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Simplifying data pipelines with Apache Kafka Cognitive Class Exam Quiz Answers

Simplifying Data Pipelines with Apache Kafka Cognitive Class Certification 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

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

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

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
  • 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

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

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.)

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

  • True
  • False

Question 6: What other open source producers can be used to code producer logic?

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

Question 7: 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 8: Kafka has a cluster-centric design which offers strong durability and fault-tolerance guarantees.

  • True
  • False

Question 9: Which of the following values of ack will not wait for any acknowledgement from the server?

  • all
  • 0
  • 1
  • -1

Question 10: A Kafka cluster is comprised of one or more servers which are called “Producers”

  • True
  • False

Question 11: 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 12: In many use cases, you see Kafka used to feed streaming data into Spark Streaming

  • True
  • False

Question 13: All Kafka Connect sources and sinks map to united streams of records

  • True
  • False

Question 14: Which is false about the KafkaProducer send method?

  • The send method returns a Future for the RecordMetadata 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

Introduction to Simplifying Data Pipelines with Apache Kafka

Apache Kafka is indeed a powerful tool for simplifying data pipelines. It acts as a distributed streaming platform, handling real-time data feeds with high throughput and fault tolerance. Here’s how Kafka simplifies data pipelines:

  1. Decoupling Producers and Consumers: Kafka allows producers and consumers to operate independently. Producers can continuously produce data without worrying about whether consumers are ready to consume it. This decoupling simplifies the overall system architecture.
  2. Scalability: Kafka is horizontally scalable, meaning you can add more brokers to the cluster to handle increased load. This scalability ensures that your data pipeline can grow with your needs without requiring significant architectural changes.
  3. Fault Tolerance: Kafka replicates data across multiple brokers, ensuring high availability and fault tolerance. Even if some brokers fail, data remains accessible, minimizing downtime and data loss.
  4. Durability: Kafka persists data to disk, ensuring that messages are not lost in case of system failures. This durability guarantees that data integrity is maintained throughout the pipeline.
  5. Stream Processing: Kafka supports stream processing frameworks like Apache Spark, Apache Flink, and Kafka Streams. These frameworks allow you to process data in real-time, enabling various use cases such as real-time analytics, ETL (Extract, Transform, Load), and event-driven architectures.
  6. Integration Flexibility: Kafka integrates with various systems and technologies, including databases, message queues, and data lakes. This flexibility allows you to build robust data pipelines that span different environments and technologies.
  7. Exactly-Once Semantics: Kafka supports exactly-once message delivery semantics, ensuring that each message is processed exactly once, even in the presence of failures. This feature simplifies application development by eliminating the need for developers to handle duplicate messages.
  8. Monitoring and Management: Kafka provides tools for monitoring cluster health, tracking message throughput, and managing configurations. These tools simplify operational tasks and help ensure the smooth operation of your data pipeline.

By leveraging these features, Apache Kafka simplifies the development, deployment, and management of data pipelines, making it a popular choice for building real-time streaming applications in various industries.

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