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Question 321

Which Kafka broker setting limits total simultaneous connections?

1. max.connections.per.ip
2. connections.max.idle.ms
3. socket.receive.buffer.bytes
4. max.connections

Correct Answer: 4

Explanation:

The max.connections broker setting limits the maximum number of connections that a broker accepts. This provides a broad connection-resource control at the broker level. It differs from max.connections.per.ip, which restricts connections originating from an individual IP address. Idle timeout and socket buffer settings address different aspects of network behavior. Connection limits can be useful in large deployments where many clients communicate with the same brokers. Administrators should consider legitimate client requirements before setting restrictive limits, because applications that maintain multiple persistent connections may otherwise experience connection failures or rejected connection attempts.

Question 322

Which Kafka setting controls socket connection establishment timeout?

1. socket.connection.setup.timeout.ms
2. connections.max.idle.ms
3. request.timeout.ms
4. socket.send.buffer.bytes

Correct Answer: 1

Explanation:

The socket.connection.setup.timeout.ms setting controls the timeout associated with establishing a socket connection. It helps prevent connection attempts from remaining unresolved indefinitely when network conditions or remote endpoints do not respond as expected. This differs from idle-connection settings, which determine how long established but inactive connections remain open. Request timeouts address request processing rather than the initial socket setup. Correctly tuning connection-establishment behavior can be useful in distributed Kafka environments where brokers and clients communicate across networks with varying latency or connectivity characteristics.

Question 323

Which broker setting controls the maximum socket receive buffer?

1. socket.send.buffer.bytes
2. socket.receive.buffer.bytes
3. replica.socket.timeout.ms
4. queued.max.requests

Correct Answer: 2

Explanation:

The socket.receive.buffer.bytes setting controls the size of the network receive buffer used by Kafka sockets. The receive buffer provides temporary memory for incoming network data before the application processes it. This setting is different from the socket send buffer, which handles outgoing data. Replica socket timeout and request-queue settings address other aspects of broker communication. Network-buffer configuration can influence throughput and connection behavior, particularly in environments with high network bandwidth or latency. Administrators should consider operating-system limits and workload characteristics when tuning socket buffer sizes.

Question 324

Which Kafka setting controls outgoing socket buffer size?

1. socket.receive.buffer.bytes
2. queued.max.requests
3. socket.send.buffer.bytes
4. socket.connection.setup.timeout.ms

Correct Answer: 3

Explanation:

The socket.send.buffer.bytes configuration determines the size of the socket send buffer used for outgoing network data. A larger buffer can provide additional space for data waiting to be transmitted, while the appropriate value depends on network characteristics and workload patterns. The receive-buffer setting applies to incoming traffic, and connection setup timeout concerns establishing sockets. The request queue setting addresses broker request processing rather than socket buffering. Network tuning should be performed with awareness of operating-system constraints because Kafka’s configured buffer sizes may also interact with system-level socket limits.

Question 325

What does queued.max.requests limit?

1. Consumer subscriptions
2. Broker queued requests
3. Topic partitions
4. Replica assignments

Correct Answer: 2

Explanation:

The queued.max.requests broker setting limits the number of requests that can be queued for processing when the request-handling system is busy. This helps control how much pending work accumulates inside a broker. The setting does not determine consumer subscriptions, topic partition counts, or replica assignments. Request-queue behavior can affect how a broker responds under heavy load because excessive pending requests can consume memory and contribute to latency. Administrators should evaluate this setting alongside network-thread and I/O-thread configurations when analyzing broker performance and request-processing capacity.

Question 326

Which Kafka setting controls replica socket timeout?

1. replica.socket.timeout.ms
2. replica.fetch.min.bytes
3. replica.fetch.max.bytes
4. replica.fetch.wait.max.ms

Correct Answer: 1

Explanation:

The replica.socket.timeout.ms setting controls the timeout for socket communication used during replica fetching. Kafka followers communicate with partition leaders to retrieve replicated data, and a socket timeout determines how long communication can remain unresolved before the operation is considered unsuccessful. This is distinct from minimum fetch size, maximum fetch size, and fetch waiting time. Replica communication settings should be tuned with awareness of network latency and cluster topology. Excessively short timeouts can cause unnecessary failures in higher-latency environments, while excessively long values may delay detection of communication problems.

Question 327

Which broker setting controls the number of I/O processing threads?

1. num.network.threads
2. num.replica.fetchers
3. num.io.threads
4. num.recovery.threads.per.data.dir

Correct Answer: 3

Explanation:

The num.io.threads broker configuration controls the number of threads used for request processing that involves disk I/O. These threads handle operations that require interaction with storage, while network threads primarily handle network communication. Replica fetcher and recovery-thread settings serve specialized purposes. Increasing I/O threads may help a storage-bound workload process requests more efficiently when sufficient CPU and storage capacity are available. However, simply increasing the value does not guarantee better performance because the appropriate setting depends on disk performance, workload patterns, CPU availability, and the overall broker architecture.

Question 328

Which broker setting controls network processing threads?

1. num.io.threads
2. num.network.threads
3. num.recovery.threads.per.data.dir
4. num.replica.fetchers

Correct Answer: 2

Explanation:

The num.network.threads broker setting controls the number of threads responsible for handling network requests and responses. These threads accept network traffic and coordinate request handling before work proceeds to other processing components. It is different from I/O threads, which handle operations involving storage, and replica-fetcher threads, which support follower replication. Network-thread tuning can matter when brokers receive high request volumes or serve many clients. Administrators should examine request rates, CPU usage, and network utilization before increasing the setting because unnecessary thread growth can also introduce scheduling overhead.

Question 329

Which Kafka setting controls recovery threads per data directory?

1. num.recovery.threads.per.data.dir
2. num.io.threads
3. log.cleaner.threads
4. num.network.threads

Correct Answer: 1

Explanation:

The num.recovery.threads.per.data.dir setting controls the number of threads used for log recovery operations per data directory. Recovery work can occur when a broker starts and needs to process log segments following an unclean or interrupted shutdown. More recovery threads can allow multiple recovery operations to proceed concurrently when sufficient storage and CPU resources exist. This setting differs from network, I/O, and log-cleaner thread counts. Administrators may consider recovery-thread tuning when startup recovery takes significant time, while also accounting for the storage subsystem’s ability to handle concurrent operations.

Question 330

Which broker configuration controls replica fetch response size?

1. replica.fetch.min.bytes
2. replica.fetch.wait.max.ms
3. replica.fetch.max.bytes
4. replica.socket.receive.buffer.bytes

Correct Answer: 3

Explanation:

The replica.fetch.max.bytes configuration controls the maximum amount of data returned in a replica fetch response. Followers use fetch requests to retrieve records from partition leaders, and response sizing affects replication throughput and memory usage. This setting is distinct from minimum fetch size and maximum waiting time, which influence when responses are returned. Socket receive buffers operate at the network layer rather than directly defining the Kafka replica response size. Administrators should consider record sizes and replication workload when selecting an appropriate value so that followers can efficiently keep up with leaders.

Question 331

Which Kafka consumer property limits total fetch response bytes?

1. max.partition.fetch.bytes
2. fetch.max.bytes
3. fetch.min.bytes
4. receive.buffer.bytes

Correct Answer: 2

Explanation:

The consumer fetch.max.bytes property controls the maximum amount of data returned for a fetch request across partitions. It therefore provides an overall fetch-response limit, whereas max.partition.fetch.bytes applies separately to each partition. fetch.min.bytes influences the minimum amount of data the broker attempts to accumulate before responding, while receive.buffer.bytes controls network buffering. Understanding these distinctions is important when tuning consumers for high-throughput workloads. Fetch limits affect memory usage, network traffic, and the amount of data a consumer can retrieve during an individual fetch cycle.

Question 332

Which consumer setting specifies minimum data before a fetch response?

1. fetch.max.bytes
2. max.partition.fetch.bytes
3. fetch.min.bytes
4. request.timeout.ms

Correct Answer: 3

Explanation:

The fetch.min.bytes consumer configuration specifies the minimum amount of data the broker should return for a fetch request when possible. The broker may wait for additional data to accumulate, subject to the relevant waiting limit. This can improve efficiency by reducing the number of small fetch responses when traffic is relatively low. It differs from maximum fetch settings, which establish upper limits. Administrators can tune the value according to workload requirements because a larger minimum may improve throughput efficiency while potentially increasing response latency when records arrive slowly.

Question 333

Which consumer setting controls network receive buffering?

1. receive.buffer.bytes
2. fetch.min.bytes
3. fetch.max.bytes
4. max.poll.interval.ms

Correct Answer: 1

Explanation:

The receive.buffer.bytes consumer setting controls the size of the TCP receive buffer used for network communication. It operates at the socket level and is separate from Kafka’s application-level fetch-size configurations. fetch.min.bytes and fetch.max.bytes determine how much Kafka data is requested or returned, while max.poll.interval.ms controls the permitted interval between consumer polling calls. Socket buffer sizing can influence network throughput, particularly under high-bandwidth or higher-latency conditions. Administrators should consider operating-system networking limits when adjusting this setting.

Question 334

Which consumer property controls maximum poll processing interval?

1. max.poll.records
2. max.poll.interval.ms
3. fetch.max.wait.ms
4. session.timeout.ms

Correct Answer: 2

Explanation:

The max.poll.interval.ms consumer property defines the maximum allowed time between successful calls to poll() before Kafka considers the consumer unable to keep up with its group responsibilities. If application processing takes too long between polls, the consumer can leave the group and trigger reassignment. This differs from max.poll.records, which limits the number of records returned by a poll, and session timeout settings, which relate to consumer liveness and heartbeats. Applications with lengthy record-processing operations may need to tune this interval appropriately.

Question 335

Which consumer setting limits records returned by one poll?

1. max.poll.records
2. fetch.max.bytes
3. max.partition.fetch.bytes
4. max.poll.interval.ms

Correct Answer: 1

Explanation:

The max.poll.records consumer setting limits the maximum number of records returned by a single poll() call. It can help applications control the amount of work retrieved at once and manage processing time between polls. The setting does not directly limit the number of bytes returned by a fetch; byte-based controls are provided by other fetch configurations. This distinction is important because record sizes can vary significantly. Applications performing expensive processing may reduce the number of records returned per poll to keep processing within acceptable timing boundaries.

Question 336

Which Kafka consumer setting controls group liveness timeout?

1. max.poll.records
2. fetch.max.bytes
3. session.timeout.ms
4. fetch.min.bytes

Correct Answer: 3

Explanation:

The session.timeout.ms setting controls the amount of time the consumer group coordinator waits for heartbeats before considering a consumer failed. If the consumer does not maintain group liveness within the configured session period, the group can begin rebalancing and assign the consumer’s partitions elsewhere. This differs from max.poll.interval.ms, which concerns how long the application can go between poll calls. Session timeout therefore relates more directly to heartbeat-based membership detection. Proper configuration helps balance failure-detection speed with tolerance for temporary communication delays.

Question 337

Which Kafka Streams property controls unhandled thread exceptions?

1. default.deserialization.exception.handler
2. default.production.exception.handler
3. default.uncaught.exception.handler
4. topology.optimization

Correct Answer: 3

Explanation:

The default.uncaught.exception.handler configuration defines the default handler used when an uncaught exception reaches a Kafka Streams processing thread. Such exceptions can indicate serious application-level failures that are not handled by more specific mechanisms. This setting is distinct from deserialization and production exception handlers, which address particular categories of processing problems. Configuring an appropriate uncaught-exception policy helps determine how a Streams application responds when a thread encounters an unexpected failure. Operational teams should understand the consequences of the selected response for application availability and recovery.

Question 338

Which Kafka Streams setting controls standby replica count?

1. replication.factor
2. num.standby.replicas
3. num.stream.threads
4. state.dir

Correct Answer: 2

Explanation:

The num.standby.replicas setting specifies how many standby copies of state stores Kafka Streams maintains. Standby replicas allow state to already exist on other application instances, which can reduce restoration time when an active task moves after a failure. Increasing standby count can improve recovery readiness but requires additional resources and network traffic. The replication factor applies to Kafka internal topics, while stream-thread count controls processing concurrency and state.dir identifies local storage. Standby configuration is especially relevant for stateful workloads where rapid task recovery is important.

Question 339

Which Kafka Streams property specifies application identity?

1. application.id
2. state.dir
3. replication.factor
4. num.stream.threads

Correct Answer: 1

Explanation:

The Kafka Streams application.id identifies a Streams application and plays an important role in its internal resource naming and consumer-group behavior. Applications running independently should generally use distinct identifiers so their processing state and coordination remain separate. The property is not a filesystem location, replication setting, or thread-count configuration. A carefully chosen application ID helps maintain clear separation between deployments and makes operational resources easier to identify. It is therefore one of the fundamental configuration properties when building a Kafka Streams application.

Question 340

Which ksqlDB statement displays available stream definitions?

1. SHOW TABLES
2. SHOW QUERIES
3. SHOW STREAMS
4. SHOW TOPICS

Correct Answer: 3

Explanation:

The ksqlDB SHOW STREAMS command lists stream definitions available to the ksqlDB environment. It helps users inspect existing stream objects before developing additional queries or troubleshooting data-processing workflows. SHOW TABLES focuses on table definitions, SHOW QUERIES displays query information, and SHOW TOPICS provides information about Kafka topics. Understanding these commands allows developers and administrators to quickly inspect the logical objects managed through ksqlDB and identify the appropriate source or destination for streaming SQL operations.