[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"me":3,"catalog:en:data-engineer\u002Fst-kafka-producer-consumer-semantics":4,"config":191},null,{"field_key":5,"field_name":6,"seniority":7,"topic_key":8,"topic_name":9,"spec_key":7,"spec_name":7,"locale":10,"cell_total":11,"field_total":12,"seniorities":13,"topics":17,"specs":94,"samples":105},"data-engineer","Data Engineer","","st-kafka-producer-consumer-semantics","St Kafka Producer Consumer Semantics","en",75,1950,[14,15,16],"junior","mid","senior",[18,21,24,27,30,33,36,39,42,45,48,51,54,57,60,63,66,69,70,73,76,79,82,85,88,91],{"key":19,"name":20,"count":11},"data-governance-lineage","Data Governance Lineage",{"key":22,"name":23,"count":11},"data-modeling-warehousing","Data Modeling Warehousing",{"key":25,"name":26,"count":11},"data-partitioning-scaling","Data Partitioning Scaling",{"key":28,"name":29,"count":11},"data-pipeline-design","Data Pipeline Design",{"key":31,"name":32,"count":11},"data-pipeline-orchestration","Data Pipeline Orchestration",{"key":34,"name":35,"count":11},"data-pipeline-reliability","Data Pipeline Reliability",{"key":37,"name":38,"count":11},"data-quality-validation","Data Quality Validation",{"key":40,"name":41,"count":11},"pe-airflow-scheduling-dependencies","Pe Airflow Scheduling Dependencies",{"key":43,"name":44,"count":11},"pe-dbt-models-materializations-tests","Pe Dbt Models Materializations Tests",{"key":46,"name":47,"count":11},"pe-file-formats-table-formats","Pe File Formats Table Formats",{"key":49,"name":50,"count":11},"pe-incremental-cdc-merge-mechanics","Pe Incremental Cdc Merge Mechanics",{"key":52,"name":53,"count":11},"pe-spark-api-semantics-udfs","Pe Spark Api Semantics Udfs",{"key":55,"name":56,"count":11},"pe-spark-execution-shuffle-partitions","Pe Spark Execution Shuffle Partitions",{"key":58,"name":59,"count":11},"st-flink-runtime-checkpointing-state","St Flink Runtime Checkpointing State",{"key":61,"name":62,"count":11},"st-flink-time-watermarks-windows","St Flink Time Watermarks Windows",{"key":64,"name":65,"count":11},"st-kafka-broker-storage-replication","St Kafka Broker Storage Replication",{"key":67,"name":68,"count":11},"st-kafka-connect-schema-registry","St Kafka Connect Schema Registry",{"key":8,"name":9,"count":11},{"key":71,"name":72,"count":11},"streaming-fundamentals","Streaming Fundamentals",{"key":74,"name":75,"count":11},"st-streaming-joins-tables","St Streaming Joins Tables",{"key":77,"name":78,"count":11},"wh-bigquery-execution-slots-cost","Wh Bigquery Execution Slots Cost",{"key":80,"name":81,"count":11},"wh-bigquery-storage-partitioning-clustering","Wh Bigquery Storage Partitioning Clustering",{"key":83,"name":84,"count":11},"wh-materialized-views-caching","Wh Materialized Views Caching",{"key":86,"name":87,"count":11},"wh-redshift-distribution-sort-vacuum","Wh Redshift Distribution Sort Vacuum",{"key":89,"name":90,"count":11},"wh-snowflake-compute-warehouses","Wh Snowflake Compute Warehouses",{"key":92,"name":93,"count":11},"wh-snowflake-storage-micropartitions","Wh Snowflake Storage Micropartitions",[95,99,102],{"key":96,"name":97,"count":98},"pipelines-etl","Pipelines \u002F ETL",450,{"key":100,"name":101,"count":98},"streaming","Streaming",{"key":103,"name":104,"count":98},"warehousing","Warehousing",[106,124,138,151,165,178],{"id":107,"topic":9,"difficulty":108,"body":109,"options":110,"correct_key":112,"explanation":123},"01a00b9b-8a97-73f5-aa40-00ea5df6e0cb",2,"In the Kafka producer, what does the `linger.ms` setting control?",[111,114,117,120],{"key":112,"text":113},"a","How long the producer waits to accumulate more records into a batch before sending it",{"key":115,"text":116},"b","How long a broker keeps a message before it becomes eligible for deletion",{"key":118,"text":119},"c","How long a consumer waits between `poll()` calls before a rebalance",{"key":121,"text":122},"d","How long the producer waits for a broker acknowledgment before retrying","`linger.ms` adds a small artificial delay before sending a batch, giving more records a chance to accumulate and improving throughput at the cost of latency. Waiting for an ack is governed by `request.timeout.ms`, poll cadence is a consumer concern, and message retention is a broker\u002Ftopic setting.",{"id":125,"topic":9,"difficulty":126,"body":127,"options":128,"correct_key":115,"explanation":137},"01a00b9b-8a98-720c-9158-ee80642fccac",3,"A producer has `batch.size=16384` (bytes) and `linger.ms=50`. Under steady, moderate traffic, which condition actually triggers a batch to be sent to the broker?",[129,131,133,135],{"key":112,"text":130},"Both conditions must be true at the same time: the batch is full AND 50ms has elapsed",{"key":115,"text":132},"Whichever happens first: the batch fills to 16384 bytes, or 50ms elapses since the batch started",{"key":118,"text":134},"Only `batch.size` matters; `linger.ms` is ignored once a batch has any records in it",{"key":121,"text":136},"Only `linger.ms` matters; `batch.size` only applies to the very first batch after startup","The two settings are an OR condition, not an AND: a batch is sent as soon as either the size threshold is reached or the linger timer expires, whichever comes first. This lets a producer batch efficiently at high throughput while still bounding worst-case latency at low throughput.",{"id":139,"topic":9,"difficulty":108,"body":140,"options":141,"correct_key":121,"explanation":150},"01a00b9b-8a99-7dc4-9d72-701b6315a6c3","A producer's `buffer.memory` fills up because the broker is temporarily unreachable and unsent batches pile up. What happens to a new `send()` call in this situation?",[142,144,146,148],{"key":112,"text":143},"It immediately throws an exception with no waiting at all",{"key":115,"text":145},"It automatically switches to writing the record straight to local disk instead",{"key":118,"text":147},"It silently drops the record without notifying the application",{"key":121,"text":149},"It blocks for up to `max.block.ms`, then throws a timeout exception","When the producer's memory buffer is full, `send()` blocks waiting for space (e.g. from a completed batch) for up to `max.block.ms`; if no space frees up in that window, it raises a timeout exception to the caller rather than silently dropping data.",{"id":152,"topic":9,"difficulty":153,"body":154,"options":155,"correct_key":112,"explanation":164},"01a00b9b-8a9b-7c29-85fd-d3faa99ba690",1,"What does the producer's `compression.type` setting actually compress?",[156,158,160,162],{"key":112,"text":157},"The whole record batch, using a codec such as `gzip`, `snappy`, `lz4`, or `zstd`",{"key":115,"text":159},"The broker's on-disk log segments, applied after the batch is already written",{"key":118,"text":161},"The TLS handshake payload between producer and broker",{"key":121,"text":163},"Only the message key of each record, leaving the value uncompressed","Compression in Kafka is applied at the batch level: the producer compresses the whole accumulated batch with the configured codec before sending, which is more efficient than compressing individual records.",{"id":166,"topic":9,"difficulty":108,"body":167,"options":168,"correct_key":121,"explanation":177},"01a00b9b-8a9c-7e50-b54d-acff399c0204","A producer repeatedly sends `ProducerRecord`s with the same key, e.g. `\"user-42\"`, to a topic that has 6 partitions and no manual partition override. What happens to these records?",[169,171,173,175],{"key":112,"text":170},"They are routed to whichever partition currently has the least data",{"key":115,"text":172},"They land on partition 0, regardless of the key's value",{"key":118,"text":174},"They are spread round-robin across all 6 partitions to balance load evenly",{"key":121,"text":176},"They are all hashed to the same partition, as long as the partition count stays at 6","The default partitioner hashes the record key to deterministically pick a partition, so the same key always maps to the same partition as long as the partition count is unchanged. This is what makes per-key ordering possible.",{"id":179,"topic":9,"difficulty":126,"body":180,"options":181,"correct_key":115,"explanation":190},"01a00b9b-8a9e-7ad9-8984-c7db8fae4287","A producer sends records with no key (`key == null`) to a topic. In modern Kafka clients (2.4+), how does the default partitioner distribute these records across partitions?",[182,184,186,188],{"key":112,"text":183},"It sends every consecutive record to a strictly different partition in round-robin order",{"key":115,"text":185},"It sticks to one partition, filling a batch there before switching to another partition",{"key":118,"text":187},"It sends all keyless records to partition 0 only, to keep ordering simple",{"key":121,"text":189},"It refuses to send keyless records unless a custom partitioner is configured","Kafka's default logic has used sticky batching for keyless records since 2.4: it stays on a partition until a batch is ready, then switches. Kafka 3.3 changed the implementation through KIP-794 but retained this batching behavior while adding adaptive broker-performance 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