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Question 81

Which Kafka feature replicates data asynchronously between clusters?

1. Cluster Linking
2. Kafka Streams
3. MirrorMaker 2
4. Schema Registry

Correct Answer: 3

Explanation:

MirrorMaker 2 is designed to replicate Kafka topics and related information between separate Kafka clusters. It can support use cases such as disaster recovery, migration, geographic distribution, and multi-cluster architectures. Unlike ordinary Kafka replication, which keeps replicas within a cluster, MirrorMaker 2 establishes replication across independent clusters. It uses Kafka Connect infrastructure and provides capabilities for topic replication and identity handling. CCDAK architects should distinguish MirrorMaker 2 from Cluster Linking because their operational models and supported use cases differ. Cross-cluster replication should be designed around recovery objectives, network capacity, security, and topic-management requirements.

Question 82

Which MirrorMaker 2 component performs cross-cluster replication work?

1. Kafka Streams application
2. MirrorSourceConnector
3. Schema Registry client
4. Consumer coordinator

Correct Answer: 2

Explanation:

MirrorSourceConnector is a Kafka Connect connector used by MirrorMaker 2 to replicate records from one Kafka cluster to another. MirrorMaker 2 uses a collection of connectors and supporting components to coordinate cross-cluster replication. The source connector consumes records from the source environment and produces them into the target cluster. This architecture allows replication to scale through Kafka Connect workers and tasks. CCDAK architects should understand the connector-based design when evaluating worker capacity, replication throughput, offset synchronization, topic naming, and failure recovery across multiple Kafka environments.

Question 83

Which Confluent feature supports Kafka cluster failover without application-level replication logic?

1. Cluster Linking
2. Consumer pause
3. Topic compaction
4. Producer batching

Correct Answer: 1

Explanation:

Cluster Linking provides a Confluent capability for connecting Kafka clusters and supporting scenarios such as disaster recovery and migration without requiring applications to implement their own cross-cluster replication logic. The architecture can maintain a relationship between source and destination environments while transferring topic data. This can simplify multi-cluster designs compared with building custom replication applications. CCDAK architects should still evaluate replication latency, network connectivity, authentication, destination readiness, and application failover procedures. Cluster Linking addresses data movement, but a complete disaster-recovery design also requires documented application and operational recovery processes.

Question 84

Which Kafka feature enables transactions across multiple topic partitions?

1. Consumer groups
2. Transactions
3. Static membership
4. Log retention

Correct Answer: 2

Explanation:

Kafka transactions allow a producer to group multiple writes into an atomic operation. Records can be written across multiple partitions and topics while remaining associated with the same transaction. Consumers configured appropriately can observe only committed transactional records. Transactions are particularly useful for consume-transform-produce workflows where output records and consumed offsets need coordinated handling. CCDAK architects should understand the additional configuration and operational requirements associated with transactional processing, including producer identity, transaction timeouts, consumer isolation, and recovery behavior.

Question 85

Which producer setting identifies a transactional producer instance?

1. transactional.id
2. transaction.timeout.ms
3. enable.idempotence
4. producer.id

Correct Answer: 1

Explanation:

The transactional.id setting identifies a transactional producer instance and enables Kafka to maintain transactional state for that producer identity. It is important for applications that use Kafka transactions because Kafka can use the identity to manage transactional operations and fencing behavior. transaction.timeout.ms controls transaction duration, while enable.idempotence addresses idempotent producer behavior rather than uniquely identifying a transactional instance. CCDAK architects should carefully design transactional producer identity, especially when multiple application instances are deployed, because incorrect identity management can cause producer fencing or unexpected transactional behavior.

Question 86

What happens when a transactional producer is fenced?

1. Its older instance loses producer authority
2. Its topic is deleted
3. Its consumer group is removed
4. Its broker becomes inactive

Correct Answer: 1

Explanation:

Producer fencing prevents an older transactional producer instance from continuing to write when another producer with the same transactional identity has taken over. This mechanism protects transactional correctness by preventing two instances from simultaneously acting as the authoritative producer for the same transactional identity. Fencing is especially important in failover scenarios where an old process may remain alive after a replacement instance starts. CCDAK architects should understand producer fencing when designing highly available transactional applications because it prevents stale instances from corrupting the intended transactional workflow.

Question 87

Which setting defines the maximum duration of a Kafka transaction?

1. transaction.timeout.ms
2. request.timeout.ms
3. retry.backoff.ms
4. metadata.max.age.ms

Correct Answer: 1

Explanation:

transaction.timeout.ms defines the maximum amount of time a transaction can remain open before Kafka considers it expired. Transactions that remain active beyond the allowed duration can be aborted by the broker. This setting should be compatible with broker-side transaction timeout limits and application processing characteristics. CCDAK architects should avoid unnecessarily long transactions because they can retain resources and delay transactional progress, while overly short limits can cause legitimate processing operations to fail. Transaction duration should therefore reflect realistic processing time, downstream latency, and recovery requirements.

Question 88

Which consumer setting determines whether transactional records must be committed before visibility?

1. fetch.max.wait.ms
2. isolation.level
3. max.poll.records
4. enable.auto.commit

Correct Answer: 2

Explanation:

The consumer isolation.level determines how transactional records are exposed to consumers. With read_committed, consumers see only records from successfully committed transactions. With read_uncommitted, transactional records can be visible regardless of their final transaction outcome. This setting is important for applications that depend on transactional processing guarantees. It does not control offset commits or fetch timing. CCDAK architects should configure consumer isolation consistently with the guarantees expected from transactional producers, especially in applications where aborted records must never reach downstream business logic.

Question 89

Which Kafka property controls the replication factor of the offsets topic?

1. offsets.topic.replication.factor
2. offsets.retention.check.interval.ms
3. transaction.state.log.replication.factor
4. default.replication.factor

Correct Answer: 1

Explanation:

offsets.topic.replication.factor controls the replication factor for Kafka’s internal consumer-offset topic. Because consumer offsets are important for recovering processing positions, appropriate replication helps protect that metadata against broker failures. This setting is distinct from the default replication factor for newly created application topics. CCDAK architects should treat internal Kafka topics as important infrastructure components rather than ignoring their durability requirements. The chosen replication factor should align with the cluster’s broker count, availability objectives, and operational recovery expectations.

Question 90

Which internal topic stores Schema Registry metadata in a Kafka-backed deployment?

1. __consumer_offsets
2. _schemas
3. __transaction_state
4. __connect_status

Correct Answer: 2

Explanation:

Schema Registry can use the _schemas Kafka topic to persist schema metadata. This internal topic supports Schema Registry’s distributed operation and allows schema information to remain available across service instances. It is separate from Kafka’s consumer offset and transaction-state topics. Because schemas form an important part of application data contracts, protecting Schema Registry’s underlying state is an important architectural consideration. CCDAK architects should understand the relationship between Schema Registry and Kafka infrastructure when planning replication, recovery, access control, and deployment of multiple Schema Registry instances.

Question 91

Which Schema Registry mode supports multiple instances sharing state?

1. Distributed mode
2. Standalone consumer mode
3. Producer-only mode
4. Embedded broker mode

Correct Answer: 1

Explanation:

Schema Registry can be deployed as multiple instances that share the same underlying Kafka-backed state, allowing clients to connect through a scalable service deployment. This architecture supports higher availability because an individual Schema Registry instance can fail without necessarily making schema services unavailable. The instances coordinate around shared schema metadata rather than maintaining unrelated independent registries. CCDAK architects should consider load balancing, listener configuration, security, storage durability, and compatibility policy when deploying Schema Registry at scale.

Question 92

Which Schema Registry feature determines whether older consumers can read new data?

1. Schema ID allocation
2. Compatibility mode
3. Subject deletion
4. REST listener

Correct Answer: 2

Explanation:

Schema Registry compatibility mode determines which schema changes are considered acceptable as schemas evolve. The selected policy can help ensure that new producers remain compatible with consumers using earlier schema versions, depending on the compatibility direction. This is especially important in independently deployed producer and consumer applications where releases do not occur simultaneously. CCDAK architects should establish compatibility requirements before allowing unrestricted schema evolution. A suitable compatibility policy reduces deployment coupling and helps prevent application failures caused by incompatible data-contract changes.

Question 93

Which compatibility mode requires both backward and forward compatibility?

1. NONE
2. BACKWARD
3. FORWARD
4. FULL

Correct Answer: 4

Explanation:

The FULL compatibility mode requires schema changes to satisfy both backward and forward compatibility requirements. This provides stronger evolution guarantees when applications need old and new versions to interoperate in both directions. BACKWARD and FORWARD focus on one direction of compatibility, while NONE does not enforce compatibility checks. CCDAK architects should select compatibility modes according to deployment sequencing and the ability of producers and consumers to evolve independently. Stronger compatibility can reduce flexibility in schema changes, so the policy should reflect actual application lifecycle requirements.

Question 94

Which Schema Registry format is based on JSON syntax and schema definitions?

1. Avro
2. Protobuf
3. JSON Schema
4. Binary JSON

Correct Answer: 3

Explanation:

JSON Schema is a schema format used to describe and validate JSON-based data structures. Confluent Schema Registry supports JSON Schema alongside formats such as Avro and Protobuf. JSON Schema can be useful when applications already exchange JSON data but still require centralized schema governance and compatibility management. CCDAK architects should compare serialization formats based on payload efficiency, language support, schema evolution, interoperability, and organizational standards. Choosing JSON Schema does not eliminate the need for compatibility planning; schema changes should still be governed carefully to prevent producer-consumer incompatibilities.

Question 95

Which serialization format uses Protocol Buffers definitions?

1. Protobuf
2. Avro
3. JSON Schema
4. CSV

Correct Answer: 1

Explanation:

Protobuf, or Protocol Buffers, is a structured serialization format supported by Confluent Schema Registry. It uses defined message schemas and provides compact binary serialization suitable for high-performance data exchange. Schema Registry can manage Protobuf schema versions and compatibility according to configured policies. Avro uses its own schema model, while JSON Schema describes JSON structures. CCDAK architects should evaluate Protobuf when strong typed contracts, compact messages, and broad language support are important. The final choice should consider existing application ecosystems, schema evolution requirements, payload characteristics, and tooling availability.

Question 96

Which Kafka feature allows a topic to have different retention behavior by policy?

1. Topic-level configuration
2. Consumer-level override
3. Broker identity mapping
4. Producer interceptor

Correct Answer: 1

Explanation:

Kafka supports topic-level configuration, allowing individual topics to have settings that differ from broker defaults. Examples include retention periods, cleanup policies, partition-level properties, and other topic-specific behavior. This capability is important because different event streams often have different business and operational requirements. A short-lived telemetry topic may need very different retention from a long-term audit stream. CCDAK architects should use topic-level configuration deliberately and maintain governance around configuration changes so production topics remain aligned with documented data-lifecycle requirements.

Question 97

Which Kafka mechanism prevents a stale replica from serving as leader after failure recovery?

1. Leader epoch
2. Topic retention
3. Schema compatibility
4. Consumer offset

Correct Answer: 1

Explanation:

Kafka uses leader epochs to distinguish different generations of partition leadership. This helps brokers and clients identify stale leadership information after failover or recovery. When a new leader is established, the leadership generation changes, allowing Kafka to reason about which replica holds authoritative leadership for the current epoch. This mechanism is particularly important during broker failures and replica recovery. CCDAK architects should understand leader epochs when analyzing failover behavior because partition leadership is dynamic, and stale information must not result in incorrect writes or replication decisions.

Question 98

Which Confluent component provides a REST interface for Kafka operations?

1. Kafka REST Proxy
2. Control Center
3. Schema Registry
4. Connect worker

Correct Answer: 1

Explanation:

Kafka REST Proxy provides HTTP-based access to Kafka functionality, allowing applications that do not use native Kafka client libraries to interact with Kafka through REST APIs. It can be useful for environments where HTTP integration is preferred or where language and platform constraints make native Kafka clients less convenient. REST Proxy is different from Schema Registry’s REST API because the latter focuses on schema operations. CCDAK architects should consider REST Proxy’s latency, throughput, security, and operational requirements before selecting it for high-volume event workloads where native clients may be more appropriate.

Question 99

Which architecture pattern isolates Kafka workloads across separate clusters?

1. Multi-cluster deployment
2. Single-partition design
3. Consumer multiplexing
4. Schema flattening

Correct Answer: 1

Explanation:

A multi-cluster Kafka deployment separates workloads or environments across independent Kafka clusters. Organizations may use this architecture for geographic isolation, regulatory boundaries, development separation, disaster recovery, organizational ownership, or workload isolation. Cross-cluster capabilities such as Cluster Linking or MirrorMaker 2 can connect selected data flows when necessary. CCDAK architects should evaluate network topology, operational ownership, security boundaries, replication requirements, and application failover procedures when designing multiple clusters. A multi-cluster architecture can provide useful isolation, but it also introduces additional operational complexity that must be managed deliberately.

Question 100

Which Kafka design principle keeps ordering guarantees limited to a partition?

1. Global ordering
2. Partition-local ordering
3. Cluster-wide sequencing
4. Broker-wide ordering

Correct Answer: 2

Explanation:

Kafka provides ordering guarantees within an individual partition rather than across all partitions in a topic. Records written to the same partition maintain their relative order as they are stored and consumed. When a topic contains multiple partitions, there is no general global ordering guarantee across those partitions. This is a fundamental architectural property that influences key selection and partition design. CCDAK architects should identify which business entities require ordered events and ensure their records are routed consistently to the same partition when ordering is essential.