無料でクラウドストレージから最新のPassTest CCDAK PDFダンプをダウンロードする：https://drive.google.com/open?id=19MTVXM4R6PW592EapOUz7D-MRCx2kl61
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| Section | Weight | Objectives |
|---|---|---|
| Kafka Connect | 15% | - Source and sink connectors configuration - Data transformation and integration patterns - Connect architecture: workers, connectors, tasks, converters |
| Apache Kafka Fundamentals | 23% | - Delivery semantics: at-most-once, at-least-once, exactly-once - Configuration basics and cluster setup - Kafka architecture and data flow - Core concepts: brokers, topics, partitions, replicas, offsets |
| Apache Kafka Application Development | 28% | - Producer API: configuration, serialization, partitioning, error handling - Transactional messaging and idempotence - Schema management: Confluent Schema Registry, compatibility rules, evolution - Consumer API: configuration, offset management, consumer groups, rebalancing |
| Application Testing | 8% | - Unit testing producers and consumers - Mocking and test utilities - Integration testing and test clusters |
| Application Observability | 13% | - Error diagnosis and troubleshooting - Logging and monitoring metrics - Tracing and performance tuning |
| Apache Kafka Streams | 12% | - Error handling and state management - Windowing and time semantics - Streams API concepts: topology, processors, state stores - Stream processing operations: map, filter, join, aggregation |
今のインタネット時代に当たり、IT人材としてConfluentのCCDAK資格証明書を取得できないと、大変なことではないなのか？ここで、我が社PassTestは一連のCCDAK問題集を提供します。あなたはCCDAK問題集を購入するかどうかと確認したい、PassTestのCCDAKデーモ版を使用して購入するかと判断します。
質問 # 48
A kafka topic has a replication factor of 3 and min.insync.replicas setting of 1. What is the maximum number of brokers that can be down so that a producer with acks=all can still produce to the topic?
正解：A
解説：
Two brokers can go down, and one replica will still be able to receive and serve data
質問 # 49
(You are experiencing low throughput from a Java producer.
Kafka producer metrics show a low I/O thread ratio and low I/O thread wait ratio.
What is the most likely cause of the slow producer performance?)
正解：B
解説：
According to the official Apache Kafka producer metrics documentation, the I/O thread ratio and I/O thread wait ratio indicate how busy the producer's network I/O thread is and how much time it spends waiting. When both metrics are low, it means the I/O thread is underutilized and not blocked on network operations.
This strongly suggests that the bottleneck is not network-related. If there were network latency or a bad data link (Option B), the I/O thread wait ratio would be high. Similarly, large batches (Option A) or compression (Option D) generally increase throughput and would result in higher I/O utilization.
An expensive or blocking callback function (for example, heavy logic in onCompletion()) executes in the producer's sender thread context. Kafka documentation explicitly warns that slow callbacks can throttle producer throughput because they delay the processing of acknowledgements and subsequent sends.
Therefore, the most likely and documented cause of low producer throughput with low I/O utilization is expensive callback logic in the producer code.
質問 # 50
(You are writing a producer application and need to ensure proper delivery.
You configure the producer with acks=all.
Which two actions should you take to ensure proper error handling?
Select two.)
正解：A、D
解説：
The Apache Kafka producer API is asynchronous by default, and proper error handling requires explicit mechanisms. The official documentation recommends using a callback function with producer.send() to determine whether a record was successfully acknowledged or failed due to an exception. The callback provides access to both RecordMetadata and any exception encountered during sending.
Additionally, certain errors-such as serialization errors or configuration issues-are thrown synchronously when calling producer.send(). These must be handled using a try/catch block that catches KafkaException or its subclasses.
Option A is invalid because ProducerRecord has no status() method. Option C is incorrect because producer.
send() returns a Future<RecordMetadata>, not a RecordMetadata object directly.
Thus, combining a callback for asynchronous errors and a try/catch block for synchronous errors is the correct and officially documented approach to ensure reliable producer error handling when using acks=all.
質問 # 51
What is a consequence of increasing the number of partitions in an existing Kafka topic?
正解：D
解説：
Increasing partitions increasesparallelism, but also means:
* Consumers in a group may have to handlemore partitions, especially if the number of consumers is lower than the number of partitions.
* This can result inincreased lag, especially under high load.
FromKafka Topic Management Docs:
"Increasing the number of partitions increases consumer work, and if consumers can't keep up, lag can accumulate."
* A is false:existing data is not redistributed.
* B is false:records with the same key always map to the same partitionbased on hash.
* D is not directly impacted by the partition count.
Reference:Kafka Topic Management > Adding Partitions
質問 # 52
This schema excerpt is an example of which schema format?
package com.mycorp.mynamespace;
message SampleRecord {
int32 Stock = 1;
double Price = 2;
string Product_Name = 3;
}
正解：C
解説：
This syntax is a clear match toProtocol Buffers (Protobuf). It defines a schema with fields, types, and tags, which is a format supported by Kafka when usingProtobuf-based schema registry serialization.
FromConfluent Schema Registry Docs:
"Kafka supports Protobuf serialization, where schemas are written in .proto files and include fields with tags."
* int32, double, and string are standard Protobuf types.
* Avro uses JSON-style schema.
* JSON Schema uses JSON object structure, not .proto.
* YAML is unrelated.
Reference:Confluent Schema Registry for Protobuf
質問 # 53
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さらに、PassTest CCDAKダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=19MTVXM4R6PW592EapOUz7D-MRCx2kl61