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| Section | Objectives |
|---|---|
| Topic 1: Kafka Fundamentals | - Kafka architecture and core concepts
|
| Topic 2: Kafka Streams and Processing | - Kafka Streams API
|
| Topic 3: Kafka Ecosystem and Confluent Platform | - Schema Registry
|
| Topic 4: Kafka Producers and Consumers | - Consumer API
|
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NEW QUESTION # 73
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 processed in orderWhich strategy should you use?
Answer: D
Explanation:
To maintain message ordering per bank account, the message key must be the account number. Kafka guarantees ordering per partition, and the partition is selected based on the hash of the key.
From Kafka 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
NEW QUESTION # 74
What are two examples of performance metrics?
(Select two.)
Answer: B,E
Explanation:
fetch-rate: Measures how frequently consumers fetch data - critical for monitoring throughput.
incoming-byte-rate: Shows how much data is being written to the broker per second - a key producer-side metric.
From Kafka JMX Metrics Guide:
"fetch-rate, incoming-byte-rate, request-latency are key performance metrics for producers and consumers." B, C, E, and F are application-layer business metrics, not Kafka performance metrics.
Reference: Kafka Monitoring > JMX Metrics
NEW QUESTION # 75
You are running a Kafka Streams application in a Docker container managed by Kubernetes, and upon application restart, it takes a long time for the docker container to replicate the state and get back to processing the dat a. How can you improve dramatically the application restart?
Answer: C
Explanation:
Although any Kafka Streams application is stateless as the state is stored in Kafka, it can take a while and lots of resources to recover the state from Kafka. In order to speed up recovery, it is advised to store the Kafka Streams state on a persistent volume, so that only the missing part of the state needs to be recovered.
NEW QUESTION # 76
An ecommerce wesbite sells some custom made goods. What's the natural way of modeling this data in Kafka streams?
Answer: C
Explanation:
Mostly-static data is modeled as a table whereas business transactions should be modeled as a stream.
NEW QUESTION # 77
(You need to send a JSON message on the wire. The message key is a string.
How would you do this?)
Answer: B
Explanation:
According to the Apache Kafka producer documentation, serialization is configured independently for message keys and values. The key and value can use different serializers depending on their data types.
In this scenario, the message key is a string, so the producer must be configured with org.apache.kafka.
common.serialization.StringSerializer for the key. The value contains JSON, so a suitable JSON serializer must be configured for the value, such as a custom JSON serializer, a library-based serializer, or a byte-array serializer after converting the JSON to bytes.
Option B correctly reflects this separation by specifying a string serializer for the key and a JSON serializer for the value. Kafka does not automatically infer serializers, and setting a serializer to null is invalid.
Additionally, there is no built-in "JSON" serializer for keys, making Option D incorrect.
Therefore, the correct approach-fully aligned with Kafka's producer configuration model-is to explicitly configure a StringSerializer for the key and a JSON-capable serializer for the value.
NEW QUESTION # 78
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