Sharding — Learn free · DataAI Nexus

Self-check

Questions 1–10 of 10

1. What is the key architectural difference between Sharding and Partitioning?
Question 1A. Sharding divides columns whereas partitioning divides rowsB. Sharding stores each shard on one dedicated node, giving a shared-nothing architecture, whereas partitions may share a nodeC. Sharding keeps all data on a single node whereas partitioning spreads itD. Sharding does not divide tables into smaller datasetsE. Sharding only works on NoSQL databases
2. How does key-based sharding decide which shard should hold newly written data?
Question 2A. By assigning shards in round-robin orderB. By consulting a manually maintained lookup tableC. By sorting the data into ranges of a given valueD. By plugging a value taken from the new data into a hash functionE. By writing to the shard with the most free space
3. Which sharding architecture divides data according to ranges of a given value?
Question 3A. Range-based shardingB. Key-based shardingC. Directory-based shardingD. Hash-based shardingE. Vertical sharding
4. What must be created and maintained for directory-based sharding?
Question 4A. A hash function for the shard keyB. A range table for each columnC. A replica of every shard on every nodeD. A single master node that holds all writesE. A lookup table that uses a shard key to track which shard holds which data
5. What is described as the main appeal of sharding a database?
Question 5A. It reduces storage costs by compressing dataB. It removes the need for indexesC. It facilitates horizontal scaling, also known as scaling outD. It makes the database easier to return to an unsharded stateE. It guarantees strict consistency across nodes
6. What is horizontal scaling?
Question 6A. Adding more machines to an existing stack to spread out the load and allow more traffic and faster processingB. Upgrading a single server with more memory and CPUC. Splitting a table's columns into separate tablesD. Moving a database to faster storageE. Reducing the number of nodes to cut cost
7. How does sharding help make an application more reliable?
Question 7A. Every shard holds a full copy of the dataB. Shards are automatically rebalanced after every writeC. Sharded databases never need backupsD. An outage is likely to affect only a single shard, so the overall impact is less than if the whole database crashedE. Sharding prevents data corruption entirely
8. Which statement is NOT a drawback of sharding?
Question 8A. If done incorrectly, it can lead to lost data or corrupted tablesB. It is natively supported by every database engineC. A sharded database can become unbalancedD. It is very difficult to return to an unsharded architectureE. Backups made before sharding will not include data written since
9. What is the problem with database backups made before the database was sharded?
Question 9A. They cannot be restored at allB. They become corrupted by the sharding processC. They double in sizeD. They are automatically deletedE. They will not include data written since the sharding took place
10. In the yearly Salary example, why is a query for years 2 and 3 faster on a sharded database than on a standard partitioned one?
Question 10A. The sharded database keeps both years in memoryB. The sharded database has fewer rowsC. Each node holding a shard processes its own dataset individually, rather than several partitions sharing one nodeD. The sharded query skips the indexE. Shards are always smaller than partitions