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NEW QUESTION # 30
You are managing a cluster with a large number of topics, and each topic has a lot of partitions. A team wants to significantly increase the number of partitions for some topics.
Which parameters should you check before increasing the partitions?
- A. Check if acks=all is beina used.
- B. Check the max open file count on brokers.
- C. Check if compression is being used.
- D. Check the producer batch size and buffer size.
Answer: B
Explanation:
Each Kafka partition maps to multiple log segment files, and each segment results in open file descriptors on the broker. When the number of partitions increases significantly, it can exceed the OS-level limit for open files per broker process, leading to failures or degraded performance. Therefore, it is essential to check and possibly increase the ulimit -n (max open files) setting on the broker machines.
NEW QUESTION # 31
Which of the following are Kafka Connect internal topics? (Choose three.)
- A. connect-distributed
- B. connect-confiqs
- C. connect-status
- D. connect-offsets
- E. connect-standalone
Answer: B,C,D
Explanation:
connect-configs stores connector configurations.
connect-status tracks the status of connectors and tasks (e.g., RUNNING, FAILED).
connect-offsets stores source connector offsets for reading from external systems.
NEW QUESTION # 32
What does Kafka replication factor provide? (Choose two.)
- A. Performance
- B. Durability
- C. Security
- D. Availability
Answer: B,D
Explanation:
Replication ensures that multiple copies of data exist across different brokers, so data is not lost if one broker fails.
With multiple replicas, Kafka can continue to serve data even if the leader or one replica fails, maintaining service availability.
NEW QUESTION # 33
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. 300 GB
- B. 250 GB
- C. 750 GB
- D. 75 GB
Answer: A
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 # 34
A broker in the Kafka cluster is currently acting as the Controller.
Which statement is correct?
- A. It is responsible for sending leader information to all producers.
- B. It can have topic partitions.
- C. All consumers are allowed to fetch messages only from this server.
- D. It is given precedence for replication to and from replica followers.
Answer: B
Explanation:
The Controller broker is a regular broker that also takes on additional responsibilities for managing cluster metadata, such as leader elections and partition assignments. It still hosts topic partitions and participates in replication like any other broker.
NEW QUESTION # 35
When using Kafka ACLs, when is the resource authorization checked?
- A. Each time the resource is accessed.
- B. Each time the resource is accessed within the configured authorization interval.
- C. The initial time the resource is accessed.
- D. When the client connection is first established.
Answer: A
Explanation:
Kafka ACLs (Access Control Lists) perform authorization checks every time a client attempts to access a resource (e.g., topic, consumer group). This ensures continuous enforcement of permissions, not just at connection time or intervals. This approach provides fine-grained security, preventing unauthorized actions at any time during a session.
NEW QUESTION # 36
What are important factors in sizing a ksqlDB cluster? (Choose three.)
- A. Number of Queries
- B. Data Encryption
- C. Number of Partitions
- D. Data Schema
- E. Topic Data Retention
Answer: A,C,D
Explanation:
The complexity of the schema (number of fields, data types, etc.) affects processing and memory usage.
Each ksqlDB persistent query consumes resources (CPU, memory), so more queries require more capacity.
More partitions increase parallelism, but also resource usage, especially in scaling and state management.
NEW QUESTION # 37
What is the primary purpose of Kafka quotas?
- A. Limit the number of clients that can connect to the Kafka cluster.
- B. Throttle clients to prevent them from monopolizing Broker resources.
- C. Guarantee faster response times for some clients.
- D. Limit the total number of Partitions in the Kafka cluster
Answer: B
Explanation:
Kafka quotas are used to limit the throughput (bytes/sec) of producers and consumers to ensure fair resource usage and prevent any single client from overwhelming the brokers.
NEW QUESTION # 38
Which option is a valid Kafka Topic cleanup policy? (Choose two.)
- A. compact
- B. default
- C. cleanup
- D. delete
Answer: A,D
Explanation:
The delete policy deletes old log segments when they exceed the retention period or size.
The compact policy retains only the latest record for each key, enabling efficient key-based storage.
NEW QUESTION # 39
You have a Kafka cluster with topics t1 and t2. In the output below, topic t2 shows Partition 1 with a leader "-1".
What is the most likely reason for this?
...
$ kafka-topics --zookeeper localhost:2181 --describe --topic t1
Topic:t1 PartitionCount 1 ReplicationFactor 1 Configs:
Topic: t1 Partition: 0 Leader: 0 Replicas: 0 Isr: 0
$ kafka-topics --zookeeper localhost:2181 --describe --topic t2
Topic:t2 PartitionCount 2 ReplicationFactor 1 Configs:
Topic: t2 Partition: 0 Leader: 0 Replicas: 0 Isr: 0
Topic: t2 Partition: 1 Leader: -1 Replicas: 1 Isr:
- A. Compression has been enabled on Broker 1.
- B. Broker 1 has another partition clashing with the same name.
- C. Leader shows "-1" while the log cleaner thread runs on Broker 1.
- D. Broker 1 failed.
Answer: D
Explanation:
A Leader of -1 indicates that no broker is currently the leader for that partition. This usually happens when the only replica for that partition is unavailable, often due to the associated broker (in this case, Broker 1) failing or being offline. Kafka cannot elect a leader if no replica is in the in-sync replica (ISR) list, which leads to leader = -1.
NEW QUESTION # 40
A customer has a use case for a ksqlDB persistent query. You need to make sure that duplicate messages are not processed and messages are not skipped.
Which property should you use?
- A. ksql.streams auto.offset.reset=latest
- B. processing.guarantee=exactly_once
- C. ksql.fail.on.production.error=false
- D. ksql.streams auto offset.reset=earliest
Answer: B
Explanation:
processing.guarantee=exactly_once ensures that messages are processed exactly once by ksqlDB, preventing both duplicates and message loss.
NEW QUESTION # 41
Which connector type takes data from a topic and sends it to an external data system?
- A. SvsIog Connector
- B. Sink Connector
- C. Source Connector
- D. Streams Connector
Answer: B
Explanation:
A Sink Connector reads data from a Kafka topic and writes it to an external data system, such as a database, file system, or cloud service.
NEW QUESTION # 42
Which use cases would benefit most from continuous event stream processing? (Choose three.)
- A. Historical dashboards
- B. End-of-day financial settlement processing
- C. Context-aware product recommendations for e-commerce
- D. Log monitoring/application fault detection
- E. Fraud detection
Answer: C,D,E
Explanation:
Real-time analysis of transactions helps detect suspicious activity instantly.
Context-aware product recommendations for e-commerce requires real-time user behavior tracking to serve personalized recommendations.
Log monitoring/application fault detection needs immediate processing to alert on system issues or failures.
NEW QUESTION # 43
You have a cluster with a topic t1 that already has uncompressed messages. A new Producer starts sending messages to t1 with compression enabled.
Which condition would allow this?
- A. Never, because topic t1 already has uncompressed messages.
- B. Only if the new Producer disables batching.
- C. If the new Producer is configured to use compression.
- D. Only if Kafka is also enabled for encryption.
Answer: C
Explanation:
Kafka allows mixed compression formats within the same topic and even the same partition. Each message batch includes metadata indicating whether and how it is compressed. Therefore, a new producer can send compressed messages to a topic that already contains uncompressed messages, as long as it is configured with a compression codec (e.g., compression.type=gzip, snappy, etc.).
NEW QUESTION # 44
What is the relationship between topics and partitions? (Choose two.)
- A. Atopic may have more than one partition.
- B. Atopic always has one partition.
- C. There is no relationship between topics and partitions.
- D. A partition is always linked to a single topic.
- E. A partition may have more than one topic.
Answer: A,D
Explanation:
Kafka topics are split into one or more partitions to enable parallelism and scalability.
Each partition belongs to exactly one topic; it cannot span multiple topics.
NEW QUESTION # 45
What are benefits to gracefully shutting down brokers? (Choose two.)
- A. It will migrate any partitions the server is the leader for to other replicas prior to shutting down.
- B. It will automatically re-elect leaders on restart.
- C. It will sync all its logs to disk to avoid needing to do any log recovery when it restarts.
- D. It will balance the partitions across brokers before restarting.
Answer: A,C
Explanation:
A graceful shutdown ensures that logs are flushed to disk, minimizing recovery time during restart.
Kafka performs controlled leader migration during a graceful shutdown to avoid disruption and ensure availability.
NEW QUESTION # 46
Kafka broker supports which Simple Authentication and Security Layer (SASL) mechanisms for authentication? (Choose three.)
- A. SASL/OAUTHBEARER
- B. SASL/SAML20
- C. SASL/OTP
- D. SASL/GSSAPI (Kerberos)
- E. SASL/PLAIN
Answer: A,D,E
Explanation:
SASL/PLAIN - A simple username/password mechanism supported by Kafka.
SASL/GSSAPI (Kerberos) - Kafka supports Kerberos authentication through the GSSAPI mechanism.
SASL/OAUTHBEARER - Kafka supports OAUTHBEARER for token-based authentication.
NEW QUESTION # 47
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