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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
  • 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

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.)

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
  • Related

Introduction to Simplifying data pipelines with Apache Kafka

Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming applications. Originally developed by LinkedIn, Kafka is now maintained by the Apache Software Foundation. It is designed to handle high-throughput, fault-tolerant, and scalable streaming of data.

Key Concepts:

  1. Topics: Kafka organizes data into topics, which are similar to a queue or a table in a traditional messaging system. Producers publish messages to topics, and consumers subscribe to topics to receive messages.
  2. Partitions: Each topic is divided into partitions, which allows Kafka to parallelize data writes and reads. Partitions also enable data replication for fault tolerance and scalability.
  3. Brokers: Kafka runs as a cluster of one or more servers called brokers. Each broker stores data for one or more partitions and can handle producer and consumer requests.
  4. Producers: Producers are responsible for publishing data to Kafka topics. They write messages to one or more partitions of a topic.
  5. Consumers: Consumers subscribe to Kafka topics and process the messages produced by producers. They can read messages from one or more partitions in a topic.

Simplifying Data Pipelines with Kafka:

  1. Unified Messaging Backbone: Kafka serves as a unified messaging backbone for real-time data integration across various systems and applications. By decoupling data producers from consumers, Kafka simplifies the development and maintenance of data pipelines.
  2. Scalability and Fault Tolerance: Kafka’s distributed architecture allows it to scale horizontally by adding more brokers to the cluster. This scalability ensures that data pipelines can handle increasing data volumes without performance degradation. Additionally, Kafka replicates data across brokers for fault tolerance, ensuring data durability and reliability.
  3. Stream Processing: Kafka supports stream processing frameworks like Apache Storm, Apache Spark, and Kafka Streams, allowing developers to perform real-time analytics and transformations on data streams within the Kafka ecosystem. This simplifies the development of complex data processing pipelines by integrating stream processing directly with data ingestion and storage.
  4. Schema Management: Kafka integrates with schema registries like Confluent Schema Registry or Apache Avro, which enable producers and consumers to serialize and deserialize data using a common schema format. This ensures data consistency and compatibility across different components of the data pipeline, simplifying data integration and interoperability.
  5. Monitoring and Management: Kafka provides tools and APIs for monitoring cluster health, tracking message throughput, and managing data retention policies. This visibility into the data pipeline simplifies operations and enables proactive maintenance to ensure optimal performance and reliability.

In summary, Apache Kafka simplifies data pipelines by providing a scalable, fault-tolerant, and unified platform for real-time data integration, processing, and analysis. Its distributed architecture, coupled with stream processing capabilities and schema management, streamlines the development and management of complex data pipelines in modern data-driven applications.

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