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Confluent Certified Developer for Apache Kafka Certification Examination Sample Questions (Q56-Q61):
NEW QUESTION # 56
(You need to set alerts on key broker metrics to trigger notifications when a Kafka cluster is unhealthy.
What are three minimum broker metrics to monitor for cluster health?
Select three.)
- A. kafka.controller:type=ControllerStats,name=UncleanLeaderElectionsPerSec
- B. kafka.controller:type=KafkaController,name=LastCommittedRecordOffset
- C. kafka.controller:type=KafkaController,name=TopicsToDeleteCount
- D. kafka.controller:type=KafkaController,name=OfflinePartitionsCount
- E. kafka.controller:type=KafkaController,name=ActiveControllerCount
Answer: A,D,E
Explanation:
The Apache Kafka monitoring and operations documentation highlights several controller-level metrics that are critical indicators of cluster health.
ActiveControllerCount must always be 1 in a healthy cluster. A value of 0 indicates no active controller, while values greater than 1 indicate a serious split-brain condition. This metric is fundamental for cluster stability.
OfflinePartitionsCount is one of the most important health metrics. Any value greater than 0 means that some partitions have no available leader, making them unavailable for producers and consumers. This directly indicates a degraded or unhealthy cluster.
UncleanLeaderElectionsPerSec tracks the rate of unclean leader elections. Unclean elections can cause data loss, and any non-zero value is a strong signal of broker failures or misconfiguration and should immediately trigger alerts.
The remaining options are not minimum health indicators. LastCommittedRecordOffset is not a standard health metric, and TopicsToDeleteCount only indicates pending administrative operations, not cluster instability.
Therefore, the three minimum and officially recommended broker metrics for monitoring Kafka cluster health are UncleanLeaderElectionsPerSec, ActiveControllerCount, and OfflinePartitionsCount.
NEW QUESTION # 57
Which configuration allows more time for the consumer poll to process records?
- A. fetch.max.wait.ms
- B. session.timeout.ms
- C. heartbeat.interval.ms
- D. max.poll.interval.ms
Answer: D
Explanation:
max.poll.interval.ms defines themaximum delay between invocations of poll()before the consumer is considered failed. It essentially gives consumers more time to process records before needing to poll again.
FromKafka Consumer Configuration Reference:
"max.poll.interval.ms: The maximum delay between invocations of poll() when using consumer group management. If poll() is not called before expiration, the consumer is considered failed."
* session.timeout.ms and heartbeat.interval.ms relate to group coordination and heartbeats.
* fetch.max.wait.ms affects how long the broker waits to accumulate data before sending a fetch response.
Reference:Apache Kafka Consumer Configs
NEW QUESTION # 58
(A consumer application needs to use an at-most-once delivery semantic.
What is the best consumer configuration and code skeleton to avoid duplicate messages being read?)
- A. auto.offset.reset=earliest and enable.auto.commit=falsewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);consumer.commitAsync();for (var record : records) {// Any processing}}
- B. auto.offset.reset=earliest and enable.auto.commit=truewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);consumer.commitAsync();for (var record : records) {// Any processing}}
- C. auto.offset.reset=earliest and enable.auto.commit=falsewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);for (var record : records) {// Any processing}consumer.commitAsync();}
- D. auto.offset.reset=latest and enable.auto.commit=truewhile (true) {final var records = consumer.poll (POLL_TIMEOUT);for (var record : records) {// Any processing}consumer.commitAsync();}
Answer: A
Explanation:
According to the official Apache Kafka consumer semantics documentation, at-most-once delivery means that records may be lost but must never be processed more than once. To achieve this guarantee, a consumer must commit offsets before processing records. If the application crashes after committing but before processing, messages are skipped rather than reprocessed, which satisfies at-most-once semantics.
Option B correctly implements this pattern. It disables automatic offset commits (enable.auto.commit=false) and explicitly calls commitAsync() immediately after polling and before processing records. This ensures that offsets are advanced regardless of whether processing succeeds, eliminating the possibility of duplicate processing.
Option C represents at-least-once semantics, because offsets are committed after processing, which can lead to duplicates on failure. Options A and D rely on automatic commits or incorrect ordering and do not provide strict at-most-once guarantees.
The Kafka documentation explicitly states that manual offset management with commits before processing is required for at-most-once delivery.
Therefore, Option B is the best and officially documented configuration and code skeleton to avoid duplicate message processing.
NEW QUESTION # 59
Match the testing tool with the type of test it is typically used to perform.
Answer:
Explanation:
Explanation:
Unit Testing # MockProducer
Integration Testing # Testcontainers
Performance Testing # Trogdor
Mock Data Generation # Connect Datagen
MockProducer: Simulates a Kafka producer in unit tests (no real broker interaction).
Testcontainers: Spawns Kafka in Docker for real environment testing.
Trogdor: Kafka's built-in performance load testing framework.
Connect Datagen: Creates sample source records for test and demo purposes.
From Kafka Developer Tools Guide:
"Kafka developers commonly use MockProducer for unit tests, Testcontainers for integration, and Trogdor for performance tests." Reference: Kafka Testing and Tools Overview
NEW QUESTION # 60
Match each configuration parameter with the correct deployment step in installing a Kafka connector.
Answer:
Explanation:
Explanation:
* 1st:Place the connector's JAR file in the directory specified by plugin.path
* 2nd:Restart the Kafka Connect cluster
* 3rd:Verify using REST API (/connector-plugins)
* 4th:Configure the connector
* 5th:(Repeat of 4th, duplicate step)
1st # Place the connector's JAR file in the directory specified by the plugin.path configuration.
2nd # Restart the Kafka Connect cluster.
3rd # Verify that the connector is installed by listing all available connectors using the Kafka Connect REST API (/connector-plugins).
4th # Configure the connector using a JSON or properties file with the necessary settings.
5th # Configure the connector using a JSON or properties file with the necessary settings.
Kafka Connect requires that custom connectors be placed in the directory defined by plugin.path. After restarting the cluster, you can use the REST API to confirm availability and then deploy the connector configuration.
FromKafka Connect Documentation:
"After placing the JAR in the plugin.path, you must restart the Connect cluster to pick it up. Use the
/connector-plugins REST endpoint to verify."
The duplication of configuration is an error in the question options and should occur only once.
Reference:Kafka Connect Plugin Installation Guide
NEW QUESTION # 61
......
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