Confluent Confluent Certified Developer CCDAK Questions & Answers

• Question 131:

  You want to perform table lookups against a KTable everytime a new record is received from the KStream. What is the output of KStream-KTable join?

  A. KTable

  B. GlobalKTable

  C. You choose between KStream or KTable

  D. Kstream

  Correct Answer: D

  Here KStream is being processed to create another KStream.

• Question 132:

  You are doing complex calculations using a machine learning framework on records fetched from a Kafka topic. It takes more about 6 minutes to process a record batch, and the consumer enters rebalances even though it's still running. How can you improve this scenario?

  A. Increase max.poll.interval.ms to 600000

  B. Increase heartbeat.interval.ms to 600000

  C. Increase session.timeout.ms to 600000

  D. Add consumers to the consumer group and kill them right away

  Correct Answer: A

  Here, we need to change the setting max.poll.interval.ms (default 300000) to its double in order to tell Kafka a consumer should be considered dead if the consumer only if it hasn't called the .poll() method in 10 minutes instead of 5.

• Question 133:

  The exactly once guarantee in the Kafka Streams is for which flow of data?

  A. Kafka => Kafka

  B. Kafka => External

  C. External => Kafka

  Correct Answer: A

  Kafka Streams can only guarantee exactly once processing if you have a Kafka to Kafka topology.

• Question 134:

  You are using JDBC source connector to copy data from a table to Kafka topic. There is one connector created with max.tasks equal to 2 deployed on a cluster of 3 workers. How many tasks are launched?

  A. 3

  B. 2

  C. 1

  D. 6

  Correct Answer: C

  JDBC connector allows one task per table.

• Question 135:

  while (true) {

  ConsumerRecords records = consumer.poll(100); try {

  consumer.commitSync();

  } catch (CommitFailedException e) {

  log.error("commit failed", e)

  }

  for (ConsumerRecord record records)

  {

  System.out.printf("topic = %s, partition = %s, offset =

  %d, customer = %s, country = %s

  ",

  record.topic(), record.partition(),

  record.offset(), record.key(), record.value());

  }

  }

  What kind of delivery guarantee this consumer offers?

  A. Exactly-once

  B. At-least-once

  C. At-most-once

  Correct Answer: C

  Here offset is committed before processing the message. If consumer crashes before processing the message, message will be lost when it comes back up.

• Question 136:

  Where are the ACLs stored in a Kafka cluster by default?

  A. Inside the broker's data directory

  B. Under Zookeeper node /kafka-acl/

  C. In Kafka topic __kafka_acls

  D. Inside the Zookeeper's data directory

  Correct Answer: A

  ACLs are stored in Zookeeper node /kafka-acls/ by default.

• Question 137:

  StreamsBuilder builder = new StreamsBuilder();

  KStream textLines = builder.stream("word-count-input");

  KTable 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=delete

  C. compression.type=lz4

  D. cleanup.policy=compact

  Correct Answer: D

  Result is aggregated into a table with key as the unique word and value its frequency. We have to enable log compaction for this topic to align the topic's cleanup policy with KTable semantics.

• Question 138:

  How can you gracefully make a Kafka consumer to stop immediately polling data from Kafka and gracefully shut down a consumer application?

  A. Call consumer.wakeUp() and catch a WakeUpException

  B. Call consumer.poll() in another thread

  C. Kill the consumer thread

  Correct Answer: A

  See https://stackoverflow.com/a/37748336/3019499

• Question 139:

  To enhance compression, I can increase the chances of batching by using

  A. acks=all

  B. linger.ms=20

  C. batch.size=65536

  D. max.message.size=10MB

  Correct Answer: B

  linger.ms forces the producer to wait before sending messages, hence increasing the chance of creating batches that can be heavily compressed.

• Question 140:

  You want to sink data from a Kafka topic to S3 using Kafka Connect. There are 10 brokers in the cluster, the topic has 2 partitions with replication factor of 3. How many tasks will you configure for the S3 connector?

  A. 10

  B. 6

  C. 3

  D. 2

  Correct Answer: D

  You cannot have more sink tasks (= consumers) than the number of partitions, so 2.