Simplifying data pipelines with Apache Kafka Cognitive Class Exam Answers

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Introduction to Simplifying data pipelines with Apache Kafka

Apache Kafka is a powerful distributed event streaming platform designed to handle real-time data feeds with high throughput and fault tolerance. It simplifies data pipelines by decoupling producers and consumers of data through its distributed commit log architecture. Here’s an introduction to how Kafka simplifies data pipelines:

1. Distributed Messaging System: Kafka acts as a distributed messaging system where data is organized into topics. Producers publish records (messages) to topics, and consumers subscribe to these topics to process the data. This decoupling allows for asynchronous communication between components of a system.
2. Scalability and Fault Tolerance: Kafka is horizontally scalable, meaning it can handle large volumes of data by adding more brokers (nodes). It replicates data across multiple brokers for fault tolerance, ensuring that messages are not lost even if some brokers fail.
3. Real-time Data Processing: Kafka enables real-time data processing by providing low-latency message delivery. This is crucial for applications requiring timely data updates and analytics, such as financial trading platforms or real-time monitoring systems.
4. Stream Processing: Kafka Streams API allows for stream processing directly within Kafka. This simplifies the architecture by integrating data processing capabilities into the messaging system itself, reducing the need for separate stream processing frameworks.
5. Integration Flexibility: Kafka integrates well with other data systems such as databases, data lakes, and analytics platforms. This makes it easier to build end-to-end data pipelines where Kafka acts as a central hub for data ingestion and distribution.
6. Event Sourcing and Log Compaction: Kafka’s log-centric design supports event sourcing, where all changes to application state are captured as immutable events. Log compaction ensures that Kafka retains the latest value for each key in a topic, simplifying stateful stream processing.
7. Monitoring and Management: Kafka provides tools and APIs for monitoring cluster health, throughput, and latency. This helps in maintaining and optimizing data pipelines for performance and reliability.

In summary, Apache Kafka simplifies data pipelines by providing a robust and scalable infrastructure for real-time data streaming and processing. Its distributed architecture, fault tolerance, and integration capabilities make it a preferred choice for building modern data-driven applications.

Simplifying data pipelines with Apache Kafka Cognitive Class Certification Answers

Modules 1 – Introduction to Apache Kafka Quiz Answers

Question 1: Which of the following are a Kafka use case?

• Messaging
• All of the above
• Stream Processing
• Website Activity Tracking
• Log Aggregation

Question 2: A Kafka cluster is comprised of one or more servers which are called “producers”

• True
• False

Question 3: Kafka requires Apache ZooKeeper

• True
• False

Modules 2 – Kafka Command Line Quiz Answers

Question 1: There are two ways to create a topic in Kafka, by enabling the auto.create.topics.enable property and by using the kafka-topics.sh script.

• True
• False

Question 2: Which of the following is NOT returned when –describe is passed to kafka-topics.sh?

• Configs
• None of the Above
• PartitionNumber
• ReplicationFactor
• Topic

Question 3: Topic deletion is disabled by default.

• True
• False

Module 3 – Kafka Producer Java API Quiz Answers

Question 1: The setting of ack that provides the strongest guarantee is ack=1

• True
• False

Question 2: The KafkaProducer is the client that publishes records to the Kafka cluster.

• True
• False

Question 3: Which of the following is not a Producer configuration setting?

• batch.size
• linger.ms
• key.serializer
• retries
• None of the above

Module 4 – Kafka Consumer Java API Quiz Answers

Question 1: The Kafka consumer handles various things behind the scenes, such as:

• Failures of servers in the Kafka cluster
• Adapts as partitions of data it fetches migrates within the cluster
• Data management and storage into databases
• a) and b) only
• All of the Above

Question 2: If enable.auto.commit is set to false, then committing offsets is done manually, which provides gives you more control.

• True
• False

Question 3: Rebalancing is a process where group of consumer instances within a consumer group, coordinate to own mutally shared sets of partitions of topics that the groups are subscribed to.

• True
• False

Module 5 – Kafka Connect and Spark Streaming Quiz Answers

Question 1: Which of the following are Kafka Connect features?

• A common framework for Kafka connectors
• Automatic offset management
• REST interface
• Streaming/batch integration
• All of the above

Question 2: Kafka Connector has two types of worker nodes called standalone mode and centralized mode cluster

• True
• False

Question 3: Spark periodically queries Kafka to get the latest offsets in each topic and partition that it is interested in consuming form.

• True
• False

Simplifying Data Pipelines with Apache Kafka Final Exam Answers

Question 1: If the auto.create.topics.enable property is set to false and you try to write a topic that doesn’t yet exist, a new topic will be created.

• True
• False

Question 2: Which of the following is false about Kafka Connect?

• Kafka Connect makes building and managing stream data pipelines easier
• Kafka Connect simplifies adoption of connectors for stream data integration
• It is a framework for small scale, asynchronous stream data integration
• None of the above

Question 3: Kafka comes packaged with a command line client that you can use as a producer.

• True
• False

Question 4: Kafka Connect worker processes work autonomously to distribute work and provide scalability with fault tolerance to the system.

• True
• False

Question 5: What are the three Spark/Kafka direct approach benefits? (Place the answers in alphabetical order.)

Question 6: Kafka Consumer is thread safe, as it can give each thread its own consumer instance

• True
• False

Question 7: What other open-source producers can be used to code producer logic?

• Java
• Python
• C++
• All of the above

Question 8: If you set acks=1 in a Producer, it means that the leader will write the received message to the local log and respond after waiting for full acknowledgement from all of its followers.

• True
• False

Question 9: Kafka has a cluster-centric design which offers strong durability and fault-tolerance guarantees.

• True
• False

Question 10: Which of the following values of ack will not wait for any acknowledgement from the server?

• all
• 0
• 1
• -1

Question 11: A Kafka cluster is comprised of one or more servers which are called “Producers”

• True
• False

Question 12: What are In Sync Replicas?

• They are a set of replicas that are not active and are delayed behind the leader
• They are a set of replicas that are not active and are fully caught up with the leader
• They are a set of replicas that are alive and are fully caught up with the leader
• They are a set of replicas that are alive and are delayed behind the leader

Question 13: In many use cases, you see Kafka used to feed streaming data into Spark Streaming

• True
• False

Question 14: All Kafka Connect sources and sinks map to united streams of records

• True
• False

Question 15: Which is false about the Kafka Producer send method?

• The send method returns a Future for the Record Metadata that will be assigned to a record
• All writes are asynchronous by default
• It is not possible to make asynchronous writes
• Method returns immediately once record has been stored in buffer of records waiting to be sent