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Question 101

Which Kafka component handles network requests from clients?

1. Log cleaner
2. Replica manager
3. Controller quorum
4. Network threads

Correct Answer: 4

Explanation:

Kafka brokers use network threads to receive and process incoming client requests before passing appropriate work to other broker components. Producers, consumers, and administrative clients communicate with brokers through the Kafka protocol, and network processing is therefore an important part of broker performance. Network threads should be considered alongside request-handler capacity, disk performance, and workload characteristics. CCDAK architects should avoid treating broker throughput as purely a storage concern because client connection handling can also become a bottleneck. Proper capacity planning considers network traffic, request rates, message sizes, and expected concurrency.

Question 102

Which broker setting controls the number of network processing threads?

1. num.network.threads
2. num.io.threads
3. queued.max.requests
4. socket.request.max.bytes

Correct Answer: 1

Explanation:

num.network.threads specifies the number of threads Kafka uses for handling network requests. These threads accept connections and process network-level request activity before requests are handled by other broker resources. Increasing the value can help when network processing becomes a bottleneck, but excessive thread counts also consume system resources. num.io.threads addresses a different stage of broker request processing. CCDAK architects should tune network and I/O resources according to request rates, connection counts, message sizes, and available CPU rather than increasing thread counts without observing actual workload behavior.

Question 103

Which broker setting controls threads responsible for request processing and disk operations?

1. num.network.threads
2. num.io.threads
3. background.threads
4. socket.receive.buffer.bytes

Correct Answer: 2

Explanation:

num.io.threads controls the number of threads Kafka uses for request processing that involves operations such as disk access. These threads can become important when workloads generate substantial produce, fetch, or administrative activity requiring broker-side processing. Network threads handle the initial network layer, while I/O threads perform subsequent work. CCDAK architects should examine CPU utilization, disk latency, request queues, and throughput before adjusting this setting. An appropriately sized I/O thread pool can help prevent broker request processing from becoming constrained when workloads involve substantial storage activity.

Question 104

What does Kafka’s page cache primarily improve?

1. Schema validation
2. Consumer assignment
3. Disk read efficiency
4. Cluster authentication

Correct Answer: 3

Explanation:

Kafka relies heavily on the operating system’s page cache to efficiently read and write log data. Frequently accessed data may remain in memory, reducing the need for repeated physical disk reads. This architecture allows Kafka to leverage the operating system rather than maintaining a large application-level cache for ordinary log data. CCDAK architects should therefore consider available system memory, disk characteristics, and workload access patterns when sizing brokers. Page-cache effectiveness can significantly influence throughput and latency, especially for workloads where consumers repeatedly access recently written records.

Question 105

Which Kafka mechanism creates an index for locating records within log segments?

1. Offset index
2. Schema index
3. Consumer index
4. Transaction index

Correct Answer: 4

Explanation:

Kafka maintains indexes associated with log segments to help locate records efficiently by their offsets. These indexes allow brokers to avoid scanning an entire segment when a consumer requests data beginning at a particular position. Kafka’s log structure combines sequential storage with supporting index information to provide efficient access. CCDAK architects should understand this storage model because partition size, segment configuration, disk performance, and indexing behavior all contribute to broker performance. The offset index is part of Kafka’s internal storage architecture rather than an application-visible database index.

Question 106

Which Kafka log structure stores records sequentially within a partition?

1. Consumer cache
2. Commit table
3. Log segment
4. Schema subject

Correct Answer: 3

Explanation:

Kafka partitions are stored as sequences of log segments. Each segment contains records written in offset order, along with supporting files such as indexes. When a segment reaches configured conditions, Kafka rolls to another segment. This structure supports efficient sequential writes and enables retention and compaction operations to work on manageable portions of the partition log. CCDAK architects should understand segments because settings involving segment size, retention, compaction, and disk usage directly influence how partition data is managed. A partition is therefore not represented by one continuously growing physical file.

Question 107

Which Kafka mechanism lets brokers replicate partition data from a leader?

1. Replica fetcher
2. Consumer coordinator
3. Schema manager
4. Connect task

Correct Answer: 1

Explanation:

Replica fetcher threads are responsible for allowing follower replicas to retrieve records from their partition leaders. This replication process keeps follower copies synchronized with the leader and helps maintain the replica set’s fault-tolerance characteristics. Replica fetching is separate from normal application consumer activity, even though both involve reading Kafka records. CCDAK architects should consider replication traffic when evaluating broker network capacity because replicas generate additional data movement beyond application-level producer and consumer traffic. Broker failures, slow disks, and network congestion can all influence replica synchronization performance.

Question 108

Which broker setting controls the number of replica fetcher threads?

1. num.network.threads
2. num.replica.fetchers
3. num.io.threads
4. background.threads

Correct Answer: 2

Explanation:

num.replica.fetchers controls the number of threads used by a broker to fetch data from partition leaders for replica synchronization. Increasing this value can provide additional replication concurrency in environments where follower synchronization is constrained, although it also consumes CPU and network resources. CCDAK architects should evaluate replication lag, broker load, disk performance, and network utilization before changing the setting. The objective is to maintain healthy replica synchronization without allowing replication activity to consume excessive resources needed for normal client traffic.

Question 109

What is a Kafka partition leader responsible for?

1. Serving partition requests
2. Managing schema versions
3. Assigning consumer groups
4. Persisting connector configuration

Correct Answer: 1

Explanation:

The partition leader is the replica responsible for handling normal produce and fetch requests for that partition. Followers replicate the leader’s log so they can take over when leadership changes. This leader-based model simplifies request routing and maintains a clear authoritative replica for each partition. Schema management belongs to Schema Registry, consumer-group coordination has separate Kafka mechanisms, and connector configuration is managed by Kafka Connect. CCDAK architects should understand leader placement because broker failures and leader distribution can influence workload balance, network utilization, and application latency.

Question 110

Which metric indicates the number of active client connections to a broker?

1. requests.completed
2. bytes.out
3. connections.current
4. request.queue

Correct Answer: 3

Explanation:

connections.current represents the current number of active network connections associated with a Kafka broker. Monitoring connection counts can help identify unusual client behavior, connection churn, or resource pressure. A large number of connections may be expected in some architectures, while rapid connection creation and termination can indicate inefficient client configuration or application instability. CCDAK architects should evaluate connection metrics alongside request rates, CPU usage, network throughput, and client deployment patterns. Connection management is particularly important in large environments where many applications or consumer instances communicate with the same brokers.

Question 111

Which Kafka metric measures the number of incoming network bytes?

1. network.bytesOut
2. network.bytesIn
3. request.rate
4. response.rate

Correct Answer: 2

Explanation:

The incoming network byte rate measures how much data Kafka brokers receive from clients and other network sources. Monitoring inbound traffic helps architects understand producer load, replication traffic, administrative activity, and potential network bottlenecks. Outbound traffic is measured separately because consumers and followers can generate substantial broker-side network utilization. CCDAK capacity planning should consider both directions because Kafka workloads often produce asymmetric network patterns. Compression, replication factor, message size, producer batching, and consumer activity can all influence the amount of data crossing broker network interfaces.

Question 112

Which broker metric measures data sent over the network?

1. network.bytesIn
2. disk.bytesWritten
3. network.bytesOut
4. request.count

Correct Answer: 3

Explanation:

network.bytesOut represents data transmitted from a broker over its network interfaces. Outbound traffic can include consumer fetch responses, replica synchronization, and other broker communication. Monitoring this metric helps identify network saturation and understand how traffic changes as workloads scale. It should be evaluated together with inbound traffic because replication and application consumption can create significant network demand. CCDAK architects should account for outbound bandwidth when sizing broker infrastructure, particularly for clusters with high consumer throughput or substantial replication across availability zones.

Question 113

Which Kafka broker setting limits the size of a socket receive buffer?

1. socket.receive.buffer.bytes
2. socket.send.buffer.bytes
3. socket.request.max.bytes
4. receive.buffer.limit

Correct Answer: 1

Explanation:

socket.receive.buffer.bytes controls the size of the socket receive buffer used by Kafka’s network connections. This setting influences how incoming network data is buffered before processing. It should be considered alongside the operating system’s networking configuration and workload characteristics. socket.send.buffer.bytes addresses outbound buffering, while request-size settings limit different aspects of Kafka requests. CCDAK architects should avoid tuning socket buffers independently from the underlying network environment because operating-system limits, latency, bandwidth, and message size all affect the appropriate configuration.

Question 114

Which Kafka broker setting controls the socket send buffer size?

1. socket.receive.buffer.bytes
2. socket.send.buffer.bytes
3. socket.request.max.bytes
4. socket.connection.max.bytes

Correct Answer: 2

Explanation:

socket.send.buffer.bytes specifies the size of the network send buffer used by Kafka connections. It affects how outbound data is buffered before being transmitted through the network stack. Proper sizing can help support workloads with substantial response traffic, but excessively large values may consume unnecessary memory. CCDAK architects should evaluate send-buffer settings alongside network bandwidth, latency, operating-system limits, and broker connection patterns. This is particularly relevant for brokers serving many consumers because fetch responses can create considerable outbound traffic during periods of high event throughput.

Question 115

Which Kafka broker setting limits queued requests waiting for processing?

1. queued.max.requests
2. request.timeout.ms
3. max.request.size
4. fetch.max.bytes

Correct Answer: 1

Explanation:

queued.max.requests controls the maximum number of requests that can be queued for processing when Kafka’s request-handling resources are busy. The setting can influence how the broker behaves under heavy request pressure. If queues become saturated, clients may experience increased latency or request failures depending on the surrounding configuration and workload. CCDAK architects should monitor request queues, processing latency, CPU utilization, and network traffic before adjusting this setting. Queue capacity should support expected traffic bursts without allowing excessive accumulation that could increase latency and resource consumption.

Question 116

Which Kafka mechanism periodically elects a new controller when required?

1. Consumer rebalance
2. Controller election
3. Schema negotiation
4. Replica compaction

Correct Answer: 2

Explanation:

Controller election selects the Kafka controller responsible for cluster-level metadata coordination. In modern KRaft architecture, controller nodes participate in a metadata quorum, and leadership changes occur according to the quorum’s coordination mechanisms. Controller leadership is separate from partition leadership, although the controller coordinates partition leader changes. CCDAK architects should distinguish these concepts because a controller manages cluster metadata responsibilities while partition leaders handle client operations for individual partitions. Understanding controller election is important when evaluating cluster availability, metadata quorum health, and failure-recovery behavior.

Question 117

Which KRaft component maintains the replicated metadata log?

1. Controller quorum
2. Consumer group
3. Connect cluster
4. Schema subject

Correct Answer: 1

Explanation:

In KRaft architecture, the controller quorum maintains the replicated metadata log used to manage Kafka cluster metadata. This replaces the ZooKeeper-based metadata architecture used by older Kafka deployments. Controller nodes participate in quorum operations and maintain consistent cluster state. The metadata log is distinct from ordinary application topic partitions, which store event records for client workloads. CCDAK architects should understand this separation because KRaft introduces different deployment and operational considerations, including controller sizing, quorum availability, and metadata recovery.

Question 118

Which KRaft role is responsible for managing Kafka metadata?

1. Broker-only role
2. Consumer role
3. Controller role
4. Connector role

Correct Answer: 3

Explanation:

The controller role in KRaft is responsible for managing Kafka cluster metadata and coordinating controller-level operations. KRaft deployments can separate broker and controller responsibilities or use combined roles depending on the architecture and deployment requirements. Controllers participate in the metadata quorum, while brokers handle client data requests and partition storage. CCDAK architects should choose an appropriate role arrangement based on cluster size, resource isolation, availability requirements, and operational strategy. Clear separation of responsibilities can be useful in larger environments where metadata and data-plane workloads have different resource needs.

Question 119

Which Kafka operation changes the number of partitions for an existing topic?

1. Partition reassignment
2. Topic partition expansion
3. Schema migration
4. Replica synchronization

Correct Answer: 2

Explanation:

Kafka supports increasing the partition count of an existing topic through partition expansion. Adding partitions can increase future producer and consumer parallelism, but it does not redistribute historical records from existing partitions into the newly created ones. Therefore, partition expansion should be planned carefully, especially when keyed ordering or partition-based distribution is important. CCDAK architects should also remember that increasing partitions is generally easier than reducing them, so initial sizing should consider expected growth. Changes can affect key distribution, consumer concurrency, and application assumptions.

Question 120

Which Kafka feature redistributes partition replicas across brokers?

1. Consumer reassignment
2. Replica reassignment
3. Schema reassignment
4. Connector migration

Correct Answer: 2

Explanation:

Replica reassignment moves partition replicas between brokers to improve distribution, replace infrastructure, respond to capacity changes, or rebalance storage and network workloads. Unlike consumer partition assignment, replica reassignment concerns the physical placement of partition copies across the Kafka cluster. Architects may use reassignment when adding brokers, retiring hardware, or correcting uneven resource utilization. The operation should be planned carefully because moving replicas creates additional network and disk activity. CCDAK designs should account for reassignment impact on production traffic, replication health, broker capacity, and operational maintenance windows.