapshots to S3 buckets and using S3 Cross-Region Replication. Option D is incorrect because it is not possible to create a CMK with the same private key as another CMK in a different Region. You can only use customer-supplied key material to create a CMK with a specific key ID in a specific Region.

Correct Answer: B. Use time range, measure name, and dimensions in the WHERE

Explanation from Amazon documents:

Amazon Timestream is a serverless time series database service that allows you to store and analyze time series data at any scale. To optimize the cost of queries, you should use the following best practices1:

Include only the measure and dimension names essential to query. Adding extraneous columns will increase data scans and therefore will also increase the query cost.

Include a time range in the WHERE clause of your query. For example, if you only need the last one hour of data in your dataset, include a time predicate such as time > ago (1h).

Include the measure names in the WHERE clause of the query when a query accesses a subset of measures in a table.

Option B follows these best practices, while option A does not. Option C is incorrect because canceling a query can save on cost if the query will not return the desired results1. Option D is irrelevant because exponential backoff is a technique to handle throttling errors, not to optimize query costs2.

Amazon Timestream is a serverless time series database service that allows you to store and analyze time series data at any scale1.It is well suited for gaming applications that generate high volumes of data from player events, such as scores, achievements, and actions2.Amazon ElastiCache for Redis is a fully managed in-memory data store that provides fast and scalable performance for applications that need sub-millisecond latency3. It can be used as a cache layer to store frequently accessed data, such as leaderboard results, and reduce the load on the database. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. It can be used to process the data from Amazon Timestream and store the leaderboard results in Amazon ElastiCache for Redis. Amazon EventBridge is a serverless event bus service that makes it easy to connect your applications with data from a variety of sources. It can be used to create a scheduled event that triggers the Lambda function once every minute, ensuring that the leaderboard data is updated regularly. The game server can then query the Redis cluster for the leaderboard data, which will be no more than 1 minute old.

Option B is incorrect because Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It is not designed for time series data, which requires efficient ingestion, compression, and querying of high-volume data streams. Option C is incorrect because Amazon Aurora is a relational database that combines the performance and availability of traditional enterprise databases with the simplicity and cost-effectiveness of open source databases. It is not optimized for time series data, which requires specialized indexing and partitioning techniques. Option D is incorrect because Amazon Neptune is a graph database that supports property graph and RDF models. It is not suitable for time series data, which requires high ingestion rates and temporal queries.