Serialization

3 h
requires JVM-01

From your world. the on-the-wire/at-rest format of a table or a Kafka topic (Avro/Parquet) vs an opaque pickle

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1What is the key difference between Avro GenericRecord and SpecificRecord? AThey produce different wire bytes for the same data and schema. BGenericRecord is a schema-driven, map-like object with untyped get/put. SpecificRecord is a generated class with typed getters/setters. The wire format is the same. CSpecificRecord has no schema. DGenericRecord cannot be serialized.

2In Flink, when does a user type fall back to the generic Kryo serializer? ANever. Flink always uses Kryo. BWhen the type is neither an Avro/Flink-recognized type nor a valid POJO (missing public no-arg constructor, or fields that are not public and lack public getters/setters). COnly for primitives. DWhenever you use generics.

3Which conditions must a class meet to be a valid Flink POJO (to avoid Kryo)? (select all) (select all that apply) AThe class is public and has a public no-argument constructor. BEvery field is public, OR has a public getter and setter following the bean naming convention. CThe class must be final. DThe class must implement java.io.Serializable with a custom writeObject.

6Does Avro binary encoding embed the schema inside every encoded record? AYes, each record carries its full JSON schema. BNo. Binary encoding assumes that the reader and the writer agree on the schema (a container file header or Schema Registry supplies it). This is why schema management exists. COnly for strings. DOnly when using SpecificRecord.

7A teammate ships events as a class with private fields, no getters, and no no-arg constructor. The teammate asks why Flink state is huge and slow. What is the likely cause? AThe type is not a valid POJO and not an Avro type, so Flink fell back to Kryo. BFlink always compresses badly. CThe fields are too small. DKryo is the fastest option and this is expected.