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Confluent Certified Administrator for Apache Kafka Sample Questions (Q44-Q49):

NEW QUESTION # 44
Which tool is used for scalably and reliably streaming data between Kafka and other data systems?

A. Kafka Schema Registry
B. Kafka Streams
C. Kafka REST Proxy
D. Kafka Connect

Answer: D

Explanation:
Kafka Connect is the tool designed for scalable and reliable integration between Kafka and external data systems (e.g., databases, cloud storage, key-value stores). It supports source connectors (to pull data into Kafka) and sink connectors (to push data from Kafka).

NEW QUESTION # 45
A Kafka cluster with three brokers has a topic with 10 partitions and a replication factor set to three. Each partition stores 25 GB data per day and data retention is set to 24 hours.
How much storage will be consumed by the topic on each broker?

A. 750 GB
B. 75 GB
C. 250 GB
D. 300 GB

Answer: D

Explanation:
10 partitions × 25 GB/day = 250 GB total per day for the topic (primary data).
With a replication factor of 3, there are 3 full copies of the data: 250 GB × 3 = 750 GB total across the entire cluster.
The cluster has 3 brokers, and Kafka tries to distribute replicas evenly among them: 750 GB ÷ 3 brokers = 250 GB per broker on average.
However, due to replication, some partitions have leaders and followers, so there's some overlap and not-perfect distribution. Each broker stores approximately 2/3 of the total topic data (since each broker holds replicas for around 2/3 of the partitions).
2/3 × 750 GB = 500 GB, but this is shared, so each broker ends up storing ~300 GB of replicated data, including its share of leaders and followers.

NEW QUESTION # 46
Your Kafka cluster has four brokers. The topic t1 on the cluster has two partitions, and it has a replication factor of three. You create a Consumer Group with four consumers, which subscribes to t1.
In the scenario above, how many Controllers are in the Kafka cluster?

A. Four
B. One
C. two
D. three

Answer: B

Explanation:
In a Kafka cluster, only one broker acts as the Controller at any given time. The Controller is responsible for managing cluster metadata, such as partition leadership and broker status. Even if the cluster has multiple brokers (in this case, four), only one is elected as the Controller, and others serve as regular brokers. If the current Controller fails, another broker is automatically elected to take its place.

NEW QUESTION # 47
You are using Confluent Schema Registry to provide a RESTful interface for storing and retrieving schemas.
Which types of schemas are supported? (Choose three.)

A. gRPC
B. Avro
C. Thrift
D. Protobuf
E. JSON

Answer: B,D,E

Explanation:
Avro is the original and most commonly used schema format supported by Schema Registry.
Confluent Schema Registry supports JSON Schema for validation and compatibility checks.
Protocol Buffers (Protobuf) are supported for schema management in Schema Registry.

NEW QUESTION # 48
Kafka Connect is running on a two node cluster in distributed mode. The connector is a source connector that pulls data from Postgres tables (users/payment/orders), writes to topics with two partitions, and with replication factor two. The development team notices that the data is lagging behind.
What should be done to reduce the data lag*?
The Connector definition is listed below:
{
"name": "confluent-postgresql-source",
"connector class": "PostgresSource",
"topic.prefix": "postgresql_",
& nbsp;& nbsp;& nbsp;...
"db.name": "postgres",
"table.whitelist": "users.payment.orders",
"timestamp.column.name": "created_at",
"output.data format": "JSON",
"db.timezone": "UTC",
"tasks.max": "1"
}

A. Increase the number of Connect Tasks (tasks max value).
B. Increase the number of Connect Nodes.
C. Increase the number of partitions.
D. Increase the replication factor and increase the number of Connect Tasks.

Answer: A

Explanation:
The connector is currently configured with "tasks.max": "1", which means only one task is handling all tables (users, payment, orders). This can create a bottleneck and lead to lag. Increasing tasks.max allows Kafka Connect to parallelize work across multiple tasks, which can pull data from different tables concurrently and reduce lag.

NEW QUESTION # 49
......

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