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[2025年12月22日]CCDAKテスト準備できるトレーニング練習テスト試験問題 [Q74-Q99]

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[2025年12月22日]CCDAKテスト準備できるトレーニング練習テスト試験問題

試験問題解答ブレーン問題集でCCDAK試験問題集PDFを使おう


CCDAK試験では、主にKafkaアーキテクチャ、トピック、パーティション、レプリケーション、Kafkaプロデューサーおよび消費者APIなど、KafkaのコアコンセプトとAPIの使用に焦点を当てています。 Kafka ConnectとKafka Streams APIは、Kafkaエコシステムの2つの重要なコンポーネントであり、候補者もこれらの分野で習熟度を示しなければなりません。 Kafka Connect APIは、Kafkaと他のデータソース間でデータを転送するコネクタの構築に使用され、Kafka StreamsライブラリはKafkaのストリーム処理に使用されます。


Confluent CCDAK（Confluent Certified Developer for Apache Kafka Certification Examination）は、Apache Kafkaの知識とスキルを検証するために設計された試験です。この試験は、エンタープライズデータストリーミングプラットフォームの主要な提供者であるConfluentによって実施され、Kafkaに熟練した開発者を対象としています。CCDAK試験は、データストリーミングやメッセージングのキャリアを追求したいプロフェッショナルにとって重要なステップであり、業界で高く評価されています。


Apache Kafkaは、IT業界の主要な分散ストリーミングプラットフォームとして登場し、企業がリアルタイムのデータフィード、メッセージング、およびストリーム処理を処理できるようになりました。 Apache Kafka（CCDAK）認定試験のConfluent Certified開発者は、Kafkaプラットフォームでの知識と経験の業界で認識されている検証です。 Kafkaと協力し、そのアーキテクチャ、設計、構成、および管理を完全に理解している開発者を対象としています。

 

質問 # 74
If I produce to a topic that does not exist, and the broker setting auto.create.topic.enable=true, what will happen?

  • A. Kafka will automatically create the topic with 1 partition and 1 replication factor
  • B. Kafka will automatically create the topic with the broker settings num.partitions and default.replication.factor
  • C. Kafka will automatically create the topic with num.partitions=#of brokers and replication.factor=3
  • D. Kafka will automatically create the topic with the indicated producer settings num.partitions and default.replication.factor

正解：B

解説：
The broker settings comes into play when a topic is auto created


質問 # 75
Match the topic configuration setting with the reason the setting affects topic durability.
(You are given settings like unclean.leader.election.enable=false, replication.factor, min.insync.replicas=2)

正解：

解説：

* unclean.leader.election.enable=false# Prevents data loss by only considering in-sync replicas when rebalancing.
* replication.factor# Specifies how many redundant copies of partitions are distributed across brokers.
* min.insync.replicas=2# Sets the standard for the number of partition instances that must keep up with the latest committed message.
* unclean.leader.election.enable=false ensures that onlyin-sync replicascan be elected as leaders. If disabled, an out-of-sync replica may become leader, potentially leading to data loss.
* replication.factor defineshow many brokerswill maintain copies of each partition, directly impacting durability and availability.
* min.insync.replicas determineshow many replicas must acknowledgea write when acks=all is used, enforcing write durability.
Reference:Apache Kafka Topic Configuration Documentation


質問 # 76
What will happen if a producer tries to send messages to a topic that does not exist in the Kafka cluster?

  • A. The brokers will hold the messages until a user creates a topic for the messages.
  • B. The messages will be sent to a topic called unnamed_topic_1.
  • C. The Producer "send()" call will block indefinitely.
  • D. Messages will only be sent if auto topic creation is enabled.

正解：D


質問 # 77
Which of the following is not an Avro primitive type?

  • A. long
  • B. int
  • C. null
  • D. date
  • E. string

正解：D

解説：
date is a logical type


質問 # 78
You need to collect logs from a host and write them to a Kafka topic named 'logs-topic'. You decide to use Kafka Connect File Source connector for this task.
What is the preferred deployment mode for this connector?

  • A. Distributed mode
  • B. SingleCluster mode
  • C. Standalone mode
  • D. Parallel mode

正解：C

解説：
Kafka Connect can run instandalone modeordistributed mode. For simple tasks likereading logs from a file on a single host,standalone modeis recommended.
FromKafka Connect User Guide:
"Standalone mode is useful when running connectors on a single machine (e.g., for development or simple deployments like log collection from a local file)." Distributed mode is preferred for scalability and fault tolerance but overkill for this use case.
Reference:Kafka Connect User Guide > Deployment Modes


質問 # 79
A client connects to a broker in the cluster and sends a fetch request for a partition in a topic. It gets an exception Not Leader For Partition Exception in the response. How does client handle this situation?

  • A. Get the Broker id from Zookeeper that is hosting the leader replica and send request to it
  • B. Send fetch request to each Broker in the cluster
  • C. Send metadata request to the same broker for the topic and select the broker hosting the leader replica
  • D. Send metadata request to Zookeeper for the topic and select the broker hosting the leader replica

正解：C

解説：
In case the consumer has the wrong leader of a partition, it will issue a metadata request. The Metadata request can be handled by any node, so clients know afterwards which broker are the designated leader for the topic partitions. Produce and consume requests can only be sent to the node hosting partition leader.


質問 # 80
Clients that connect to a Kafka cluster are required to specify one or more brokers in the bootstrap.
servers parameter.
What is the primary advantage of specifying more than one broker?

  • A. It is the mechanism to distribute a topic's partitions across multiple brokers.
  • B. It provides redundancy in making the initial connection to the Kafka cluster.
  • C. It forces clients to enumerate every single broker in the cluster.
  • D. It provides the ability to wake up dormant brokers.

正解：B

解説：
The bootstrap.servers setting is used by clients (producers and consumers) toinitially connectto the Kafka cluster. It does not need to list all brokers-just a few that are up and reachable. The client will then use the cluster metadata to discover the full broker list.
FromKafka Java Client Configuration Docs:
"This is a list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping." Hence,listing multiple brokers provides redundancyand fault tolerance in the event one is down during client startup.
Reference:Apache Kafka Java Client Configuration > bootstrap.servers


質問 # 81
A stream processing application is consuming from a topic that has five partitions. You run three instances of your application, each with num.streams.threads set to five.
You need to identify the amount of total stream tasks that will be created and the amount that will be actively consuming messages from the input topic.
Which option is correct?

  • A. 5 created, 1 actively consuming
  • B. 15 created, 5 actively consuming
  • C. 5 created, 5 actively consuming
  • D. 15 created, 15 actively consuming

正解：D


質問 # 82
In Kafka, what are Topics split into?

  • A. Chunks
  • B. Partitions
  • C. Consumers
  • D. Sub Topics

正解：B


質問 # 83
A consumer has auto.offset.reset=latest, and the topic partition currently has data for offsets going from 45 to 2311. The consumer group never committed offsets for the topic before. Where will the consumer read from?

  • A. it will crash
  • B. offset 0
  • C. offset 45
  • D. offset 2311

正解：D

解説：
Latest means that data retrievals will start from where the offsets currently end


質問 # 84
You are building a system for a retail store selling products to customers.
Which three datasets should you model as a GlobalKTable?
(Select three.)

  • A. Customer profile information
  • B. Catalog of products
  • C. All purchases at a retail store occurring in real time
  • D. Log of payment transactions
  • E. Inventory of products at a warehouse

正解：A、B、E

解説：
AGlobalKTableis a replicated, read-only table available in full on all instances. It's best forreference or lookup datasetssuch as:
* Product catalog
* Customer profiles
* Warehouse inventory
FromKafka Streams Developer Guide:
"Use GlobalKTable when you need to perform joins using non-partition-aligned reference data that's small enough to replicate."
* Purchases and transactions are high-throughput, time-sensitive streams, not static reference data.
Reference:Kafka Streams Concepts > GlobalKTable


質問 # 85
When using plain JSON data with Connect, you see the following error messageorg.apache.kafka.connect.
errors.DataExceptionJsonDeserializer with schemas.enable requires "schema" and "payload" fields and may not contain additional fields. How will you fix the error?

  • A. Set key.converter.schemas.enable and value.converter.schemas.enable to false
  • B. Set key.converter, value.converter to AvroConverter and the schema registry url
  • C. Use Single Message Transforms to add schema and payload fields in the message
  • D. Set key.converter, value.converter to JsonConverter and the schema registry url

正解：A

解説：
You will need to set the schemas.enable parameters for the converter to false for plain text with no schema.


質問 # 86
In the Kafka consumer metrics it is observed that fetch-rate is very high and each fetch is small. What steps will you take to increase throughput?

  • A. Increase fetch.max.bytes
  • B. Increase fetch.max.wait
  • C. Increase fetch.min.bytes
  • D. Decrease fetch.min.bytes
  • E. Decrease fetch.max.bytes

正解：C

解説：
This will allow consumers to wait and receive more bytes in each fetch request.


質問 # 87
A topic has three replicas and you set min.insync.replicas to 2. If two out of three replicas are not available, what happens when a consume request is sent to broker?

  • A. A new leader for the partition will be elected
  • B. An empty message will be returned
  • C. Data will be returned from the remaining in-sync replica
  • D. NotEnoughReplicasException will be returned

正解：C

解説：
With this configuration, a single in-sync replica is still readable, but not writeable if the producer using acks=all


質問 # 88
How do you read a table or stream from the beginning of a topic?

  • A. CREATE STREAM shipped_orders AS
    SELECT
  • B. itemId as itemId,
  • C. SET 'auto.offset.reset' = 'earliest';
    SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
  • D. SET 'auto.offset.reset' = 'latest';
    SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
  • E. SELECT STRUCT(f1 := v1, f2 := v2) FROM s1 EMIT CHANGES;
  • F. id as paymentId
    FROM orders o
    INNER JOIN payments p WITHIN 1 HOURS ON p.id = o.id
    INNER JOIN shipments s WITHIN 2 HOURS ON s.id
    = o.id;
  • G. id as orderId
  • H. id as shipmentId,

正解：C


質問 # 89
Compaction is enabled for a topic in Kafka by setting log.cleanup.policy=compact. What is true about log compaction?

  • A. After cleanup, only one message per key is retained with the first value
  • B. Kafka automatically de-duplicates incoming messages based on key hashes
  • C. After cleanup, only one message per key is retained with the latest value
  • D. Each message stored in the topic is compressed

正解：C

解説：
Compaction changes the offset of messages
Explanation:
Log compaction retains at least the last known value for each record key for a single topic partition. All compacted log offsets remain valid, even if record at offset has been compacted away as a consumer will get the next highest offset.


質問 # 90
The rule "same key goes to the same partition" is true unless...

  • A. the number of producer changes
  • B. the number of partition changes
  • C. the replication factor changes
  • D. the number of kafka broker changes

正解：B

解説：
Increasing the number of partition causes new messages keys to get hashed differently, and breaks the guarantee "same keys goes to the same partition". Kafka logs are immutable and the previous messages are not re-shuffled.


質問 # 91
A Kafka producer application wants to send log messages to a topic that does not include any key. What are the properties that are mandatory to configure for the producer configuration? (select three)

  • A. value
  • B. key.serializer
  • C. partition
  • D. bootstrap.servers
  • E. value.serializer
  • F. key

正解：B、D、E

解説：
Both key and value serializer are mandatory.


質問 # 92
You create a topic named stream-logs with:
* A replication factor of 3
* Four partitions
* Messages that are plain logs without a keyHow will messages be distributed across partitions?

  • A. All messages will be written to the same log segment.
  • B. The first message will always be written to partition 0.
  • C. Messages will be distributed round-robin among all the topic partitions.
  • D. Messages will be distributed among all the topic partitions with strict ordering.

正解：C

解説：
If amessage key is not provided, Kafka's default partitioner usesround-robin distributionacross available partitions.
FromKafka Producer Design:
"If no key is provided, the default partitioner distributes messages round-robin to available partitions."
* A is incorrect - not all go to partition 0.
* C is invalid - Kafka doesn't group messages into one segment without a key.
* D is false - ordering isnot preservedacross partitions without a key.
Reference:Kafka Producer Partitioner Behavior


質問 # 93
There are 3 brokers in the cluster. You want to create a topic with a single partition that is resilient to one broker failure and one broker maintenance. What is the replication factor will you specify while creating the topic?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

正解：C

解説：
1 is not possible as it doesn't provide resilience to failure, 2 is not enough as if we take a broker down for maintenance, we cannot tolerate a broker failure, and 6 is impossible as we only have 3 brokers (RF cannot be greater than the number of brokers). Here the correct answer is 3


質問 # 94
You want to send a message of size 3 MB to a topic with default message size configuration. How does KafkaProducer handle large messages?

  • A. KafkaProducer divides messages into sizes of message.max.bytes and sends them in order
  • B. MessageSizeTooLarge exception will be thrown, KafkaProducer will not retry and return exception immediately
  • C. KafkaProducer divides messages into sizes of max.request.size and sends them in order
  • D. MessageSizeTooLarge exception will be thrown, KafkaProducer retries until the number of retries are exhausted

正解：B

解説：
MessageSizeTooLarge is not a retryable exception.


質問 # 95
Which feature determines the maximum parallelism at which a Kafka Streams application can run?

  • A. Configured Kafka Streams application instances
  • B. Input topics
  • C. Partitions of the input topic(s)
  • D. Brokers in the Kafka cluster

正解：C


質問 # 96
You have a topic t1 in a Kafka cluster. A producer running on host A can successfully write messages to t1.
Then, you move that producer code to run on host B, but it fails writing messages to t1.
What is a likely reason for that failure?

  • A. Topic T1 no longer exists in the same environment as host B.
  • B. Kafka ACLs are not configured to allow host B's IP address.
  • C. Another producer started writing to t1.
  • D. The producer is connecting to a different broker in the same cluster.

正解：B


質問 # 97
StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> textLines = builder.stream("word-count-input"); KTable<String, Long> wordCounts = textLines
.mapValues(textLine -> textLine.toLowerCase())
.flatMapValues(textLine -> Arrays.asList(textLine.split("\W+")))
.selectKey((key, word) -> word)
.groupByKey()
.count(Materialized.as("Counts"));
wordCounts.toStream().to("word-count-output", Produced.with(Serdes.String(), Serdes.Long())); builder.build(); What is an adequate topic configuration for the topic word-count-output?

  • A. max.message.bytes=10000000
  • B. cleanup.policy=delete
  • C. cleanup.policy=compact
  • D. compression.type=lz4

正解：C

解説：
Result is aggregated into a table with key as the unique word and value its frequency. We have to enable log compaction for this topic to align the topic's cleanup policy with KTable semantics.


質問 # 98
To enhance compression, I can increase the chances of batching by using

  • A. acks=all
  • B. max.message.size=10MB
  • C. batch.size=65536
  • D. linger.ms=20

正解：D

解説：
linger.ms forces the producer to wait before sending messages, hence increasing the chance of creating batches that can be heavily compressed.


質問 # 99
......

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