CCDAK Self-Study Guide for Becoming an Confluent Certified Developer for Apache Kafka Certification Examination Expert [Q57-Q71]

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CCDAK Self-Study Guide for Becoming an Confluent Certified Developer for Apache Kafka Certification Examination Expert

CCDAK Study Guide Realistic Verified CCDAK Dumps

NO.57 What client protocol is supported for the schema registry? (select two)

HTTP

HTTPS

JDBC

Websocket

SASL

clients can interact with the schema registry using the HTTP or HTTPS interface

NO.58 A consumer has auto.offset.reset=latest, and the topic partition currently has data for offsets going from 45 to 2311. The consumer group never committed offsets for the topic before. Where will the consumer read from?

offset 2311

offset 0

offset 45

it will crash

Latest means that data retrievals will start from where the offsets currently end

NO.59 A topic “sales” is being produced to in the Americas region. You are mirroring this topic using Mirror Maker to the European region. From there, you are only reading the topic for analytics purposes. What kind of mirroring is this?

Passive-Passive

Active-Active

Active-Passive

This is active-passing as the replicated topic is used for read-only purposes only

NO.60 The exactly once guarantee in the Kafka Streams is for which flow of data?

Kafka => Kafka

Kafka => External

External => Kafka

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

NO.61 What’s a Kafka partition made of?

One file and one index

One file

One file and two indexes per segment

One file and two indexes

Kafka partitions are made of segments (usually each segment is 1GB), and each segment has two corresponding indexes (offset index and time index)

NO.62 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?

KTable

GlobalKTable

You choose between KStream or KTable

Kstream

Here KStream is being processed to create another KStream.

NO.63 Your producer is producing at a very high rate and the batches are completely full each time. How can you improve the producer throughput? (select two)

Enable compression

Disable compression

Increase batch.size

Decrease batch.size

Decrease linger.ms

Increase linger.ms
Explanation:
batch.size controls how many bytes of data to collect before sending messages to the Kafka broker. Set this as high as possible, without exceeding available memory. Enabling compression can also help make more compact batches and increase the throughput of your producer. Linger.ms will have no effect as the batches are already full

NO.64 A kafka topic has a replication factor of 3 and min.insync.replicas setting of 2. How many brokers can go down before a producer with acks=1 can’t produce?

min.insync.replicas does not impact producers when acks=1 (only when acks=all)

NO.65 What data format isn’t natively available with the Confluent REST Proxy?

avro

binary

protobuf

json

Protocol buffers isn’t a natively supported type for the Confluent REST Proxy, but you may use the binary format instead

NO.66 A Zookeeper ensemble contains 5 servers. What is the maximum number of servers that can go missing and the ensemble still run?

majority consists of 3 zk nodes for 5 nodes zk cluster, so 2 can fail

NO.67 We have a store selling shoes. What dataset is a great candidate to be modeled as a KTable in Kafka Streams?

Money made until now

The transaction stream

Items returned

Inventory contents right now

Aggregations of stream are stored in table, whereas Streams must be modeled as a KStream to avoid data explosion

NO.68 Which of the following Kafka Streams operators are stateful? (select all that apply)

flatmap

reduce

joining

count

peek

aggregate

Seehttps://kafka.apache.org/20/documentation/streams/developer-guide/dsl-api.html#stateful-transformations

NO.69 Partition leader election is done by

The consumers

The Kafka Broker that is the Controller

Zookeeper

Vote amongst the brokers

The Controller is a broker that is responsible for electing partition leaders

NO.70 If a topic has a replication factor of 3…

3 replicas of the same data will live on 1 broker

Each partition will live on 4 different brokers

Each partition will live on 2 different brokers

Each partition will live on 3 different brokers

Replicas are spread across available brokers, and each replica = one broker. RF 3 = 3 brokers

NO.71 Your topic is log compacted and you are sending a message with the key K and value null. What will happen?

The broker will delete all messages with the key K upon cleanup

The producer will throw a Runtime exception

The broker will delete the message with the key K and null value only upon cleanup

The message will get ignored by the Kafka broker

Sending a message with the null value is called a tombstone in Kafka and will ensure the log compacted topic does not contain any messages with the key K upon compaction

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