CCDAK過去問題を使用すると、Confluent Certified Developer for Apache Kafka Certification Examinationの半分を合格したことになります

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Confluent CCDAK Exam Syllabus Topics:

Section	Objectives
Kafka Ecosystem and Confluent Platform	- ksqlDB and connectors 1. Kafka Connect basics 2. Stream querying concepts - Schema Registry 1. Avro, Protobuf, JSON Schema usage
Kafka Producers and Consumers	- Producer API 1. Serialization and partitioning strategies 2. Reliability and acknowledgments - Consumer API 1. Consumer groups 2. Rebalancing and offset commits
Kafka Fundamentals	- Data flow in Kafka 1. Offset management 2. Producer and consumer model - Kafka architecture and core concepts 1. Replication and fault tolerance 2. Brokers, topics, partitions
Kafka Streams and Processing	- Event processing patterns 1. Exactly-once semantics 2. Windowing and aggregation - Kafka Streams API 1. Stream processing fundamentals 2. Stateful vs stateless processing

>> CCDAK過去問題 <<

CCDAK学習範囲、CCDAKウェブトレーニング

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Confluent Certified Developer for Apache Kafka Certification Examination 認定 CCDAK 試験問題 (Q11-Q16):

質問 # 11
Which of the following event processing application is stateless? (select two)

A. Find the minimum and maximum stock prices for each day of trading
B. Read log messages from a stream and writes ERROR events into a high-priority stream and the rest of the events into a low-priority stream
C. Read events from a stream and modifies them from JSON to Avro
D. Publish the top 10 stocks each day

正解：B、C

解説：
Stateless means processing of each message depends only on the message, so converting from JSON to Avro or filtering a stream are both stateless operations

質問 # 12
You have a consumer group with default configuration settings reading messages from your Kafka cluster.
You need to optimize throughput so the consumer group processes more messages in the same amount of time.
Which change should you make?

A. Decrease the session timeout of each consumer.
B. Remove some consumers from the consumer group.
C. Disable auto commit and have the consumers manually commit offsets.
D. Increase the number of bytes the consumers read with each fetch request.

正解：D

解説：
To increase consumerthroughput, one effective strategy is toincrease the amount of datafetched in each poll by raisingfetch.max.bytesormax.partition.fetch.bytes. This allows each poll to retrieve more records per request, improving processing efficiency.
FromKafka Consumer Config Docs:
"Increasing fetch size allows consumers to retrieve larger batches of messages, improving throughput and reducing request overhead."
* Removing consumers (A) may reduce parallelism.
* Manual commit (C) adds complexity, not throughput.
* Decreasing session timeout (D) risks unnecessary rebalances.
Reference:Kafka Consumer Configuration > fetch.max.bytes

質問 # 13
Your streams application is reading from an input topic that has 5 partitions. You run 5 instances of your application, each with num.streams.threads set to 5. How many stream tasks will be created and how many will be active?

A. 25 created, 5 active
B. 25 created, 25 active
C. 5 created, 1 active
D. 5 created, 5 active

正解：A

解説：
One partition is assigned a thread, so only 5 will be active, and 25 threads (i.e. tasks) will be created

質問 # 14
To prevent network-induced duplicates when producing to Kafka, I should use

A. retries=200000
B. batch.size=1
C. enable.idempotence=true
D. max.in.flight.requests.per.connection=1

正解：C

解説：
Producer idempotence helps prevent the network introduced duplicates. More details herehttps://cwiki.apache.
org/confluence/display/KAFKA/Idempotent+Producer

質問 # 15
You create a producer that writes messages about bank account transactions from tens of thousands of different customers into a topic.
* Your consumers must process these messages with low latency and minimize consumer lag
* Processing takes ~6x longer than producing
* Transactions for each bank account must be processedin orderWhich strategy should you use?

A. Use a unique identifier such as a universally unique identifier (UUID) as the message key.
B. Use the bank account number found in the message as the message key.
C. Use the timestamp of the message's arrival as its key.
D. Use a combination of the bank account number and the transaction timestamp as the message key.

正解：B

解説：
To maintainmessage ordering per bank account, themessage key must be the account number. Kafka guaranteesordering per partition, and the partition is selected based on thehash of the key.
FromKafka Producer Docs:
"All records with the same key will be sent to the same partition, and Kafka preserves the order of records in a partition." Using account number ensures:
* All messages for the same account go to the same partition
* Order is maintained
* Workload is evenly distributed if account keys are varied
Other options break ordering or introduce unnecessary randomness.
Reference:Kafka Producer Concepts > Partitions and Keys

質問 # 16
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近年、IT領域で競争がますます激しくなります。IT認証は同業種の欠くことができないものになりました。あなたはキャリアで良い昇進のチャンスを持ちたいのなら、JPNTestのConfluentのCCDAK「Confluent Certified Developer for Apache Kafka Certification Examination」試験トレーニング資料を利用してConfluentの認証の証明書を取ることは良い方法です。現在、ConfluentのCCDAK認定試験に受かりたいIT専門人員がたくさんいます。JPNTestの試験トレーニング資料はConfluentのCCDAK認定試験の１００パーセントの合格率を保証します。

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