Querying interview questions

1. A.a batch paradigm that splits work into a per-record map step and an aggregating reduce step across a cluster
2. B.the property whereby most non-relational stores cannot combine records server-side, pushing that work to the data model or application
3. C.a read that targets items by their indexed key and is efficient because it touches only matching partitions
4. D.a read that examines every item in a table and is costly because it ignores key structure

Answer + AI explanation with Pro

1. A.Scan operation
2. B.Query operation
3. C.Absence of joins
4. D.MapReduce

Answer + AI explanation with Pro

1. A.Query operation — a read that examines every item in a table and is costly because it ignores key structure
2. B.Query operation — a read that targets items by their indexed key and is efficient because it touches only matching partitions
3. C.Query operation — the property whereby most non-relational stores cannot combine records server-side, pushing that work to the data model or application
4. D.Query operation — a batch paradigm that splits work into a per-record map step and an aggregating reduce step across a cluster

Answer + AI explanation with Pro

1. A.a read that examines every item in a table and is costly because it ignores key structure
2. B.a read that targets items by their indexed key and is efficient because it touches only matching partitions
3. C.an extra lookup structure on a non-primary attribute so reads can filter by fields other than the main key
4. D.a batch paradigm that splits work into a per-record map step and an aggregating reduce step across a cluster

Answer + AI explanation with Pro

1. A.Absence of joins
2. B.Secondary index
3. C.Query operation
4. D.Scan operation

Answer + AI explanation with Pro

1. A.Scan operation — a batch paradigm that splits work into a per-record map step and an aggregating reduce step across a cluster
2. B.Scan operation — the property whereby most non-relational stores cannot combine records server-side, pushing that work to the data model or application
3. C.Scan operation — an extra lookup structure on a non-primary attribute so reads can filter by fields other than the main key
4. D.Scan operation — a read that examines every item in a table and is costly because it ignores key structure

Answer + AI explanation with Pro

1. A.a read that examines every item in a table and is costly because it ignores key structure
2. B.a read that targets items by their indexed key and is efficient because it touches only matching partitions
3. C.an extra lookup structure on a non-primary attribute so reads can filter by fields other than the main key
4. D.the property whereby most non-relational stores cannot combine records server-side, pushing that work to the data model or application

Answer + AI explanation with Pro

1. A.Absence of joins
2. B.MapReduce
3. C.Query operation
4. D.Scan operation

Answer + AI explanation with Pro

1. A.Absence of joins — a read that targets items by their indexed key and is efficient because it touches only matching partitions
2. B.Absence of joins — a batch paradigm that splits work into a per-record map step and an aggregating reduce step across a cluster
3. C.Absence of joins — an extra lookup structure on a non-primary attribute so reads can filter by fields other than the main key
4. D.Absence of joins — the property whereby most non-relational stores cannot combine records server-side, pushing that work to the data model or application

Answer + AI explanation with Pro

1. A.an extra lookup structure on a non-primary attribute so reads can filter by fields other than the main key
2. B.a read that targets items by their indexed key and is efficient because it touches only matching partitions
3. C.the property whereby most non-relational stores cannot combine records server-side, pushing that work to the data model or application
4. D.a batch paradigm that splits work into a per-record map step and an aggregating reduce step across a cluster

Answer + AI explanation with Pro

1. A.Absence of joins
2. B.Secondary index
3. C.Scan operation
4. D.MapReduce

Answer + AI explanation with Pro

1. A.MapReduce — a read that examines every item in a table and is costly because it ignores key structure
2. B.MapReduce — a batch paradigm that splits work into a per-record map step and an aggregating reduce step across a cluster
3. C.MapReduce — a read that targets items by their indexed key and is efficient because it touches only matching partitions
4. D.MapReduce — the property whereby most non-relational stores cannot combine records server-side, pushing that work to the data model or application

Answer + AI explanation with Pro

1. A.a read that examines every item in a table and is costly because it ignores key structure
2. B.a batch paradigm that splits work into a per-record map step and an aggregating reduce step across a cluster
3. C.an extra lookup structure on a non-primary attribute so reads can filter by fields other than the main key
4. D.a read that targets items by their indexed key and is efficient because it touches only matching partitions

Answer + AI explanation with Pro

1. A.Query operation
2. B.Scan operation
3. C.Secondary index
4. D.Absence of joins

Answer + AI explanation with Pro

1. A.Secondary index — a batch paradigm that splits work into a per-record map step and an aggregating reduce step across a cluster
2. B.Secondary index — the property whereby most non-relational stores cannot combine records server-side, pushing that work to the data model or application
3. C.Secondary index — a read that targets items by their indexed key and is efficient because it touches only matching partitions
4. D.Secondary index — an extra lookup structure on a non-primary attribute so reads can filter by fields other than the main key

Answer + AI explanation with Pro