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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions:

1. We have a store selling shoes. What dataset is a great candidate to be modeled as a KTable in Kafka Streams?

A) The transaction stream
B) Money made until now
C) Inventory contents right now
D) Items returned

2. StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> textLines = builder.stream("word-count-input"); KTable<String, Long> wordCounts = textLines
.mapValues(textLine -> textLine.toLowerCase())
.flatMapValues(textLine -> Arrays.asList(textLine.split("\W+")))
.selectKey((key, word) -> word)
.groupByKey()
.count(Materialized.as("Counts"));
wordCounts.toStream().to("word-count-output", Produced.with(Serdes.String(), Serdes.Long())); builder.build(); What is an adequate topic configuration for the topic word-count-output?

A) max.message.bytes=10000000
B) cleanup.policy=compact
C) compression.type=lz4
D) cleanup.policy=delete

3. How do you read a table or stream from the beginning of a topic?

A) SET 'auto.offset.reset' = 'latest';
SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
B) id as orderId
C) id as paymentId
FROM orders o
INNER JOIN payments p WITHIN 1 HOURS ON p.id = o.id
INNER JOIN shipments s WITHIN 2 HOURS ON s.id
= o.id;
D) id as shipmentId,
E) SET 'auto.offset.reset' = 'earliest';
SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
F) CREATE STREAM shipped_orders AS
SELECT
G) itemId as itemId,
H) SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;

4. What isn't a feature of the Confluent schema registry?

A) Store avro data
B) Store schemas
C) Enforce compatibility rules

5. Which configuration determines how many bytes of data are collected before sending messages to the Kafka broker?

A) send.buffer.bytes
B) max.block.size
C) batch.size
D) buffer.memory

Solutions: