Spark 4 & Connect interview questions

1. A.a Spark 4 API that lets developers implement custom batch and streaming data sources in pure Python without writing Scala or Java
2. B.the Spark 4 semi-structured data type that stores arbitrary JSON-like values efficiently with fast field extraction and no fixed schema
3. C.a client-server architecture in Spark 4 that decouples the client from the cluster, sending unresolved logical plans over gRPC for thin multi-language clients
4. D.the Spark 4 client-server architecture that decouples the application from the driver, letting thin clients submit DataFrame plans over a gRPC protocol

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1. A.String collation support
2. B.SQL user-defined function
3. C.Spark Connect
4. D.ANSI by default

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1. A.Spark Connect — the Spark 4 setting that switches an application between classic in-process execution and Spark Connect without code changes
2. B.Spark Connect — a client-server architecture in Spark 4 that decouples the client from the cluster, sending unresolved logical plans over gRPC for thin multi-language clients
3. C.Spark Connect — a Spark 4 data type for semi-structured data that stores JSON-like values in an efficient binary form for much faster access than parsing strings
4. D.Spark Connect — the Spark 4 feature letting users define reusable functions in pure SQL with CREATE FUNCTION, separate from registering language UDFs

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1. A.the Spark 4 client-server architecture that decouples the application from the driver, letting thin clients submit DataFrame plans over a gRPC protocol
2. B.a Spark 4 data type for semi-structured data that stores JSON-like values in an efficient binary form for much faster access than parsing strings
3. C.the Spark 4 setting that switches an application between classic in-process execution and Spark Connect without code changes
4. D.a Spark 4 API that lets developers implement custom batch and streaming data sources in pure Python without writing Scala or Java

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1. A.SQL user-defined function
2. B.ANSI mode default
3. C.ANSI by default
4. D.VARIANT type

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1. A.VARIANT type — the Spark 4 requirement that Java 17 is the minimum runtime, with Java 21 also supported, dropping older JVMs
2. B.VARIANT type — a client-server architecture in Spark 4 that decouples the client from the cluster, sending unresolved logical plans over gRPC for thin multi-language clients
3. C.VARIANT type — a Spark 4 data type for semi-structured data that stores JSON-like values in an efficient binary form for much faster access than parsing strings
4. D.VARIANT type — a Spark 4 user-defined table function written in Python that returns a table whose schema can depend on the input arguments

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1. A.the Spark 4 change making ANSI SQL semantics the default, so overflow and invalid casts raise errors instead of returning null
2. B.a Spark 4 API that lets developers implement custom batch and streaming data sources in pure Python without writing Scala or Java
3. C.a Spark 4 data type for semi-structured data that stores JSON-like values in an efficient binary form for much faster access than parsing strings
4. D.a client-server architecture in Spark 4 that decouples the client from the cluster, sending unresolved logical plans over gRPC for thin multi-language clients

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1. A.Spark Connect
2. B.SQL user-defined function
3. C.Polymorphic Python UDTF
4. D.ANSI mode default

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1. A.ANSI mode default — a Spark 4 data type for semi-structured data that stores JSON-like values in an efficient binary form for much faster access than parsing strings
2. B.ANSI mode default — a client-server architecture in Spark 4 that decouples the client from the cluster, sending unresolved logical plans over gRPC for thin multi-language clients
3. C.ANSI mode default — a Spark 4 API that lets developers implement custom batch and streaming data sources in pure Python without writing Scala or Java
4. D.ANSI mode default — the Spark 4 change making ANSI SQL semantics the default, so overflow and invalid casts raise errors instead of returning null

Answer + AI explanation with Pro

1. A.the Spark 4 change making ANSI SQL semantics the default, so overflow and invalid casts raise errors instead of returning null
2. B.a Spark 4 feature that writes logs as JSON with consistent fields so they can be queried with Spark SQL
3. C.a Spark 4 API that lets developers implement custom batch and streaming data sources in pure Python without writing Scala or Java
4. D.a Spark 4 data type for semi-structured data that stores JSON-like values in an efficient binary form for much faster access than parsing strings

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1. A.Spark Connect
2. B.spark.api.mode
3. C.Spark Connect client
4. D.Python Data Source API

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1. A.Python Data Source API — a client-server architecture in Spark 4 that decouples the client from the cluster, sending unresolved logical plans over gRPC for thin multi-language clients
2. B.Python Data Source API — the Spark 4 setting that switches an application between classic in-process execution and Spark Connect without code changes
3. C.Python Data Source API — a Spark 4 user-defined table function written in Python that returns a table whose schema can depend on the input arguments
4. D.Python Data Source API — a Spark 4 API that lets developers implement custom batch and streaming data sources in pure Python without writing Scala or Java

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1. A.the Spark 4 feature letting users define reusable functions in pure SQL with CREATE FUNCTION, separate from registering language UDFs
2. B.a lightweight language binding (available for Python, Scala, Go, Rust, and Swift) that sends unresolved logical plans to a remote Spark server
3. C.a Spark 4 user-defined table function written in Python that returns a table whose schema can depend on the input arguments
4. D.the Spark 4 client-server architecture that decouples the application from the driver, letting thin clients submit DataFrame plans over a gRPC protocol

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1. A.ANSI mode default
2. B.Structured logging
3. C.Polymorphic Python UDTF
4. D.Spark Connect

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1. A.Polymorphic Python UDTF — a Spark 4 user-defined table function written in Python that returns a table whose schema can depend on the input arguments
2. B.Polymorphic Python UDTF — a Spark 4 feature letting columns declare case-insensitive or locale-aware comparison and sorting rules
3. C.Polymorphic Python UDTF — the Spark 4 change making ANSI SQL semantics the default, so overflow and invalid casts raise errors instead of returning null
4. D.Polymorphic Python UDTF — the Spark 4 change making spark.sql.ansi.enabled true so overflow, divide-by-zero, and bad casts raise errors instead of returning null

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1. A.the Spark 4 requirement that Java 17 is the minimum runtime, with Java 21 also supported, dropping older JVMs
2. B.a Spark 4 API that lets developers implement custom batch and streaming data sources in pure Python without writing Scala or Java
3. C.a Spark 4 feature letting columns declare case-insensitive or locale-aware comparison and sorting rules
4. D.the Spark 4 semi-structured data type that stores arbitrary JSON-like values efficiently with fast field extraction and no fixed schema

Answer + AI explanation with Pro

1. A.SQL user-defined function
2. B.String collation support
3. C.ANSI mode default
4. D.Spark Connect client

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1. A.String collation support — a lightweight language binding (available for Python, Scala, Go, Rust, and Swift) that sends unresolved logical plans to a remote Spark server
2. B.String collation support — a Spark 4 user-defined table function written in Python that returns a table whose schema can depend on the input arguments
3. C.String collation support — the Spark 4 change making ANSI SQL semantics the default, so overflow and invalid casts raise errors instead of returning null
4. D.String collation support — a Spark 4 feature letting columns declare case-insensitive or locale-aware comparison and sorting rules

Answer + AI explanation with Pro

1. A.the Spark 4 feature letting users define reusable functions in pure SQL with CREATE FUNCTION, separate from registering language UDFs
2. B.a lightweight language binding (available for Python, Scala, Go, Rust, and Swift) that sends unresolved logical plans to a remote Spark server
3. C.a Spark 4 feature that writes logs as JSON with consistent fields so they can be queried with Spark SQL
4. D.the Spark 4 change making ANSI SQL semantics the default, so overflow and invalid casts raise errors instead of returning null

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1. A.Spark Connect
2. B.String collation support
3. C.Spark Connect client
4. D.Structured logging

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1. A.Structured logging — the Spark 4 client-server architecture that decouples the application from the driver, letting thin clients submit DataFrame plans over a gRPC protocol
2. B.Structured logging — a Spark 4 feature that writes logs as JSON with consistent fields so they can be queried with Spark SQL
3. C.Structured logging — a Spark 4 data type for semi-structured data that stores JSON-like values in an efficient binary form for much faster access than parsing strings
4. D.Structured logging — the Spark 4 semi-structured data type that stores arbitrary JSON-like values efficiently with fast field extraction and no fixed schema

Answer + AI explanation with Pro

1. A.a Spark 4 feature that writes logs as JSON with consistent fields so they can be queried with Spark SQL
2. B.a Spark 4 user-defined table function written in Python that returns a table whose schema can depend on the input arguments
3. C.the Spark 4 change making ANSI SQL semantics the default, so overflow and invalid casts raise errors instead of returning null
4. D.the Spark 4 requirement that Java 17 is the minimum runtime, with Java 21 also supported, dropping older JVMs

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1. A.Java 17 baseline
2. B.ANSI mode default
3. C.Spark Connect client
4. D.Spark Connect

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1. A.Java 17 baseline — a client-server architecture in Spark 4 that decouples the client from the cluster, sending unresolved logical plans over gRPC for thin multi-language clients
2. B.Java 17 baseline — the Spark 4 requirement that Java 17 is the minimum runtime, with Java 21 also supported, dropping older JVMs
3. C.Java 17 baseline — the Spark 4 setting that switches an application between classic in-process execution and Spark Connect without code changes
4. D.Java 17 baseline — a Spark 4 feature letting columns declare case-insensitive or locale-aware comparison and sorting rules

Answer + AI explanation with Pro

1. A.a Spark 4 user-defined table function written in Python that returns a table whose schema can depend on the input arguments
2. B.the Spark 4 semi-structured data type that stores arbitrary JSON-like values efficiently with fast field extraction and no fixed schema
3. C.the Spark 4 client-server architecture that decouples the application from the driver, letting thin clients submit DataFrame plans over a gRPC protocol
4. D.the Spark 4 requirement that Java 17 is the minimum runtime, with Java 21 also supported, dropping older JVMs

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1. A.Spark Connect
2. B.VARIANT type
3. C.ANSI mode default
4. D.ANSI by default

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1. A.Spark Connect — the Spark 4 change making spark.sql.ansi.enabled true so overflow, divide-by-zero, and bad casts raise errors instead of returning null
2. B.Spark Connect — the Spark 4 client-server architecture that decouples the application from the driver, letting thin clients submit DataFrame plans over a gRPC protocol
3. C.Spark Connect — a Spark 4 API that lets developers implement custom batch and streaming data sources in pure Python without writing Scala or Java
4. D.Spark Connect — a Spark 4 feature letting columns declare case-insensitive or locale-aware comparison and sorting rules

Answer + AI explanation with Pro

1. A.the Spark 4 setting that switches an application between classic in-process execution and Spark Connect without code changes
2. B.the Spark 4 requirement that Java 17 is the minimum runtime, with Java 21 also supported, dropping older JVMs
3. C.a lightweight language binding (available for Python, Scala, Go, Rust, and Swift) that sends unresolved logical plans to a remote Spark server
4. D.the Spark 4 change making spark.sql.ansi.enabled true so overflow, divide-by-zero, and bad casts raise errors instead of returning null

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1. A.VARIANT type
2. B.ANSI mode default
3. C.spark.api.mode
4. D.Spark Connect client

Answer + AI explanation with Pro

1. A.spark.api.mode — the Spark 4 feature letting users define reusable functions in pure SQL with CREATE FUNCTION, separate from registering language UDFs
2. B.spark.api.mode — the Spark 4 requirement that Java 17 is the minimum runtime, with Java 21 also supported, dropping older JVMs
3. C.spark.api.mode — the Spark 4 setting that switches an application between classic in-process execution and Spark Connect without code changes
4. D.spark.api.mode — the Spark 4 change making spark.sql.ansi.enabled true so overflow, divide-by-zero, and bad casts raise errors instead of returning null

Answer + AI explanation with Pro